\n",
+ "\n",
+ "This material is copyright Axel Brando and made available under the Creative Commons Attribution-ShareAlike 4.0 International License (http://creativecommons.org/licenses/by-sa/4.0/). Code is also made available under the Apache Version 2.0 License (https://www.apache.org/licenses/LICENSE-2.0). \n",
+ "\n",
+ "Please, to use this material and code follow the instructions explained in the main repository [https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation](https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation#bibtex-reference-format-for-citation)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "
Time series regression problem by using DNN with Adversarial Training
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using TensorFlow backend.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import tensorflow as tf\n",
+ "tf.python.control_flow_ops = tf\n",
+ "\n",
+ "#config = tf.ConfigProto()\n",
+ "#config.gpu_options.allow_growth=True\n",
+ "#sess = tf.Session(config=config)\n",
+ "\n",
+ "from keras import backend as K\n",
+ "\n",
+ "import os\n",
+ "import numpy as np\n",
+ "from pandas.io.parsers import read_csv\n",
+ "from sklearn.utils import shuffle\n",
+ "import random\n",
+ "from datetime import datetime"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### We load our dataset\n",
+ "Because our dataset is private we will only expose a dummy code"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "X = np.load('Normalised-X_train.npy')\n",
+ "y = np.load('y_train.npy')\n",
+ "X_val = np.load('Normalised-X_val.npy')\n",
+ "y_val = np.load('y_val.npy')\n",
+ "X_orig = np.load('Original-X_train.npy')\n",
+ "X_val_orig = np.load('Original-X_val.npy')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from keras.models import Sequential,Graph\n",
+ "from keras.layers.core import Dense, Dropout\n",
+ "from keras.callbacks import History\n",
+ "from keras.layers.recurrent import LSTM\n",
+ "from keras.models import model_from_json\n",
+ "from keras.regularizers import l2, activity_l2\n",
+ "\n",
+ "from keras.objectives import mean_absolute_error\n",
+ "\n",
+ "from keras import backend as K\n",
+ "\n",
+ "import numpy as np\n",
+ "\n",
+ "c = 1 #The number of outputs we want to predict\n",
+ "m = 1 #The number of distributions we want to use in the mixture\n",
+ "\n",
+ "#Note: The output size will be (c + 2) * m\n",
+ "\n",
+ "def log_sum_exp(x, axis=None):\n",
+ " \"\"\"Log-sum-exp trick implementation\"\"\"\n",
+ " x_max = K.max(x, axis=axis, keepdims=True)\n",
+ " return K.log(K.sum(K.exp(x - x_max), \n",
+ " axis=axis, keepdims=True))+x_max\n",
+ "\n",
+ "\n",
+ "def mean_log_Gaussian_like(y_true, parameters):\n",
+ " \"\"\"Mean Log Gaussian Likelihood distribution\n",
+ " Note: The 'c' variable is obtained as global variable\n",
+ " \"\"\"\n",
+ " components = K.reshape(parameters,[-1, c + 2, m])\n",
+ " mu = components[:, :c, :]\n",
+ " sigma = components[:, c, :]\n",
+ " alpha = components[:, c + 1, :]\n",
+ " alpha = K.softmax(K.clip(alpha,1e-8,1.))\n",
+ " \n",
+ " exponent = K.log(alpha) - .5 * float(c) * K.log(2 * np.pi) \\\n",
+ " - float(c) * K.log(sigma) \\\n",
+ " - K.sum((K.expand_dims(y_true,2) - mu)**2, axis=1)/(2*(sigma)**2)\n",
+ " \n",
+ " log_gauss = log_sum_exp(exponent, axis=1)\n",
+ " res = - K.mean(log_gauss)\n",
+ " return res\n",
+ "\n",
+ "\n",
+ "def mean_log_LaPlace_like(y_true, parameters):\n",
+ " \"\"\"Mean Log Laplace Likelihood distribution\n",
+ " Note: The 'c' variable is obtained as global variable\n",
+ " \"\"\"\n",
+ " components = K.reshape(parameters,[-1, c + 2, m])\n",
+ " mu = components[:, :c, :]\n",
+ " sigma = components[:, c, :]\n",
+ " alpha = components[:, c + 1, :]\n",
+ " alpha = K.softmax(K.clip(alpha,1e-2,1.))\n",
+ " \n",
+ " exponent = K.log(alpha) - float(c) * K.log(2 * sigma) \\\n",
+ " - K.sum(K.abs(K.expand_dims(y_true,2) - mu), axis=1)/(sigma)\n",
+ " \n",
+ " log_gauss = log_sum_exp(exponent, axis=1)\n",
+ " res = - K.mean(log_gauss)\n",
+ " return res\n",
+ "\n",
+ "\n",
+ "def scoring_rule_adv(y_true, y_pred):\n",
+ " \"\"\"Fast Gradient Sign Method (FSGM) to implement Adversarial Training\n",
+ " Note: The 'graphADV' pointer is obtained as global variable\n",
+ " \"\"\"\n",
+ " \n",
+ " # Compute loss \n",
+ " #Note: Replace with 'mean_log_Gaussian_like' if you want a Gaussian kernel.\n",
+ " error = mean_log_LaPlace_like(y_true, y_pred)\n",
+ " \n",
+ " # Craft adversarial examples using Fast Gradient Sign Method (FGSM)\n",
+ " # Define gradient of loss wrt input\n",
+ " grad_error = K.gradients(error,graphADV.input) #Minus is on error function\n",
+ " # Take sign of gradient, Multiply by constant epsilon, Add perturbation to original example to obtain adversarial example\n",
+ " #Sign add a new dimension we need to obviate\n",
+ " \n",
+ " epsilon = 0.08\n",
+ " \n",
+ " adversarial_X = K.stop_gradient(graphADV.input + epsilon * K.sign(grad_error)[0])\n",
+ " \n",
+ " # Note: If you want to test the variation of adversarial training \n",
+ " # proposed by XX, eliminate the following comment character \n",
+ " # and comment the previous one.\n",
+ " \n",
+ " ##adversarial_X = graphADV.input + epsilon * K.sign(grad_error)[0]\n",
+ " \n",
+ " adv_output = graphADV(adversarial_X)\n",
+ " \n",
+ " #Note: Replace with 'mean_log_Gaussian_like' if you want a Gaussian kernel.\n",
+ " adv_error = mean_log_LaPlace_like(y_true, adv_output)\n",
+ " return 0.3 * error + 0.7 * adv_error\n",
+ "\n",
+ "graph = Graph()\n",
+ "graph.add_input(name='input', input_shape=(12,))\n",
+ "graph.add_node(Dense(500, activation='relu'), name='dense1_1', input='input')\n",
+ "graph.add_node(Dropout(0.25), name='drop1_1', input='dense1_1')\n",
+ "\n",
+ "graph.add_node(Dense(500, activation='relu'), name='dense2_1', input='drop1_1')\n",
+ "graph.add_node(Dropout(0.25), name='drop2_1', input='dense2_1')\n",
+ "\n",
+ "graph.add_node(Dense(500, activation='relu'), name='dense3_1', input='drop2_1')\n",
+ "graph.add_node(Dropout(0.25), name='drop3_1', input='dense3_1')\n",
+ "\n",
+ "\n",
+ "graph.add_node(Dense(output_dim=500, activation=\"relu\"), name='FC1', input='drop3_1')\n",
+ "graph.add_node(Dense(output_dim=c*m), name='FC_mus', input='FC1')\n",
+ "graph.add_node(Dense(output_dim=m, activation=elu_modif), name='FC_sigmas', input='FC1') #K.exp, W_regularizer=l2(1e-3)\n",
+ "graph.add_node(Dense(output_dim=m, activation='softmax'), name='FC_alphas', input='FC1')\n",
+ "graph.add_output(name='output', inputs=['FC_mus','FC_sigmas', 'FC_alphas'], merge_mode='concat',concat_axis=1)\n",
+ "graphADV = graph\n",
+ "graph.compile('rmsprop', {'output':scoring_rule_adv})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from keras.callbacks import Callback, ModelCheckpoint\n",
+ "class LossHistoryDAdvDMDN3(Callback):\n",
+ " def on_train_begin(self, logs={}):\n",
+ " self.losses = []\n",
+ "\n",
+ " def on_batch_end(self, batch, logs={}):\n",
+ " self.losses.append(logs.get('loss'))\n",
+ "lossHistory = LossHistory()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Train on 1350000 samples, validate on 150000 samples\n",
+ "Epoch 1/500\n",
+ "1350000/1350000 [==============================] - 16s - loss: 29.7862 - val_loss: 9.0249\n",
+ "Epoch 2/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 7.8454 - val_loss: 7.2621\n",
+ "Epoch 3/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.9666 - val_loss: 6.8823\n",
+ "Epoch 4/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.7584 - val_loss: 6.7566\n",
+ "Epoch 5/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.6692 - val_loss: 6.6874\n",
+ "Epoch 6/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.6185 - val_loss: 6.6641\n",
+ "Epoch 7/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.5885 - val_loss: 6.6248\n",
+ "Epoch 8/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.5699 - val_loss: 6.6047\n",
+ "Epoch 9/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.5517 - val_loss: 6.6078\n",
+ "Epoch 10/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.5379 - val_loss: 6.5967\n",
+ "Epoch 11/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.5248 - val_loss: 6.5887\n",
+ "Epoch 12/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.5171 - val_loss: 6.5819\n",
+ "Epoch 13/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.4988 - val_loss: 6.5552\n",
+ "Epoch 14/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.4737 - val_loss: 6.5455\n",
+ "Epoch 15/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.4590 - val_loss: 6.5025\n",
+ "Epoch 16/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.4377 - val_loss: 6.5089\n",
+ "Epoch 17/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.4312 - val_loss: 6.4698\n",
+ "Epoch 18/500\n",
+ "1350000/1350000 [==============================] - 16s - loss: 6.4094 - val_loss: 6.4545\n",
+ "Epoch 19/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.4060 - val_loss: 6.4500\n",
+ "Epoch 20/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.4084 - val_loss: 6.5336\n",
+ "Epoch 21/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.3969 - val_loss: 6.4833\n",
+ "Epoch 22/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.3805 - val_loss: 6.4935\n",
+ "Epoch 23/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.3784 - val_loss: 6.4120\n",
+ "Epoch 24/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.3646 - val_loss: 6.4622\n",
+ "Epoch 25/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.3510 - val_loss: 6.3678\n",
+ "Epoch 26/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.3483 - val_loss: 6.4232\n",
+ "Epoch 27/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.3302 - val_loss: 6.3942\n",
+ "Epoch 28/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.3353 - val_loss: 6.3898\n",
+ "Epoch 29/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.3183 - val_loss: 6.3670\n",
+ "Epoch 30/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.3179 - val_loss: 6.3423\n",
+ "Epoch 31/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.3086 - val_loss: 6.3436\n",
+ "Epoch 32/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.3072 - val_loss: 6.3940\n",
+ "Epoch 33/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.3121 - val_loss: 6.3615\n",
+ "Epoch 34/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 3447.8928 - val_loss: 6.3385\n",
+ "Epoch 35/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.2895 - val_loss: 6.4213\n",
+ "Epoch 36/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.2824 - val_loss: 6.3364\n",
+ "Epoch 37/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.2663 - val_loss: 6.3140\n",
+ "Epoch 38/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.2581 - val_loss: 6.3299\n",
+ "Epoch 39/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.2606 - val_loss: 6.2986\n",
+ "Epoch 40/500\n",
+ "1350000/1350000 [==============================] - 15s - loss: 6.2672 - val_loss: 6.3022\n",
+ "Epoch 41/500\n",
+ "1350000/1350000 [==============================] - 19s - loss: 6.2610 - val_loss: 6.3703\n",
+ "Epoch 42/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2592 - val_loss: 6.3463\n",
+ "Epoch 43/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2458 - val_loss: 6.2723\n",
+ "Epoch 44/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2464 - val_loss: 6.2616\n",
+ "Epoch 45/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2539 - val_loss: 6.3413\n",
+ "Epoch 46/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2569 - val_loss: 6.3025\n",
+ "Epoch 47/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2521 - val_loss: 6.3246\n",
+ "Epoch 48/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 9.4204 - val_loss: 6.3016\n",
+ "Epoch 49/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2429 - val_loss: 6.3070\n",
+ "Epoch 50/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2369 - val_loss: 6.2675\n",
+ "Epoch 51/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2432 - val_loss: 6.3136\n",
+ "Epoch 52/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2465 - val_loss: 6.2969\n",
+ "Epoch 53/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 15.8449 - val_loss: 6.2883\n",
+ "Epoch 54/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2374 - val_loss: 6.3141\n",
+ "Epoch 55/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2348 - val_loss: 6.2923\n",
+ "Epoch 56/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2271 - val_loss: 6.2633\n",
+ "Epoch 57/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2277 - val_loss: 6.2775\n",
+ "Epoch 58/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2197 - val_loss: 6.2851\n",
+ "Epoch 59/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2308 - val_loss: 6.2539\n",
+ "Epoch 60/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2341 - val_loss: 6.3026\n",
+ "Epoch 61/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2077 - val_loss: 6.2957\n",
+ "Epoch 62/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2163 - val_loss: 6.2217\n",
+ "Epoch 63/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2180 - val_loss: 6.2576\n",
+ "Epoch 64/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2375 - val_loss: 6.2979\n",
+ "Epoch 65/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2105 - val_loss: 6.2852\n",
+ "Epoch 66/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2079 - val_loss: 6.3013\n",
+ "Epoch 67/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.2138 - val_loss: 6.2557\n",
+ "Epoch 68/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.1919 - val_loss: 6.2478\n",
+ "Epoch 69/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.1982 - val_loss: 6.3009\n",
+ "Epoch 70/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.1915 - val_loss: 6.1961\n",
+ "Epoch 71/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.1742 - val_loss: 6.2161\n",
+ "Epoch 72/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.1656 - val_loss: 6.1442\n",
+ "Epoch 73/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 15.0535 - val_loss: 6.1802\n",
+ "Epoch 74/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.1711 - val_loss: 6.1906\n",
+ "Epoch 75/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.1792 - val_loss: 6.1825\n",
+ "Epoch 76/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.1871 - val_loss: 6.2463\n",
+ "Epoch 77/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.1718 - val_loss: 6.1760\n",
+ "Epoch 78/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.1312 - val_loss: 6.1733\n",
+ "Epoch 79/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.1613 - val_loss: 6.0663\n",
+ "Epoch 80/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.1686 - val_loss: 6.1434\n",
+ "Epoch 81/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.1790 - val_loss: 6.1783\n",
+ "Epoch 82/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 27.0177 - val_loss: 6.2367\n",
+ "Epoch 83/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.0932 - val_loss: 6.0413\n",
+ "Epoch 84/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.0167 - val_loss: 5.9920\n",
+ "Epoch 85/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9700 - val_loss: 5.9577\n",
+ "Epoch 86/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 8.6538 - val_loss: 5.9091\n",
+ "Epoch 87/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9227 - val_loss: 5.8441\n",
+ "Epoch 88/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.8916 - val_loss: 5.8381\n",
+ "Epoch 89/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9454 - val_loss: 6.6176\n",
+ "Epoch 90/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9977 - val_loss: 5.9074\n",
+ "Epoch 91/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9264 - val_loss: 5.8940\n",
+ "Epoch 92/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9301 - val_loss: 5.8595\n",
+ "Epoch 93/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9098 - val_loss: 6.0277\n",
+ "Epoch 94/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9353 - val_loss: 5.7810\n",
+ "Epoch 95/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.8626 - val_loss: 5.8708\n",
+ "Epoch 96/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.8642 - val_loss: 5.7998\n",
+ "Epoch 97/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9136 - val_loss: 5.9348\n",
+ "Epoch 98/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.0246 - val_loss: 6.2318\n",
+ "Epoch 99/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9882 - val_loss: 5.9040\n",
+ "Epoch 100/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9004 - val_loss: 5.7999\n",
+ "Epoch 101/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.8963 - val_loss: 5.8555\n",
+ "Epoch 102/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9758 - val_loss: 5.9900\n",
+ "Epoch 103/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9700 - val_loss: 5.8655\n",
+ "Epoch 104/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.8902 - val_loss: 5.8082\n",
+ "Epoch 105/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9825 - val_loss: 5.8998\n",
+ "Epoch 106/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9408 - val_loss: 6.4737\n",
+ "Epoch 107/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9366 - val_loss: 5.9261\n",
+ "Epoch 108/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 7.1548 - val_loss: 5.7919\n",
+ "Epoch 109/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.7795 - val_loss: 5.8612\n",
+ "Epoch 110/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.7870 - val_loss: 5.7665\n",
+ "Epoch 111/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.7701 - val_loss: 5.9195\n",
+ "Epoch 112/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.8535 - val_loss: 5.8555\n",
+ "Epoch 113/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.8091 - val_loss: 5.8830\n",
+ "Epoch 114/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 585.9838 - val_loss: 5.7278\n",
+ "Epoch 115/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.7428 - val_loss: 5.6699\n",
+ "Epoch 116/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.7904 - val_loss: 5.7556\n",
+ "Epoch 117/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.7956 - val_loss: 5.6494\n",
+ "Epoch 118/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.7015 - val_loss: 5.6298\n",
+ "Epoch 119/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.7269 - val_loss: 5.6338\n",
+ "Epoch 120/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6662 - val_loss: 5.6037\n",
+ "Epoch 121/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.7103 - val_loss: 5.6566\n",
+ "Epoch 122/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6448 - val_loss: 5.7756\n",
+ "Epoch 123/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.7559 - val_loss: 5.6725\n",
+ "Epoch 124/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 10769.7179 - val_loss: 5.7412\n",
+ "Epoch 125/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6813 - val_loss: 5.6273\n",
+ "Epoch 126/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5582.1341 - val_loss: 5.8424\n",
+ "Epoch 127/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.7189 - val_loss: 5.6970\n",
+ "Epoch 128/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6084 - val_loss: 5.5500\n",
+ "Epoch 129/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6861 - val_loss: 5.5956\n",
+ "Epoch 130/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 23.6628 - val_loss: 5.6905\n",
+ "Epoch 131/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9408 - val_loss: 5.8454\n",
+ "Epoch 132/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6.0466 - val_loss: 6.0706\n",
+ "Epoch 133/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9169 - val_loss: 5.7521\n",
+ "Epoch 134/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.7096 - val_loss: 5.6641\n",
+ "Epoch 135/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6524 - val_loss: 5.5616\n",
+ "Epoch 136/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6123 - val_loss: 5.7346\n",
+ "Epoch 137/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6221 - val_loss: 5.6257\n",
+ "Epoch 138/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5994 - val_loss: 5.4892\n",
+ "Epoch 139/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5396 - val_loss: 5.4075\n",
+ "Epoch 140/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6863 - val_loss: 5.4344\n",
+ "Epoch 141/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6397 - val_loss: 5.5309\n",
+ "Epoch 142/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 1301.1752 - val_loss: 5.5103\n",
+ "Epoch 143/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4840 - val_loss: 5.4815\n",
+ "Epoch 144/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5528 - val_loss: 5.4713\n",
+ "Epoch 145/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5382 - val_loss: 5.4469\n",
+ "Epoch 146/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6885 - val_loss: 5.4334\n",
+ "Epoch 147/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4501 - val_loss: 5.4186\n",
+ "Epoch 148/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6716 - val_loss: 5.4964\n",
+ "Epoch 149/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6151 - val_loss: 5.5339\n",
+ "Epoch 150/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5257 - val_loss: 5.5284\n",
+ "Epoch 151/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5840 - val_loss: 5.5285\n",
+ "Epoch 152/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4799 - val_loss: 5.5835\n",
+ "Epoch 153/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5972 - val_loss: 5.5463\n",
+ "Epoch 154/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6395 - val_loss: 5.5798\n",
+ "Epoch 155/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 408.8311 - val_loss: 5.6334\n",
+ "Epoch 156/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6775 - val_loss: 5.5055\n",
+ "Epoch 157/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6688 - val_loss: 5.5794\n",
+ "Epoch 158/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6156 - val_loss: 5.4109\n",
+ "Epoch 159/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5038 - val_loss: 5.4449\n",
+ "Epoch 160/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5090 - val_loss: 5.4629\n",
+ "Epoch 161/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5011 - val_loss: 5.6612\n",
+ "Epoch 162/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 3068.4308 - val_loss: 5.3879\n",
+ "Epoch 163/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4819 - val_loss: 5.5101\n",
+ "Epoch 164/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4522 - val_loss: 5.5187\n",
+ "Epoch 165/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6106 - val_loss: 5.5516\n",
+ "Epoch 166/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5072 - val_loss: 5.9363\n",
+ "Epoch 167/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5463 - val_loss: 5.4324\n",
+ "Epoch 168/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4712 - val_loss: 5.6606\n",
+ "Epoch 169/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5269 - val_loss: 5.4539\n",
+ "Epoch 170/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5148 - val_loss: 5.5766\n",
+ "Epoch 171/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4935 - val_loss: 5.4558\n",
+ "Epoch 172/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5629 - val_loss: 5.4514\n",
+ "Epoch 173/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4675 - val_loss: 5.5133\n",
+ "Epoch 174/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5323 - val_loss: 5.4086\n",
+ "Epoch 175/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.8438 - val_loss: 5.4348\n",
+ "Epoch 176/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4839 - val_loss: 5.7951\n",
+ "Epoch 177/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4741 - val_loss: 5.5559\n",
+ "Epoch 178/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 12.7058 - val_loss: 5.3937\n",
+ "Epoch 179/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4725 - val_loss: 5.3703\n",
+ "Epoch 180/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3955 - val_loss: 5.3879\n",
+ "Epoch 181/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6200 - val_loss: 5.5476\n",
+ "Epoch 182/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4326 - val_loss: 5.3644\n",
+ "Epoch 183/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4381 - val_loss: 5.3311\n",
+ "Epoch 184/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 7617.3747 - val_loss: 5.3891\n",
+ "Epoch 185/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4665 - val_loss: 5.4115\n",
+ "Epoch 186/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3880 - val_loss: 5.4114\n",
+ "Epoch 187/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4310 - val_loss: 5.3774\n",
+ "Epoch 188/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4173 - val_loss: 5.4025\n",
+ "Epoch 189/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3575 - val_loss: 5.3219\n",
+ "Epoch 190/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3631 - val_loss: 5.4493\n",
+ "Epoch 191/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4110 - val_loss: 5.3572\n",
+ "Epoch 192/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3687 - val_loss: 5.3683\n",
+ "Epoch 193/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4313 - val_loss: 5.7288\n",
+ "Epoch 194/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5728 - val_loss: 5.3819\n",
+ "Epoch 195/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3935 - val_loss: 5.3697\n",
+ "Epoch 196/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 7.0695 - val_loss: 5.7113\n",
+ "Epoch 197/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5963 - val_loss: 5.5194\n",
+ "Epoch 198/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5671 - val_loss: 5.9102\n",
+ "Epoch 199/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.9574 - val_loss: 5.5739\n",
+ "Epoch 200/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5984 - val_loss: 5.4882\n",
+ "Epoch 201/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5384 - val_loss: 5.3880\n",
+ "Epoch 202/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4632 - val_loss: 5.3799\n",
+ "Epoch 203/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4025 - val_loss: 5.3593\n",
+ "Epoch 204/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 50.4140 - val_loss: 5.3176\n",
+ "Epoch 205/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3351 - val_loss: 5.3260\n",
+ "Epoch 206/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4074 - val_loss: 5.3286\n",
+ "Epoch 207/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3534 - val_loss: 5.3401\n",
+ "Epoch 208/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3016 - val_loss: 5.3187\n",
+ "Epoch 209/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4013 - val_loss: 5.3492\n",
+ "Epoch 210/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3117 - val_loss: 5.3745\n",
+ "Epoch 211/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4403 - val_loss: 5.3551\n",
+ "Epoch 212/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4858 - val_loss: 5.3500\n",
+ "Epoch 213/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3987 - val_loss: 5.3363\n",
+ "Epoch 214/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4640 - val_loss: 5.4717\n",
+ "Epoch 215/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5538 - val_loss: 5.6265\n",
+ "Epoch 216/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5769 - val_loss: 5.3770\n",
+ "Epoch 217/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6079 - val_loss: 5.5459\n",
+ "Epoch 218/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3827 - val_loss: 5.3975\n",
+ "Epoch 219/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3924 - val_loss: 5.3477\n",
+ "Epoch 220/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5503 - val_loss: 5.4093\n",
+ "Epoch 221/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4405 - val_loss: 5.4235\n",
+ "Epoch 222/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3287 - val_loss: 5.2771\n",
+ "Epoch 223/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4946 - val_loss: 5.3646\n",
+ "Epoch 224/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6374 - val_loss: 5.4292\n",
+ "Epoch 225/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3747 - val_loss: 5.3255\n",
+ "Epoch 226/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4149 - val_loss: 5.4493\n",
+ "Epoch 227/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3900 - val_loss: 5.5252\n",
+ "Epoch 228/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3887 - val_loss: 5.3667\n",
+ "Epoch 229/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4983 - val_loss: 5.5366\n",
+ "Epoch 230/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3957 - val_loss: 5.4301\n",
+ "Epoch 231/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4663 - val_loss: 5.6009\n",
+ "Epoch 232/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3826 - val_loss: 5.5905\n",
+ "Epoch 233/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4812 - val_loss: 5.4994\n",
+ "Epoch 234/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4898 - val_loss: 5.7095\n",
+ "Epoch 235/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4411 - val_loss: 5.3684\n",
+ "Epoch 236/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3504 - val_loss: 5.4569\n",
+ "Epoch 237/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4028 - val_loss: 5.3548\n",
+ "Epoch 238/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 6175.7367 - val_loss: 5.3995\n",
+ "Epoch 239/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3553 - val_loss: 5.3867\n",
+ "Epoch 240/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3236 - val_loss: 5.4396\n",
+ "Epoch 241/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3268 - val_loss: 5.6074\n",
+ "Epoch 242/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3556 - val_loss: 5.3263\n",
+ "Epoch 243/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3021 - val_loss: 5.2896\n",
+ "Epoch 244/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3366 - val_loss: 5.2819\n",
+ "Epoch 245/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2979 - val_loss: 5.3444\n",
+ "Epoch 246/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3292 - val_loss: 5.3404\n",
+ "Epoch 247/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 3057.1032 - val_loss: 5.4018\n",
+ "Epoch 248/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5671 - val_loss: 5.3944\n",
+ "Epoch 249/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3906 - val_loss: 5.3845\n",
+ "Epoch 250/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3935 - val_loss: 5.2740\n",
+ "Epoch 251/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3750 - val_loss: 5.7197\n",
+ "Epoch 252/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5055 - val_loss: 5.2997\n",
+ "Epoch 253/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3740 - val_loss: 5.3308\n",
+ "Epoch 254/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4213 - val_loss: 5.3785\n",
+ "Epoch 255/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5006 - val_loss: 5.4361\n",
+ "Epoch 256/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3856 - val_loss: 5.3604\n",
+ "Epoch 257/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 7.0479 - val_loss: 5.3835\n",
+ "Epoch 258/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4141 - val_loss: 5.3147\n",
+ "Epoch 259/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4218 - val_loss: 5.3158\n",
+ "Epoch 260/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3392 - val_loss: 5.4026\n",
+ "Epoch 261/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3413 - val_loss: 5.2921\n",
+ "Epoch 262/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5027 - val_loss: 5.4501\n",
+ "Epoch 263/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3184 - val_loss: 5.3298\n",
+ "Epoch 264/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3173 - val_loss: 5.3724\n",
+ "Epoch 265/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3095 - val_loss: 5.3588\n",
+ "Epoch 266/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3927 - val_loss: 5.4756\n",
+ "Epoch 267/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3247 - val_loss: 5.2738\n",
+ "Epoch 268/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3691 - val_loss: 5.3556\n",
+ "Epoch 269/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4105 - val_loss: 5.3146\n",
+ "Epoch 270/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 454.4595 - val_loss: 5.3335\n",
+ "Epoch 271/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3670 - val_loss: 5.3556\n",
+ "Epoch 272/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5132 - val_loss: 5.2682\n",
+ "Epoch 273/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4675 - val_loss: 5.3033\n",
+ "Epoch 274/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4767 - val_loss: 5.4167\n",
+ "Epoch 275/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3856 - val_loss: 5.2808\n",
+ "Epoch 276/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5216 - val_loss: 5.3954\n",
+ "Epoch 277/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3470 - val_loss: 5.3434\n",
+ "Epoch 278/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4569 - val_loss: 5.5649\n",
+ "Epoch 279/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3842 - val_loss: 5.2653\n",
+ "Epoch 280/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4204 - val_loss: 5.3244\n",
+ "Epoch 281/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3138 - val_loss: 5.4928\n",
+ "Epoch 282/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4510 - val_loss: 5.3752\n",
+ "Epoch 283/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3164 - val_loss: 5.3719\n",
+ "Epoch 284/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3397 - val_loss: 5.3509\n",
+ "Epoch 285/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3559 - val_loss: 5.3901\n",
+ "Epoch 286/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4933 - val_loss: 5.4493\n",
+ "Epoch 287/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3609 - val_loss: 5.4094\n",
+ "Epoch 288/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3183 - val_loss: 5.3393\n",
+ "Epoch 289/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3449 - val_loss: 5.3560\n",
+ "Epoch 290/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.6701 - val_loss: 5.2981\n",
+ "Epoch 291/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2895 - val_loss: 5.2927\n",
+ "Epoch 292/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3315 - val_loss: 5.2608\n",
+ "Epoch 293/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4342 - val_loss: 5.3210\n",
+ "Epoch 294/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3563 - val_loss: 5.3807\n",
+ "Epoch 295/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4246 - val_loss: 5.6176\n",
+ "Epoch 296/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4019 - val_loss: 5.5455\n",
+ "Epoch 297/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4288 - val_loss: 5.4063\n",
+ "Epoch 298/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3590 - val_loss: 5.3401\n",
+ "Epoch 299/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3349 - val_loss: 5.3297\n",
+ "Epoch 300/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3834 - val_loss: 5.3420\n",
+ "Epoch 301/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4669 - val_loss: 5.5065\n",
+ "Epoch 302/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3363 - val_loss: 5.3237\n",
+ "Epoch 303/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2993 - val_loss: 5.2684\n",
+ "Epoch 304/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3281 - val_loss: 5.4332\n",
+ "Epoch 305/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3289 - val_loss: 5.4076\n",
+ "Epoch 306/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3792 - val_loss: 5.3153\n",
+ "Epoch 307/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3734 - val_loss: 5.4042\n",
+ "Epoch 308/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3494 - val_loss: 5.3745\n",
+ "Epoch 309/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3432 - val_loss: 5.3205\n",
+ "Epoch 310/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3885 - val_loss: 5.2971\n",
+ "Epoch 311/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3399 - val_loss: 5.3237\n",
+ "Epoch 312/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5838 - val_loss: 5.5525\n",
+ "Epoch 313/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3769 - val_loss: 5.3655\n",
+ "Epoch 314/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3485 - val_loss: 5.4132\n",
+ "Epoch 315/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3534 - val_loss: 5.2680\n",
+ "Epoch 316/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3005 - val_loss: 5.2987\n",
+ "Epoch 317/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3509 - val_loss: 5.3846\n",
+ "Epoch 318/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3091 - val_loss: 5.3816\n",
+ "Epoch 319/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3219 - val_loss: 5.5345\n",
+ "Epoch 320/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3051 - val_loss: 5.2698\n",
+ "Epoch 321/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4930 - val_loss: 5.3516\n",
+ "Epoch 322/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3969 - val_loss: 5.3177\n",
+ "Epoch 323/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3131 - val_loss: 5.2992\n",
+ "Epoch 324/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3954 - val_loss: 5.2760\n",
+ "Epoch 325/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3031 - val_loss: 5.3876\n",
+ "Epoch 326/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3080 - val_loss: 5.2751\n",
+ "Epoch 327/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4066 - val_loss: 5.2957\n",
+ "Epoch 328/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4625 - val_loss: 5.3876\n",
+ "Epoch 329/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3035 - val_loss: 5.3093\n",
+ "Epoch 330/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3598 - val_loss: 5.2567\n",
+ "Epoch 331/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4470 - val_loss: 5.2945\n",
+ "Epoch 332/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3521 - val_loss: 5.3470\n",
+ "Epoch 333/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3280 - val_loss: 5.2740\n",
+ "Epoch 334/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3643 - val_loss: 5.5837\n",
+ "Epoch 335/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3673 - val_loss: 5.3204\n",
+ "Epoch 336/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4018 - val_loss: 5.3159\n",
+ "Epoch 337/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3116 - val_loss: 5.3364\n",
+ "Epoch 338/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3302 - val_loss: 5.5545\n",
+ "Epoch 339/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3175 - val_loss: 5.2624\n",
+ "Epoch 340/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4350 - val_loss: 5.5940\n",
+ "Epoch 341/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3937 - val_loss: 5.5817\n",
+ "Epoch 342/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3737 - val_loss: 5.2985\n",
+ "Epoch 343/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3390 - val_loss: 5.4301\n",
+ "Epoch 344/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4075 - val_loss: 5.4396\n",
+ "Epoch 345/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3274 - val_loss: 5.3246\n",
+ "Epoch 346/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5045 - val_loss: 5.4144\n",
+ "Epoch 347/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3435 - val_loss: 5.3835\n",
+ "Epoch 348/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2870 - val_loss: 5.4418\n",
+ "Epoch 349/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2757 - val_loss: 5.3978\n",
+ "Epoch 350/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2970 - val_loss: 5.5094\n",
+ "Epoch 351/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3160 - val_loss: 5.3145\n",
+ "Epoch 352/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3873 - val_loss: 5.4005\n",
+ "Epoch 353/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5345 - val_loss: 5.4274\n",
+ "Epoch 354/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3516 - val_loss: 5.4037\n",
+ "Epoch 355/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3436 - val_loss: 5.3413\n",
+ "Epoch 356/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5026 - val_loss: 5.3142\n",
+ "Epoch 357/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3134 - val_loss: 5.3330\n",
+ "Epoch 358/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3357 - val_loss: 5.3326\n",
+ "Epoch 359/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3476 - val_loss: 5.3094\n",
+ "Epoch 360/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4053 - val_loss: 5.3954\n",
+ "Epoch 361/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2666 - val_loss: 5.3279\n",
+ "Epoch 362/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2908 - val_loss: 5.3139\n",
+ "Epoch 363/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3158 - val_loss: 5.2449\n",
+ "Epoch 364/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3100 - val_loss: 5.3535\n",
+ "Epoch 365/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3767 - val_loss: 5.5399\n",
+ "Epoch 366/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4194 - val_loss: 5.4521\n",
+ "Epoch 367/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3103 - val_loss: 5.3095\n",
+ "Epoch 368/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3288 - val_loss: 5.3200\n",
+ "Epoch 369/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3641 - val_loss: 5.4738\n",
+ "Epoch 370/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2728 - val_loss: 5.3727\n",
+ "Epoch 371/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3420 - val_loss: 5.4836\n",
+ "Epoch 372/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4054 - val_loss: 5.4744\n",
+ "Epoch 373/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3180 - val_loss: 5.3983\n",
+ "Epoch 374/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3924 - val_loss: 5.3881\n",
+ "Epoch 375/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3811 - val_loss: 5.4244\n",
+ "Epoch 376/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5263 - val_loss: 5.4206\n",
+ "Epoch 377/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2915 - val_loss: 5.2659\n",
+ "Epoch 378/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3573 - val_loss: 5.3419\n",
+ "Epoch 379/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3693 - val_loss: 5.2998\n",
+ "Epoch 380/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3165 - val_loss: 5.3849\n",
+ "Epoch 381/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2897 - val_loss: 5.5561\n",
+ "Epoch 382/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5070 - val_loss: 5.6117\n",
+ "Epoch 383/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4517 - val_loss: 5.4169\n",
+ "Epoch 384/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3123 - val_loss: 5.3732\n",
+ "Epoch 385/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2567 - val_loss: 5.2507\n",
+ "Epoch 386/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4730 - val_loss: 5.3082\n",
+ "Epoch 387/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3508 - val_loss: 5.4098\n",
+ "Epoch 388/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4074 - val_loss: 5.3306\n",
+ "Epoch 389/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2884 - val_loss: 5.4003\n",
+ "Epoch 390/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2827 - val_loss: 5.2466\n",
+ "Epoch 391/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3828 - val_loss: 5.4468\n",
+ "Epoch 392/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3117 - val_loss: 5.3865\n",
+ "Epoch 393/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2901 - val_loss: 5.2659\n",
+ "Epoch 394/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2805 - val_loss: 5.2908\n",
+ "Epoch 395/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2720 - val_loss: 5.3167\n",
+ "Epoch 396/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3783 - val_loss: 5.4293\n",
+ "Epoch 397/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2569 - val_loss: 5.2149\n",
+ "Epoch 398/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3574 - val_loss: 5.3978\n",
+ "Epoch 399/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3202 - val_loss: 5.3253\n",
+ "Epoch 400/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3115 - val_loss: 5.3875\n",
+ "Epoch 401/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3737 - val_loss: 5.7330\n",
+ "Epoch 402/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3569 - val_loss: 5.3127\n",
+ "Epoch 403/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2674 - val_loss: 5.1900\n",
+ "Epoch 404/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3973 - val_loss: 5.3643\n",
+ "Epoch 405/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3747 - val_loss: 5.3957\n",
+ "Epoch 406/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2847 - val_loss: 5.4734\n",
+ "Epoch 407/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3755 - val_loss: 5.3461\n",
+ "Epoch 408/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3320 - val_loss: 5.3933\n",
+ "Epoch 409/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3332 - val_loss: 5.4947\n",
+ "Epoch 410/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2682 - val_loss: 5.3511\n",
+ "Epoch 411/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3633 - val_loss: 6.0042\n",
+ "Epoch 412/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3954 - val_loss: 5.2737\n",
+ "Epoch 413/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3937 - val_loss: 5.3873\n",
+ "Epoch 414/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3292 - val_loss: 5.3222\n",
+ "Epoch 415/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3162 - val_loss: 5.2994\n",
+ "Epoch 416/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4137 - val_loss: 5.3138\n",
+ "Epoch 417/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3479 - val_loss: 5.4031\n",
+ "Epoch 418/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2956 - val_loss: 5.6062\n",
+ "Epoch 419/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4001 - val_loss: 5.3372\n",
+ "Epoch 420/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2871 - val_loss: 5.3819\n",
+ "Epoch 421/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 9364.3704 - val_loss: 5.2973\n",
+ "Epoch 422/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2912 - val_loss: 5.3987\n",
+ "Epoch 423/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2375 - val_loss: 5.3437\n",
+ "Epoch 424/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3314 - val_loss: 5.2303\n",
+ "Epoch 425/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2617 - val_loss: 5.1938\n",
+ "Epoch 426/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3775 - val_loss: 5.3433\n",
+ "Epoch 427/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2600 - val_loss: 5.3693\n",
+ "Epoch 428/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3152 - val_loss: 5.3140\n",
+ "Epoch 429/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2293 - val_loss: 5.1917\n",
+ "Epoch 430/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3716 - val_loss: 5.2965\n",
+ "Epoch 431/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3417 - val_loss: 5.3220\n",
+ "Epoch 432/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2723 - val_loss: 5.2462\n",
+ "Epoch 433/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3396 - val_loss: 5.3635\n",
+ "Epoch 434/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3752 - val_loss: 5.3676\n",
+ "Epoch 435/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2749 - val_loss: 5.3837\n",
+ "Epoch 436/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 4963.1908 - val_loss: 5.3364\n",
+ "Epoch 437/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3237 - val_loss: 5.3100\n",
+ "Epoch 438/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2531 - val_loss: 5.4548\n",
+ "Epoch 439/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5036 - val_loss: 5.4230\n",
+ "Epoch 440/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3139 - val_loss: 5.2591\n",
+ "Epoch 441/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2607 - val_loss: 5.3472\n",
+ "Epoch 442/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2524 - val_loss: 5.3462\n",
+ "Epoch 443/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3498 - val_loss: 5.4126\n",
+ "Epoch 444/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3024 - val_loss: 5.3522\n",
+ "Epoch 445/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2204 - val_loss: 5.2957\n",
+ "Epoch 446/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.5000 - val_loss: 5.4656\n",
+ "Epoch 447/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2704 - val_loss: 5.3990\n",
+ "Epoch 448/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2862 - val_loss: 5.2769\n",
+ "Epoch 449/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3106 - val_loss: 5.2438\n",
+ "Epoch 450/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 23323.0043 - val_loss: 5.2710\n",
+ "Epoch 451/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2983 - val_loss: 5.3548\n",
+ "Epoch 452/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2687 - val_loss: 5.3689\n",
+ "Epoch 453/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2622 - val_loss: 5.2175\n",
+ "Epoch 454/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2381 - val_loss: 5.2472\n",
+ "Epoch 455/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2681 - val_loss: 5.3339\n",
+ "Epoch 456/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2813 - val_loss: 5.2747\n",
+ "Epoch 457/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2638 - val_loss: 5.2408\n",
+ "Epoch 458/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2527 - val_loss: 5.2887\n",
+ "Epoch 459/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2657 - val_loss: 5.2155\n",
+ "Epoch 460/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2692 - val_loss: 5.3070\n",
+ "Epoch 461/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3158 - val_loss: 5.4912\n",
+ "Epoch 462/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3117 - val_loss: 5.3455\n",
+ "Epoch 463/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2725 - val_loss: 5.4203\n",
+ "Epoch 464/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2847 - val_loss: 5.2334\n",
+ "Epoch 465/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3704 - val_loss: 5.3898\n",
+ "Epoch 466/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3424 - val_loss: 5.5012\n",
+ "Epoch 467/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3544 - val_loss: 5.3108\n",
+ "Epoch 468/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4463 - val_loss: 5.3119\n",
+ "Epoch 469/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3654 - val_loss: 5.3536\n",
+ "Epoch 470/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2549 - val_loss: 5.2207\n",
+ "Epoch 471/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2909 - val_loss: 5.2353\n",
+ "Epoch 472/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3846 - val_loss: 5.3198\n",
+ "Epoch 473/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3268 - val_loss: 5.3093\n",
+ "Epoch 474/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3031 - val_loss: 5.3124\n",
+ "Epoch 475/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2553 - val_loss: 5.2371\n",
+ "Epoch 476/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4931 - val_loss: 5.4623\n",
+ "Epoch 477/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3338 - val_loss: 5.2936\n",
+ "Epoch 478/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2650 - val_loss: 5.2405\n",
+ "Epoch 479/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2982 - val_loss: 5.5918\n",
+ "Epoch 480/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3484 - val_loss: 5.2875\n",
+ "Epoch 481/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3529 - val_loss: 5.3791\n",
+ "Epoch 482/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3501 - val_loss: 5.4574\n",
+ "Epoch 483/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3209 - val_loss: 5.2467\n",
+ "Epoch 484/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3486 - val_loss: 5.3046\n",
+ "Epoch 485/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2712 - val_loss: 5.2874\n",
+ "Epoch 486/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2876 - val_loss: 5.3156\n",
+ "Epoch 487/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4454 - val_loss: 5.4231\n",
+ "Epoch 488/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.4337 - val_loss: 5.3273\n",
+ "Epoch 489/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2881 - val_loss: 5.3914\n",
+ "Epoch 490/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3096 - val_loss: 5.2397\n",
+ "Epoch 491/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3714 - val_loss: 5.4302\n",
+ "Epoch 492/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3738 - val_loss: 5.4014\n",
+ "Epoch 493/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3299 - val_loss: 5.3533\n",
+ "Epoch 494/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2697 - val_loss: 5.2619\n",
+ "Epoch 495/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2564 - val_loss: 5.3583\n",
+ "Epoch 496/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3015 - val_loss: 5.2606\n",
+ "Epoch 497/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.3067 - val_loss: 5.4087\n",
+ "Epoch 498/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2464 - val_loss: 5.3962\n",
+ "Epoch 499/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2877 - val_loss: 5.2634\n",
+ "Epoch 500/500\n",
+ "1350000/1350000 [==============================] - 24s - loss: 5.2680 - val_loss: 5.3069\n",
+ "\n",
+ "********************************* End *********************************\n",
+ "\n",
+ "Duration: 3:20:19.955397\n"
+ ]
+ }
+ ],
+ "source": [
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "epoch=500\n",
+ "graph.fit(data={'input':X.squeeze(),'output':y}, batch_size=100000, nb_epoch=epoch, \n",
+ " validation_split=0.1,callbacks=[lossHistory])\n",
+ "end_time = datetime.now()\n",
+ "a=0\n",
+ "print \n",
+ "print \"********************************* End *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#dummy code\n",
+ "graph.load_weights('MDN-weights.hdf5')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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p0sMv4+h2DMGIiNrHk/de04Sgab7oUcOpT60hSRJisVi7h0HkGwxqiKgtDMMoVtAAhQ/o\nRCLh6sFeK0IdhgPklJ/CRqJOx/fogLL60/jhb+eDqU/dgBU1ROUY1BBRy1gNgq0VnACUTXFyckDN\ng28iIuo0DHHn+Skc8cHUp2445mGPGqJyDGqIyHOmaRaX2LZrEOxEkJsJOw2j2slvzx0RUTcJwueE\np/zSnwYojCWVavcoOh6DGqJyDGqIyDNOGgQ7CQaCFHgQEXUS0zRr9hEjcoOXS3NbxxoN96hRFBjs\nUeM5BjVE5RjUEJHrrOlNuVwOkUikboPgoGJQRERE3c6T8M5PFTU+mIbVDV9OsUcNUTkGNUTkGiug\nsRoE5/N5RKPRhg4uvJhqw+k7REREAeSnoEbT2t6jphuwooaoHIMaIlq0yhWcmu0/06igBjBBHXcQ\ncDocEVEHUVXAo6lPTgmKwuW5W4AVNUTl/PEOSESBY63gpKpq1QbBzfSdcRODkeYJglD8uxIRUWsw\ncC4QNA2mRxU1jp9jTn1qCVbUEJVjUENEjri5glOzGMAQEVEjuuEEt908eY59sCR2kar6ZywdTJIk\nZDKZdg+DyDcY1BBRQ6z+M5IkIRwO11zBydLpFTU8+KdmMWj0Fp9fooDzUY8aTn1qDUVRkEgk2j0M\nIt/ovGVYiMhV1vQmWZahKArm5uYgimLLq2hKBbWiJqjjJncx4PMWn1+iDuBhjxrHn8Oc+tQSkiQh\nFou1exhEvsGKGiKyZdcg2Onym34JJrrhAIeIiKhTeNmjBnAY6HLqU0tIksSKGqISDGqIqIzVf0bX\ndQAL+894Fbw4DXUa2ZbhTPP8ErI1KmjjDRo+t0TUUj6a+uSrfjkdTFEUNhMmKsGghogcNwh2UqHi\nxQkeAxii1mEIRtQ6QawA9WTMflueu81jCeLrwikuz01Uzh/vgETUFlaDYGt6E1A7oHF6kOBke69O\nBq39enWA44fHSERE1FE0zbOKGsfHBD6Z+tTpQQ2X5yYqx6CGqAuZplnsPyNJEgzDQDKZDMxBAAMP\nIiKizuWrlZY49aklWFFDVI5BDVEXqaygKa2eaTSkcVKh0u7lub3cLxFRNwraFAy+/weUj3rUCKrq\naWNjKmAzYaJyDGqIuoDdCk6lAU3QDmSDOOag4XNMRJ0iSMFSEHV6jxo/TH0KWkDaDFbUEJXzyTsg\nEbnNNM1iBU21FZya4VWVTBCDgWaey6A9xiDhc0tE1CH81KOGU59agj1qiMoxqCHqME5XcApqQBLE\nAKjTvw1rpyA9t356TRKRv3RD5UQj/DTdSFAU34ylk8myzKlPRCXEdg+AiNxhVc/IsoxDhw7BMAwI\nggBRFF096GNFDREREXmKU5/KdEOAp2kawn75mxP5AP81EAVc6fQm64O8tJKmnk4PSLx6fIZhQJIk\nKIpie592l3Vdrzqeardxsk0jt6l2HRERkW/4qJkwpz61Do9PiA5jUEMUUHYrOImi90VyfqiSaWe4\nZAU0siwjEokgGo2WHVjYjcu6zuobVHkgYl1feT92+6h1XSPb2LELfKwx5XK5qtvUutzqbYiIqD28\nqPYQNM2zcMTReE0TgqL4JzQioq7BoIYoYGqt4GTxQ5jilJfjcGO/pQFNNBpFOp2GKIpQFKXhAz4r\n/Ii28Zu5RgMf0zQhSRJisZhrIVFl+NTsfqr9Pa2m2YD/gqTKMM/qJeVkv0REXcMvfWE0DWY4DLTg\ni7BaumHqkx+ORYn8hEENUQBYJ3aqqjbUINhLTkMgu5NzN8bQ6H4X+xwZhoF8Pg9FUYoBTSgUAhDM\ng4pGAwGrx5H1WP3MNE3k83lEIpHi/Ha3AqB62yzmNvl8vuZ+KrUrbLICMCscdvu+ibzUDSe4Hckv\nPWpYTdMSQTyeIvKaD94Biaga61v3XC5XPGluJKBhRc3i6bpe7EETjUaRyWRaMrWMnCv9N1H5fz+y\nwr9UKlV1G6+CpWrb1Kp+Kg2KF3vfdtyeVmf166q8f69CrWrXkX8E6e/DYGmepgHJpCe7dnSswf40\nLcXXPtFhDGqIfMg0zeL0JtM0i9NtIj74VscPIZCXYygNaGKxGAOagOi0gzs/hQGKosA0TcRisUXv\nqxWVTtbz5Le+T9W2sYKlehVLTvfr9jZEdrz6gsXr5bkbfX0LmuaLKVgM8Ii6D4MaIh+xW8GpmeW1\n/RCmONXucei6jnw+D1VVPQtovJoK5oV2/z2oM7ViKpRhGAiFQi0Lthc7Zc4ab+X7jR/6PpUqDcAM\nw3C04l07tikdb7VG7uQe159bVS30hmk3VtS0DP99EpXzwTsgEdk1CC49aPfTSbMfQiA3x9CKgIao\nlJ/+PdPiLbYaRVEUhMNhX/WDqhXuWP2g7Mbbrr5P9bbRdb2hYMnuusWERM1sYxhGsaq2qyuf/LI8\nN4OaltA0zVfvgUR+wKCGqE2sb/isChrAvQbBfghTnGr1OEoDmng8jmQy2VRA07EHyUTzglQJRu6o\nFQhY/dKCclIlSRJCoVCx0XipVjUdB+pPvbMuW8cGsiwvqvKp1nVubmONSVVVd6fZaZpnQY1pmg1/\n3ns9BasRfjhG85okSYjH4+0eBpGvMKghajGrbFzTtIZXcGpFiNFoWbjTsfjtAEPTNEiSVAxoUqkU\nwxYiog4XlGoUpz2hWrnCXa2pd9aUbTfuGwDCuRwUw0B+dhaAu5VO1lit1Q1r3SaUy8GMRMp6SLWr\n75MfX69ukWWZQQ1RBQY1RC1ilTLruo65uTmIooh4PN7wB6+TwMNpRY1XnOzbqyoga1tN05DP56Fp\nmqsBjZO+B36pXiIios7Qir5PtVhTt908yTZNExHThNDTA3F+dTw3p8yVVofV6/skSBLMSMSTVe+c\nhjv5fN6X0/XcIEmSKw3riToJgxoij1Wu4FT6oeblCb5nKzEErKLGql6SJAmJRAI9PT0d/a2U24LS\ngJMhGBHZCcp7GB0mCEJx6lNpqOIWJw3HRUGAGIshkUgs6j4XU21kHUdGIhHPp9453aZSI2HOwYMH\ncfnllyMcDiMSiRQf1+7du3HppZcWryv9fel/J510Es4888xiT8FIJIKnnnoKExMT+NjHPobR0VGM\njIzg0UcfRSaTAQDceuut2L59O8LhML7zne/glFNOqftYiNqNQQ2RR6wqDrsGwc2s4uSkR0Szq0S5\nfTDrdH9uVtSoqgpJkooN6np7ez15fF49d+3WaY+HiIgCxMNmwo4qYRUFpgvNhBdTjWJN0bLrs9RO\njU5jq7wum83ic5/7HDRNg6qq0DQN+/fvhyRJOOGEE6CqavF66+fSY7rZ2VmIoojHH38c2Wy2uN9t\n27bhpJNOwpe//GV885vfxK233opt27bhhRdewKOPPoqdO3di3759OOmkk/Dyyy/zOId8z1//4ok6\ngN0KTrUa8HnFy4oaLxqLuvWBqaoq8vk8DMNAPB4vNrzkB3J3GB3dg+333wlJmkQ83oeLL70Sw8Mj\n7R4WERE54IcmvgAKqz75YRw+1Gz41Nvbi1NPPbXsuqeffhpvvfUWLrvssobu2+oxVOrnP/85fvvb\n3wIALrjgApx44onYtm0b/umf/gnnnXcewuEwRkZGsG7dOjz11FM44YQTGrovonbhGrRELrA+MGRZ\nhizL0DStWEFT7YPMq54z1vZOeBUctWq/pmlCVVVMT09jbm6uuMx2PB6HKIq+mRLD6Tneev31UWy7\n5Qqc+B4JZ/55L058j4Rtt1yB0dE97R4aERE5oaqAHypIGNS0RD6fd9TnSBAEnHzyyTj++ONx//33\nAwD279+PpUuXAgCWLVuGAwcOAADeeOMNrF69unjblStX4o033nBx9ETe8ME7IFFwWQFNPp8HAIRC\nIdeW2F4MLwMBP4U6pU2CDcNAIpFANBpt+/NP7fHDB+/GuWesRiJeOKhOxCM494zV2H7/nfjbr3+7\nzaMjIqovaFNpPRuvh8tzOyGoqitTnxYjaK+JZjhd9el3v/sdli9fjrGxMZxyyilYv3592xtrE7mN\nFTVETbACAlmWoSgKJEmCYRhVK2gqeV1R45SfwpdGWM31KitoYrEYP5hdFJQKIF1XMDW+E9OTu4sh\njSURj2B25g0Yulrl1u0ThOeWiKgtFAXwKCBxFHyoqmfjoMMkSXIU1CxfvhwAMDQ0hDPOOANPPfUU\nli5div379wMA3n77bSxZsgRAoYJm7969xdvu27cPK1eudHH0RN5gUEPkgDXFRpbl4lKNjYYzrRTU\nihqg9smraZpQFAVzc3MwTROJRKJuQBOUsIEaZ5omcnNv463X/wM7/3AP/veTX8GBNx5HJNqDvFQe\nyOQlFbo2g//95Ffw0nMP4uDb/w1NzbVp5AV+e78gIvIbQVVhcupT15BlueHluXO5HGZnZwEAc3Nz\n+Nd//Vds2rQJH/3oR/HQQw8BAH74wx/i9NNPBwB89KMfxU9+8hMoioLXXnsNu3fvxrve9S5PHgeR\nm3zwDkjkf7VWcLIue91zppsraqyAzJpiFovFkMvlEPXBt1xOHiNDo+Zpag5TEy9h6tAuTI7vBAQR\nff0bsHTVn2Jd9mIYhojPZD6Eb3/rb4rTn/KSikd/thfXXH8PViwfxMTB53HowB/w2ouPoic9guzQ\nJvQPbUI01tfuh0dERKU49amoG6Y+Oamo2b9/P84880wIggBN0/CJT3wCp5xyCv7kT/4E5557LrZv\n347h4WE8+uijAIBjjjkG5557Lo455hhEIhHcc889Hf98UmdgUENUg2EY0DQNuq4DsF/ByeKnE/Ag\nVtSU7ru0usaaWgYAiUQCkfkDt1yusaoIhiPBZJoG5mb2YfLQTkyN78Tc7JvozRyBvoENWL7m/0Q8\nuaTs36KiKFizZhjXXH8Xtt9/J2RpCrF4Btdcf1dx1aclK96NJSveDV2TMTm+ExNjz2Hvq79APDmE\n/sFCaJNILWvTIyYiL/D931uehQgeLs/tiIdTsOgwSZKQSqUa2nbt2rV45plnFlzf39+Pf//3f7e9\nzbXXXotrr712UWMkajUGNUQVrP4nqqoWl/6r1yDY61WWglxRAzg/kLMCmnw+D1EUiwFNI9OjKLgU\nZRpTh17E5PhOTI2/iEgkhczARqwc+SDSfUdCDNU/WB4eHqnbODgUjmFgyRYMLNkCw9AxM7kb42PP\nYecz90AMxdA/uAnZoc3oSa+BIHCGMFGpIL7/8ttzfxod3YPt998JSZpEPN6Hiy+9shisexnUODom\n4dSnlpBlGYODg+0eBpGvMKghmmet4KRpWsMBjaXZFYsa5adgx+vQyFriXBRFpFIphMPhmlVMjfx9\ngnhi4Qde/60NQ8fs1GvzwcwuSPlDyGTXIdO/EWuO+DBiiX7P7tsiiiFk+tcj078eI0efjbmZvRgf\nexav7voxNDVXmB41uAnp7DqIIj8yiQAGH14yTbNsanWnGh3dg223XDE/VbUXeUnCtluuKFZB+qZH\njQ+aCXfD1CdFURruUUPULXzwDkjkD6qqQtf1Yjjj5Yei3z5wWzmdyY5pmpBluVhJk0qlilOcqu3T\nyf37BadhAXJ+HJPjOzE5vgvTEy8jnhhApn8jhtedhZ70CEQx1LaxCYKAnvQa9KTXYM2RH0Y+dwAT\nY89h32u/RH7HD9E3sBH9g5vRN7ARoXDjq1MQEVG57fffWewnBhRW6Dv3jNXYfv+dhapIv/SoUZS2\n96jpBpIkIZFItHsYRL7CoIZoXmWDYKe3tapwGt2eFTWHAxpJkhAKhSAIQrGKptHb+ymIoYUMXcH0\n5CuFcObQLmjqHPr616N/aDPWrj8H0Wi63UOsKpFcgsTwB7Bi+ANQ5ClMHHweB976L7y668fo7TsS\n2cFNyA69w9ePgYjITwxDw8zkq5ia2I1EfG3Z7xLxCGRpqnDBw0oWTn3yH1mWHS3PTdQNGNQQzevm\nE/5WBztWQJPP5xEOh9HT04NwOIypqSlH+232/t0kiqKjkK7TmaYJKXdgPpjZiZmp15DqWYm+gY04\n6phPItW70pO+L07DUqeisQyWrnwflq58HzRNwuShFzAx9ixef+WfkEwtQ3ZoM/oHNyGeHPJsDO3U\n7ZVgRGSvkYpZKbcfk+O7MDX+ImYmX0EitQyRSAJ5SS1W1ABAXlIRi2cAFCpZfBGQqCrQ09PWIXTD\n+6+TVZ9jR2FdAAAgAElEQVSIugWDGiIX+K1Cxutwotl9m6YJSZIgSRLC4TB6e3sbrp7xKyfPxejo\nHtz3/b+Hps0ubJwYYJqWx/TES5g8tAuTh3YCMNE3sBFLV7wX6469AOFIst1DdFU4HMfg0ndicOk7\nYRgapidexvjYs9jx9HcQjvSgf2gTsoObkepd1REBcCc8BiJqHVWdw/T4S/PhzC4AQF//RgwtPwFH\nHXM+wpEULh8q7VETQV5S8ejP9uKa6+8q7ETTfNGjRlAUGD6Y+tTp78OsqCFaqP3vgEQdoFW9R7ya\n6uNk/M2scGUYRnGZ7VoBjdNxNLJtK/42jfxdRkf34JvfuLJq40Q/ev311/GjR74PRZ4qC5ZM00Bu\n9g1MHipMZ5qb3YfezFr09W/AstX/BxLJpR1/UGkRxTD6Bjaib2AjTPMczE6PYnzsWeze8RAMQ0P/\n0GZkBzeht+/Isv47jfRuIiIKAtPQMT35CqbGd2Hy0C5Iuf3o7TsSmf4NWLHm/Ygnlyx4rxseHsE1\n19+F7fffCVmaQiyeKf889NPy3HbjME1AlgGGC65gUEO0EIMaonmtPGFqpkLGy/0DzipDGt3WMAwY\nhoHZ2VlEIpGOqKBp1v33fdu2ceJdf/8lXHn5JyGIIQjC/H9iCIIgll0Wiz+XXF/c9vDvRNvfieXb\nNfB6Gh3dgzv+7ov42JlrisHSN276ND5x3oeQio8hFEmir38DVoychHTfUQg1sHR2pxMEEb2ZtejN\nrMWaIz+KfG5/cXqUnD+EvsFj0T+0CZn+DWW3q7lELREtStCmjQQhwDVNE3L+ICbHd2Hi4E7MTO1G\nIjmETP8GrDnqo+jNrG1olbzh4ZFC42A7HgU1jl8PmlbWKyf0xBMIPfEEIEmI3nsvlL/+awCAvnUr\n9K1b3RxqV+HUJ6KFuvOMichlzVZt+OWAzO2KGsMwik2CASCRSDT0AexFRU27mKaBuZm981UnO3Fo\n/x+RiG8p2yYRj0A3NITCCZimDtPUYejK/M8GDFOHaejF35mGUfLz4esN06jYTi/uo3Jb0zSA+RBI\ntMIgm5Bo+w9/i4+deURZsHTe2UfhsV/9N/72699BPDHQjqc1MARBQDK1DMnUMqwcOQWyNIGJg8/j\n7X1P4pUXfoRU+ggMLNmC2Xwat992daAqrYiCxg+fs0FXOsV1avxFGIaKTP8GZIeOw+qjzkZPj4uf\nCaYJQdcBD7/YabjPXcWqT1YgIxw4gMiPfgTluuu8GmKRX44VvSTLMld9IqrAoIaoDZr5wHUyVcLr\nihpr+8qxGIYBSZIgyzIikQjS6TTm5uYQCrV3yeVWBTqqMjtf+v0CJsdfRCSSQt/ARqw64s8xuHQU\neUlZ0DgxnVmLlSMnt2R8QOHvVgxwagRB0fjzZWMFCmGNIMQY0jQhFs9i2aqtWLZqKzQ1h7fffBqT\nh3bgrrsfwblnbKy+RC25itVLRI0xDR2zM3sLn2nju5CbffPwFNdVW5FILYcgCFAUxf3PWFUt9Kfx\nQzhRbeqToni2KlU3YkUN0UIMaohc0EwY4GWPCi+nVtltWxrQRKNRpNPpsnDGiyoZP1TUmKaB2ak9\nOHjgOUyN74KUO4B0dt18OHNaWaBxyWVX1W6c2CKCIEAQQgBqh2fJ1FLkJanqihx+1O7XQ6PCkSSy\ng/8DK1a/F6neJ20DsVzuQFd8i9pKo6OlzUuDU73EcIlaRc6PFxsAT028jFgsg0z/BqxaeyrSmSMg\ntmqKq1/60wDVlwn3y6pUHYIVNUQLMaghmrfYEyKvTxJbvYR2I+oFNNa+28nt502RpzFx8AWMj+3A\nxKGdiMbSSGc3YM2RH0Fv3xFV5+VbjRN/8P1vQ1NnEI/3+foE8eJLr8StN18+36OmfcFSo9r9OmtW\nPJG1DcSkubfxx//v5sKy30Ob0ZMe9mRZ826y/f47bftE3ff92/CVr960oLeTNTWwMFWwPa+voIZL\nAAOmINA1GdOTLxeXztbUHDL965EdfAdGjj4b0VibgnkPq1UcB+BVxiKoatmUKC+ZpglR7Oz3f0VR\nEGHwRVSGQQ2RCxYzlcmr7QFv5zXPzc1BVVVEo1FkMhlXDiKcPsaWrLRl6Jieeq0QzBzcgXzuILID\n65EdPBZr15+JSDQDVVUbevzDwyO47vpbkUgkfH/QNTw8gi986e/w8D/cC1WeWbgiB7ni4kuvtK20\nuvq6+zA0EMb42LN4dddPoKk5ZIc2oX9wM9LZoxpq1EkF1sno1MTLSMSPKPtdIh7B5KHnsPMPdxen\nAhoVUwMBs2aj72KoU9awu2Lbkt+JFU3BNd1AJByFGIosaPx9z13ftw2XvnfPzbjmmquL+xcX3PfC\nMRYbi7cgdApywNTJCr3T3sDU+E5Mjr+IuZnX0dM7jMzABqw79gIke1b4IhAWfLI0NzDfo4ZTnzzH\nClKihfzxLkgUcH6YhlPKq1WidF0vNggWBKGhgMar58bpY3RyECBLk4WqmYM7MHloF2LxfvQPHosj\nNpyDdN8RZcss67ruaBxBsmbNMG742rf4LZeH6i1Rm+pdjdVHnIZ87gAmxp7Dvtf+BfkdB5Ad2Ijs\n0Gb0DWxEKBRr74PwmcLJ6FuYm96NyfFdmJvZi570GkSiPchL6oLqpYElW/A/3vu1mvszKxt2mzqM\n0qbddZp5G2W/q+gRpUiAIMA0NOhm+X6k/DgS8f6y8STiEcxOv4J9r/3Stlm43Vis8AmmYRs02QVK\nhdXmFm6r6yYikejhbYur0h3ex3e/+7BtwMTeS/U1c8Jaq3pJkScxOf4ipg7twtTEiwhHegor9q15\nP9LZoxb9/uHJCTanPnUlBjVE5RjUELWJ1xU1bvbAsQIaRVEQi8UgCALi8bgnVSFuhzqNrVKlY3ry\nFUyM7cD4wRcgS+PoG9iA/sFjceSGcxCL97k6Jj+FetR+NZeonZdILkFi+ANYMfwBKPIUxseew4E3\nfo9Xd/5fSGfXITu0GdnBdyASSbVo1P5SPBkd34XJQ4cbeZeejF7e/+Gm+kQVAggR8KiKKZfLIRaL\n2TZd7+v/L9upcf2Dx+LYd17h+L6KoVNlaFS2ilz1FeZMU0c+l0M0GoaJyvDq8GVVzdv2Xpqd2Qdd\nlxkuusiueukbN30Wn/rUWeiJH4IqTyPdvx59/eux5qiPIFYR/PmSn4KaGlOfnFbUCIcOwRRFIJt1\ndDtWmxB1JwY1RC7wW0WNU9XGr+s68vk8VFVFLBYrVtAoirLofVfb1ov92pGlCYyP7ZivmnkRieQQ\nsoPH4KhjzkM6MwJBbGylqmaql4iA5gO7aCyDZav+FMtW/Sk0NYeJQzswMfYsRl/6KVK9q9E/tBnZ\noU2IxZ2dDASJoSuYnnyluCJN6cno0lWnIJEcXFAJVq96yY+qTY1rtldUMXRCuF4/8apmZ2eRSqVq\nvpdl+v7VNmDStTk8/eRXWxoudvpJrl3vpfPOPgI/+/lv8LUbv4lU72pfTGdyRFU9W5rb6etBUJQF\n07AmJybwwi9/idDYGNSf/ATv+OAH0VcjfAk98UThv8cfB6JR6O99L4DDS313uyAfPxN5iUEN0Tw3\nDuScHAC0qqKmGaUBTTweRzKZLKueCVowJQgCdF3FzNSrxaoZRZ5CdmAjBpcch3XHfBzRWLrdw/Sd\noP2dg8StE8dwJImhZcdjaNnx0HUFU+MvYnzsWezb88vClL35ZsSJ1DJX7q9dTNNEbu5NTM1P4ZiZ\n3oNUzypk+tfjyI1/WXYyak3PtNNI9ZKfBDFcAmoFTHdj5YolmDi4A+MHuytcdJOiTGN6/CVMju/C\nwf1/QCK+uez3iXgEoVAKPenhNo1wcQRNs+8L0w4VlTOTExN45o478P7XX0fCMDD38st4fOdObLnq\nqqphjRXIxGZnYSxfDvUK59Vwna6wImXnBqpEzWBQQ+SCVjQT9pI1Fk3TIElS1YBmMft2e9u9e1/H\nDx+6G6oyg3iiD5d9+gsYGRkp20bKH8L42A7sf/MZ5GZfQzK1DNnBY3D0Oz6B3sxI8L5lpJr88u+p\nXUKhKPqHNqF/aFOhCfbkKxg/+Cx2PnMvxFC0GNqketcE4oBYVWYwNf5icUUaMRRFX/8GLF21Feuy\nFyMcjrd7iK6o97oNWrgE1A+YhpYfj6HlVri4qxAuvvYviCUGOiZcdJNhaJiZfLW4dLYsjSOdXYe+\n/vXIDh5j23spFm/Tik1u8PHUp+d/9SucGA5DV8M4qGeRCYVwIoAnf/Ur/Ol559XfVxOPq9s/24i6\nFYMaIpe42ROm1v692F7Xdei6jpmZGcTj8bpl7e0Omfbs2YNvbftrfOzM1UjEU8hLOdz8t5/F9V+9\nC31prTilSVNnkR08Bn2Dm7DxuE8hnnC310yzeNDlviAED60kiCFk+o9Gpv9ojKw7G3Mzr2N87Fns\nfuFHMHQJ2cHN6B/ahN6+o8qaY7eTdTJamM70ImTpENJ9R6FvYANWjZyKeHKw3UP0TCe+fhsJmEIl\nAaJh6JiZ3I3xseew85l7IIZiJeFiAKfvLIJpmsjPvV0MZmYmX0UitRyZ/vVYe/RfIJUeLv67vewz\nq1ydHtfMWF3vV+fx8txOVC7Drew5hPGxMGYPxDErrcCytwUAYSiRQ/V3tojH1YnvEURUG4MaojZp\nd9gBAJqmIZ/PQ9M0CIKAvr4+1w8GnFbUGIZRd7v7fvDt+ZDm8Jz8c05fhVu/fiE+95kzkB08Fhs2\nX4Ce9BoIgojJyUlEor2LeRiu4cEWtZogCOhJD6MnPYw1R34E+bn9GB97Fq+/8v9Alg6hb+AY9A9t\nRqZ/A0Kh1i03a5ompNz+YsXMzOQrSKSWIdO/AWuPPrvsZJQ6nyiGkOlfj0z/eowcfRbmZvbOh4uP\nwNBlX4aLblLVOUyNv4hDB57H3PQrAAT0DWzA0PJ346hjPolwlV4+QZ0eV5OmedajBnD4OVxR3RMd\nGUC/Oo746znEErNYtsyEouuIjgzUv19FgRljI+1KhmF4sjgFUdAxqCGat9gTaK+DFzcrakoDmkQi\ngUQigdnZWc/667hNyk8iES8/aE3EI+hJj2DLu7/U8vEwfKEgSaSWYmXqZKwcORmyNIGJg8/h7X3/\nL1554UdI9x+N/sHNyA4ei3AkWbyNW//eNXUOU/O9NabGdwEA+vo3Ymj5u3DUMedXPRml7iIIYkW4\n+PZ8uPjPkPOH0Dd4LPqHNiHTv7Gl4aKbDEPH7NRrxel9Um4/evuOQqJnLVatPQXJ1NKGP1uCOD2u\nFkFV/dOjpqIK5h0f/GChJ42mQRBFKLqOxzUNWz74wfr7kmX/TOnyEVmWEWOARbQAgxqiNmlH2KGq\nKiRJgq7riMfj6OnpKVaxeDWWRqtkrG0bGUc80Ye8lFswJz+Rsv9Gq93BUpDxeetssXgWy1b9GZat\n+jOo6hwmD+7A+Ngfseelf0RPeg2yQ5uRzh4DCImm9m8YOman9xSnM0lzb6O370hk+jdgxZr3I55c\nwqCT6kqklmFlahlWjpxSCBfHnsPbe58ohIvZowvNiAeP9XXQZ5om5PzBYkg5PbEb8eQQMv3rseao\nj6I3sxaiGMbc3BwSiUR3/7vwcY+avmwWW666Ck9ffiPU2BxS69ZhS51Vn4qaWNIb6PyVyyRJQjze\nGT3HiNzEoIbIJX6uqFFVFfl8HoZhlAU0rRqL2y779Bfw9Rs/UzYn///++T585Wvfa8t4OvUgqt1/\nZ2qtSCSFoeXvwtDyd0HXZUwdKjR53fvqY4jFBzGwdEuhyWtyCQBgdHQPtt9/JyRpEvF4Hy6+9EoM\nD49Ayh3E5PhOTI2/iOnJ3YgnBpDp34A1R36keDJK1KxYPItlq/8My1YXwsWJg88Xw8VUeni+r80m\nRGPt70mmqTlMT7xcnN5nGBr6+tdjYMk7ccSGjyMS7Wn3EBfNk88/Py3PbVPd05fN4p3rN0AxE1hS\nr4Fw6b449cmWJEmsqCGywaMlohKLPTH1evlsJ9uXruJkGAYSiQSi0ajtAYqXJ+RerPo0MjKCL1/7\nHWx/4C5oamHVp6987XsLVn1qZgxOOV1SkuEHBUEoFEP/kuPQv+Q4qKqC8bGdyM28iBeevgvhSBJ5\neRke/IdHcd5Za5GI9yIvSfj6jRfhrI+8E0uXJJHp34CBJVtwxIaP+aY/FHWeSCSFJctPwJLlJ0DX\nZUwe2omJsWex99VfIJ4cQv/gJsRTRyGVak2ljWnomJ3Zi6nxnZgcfxG52TfRm1mLvv4NWLZqKxKp\n5R0Z6rtO03xbUXP4ehVC1OFpFKc+2WJFDZE9BjVELmnm4MuLYMc0TaiqWlzJKZlMVg1omuWHsGF4\neAQ3fO22lh2Ak7/54TXZqUQxhN6+dVi64jiMHP0XmJ1+HX9745fnQ5rDDb0/cc5G/PsTOdz8jdt5\nMkotFwrFMLBkCwaWbIFhaJie3I3xA8/irb0PYF8kiWzZClLuvT7l/HhxOtPUxMuIxfuQ6d+AVWtP\nRTpzBESHPXQ6tULTCT/3qLGYigbB6Rg59cmWLMsMaohsMKghcomXy2db29fq9WIFNPl8HgAgiiJi\nsZijclIvDgacPk4vTrZ5Ek9+E9TXpCCI6M2MIBTuKesRBRTCGl1rvCk5kVdEMYy+/g3IZNdjaOWp\ngDFeWEFqxz/AMNRCT5uhzUhnjoBQsoJUtel8pXRNxvTk4elMmppDX/96ZAffgZGjz0Y0lmnxo+1A\nfulRo+uF/4cWrjJmNlFRw6lP9lhRQ2SPQQ1RmzhpsltLZUCTSCQQiUSQy+UcjcUJr04yOYWIKBji\n8T7kJWlBQ+9YnCepncx6zw1SGCcIInoyI+jNjJSvILX7Z5ClCWQHjkV2aDOmZuO47dYvzPc+K0zn\n23bLFbj6ujsx2B8uTmeam9mLnvQaZPo3YN2xFyDZswKCwKWFXaUongU1jr6QqjEOU9UgRByeRnn4\nuIKMQQ2RPQY1RCUWc/Lf6mbC1QKa0gOQZip8vDgAZ6ByWJACJrfCROo8F196JbbdckVZQ+9Hf7YX\n11x/V7uHRlSVIAhI9ixHsmc5Vq39IOT8OMYPPou39j6Oe7//U5x7xsay6XznnrEat2+7BJddeFJx\npbJ09iiEQqyKsHhSieuXHjXV+tOgMPVJTDlcDa/G/qoJYjjqFJfnJrLHoIaoTZo9YTdNE4qiQJIk\nAPYBjbV/rzhtEOzFfp0IUjhCFATDwyO45vq7sP3+OyFLU4jFM7jm+rsWTBMh8rNYoh/LV5+I5atP\nRLLnv22n8yVTq3Dcu69r0wi7lE961Nj1ygk98QRCTzyBV575A+6R38B12ig2DS6FvnUr9K1ba++P\nU59ssaKGyB6DGiKXtCIM0HUdU1NTEEURyWQS4XC4ZhDi9SpUjWL4QtR5hodH8Ldf/3a7h0HkikRy\nwHY6XyI52MZRdSkPl+d2PI6KChj5Pe/B1373Szy5bAL7Nozjowd/iXOWnoMb3/Oe+idVnPpki82E\niexxUi2RS7xqJmyaJmRZRi6Xg2EYSKVS6O3tta2iqdy/V1hRQ37EvzERNeviS6/Eoz/bi7ykAkBx\nOt/Fl17ZsjHwPWyeh1OfHPeoqQhqLvzChbh3+l6Mrx2HGTbx1qa3cM/UPbjoCxc1tT9XxxtQrKgh\nsueDuJqI7FgBjSRJEEUR8Xgcqqoi0uDBi9P+IkELM4I2XktQx+13nX4gS0TOOXmv9dN0viC9n3nS\no8Yvqz7ZVMAc3X80/tf+/4UeuQdhNYyIGgHmgHVL1zW2P059WoAVNUT2GNQQlVjMwYZbFTWlAU0o\nFEIqlUIkEoGqqlBVtenxuclpRY0X2zrFcMQ5hkpEFHROPtc5nc8n/NSjpqIC5pLLLsHDX30YO96x\no3jdkueX4NJPX1p/f4rii8flN7IsI5PhioFElRjUELWJ3SpOsiwjn88jHA6jp6cH4ZI52l5NrWp2\n+3bzavoVERERtZFfernYTFVauXIl/mzgz/Dmy28Wr1sxsAIrVqxoan/1dMPUJ676RGSPQQ2RS5pd\nytg0TUiSBEmSEA6H0dvbWxbQlO7fL0FKECtqiPyGr3UiIhuaBng0FcY0TYhigy06qwRGD3zrgWbu\nGEITQU03YI8aInsMaohc0kzIYBgGJicnEYlEqgY0rRpPEEMSvwRAThsmNxPoUWfp9G9IiYiaJagq\nDB9U1NhNfWqaqsIMh4FGQ6IuIssyEolEu4dB5DsMaohKtOLkyTCMYg8a0zSRyWQQCoUaGptfghSv\nq2QaKfXliS4REXWSIE5z8WTMflme280KGDYSrooVNUT2GOsSuaReIGEYBnK5HKampqDrOnp6eiAI\nQkMhjRfjWez2XvDqANXrx9bu580rfnhNNMp67QRlvEREVIWHqz45Xp7brcCoyb47QQzvnOKqT0T2\nfBBXE3U2wzAgSRJkWUY0GkU6nUYoFHI8/cVPJ83NNPJ1+2DDT88H0B0HU9Sd/PZvrRFBGy8RldA0\nXzQTdnPqk6Ao7k2j6jCsqCGyx6CGyCWVJzPVAppq23s9Hre394o1jqCEHoIgsGEykY8E5b0jiIL0\n3gwEb7xUIPhkeW6oKqc+tQAraojsMaghcpFpmjAMA/l8Hoqi2AY0drdp5EDSq8qUZjQbArWroobh\nCBERkbs8+1z1cOqTI24uE86pT1VJksRmwkQ2GNQQlVjMh6EV0kxNTSEajSKTydRcAtLrD96gV9QE\nQenS6taYK/+udn9nK8xrZHunl93aBxERUSM8aSbshx41LlbUcOpTdayoIbLHoIZokXRdhyRJUBQF\npmmir6+vZkBTymmliV+mBfkpBGp0hSg37980TciyjHw+j3A4XPwmyO5+Ki9rmgYAiMwfhNbbvvJy\nZW8jp7dv5HkofT5N00Qul6v6ez9crnYdEVGQ+OHz3Rd8UlHjarjCqU9VybKMGJ8bogUY1BA1qTSg\nicVi6OnpwdzcXMMhjdf8FKY40eg42nEwa5omVFVFLpeDKIro7e1FOBwuhnRWv5p6dF1H2A9Lj8I+\nuLGus/osVR5ALTYc8jJsmpubA9D+8KjeZdM0i/9V24aIqBsJqgrTD5+RnPrUEmwmTGTPB++CRMGi\n6zry+TxUVUUsFitOcTIMw3HQ0elhSrPbu82N+7cCGgBIJpOIRCIdcfBUrzrFyyXk3WL9befm5pBI\nJGxDkVqXG9nGi7BJ13UoirJgLEBrprg1etkKlCofM6fQEZEnfFJRw6lPrWGapm++5CTyEwY1RCVq\nnWiUBjTxeBzJZLLlHyxeNtAVBMHxkuFeaOYxenmCqGka8vk8dF1HIpFANBq1PUH1Q4DWray/h1XR\nFIQDPlmWIQgCovMH7outKmrkcrNhk/V/q69Ste0rtav/kmEYEAQBmqaxXxORhzz7/PVweW5H0805\n9all+N5LtBCDGqI6NE2DJEnFgCaVSlU9gfC6oqYZXgYZTvYdtCCjNJhLJBLo6enhgQR5xs+BgdX8\nOpVK1dyu1WFTte2tAMoueHbas6kVVUumaRZDJfZrIoJ/Kmo49aklOv3xETWLQQ1RFVYlhaZpNQOa\nSk4/VL2aQuT0g8/rfTvhdtWQk8dWurx6LBZDX1+fqwcRQaq+CdJYqf38EjZVVis1yo0pcc1OodN1\nvaFm5IttDr7Yy1ZYVjplz8/Nwfn+FUy+6VGjqoBLy0Zz6pM9/hslqs4H74JE/lIa0DippGjmwLIV\nJzJefRvj1bSjdp3clS613cjy6kTUWdpVnTI7O4tYLObK+40bQY/TbbxeiW4xU+KsYEmWZUf79CJ8\nakQ3VE80xMXeMIshKArMdNqdnSmKLx6TX/F1T7QQgxqiEoZhYHZ2FvF4vKmpLk7DC6+b8joZv5fV\nE17t242KGusgPp/PIxKJIJ1O+755LhF1FrdOUryuajIMA5qmebqUrptVS7quA7CvCirV7rCpNFiy\nVturtb2Tfbp1uaU8nPrkKAxTFJhuTn1qIqhheEfUvRjUEJUIhULIZDId86HYyeHLYlnl+/l8vmyp\nbQouTtUiCj43AwPrPcHpNLjFajbosYKlyi8Lmu3XVO1yo9uUqtZfyTRN5HI5d4MhRYEmCDA0zb19\nNsMnqz51yjEpETnDsxKiCm4cFPpleyeCuu9mKmpKl9pOpVKIlHxjtmfPHvzw/h9AkyWEY3FccOmn\nMTIyUnf/jQpimMBv9IgI4HtBoxYTGBiGUfaZ1A6NBjuGYRT7ubnVrwkAoKrQBAG6qja9PzvW38H6\n/K/3dwpLEjRBKFY4LSYoCkkShEikGMY1cvtuwPcUouoY1BBVCOKJdDVBDF+8bGqsaRpyuRwMw7Bd\nanvPnj34zq1fx1WfOBvJRBy5vIQ7bv06/urar9YNazoRD56IiLqPk35NgiC4Pl1Y0HXEenpgLqKR\nr91xhLVYQDweb6h5t6iqEGKx4uNzFDZV/D6cz8MMhcr6JTUaNmmaVmySXsoPU+IWW9WkKErLK96I\ngoJBDZGLWFHT+n03Qtd1mKaJmZkZJBIJxGIx24OHH97/g2JIAwDJRBxXfeJsPHT/D/C1m79R8z74\nrVBjRkdH8ciDD0BTJISjcZx/0SUYHh5u97DaolMCYSIiV7nQo8bu89hq2N1osCRqGkKJhCsVTlHT\nBJJJJJPJhm9jmmaxf144HPZkilsr+zUZhoEtW7YUH080GkU4HMb4+DhOOOEERCIRRCIRRKPR4s+l\nlz/2sY/h1FNPtd33L3/5S1x11VUwDAOXXHIJrr766qrjIAoKBjVELmp3IFHJ6/H4oaKm3kGBtdQ2\ngLpLbWuyVAxpLMlEHJosVbkFOTE6Ooq7v3Ur/vr8vyhWLP39t27F5790rW/DGq+CJb+9V9QSpLES\nBVnQAn/PVpX0sJmwI26uPtVEM2FBEBb8FzSVvZR+97vfQVVVaJoGRVEwNjaGu+66CzfeeCMURYGq\nqlBV1fbnFStW2N6HYRi4/PLL8etf/xorVqzA8ccfj9NPPx0bNmxo1cMk8gSDGqI2CnJFjZ/7sljf\nQl88Hk8AACAASURBVMmyjGg0inQ6jampqbpjDscK4UFpWJPLF3rVeDHGIB50LcYjDz5QDGmAQgj2\n1+f/Be773ndxzZe+CAgCAAGCKACCePhnFC4LglDYRhAgCOKCn03DcPV1xmCJKNgYMAaUqrq32tIi\nCG6v+hR3/1jC70qPcwRBwPLly8t+H4/Hkc1mccIJJzR9H0899RTWrVtX/Kw777zz8POf/5xBDQUe\ngxqiCosJFVoRvPgl2HE6lkYtpqKm2lLb1jb1wpELLv007qjsUfPIP+KvrrthcQ+qYszdSlPsK5Z0\nRYI2OwWYJmCaME2jys/m/M8GTJiAYQIo2cYwoWH+9TAf3hwOd6oHPQuDn8L2D919r32wdO9duPoL\nV9nfvsGgydAKDSUN0W4szb1GghgsAQyXyFvd/J4bWB5V1Dj+gqTJJbXtCIoCM512fLtO/1JHlmXE\nFxlgvfHGG1i9enXx8qpVq/DUU08tdmhEbceghihAWFFjr3Sp7VAotGCpbUEQsHfvXvz9bTdDyU8j\nmkjjks9euaBB8MjICP7q2q/ioflVnwwlj89+9rOurvoUNNbfzo3HGI7aVyxFe7NIrjl60fvP5/MI\nh8MIh0MV4Y6xMOgp/lweBpVeNk3YB0uqCkNT696+9v0XxqAAMA0DwOHbA6gImipDJ/ug6aF7fmAf\nLN3zHXzpr66oW5FUGVRZP5sQYEoSdBHAfAglQCj8jMUFTUEMlxgsEXmsQ6c+Nbs8dyeTJAmxWKzd\nwyDyJQY1RC5qJuyobNzm9vZBq6hxul9N04pLZ1YutW3Zs2cP7v7WV3HJh49DIp5FXlLwzRuuwtU3\n3WEb1liNg5XpCcyOvtiWb7RGR0fx0H13Q8nPIJroxYWXfT7wJ4PnX3QJvn3rTfjChR8/fFL+yD/i\n81+61rX7EIrBw/zlRewrkuqtEixlkFixdlHjVBQFpmkuOEAtvPZNB0HT4Z9NMWQbLBm6ATEcObwv\nw4Bh6nUrmEovG7qGnCDWDb0AE4UKIvvQp7K66aHv2U+H+8Hdd+BLV3yu7u3tQidd0yAIItRopIHb\nz/9cFjxVf9UEMVgCGC5RgBgGBMMA3FxJSpYhvvwyjGOOcXQzQVHcC4xcrM7pJJIkLbqiZuXKlXj9\n9deLl/ft24eVK1cudmhEbceghshFQW666XVFTaMBU6P71TQNqqoCOBzQVLvtA9+7cz6kKRwkJeJR\nXPihTfjutmvwhcsvgSCIEMRQ4b+SnyGIMDUZ47v/G6FkEoIwv40olv1smAJ03UAoFAbKfi8W93f4\nhLC+0dFR/N1NX8RFp21GIj6AvKTg7276Iv7mhtsDfXK1ZvUqXHj2R/DAL/4DuqYhHI35+gT3/Isu\nwd9XnpS7HCxVKoYGTQRNkWSPbbAU6UkjtmRV02MyTRNzc3Po6elpaNvS0Ka0WsgudDLDEdtwyTSB\ncLLXvjrJ0GHYBliFYMnQCyGUIQiNTaOzC5pKQ5uSoOfB+35oHyzd9W188fOfqVmtVC0oMhQFijxb\nfL+oVd1UL7SyC5qCGi51o6BNc/FkvFZ/Ghf2G3riCYSeeALC5CQiP/4x5M9+FiFNg/j+90PfurXx\nsbhAaDKoCdprwik3pj4df/zx2L17N0ZHR7F8+XL85Cc/wY9//GOXRkjUPgxqiNrIj8t5OzkoaEco\npes68vk8VFVFKBQqLt1Yi5KfRiKeLbsuEY/CMAxEYj0wDR2mqcPUVRjG/LQUQ4dp6BAMAeFJGbMT\no/PbGYWmtdZt5rczDH2+ma0OzP//8HZG4aTSCm0gQAyFCydlpaGOGIIghHDPw/88H9IcDpYuOm0z\nHrjndnzt5m8hFAlmQ0Jl4gBGjjoaXzn5jHYPBUD9qqXh4WF8/kvXYvuDD0BTZAZLDSiGBtblOl+K\nRxIp+3Ap1YvowLKmxiDLMgRBqPu+YKcstLHCnfl+SCZMCNGYfbAkhhDtX2J/+8qfK4ImKAp0TZrP\ns+pXN9WqtAKwYErcg/f/g224tP3BB3D9jTc19RxTZ/BlpZWL0570rVuhb90K4eWXEfr1ryFdcw1k\nWW58iWw3q2A49cmWG0FNKBTCd7/7XZxyyinF5bk3btzo0giJ2odBDVGFxXxzEfSKGq+2d2PJ7dKl\ntuPxOFKpFPL5fEP7jCbSyEtKMfgAgLykINW/CkPr3lPztqZpYuL5/0J2+HhE01nbbXRdh6qqEEWx\n5n4wHwDNzc0iFo1AEADTqAh2TB2h2H+UjRUohDVzh/bilf+4F4IYQjTVj2gyi0iqH9FUdv5yH8Sw\nPw8ETdOEMvYmEquObPdQADRetTQ8POybk9lOC5YAf4RLpRYETRW/D8eT9sFSsgeRzGBT96nPziKR\nSrnyrbldUCTE/tE2XNIUedH3R8Hl20orD/rTND2Fqc3Lc3cDN6Y+AcCpp56KF1980YUREfkHgxqi\nNvJjRY0TrQilDMOAJEmQZRmxWAyZTKZmIGLnks9eiVu/ciUunp/+lJcUPPSL53D1TXfUva0gCEgs\nW4Pc26NVg5pGTrAKJ4CF6VJiOIZwLFH1ccR7h2yDpfSyo7Hu5KugK3NQ5iagzI1DmZvA9Js7ocyN\nQ81NIhRNFIKbZCHAKQY5yb75ah5n3HoNaTOTgCAglMosel9OmKYBXc5Bk2ehSjPQpFlo0gzuvev7\ntlVLd9/6RXz+wjNtp8NZFVGHf3e4GgpCxZS3+e1Qcdn6v6YXKqzMWLx8Kl3ZfR3ePojBEhDMcKnW\nmP0WLFWyC5rC8YRtuBSKsoGnm3xZnVLDIw/a94dqd6WVoGnuL83dZEji1vLcExOT+M1rCsZ+PYY+\n5Ul88IPvQDbb19BtOfWJqHsxqCFykdfLbXvNyQo/XlfUVFtqu5n9joyM4PKrb8H/fOQBqNIsoom0\nbSPhauIDy5B741VouRmEk70N3WYxLrzs8yUn5YVg6cHHnsXf3HA7BEFAONaDcKwHyf7VZbczTQOa\nNANlthDgKLlxzB0ahTI3AU2aRjjeW6i8qQhyIoneQqjgIeXgm4gNrXT1gNPQlEL4Is9Ck2aRnxmH\nqeagKzlo8nwoI88hFIkjHO9FON6DSKwH4XgvTIi2VUuhWC8G171vvsqpdIpb4WeYRskUuJL/6wpQ\nUhVlVkx/K/RCOXzZsKbWwSzfz4Lpc4VlvO/7xydw0YdPWBAsffeWq/C58z9c0RspVGVa3eHfw6aP\nUuE2oQWhEgQRsqICebs+TaJNgFX4+fW9b+DbN385UOFSvUAsaMESYB8u/d2DP8KlF14A0zAKf8MO\n1arPVzeqU0xz/r1A12EI89PgjMJUW+u9BfNTb633p8O/N2x+P38bwyx5Tzl8G3n8wKIrrTwJEdxs\n4GtptkrHhYqaiYlJ3HHHM0hMnQ1I78bYy6uxc+fjuOqqLQ2HNZ2MQQ1RdQxqiFzmVUNea/tOr6gx\nTROGYWBqasp2qe1mrVmzBjfecntT+xJEEYmlq5F763Wkjzx20WOp93cZHh7G39xwe9nJVyONhAVB\nRCSRQSSRQWqofFUi09Ch5CahzlfiyLNjmNn/EpS5cehKHpFk3+EpVCX/N83GGyBXO1nUpRz0/CyS\nI43NGTdNA5o8Nx+0FCpgNMmqiJkpXmcaRlkAg0gCkUQfUoPDhevivQjHemwriZJ9P7etWor3DiI1\n4P1Jt6qq0HW9oQNU0zQQ//VrtsFSJDWA5Vs+sjBAKvnZOnkr75tUHgYZulp+m4owSlMV5EVhwTQ9\nK1yyC7B+8D9/Yxsu3fX1K/HZvzy1JPCpCIqqVjMt7OeEsuqmws+abkAUQwhHYrbVTKhoBi6UVEM9\n+P3v2FZaPXTf3fjazbcBCFawBNhXLV1+zQ0YEiTMvfo8kiMbIYYbO4l16z2/lavataIaoVp1yg/u\nvgNf+txnygISu9Ck2K9ovkG0Mv/ahNVguvT/xXD08GsZYqjwNxTsfr/w9oIQQjT7L7aVVuF2V1p5\nMfVpvimw42DJhelKv/rV89C0d2Pf9E+gv/wyzHweA2vfjV/96r9x3nl/uqh9d4J8Po+BgYF2D4PI\nlxjUELnI6+ClGV4FO07HXm9b0zShqiry+TwMw0Bvb6/tUtuVY3CyXPlixIdWYvzZ30OX8wjFEp7f\n3/DwcPHE0A2CGEKsZwCxnoUHRIauFqdSqXMT+P/ZO+8wN8pz7d/T1bfvuu7KFXCMYyDJ4aRBEgIh\njXykkdADBptmMLYxGAw2uOBesI1tCJyQk0YqSUgICaQDhxYwYGywrXX39lWf/v0xRSNppJW00q5s\n5r68l6a8884rWWXe39zP8yR6DqH/0E4IsV4osqhDm/oskGMkNR5osih0HQHbMAIESUKWeD38KGq6\nXqwhSRIfgcTHQTFu0C6fDmE0GOOpH5sCMC4/SJpL+8wlk0kzwfRAyudaqjYRBJkzzxLnrYcr0FzR\n8xdT9ckq15/es4VLbKAZrWd/V3cJZLqJlAxIlOlmsgFFsgBFTB0jSSKgKlp+GRt3kzlBzjy/qiB8\nbA/crk9ljbn/8NvY8+yGNOCT7kJKBz7IWLe6nZABpwRJBs+5sivPWeAULHDK2uf3t67NAZYewqL7\nV5qfDzu4pKoq+KPtiL33BjzjpoByFZZgdbDg40StapcPLkl8Inf1spqG3PDEup0gIQhCyYmwi9Xl\n36vSMD5RBMpwcyarz1JCn8pQ9enwrn6En/87WqNRoK8PsqLgSCiEw7Q44LHV5LiulIy8g44cOcqW\nA2ocOcpQNScTrmQJbUPlhC+FjkOSJMTjcSiKApZlIYpiQZPtYjTY/xuSpuFqGoX4sYPwt00u48iG\nXyTFwBVotp3wR/t7QcgxSIl+CLEexDr2olcPqyJICqynDlt/9EfbyeK2NYtw0zXfhkf1oTf8PsQ3\n+9NdMPoj66mDp36sCWBozltSPp1iVKprabh0IoElQ6zbbw+XPHVgPZXLVTSYqk81zx+wzw816lRM\nOOe6LOBjGw5nqQ5nHw4np4XWKWISkipo309KNjyyrSan9xPt3A+3a1Tac9DA0jvY/YeVevlua76j\nbIjEsTWQ3o0hLnRBVsW8uZlUEFBVIMpyGW6mdFdTrtxMIEk8umW17ffFY9s24J7Fy1NwisguNT5c\nygeXxjQ3QOUTuauX1TUN48hzAybDafXoY49C6O0EG6gf9jA+oII5akqBP2Vw1MT630Gz72yQBAHF\n4wHp96NZFhDrfwPAlwrqo1o+B5VQMpkExzn5shw5spMDahw5GkZVWzLhYvPODFayLCMej0OWZbjd\nbrAsa1ZQKnQMQ3nHyd0yFr1vvQTvqCBIJv3i7WS9kKJYFxjGD2/9mLTtqqpCFuIQYj1Q8Sdb54Qk\nifC4m0CCwqgJX7F1wQynyu1aqqRONLAEnJhwKd+YKbYyTrpoNApviVWfAn/aYwuWakZ/CKdcOD9H\nTiXDtZTaLsdj8HUzIAMBEG53ztxMsixBEkUQFKlBJknPzZThitKOsXczxboPwu1K/z5xu1iEj+3B\n3ue3pMLpoNon8S7CzaSqAAgKNM3YupnsczNl9/fIQ2ts4dKrf34Ktf/1EXz3ssuw9tHHMOfyb1aV\nO6WQnEt337cEiUN7QTAsXC1jB+600hJFCKqKxXfPL1toHCGKpZXGLgOomV5D4s3ovyArbhCCACXc\nB0J9HR+uKTOMOkGVTCbhdlfepezI0YkoB9Q4clRGncjluQ1VYvyZr0tmqW2fz2dOUtrbQ3ho62qI\nchRedx1mXXd7wUl/ixlDKaJYDlx9MxIdh+AdPX5YxzLc0pIae0FzXnhqR9pOFv0NY0FKKrzByaA8\nxYXNOMrWiQSWgBMTLp1oYx4o8bhRaW5A1QJywxjE970NivHCNXK8LTiSJAmiKA5qYhV4eqc9XBo1\nBZPPv83cltNNVISbSRQEqKoMiiTt3UyW3Ez53EyJvqNwu1rNsREEhVGjpsHlF9HT+RYIOYkvf3wk\n1j+2DQANqCIu/ORkJHf9HHt2W8LebHIqWXMzqSBAkBQois4JouyqwqX3k+r/kU3rbAHT97euxcJ7\n7jNDrkiPF0LXUbCNI4pyM1UimfCxgwdBdh7DV8/0ly80Tg99Kmq8igJCkgp24oT2h/CDDU8i3CEj\n0EzhitnfRHBcEPWnjcaN4k78df/P0dlwLuqbSHwuyODt004v7bmcZHKSCTtylFsOqHHkaBh1ojtq\nioUNA5XaDoVCuOf+WTjvayPAuRjwyT4sXDwDS+/dURZYUw65R7Sib9er8IxoBUFpX6Ht7SFsfng1\nEsk+eFx1uO7a2WhrCw7vQIdQuSaLCxYsAUmIDqT5AOtEg0vAiTXmcoIlinPDO+nDiId2Id7+Ljxj\nJ4Ogyh+CWKjTiiBIEBSJwVyqCoIWUjbY0Ar/U6+acIlh/aitm4xYtBO/+McbuHvJCgDAJADnXKq1\nV1VVr/SWXs0tFQ5nAUEWUCQIPKAqoCiigNxMIhQxaduP0TYRPg63K5j2XNwuFtHOEA6+/DPLMSoa\nG0/H3r9shSwlkeZmygpxSwEnQMt/RlrBkk1uJqRBpczqdFTaOZ768aO4pt6LrjwJvYtWKaFPRn6a\nAq6LQvtDWHzNE5gg/hdGvL8fidOnYPE1T+DeRy/H1AsuwH927cK3qWMgphPgA378VZIw/YILBuz3\nZC/NDTigxpGjfHJAjSNHGarmHDVDoUqN36jkxLJsFqAxtHX7Gh3SaF9NnIvG5y4agS3b1mDl8k1Z\n7SuZ/DiXaJcHjL8Wic6j8IwYi1AohLuXXI/PXTQCnIsDnwzj3qU3YvHCzVUNa9rbQ9j+yAbEk72D\nhku5JosBoQ9c05iBOyiTTobPnyNHxaqcYImkGXjHT0Xi0PuI7n0T3nFTQDLlzR9xormWAA0urV0y\nF7Mv/TJqa8eis2M3Nj/5XM4wPtPNBAqgCg9xoQeRX8lO/l+/ZJ9zaeQpmHDu9Wlt4+3voq31VLAN\nI2CWCs/KjZQCT1Bl8HwSUBXQFJm1LxsuWRKFqzIUMcPNZLicon0Am/6auV0shESk9BeilGTCRYQ9\nPbFgEyYePQus3AXwPFyHj2GiMgFPLNiEe366BtNvvRX/euwx8HV1wPjxmH7BBaitqyvhiZx8SiaT\nDqhx5CiHHFDjyFEZVY0OmeF01KiqCkEQEI/HoaoqampqQOW5QxtL9IJzpV+gcS4a+4+8gB8/MwMk\nSYEkaZAEBZLULoIJggRFMaBIGgSRvp8kaPNRlhXQNAuGZvVt1rbasVTGY+b5zP68JISj+5B0K9iw\n+QEd0qTg0nlfG4FtO9Zh6f3rtDuGVab29hDuXXqjDsXKA5cyJ4tSLIz4gQ7Qgfoyjbq8cMlR4TK+\nFz4Id3cdAQRJwj12EviOQ4i+9ya8wdPK7oobKtdSucDt2FEjcc+cO9Bx7DC2//r34BWqInBJVVXb\nmxilqpg8UXSgHmJfl1ahjyB0x2j+aQJRZrAEAL7AjyAf60jblkgKYN3+kvssqTy3IBRcJjwWbUKd\nxw81mQRBklBcLtBwoTeqJZKuravDuTSN+IwZUJuGN7l0tclx1DhylFsOqHHkqApULROgcjkSrKW2\nCYKA1+tFLBbLC2kAwOuuA5/sM6EHAPBJCeNGnY1vfX4dFEWCoshQVBmKIkEQkogn4vB4Xdp2RTL3\nGY+qKkNWJCQScZAkQFKEZb/WRpIF22ONNqoqQdbvLhrHTCSnoPPtnTjW9Q4+4jol7XlwLhoHjr+C\nnz9/AwCkwBBBa/ZukCBJGpQVOmWAI629Zg8nCSssst9GZoIqC1wizPNr+x/auiLLuXTe10bg4R1r\nsOS+VanzEGTJ7wmh6wi4xlFle19XAi4NlewA0+jRYxz3j6OqFUEQcLWMBcW5Edv/NtxjJoKpaRju\nYZWkwX4HSZE+xA/sga++GQ1TzsKUz11UppFVXsW4lxh/PRKH9kJV5IpX3cunCz7/RRx8MeUEKksS\n8iKgiyFCkgpOQOyZREEQa8HFeaC3F2huhiAL8ExKvY6llPqulmvDSspx1DhylFsOqHHkqIyqdPns\nYu9sVzIUJFffBqBRVRVutxuMfherEM267nYsXDzDdKjwSQl/+c0xLL33flAkA4pMv8hhaREUkUDA\nHxiwbwMUleuCQOjvRt3B99E6crctXGob8TF887ProKpKOhhSZSSSMRAEQFmhkZ53QFEtMMoCmzL7\nMLZLMm9pL+l9GO2kjD60fR19e8DZwKWDHa/jt/+ab45DAzXp0CcdClG2YImDGxMxCXsjLwCdRAos\nZQCjbAdUBliynHPT1tW2cGnL9lVYfO9ykAQFSZZBkTRUlamai9tcgGnRgo0YOXLUwB0MkzLh0mXf\nnYFTTz1tuIflaIjF1DaCYDnE9++CwidA1rUM95CGTKqqgj92AELPcXhaJ4P21w73kEpSoe4lgqZB\neXyQIn0FQ7lyO4AAoKW+HvWnTsWS1yLlC40TxaJBTTGhT1d/PIh7//i/mKB+EqzXCym0F/vVv2Px\ntZ9LNeL5QVeQOhnlOGocOcotB9Q4cjTMMoBHNUwsBwN2JElCIpFIK7VtfU4HDrRjyYP3IBzvQcBT\nj9kz52YlCA4Gg1h673as37QCshKDx12HpffenzOR8HDmJGEC9SAIAjddORMLVtyaBpf+/OtjWLzw\nHn2MJCiKhfX+JKFyoCgKTLEXjmXSc785Dj4ZzoZLLR/FxeesA6BdgKuqgngiBpLUoVKW0ygDLOmA\nh+lPQFIUNPtPywJLaRBKFix9KJa+MtoqErr69oFzTUp7HpyLxpHOnXjmpcWW9hJUVQFBkGmhb3Zg\nyRY2Ge4my7EkSVr6SHcs2cImA0IRNDZusQdMW3esxp13LAJJSWl9GC6m4ZQdXFq+5nYsuXsLgsFx\nwzo2R0Mv2uOHb9I0xPa/AzIZB9E4dHmnhkuKwCPevhsEScI3eTpI5oMxwWYC9RDDPcPrnhJFuAOB\nsobGEaXmqCnwN3rst76JRR/9KH6w4Ukc16s+LZq9AGPHBdP7G2RS65NRjqPGkaPcckCNI0cZqvZk\nwsWAnaFw1MiyjEQiYZZstZbaNhRqD2Hhytsw9UtNGO2iISQ7MHvRtdiw5JEsCNPWFsSihctRX1++\n3CbW8RajUCiEDQ+vtoVLBEHAPaIN6DiEpfdux0NbVyGR7IfHVYvFC++p6nCc666dbZmIZ8MlQHt+\nBEGBptiioJIqy4gcexneSdNBcaWX8M3Us02HbOFSa8tZuOhTa7R1PV+C5uKS7cGSxcFkgp0091GG\nu8nGxaQoIiQ1adNHdr/d4XZwrolpz4Vz0TjW/Tb+uXMtVFjOqco6ZCoshC0dLGXnXLK6lFLhbHnA\nkr59w+b1WXDp/ItHYeuO1bj/vlWgKVar2lIFcNkqwwUUjffA46rFzOtuq+rPoaGDBw/giR9tr+qq\ncSTrgm/iNMRC70I+/B7U8R8yq96dbBL7u5E49D7YxlHgmsdU3fu8kmICDeA7Dg3vzSNRLL5CUwF9\nFpujhhDFgkOfACA4LohF6+fZ75Rl7bHIKmrVchOvkhIEwQE1jhzl0Mn5K+vI0TCqWBhQbZVqCh2L\noihQFAXhcNi21LZVGx9eg6lfagKrT/xYF43TvlCPdVsfxJrlm0DZTPqG+wIlFAph9qJrcdoX6nPC\nJa6+CbHDezFqTB2WLllX+AVgBf/P29tDeGjHeoQTvQi463DTjFuzJn1tbUEsXrhZD23pKytcEno7\nQHkDZYU0QGFwCUi9bwiCBgkaoIb3DuYzje22gGlM8xn4/EeWwO1Of51UVbGAogFgU6Fhc5a2siJC\nkpPZoXQW2NQbOQTONSFtXJp76U08/cJdkGQBqqpoEI9kQVGsvsxkrNvs17fbr7NZ+0mysDC2dBeQ\nC3wyekLkMGpvD2H5mttx/sWjqj73EkHR4FpPQfLIfvS+8woe/f3vcKT7UNXCpWKlKgqSR/dD7O+B\nJ3gaaO/AIbUnm0jOBYJmIMcjw/f8SwlTGkgl5KgpJvRpQDlhTzklyzLocoM5R45OEjmfDEeOyqih\nAAuVLEldyPhVVUUymUQymYSqqqitrR0wRj0c78FoV/rXDeui8fLBl/DAb66Gosog9WpLlJ5sl9Yr\nOVEkZSbdzVonKCgK4OJc2j5C228k6qX0Y0iSgiIpIEkKbs6Tp0/aXF+xcRlO+0J9Flxau2U5Vi1d\nD1I/xjOiFYljB+BqPcXuqQ+p2ttDuH3JLEy5sAE1LhpCsgu3L5mFNYu22sKapfevG7DPYt5DqqpC\n6DoC95gJAzcuUoXApWqDnkBuwLRowUbb9gRBgiJIgByesDgAeLrxPVu4NLblLHzt0+sBAIoiQ1YE\nyLKgwx/Bsi5krKf2i3x/nvZi1vGKIluADpMD6LB4fMdvbUPMVm6YhxtvvhIAYfl+I0CAAAgCBAAQ\nhL4N+jZtXVslLO1haUcaLfK0s5zDXE5vv37zOh3SpI9709alWLjwHpucTtnhe0MNtI/yKv723O9w\nxfmfxpvRv+N49GjVwqVM5aocJ/MJxNvfBclwWqgTPTyfv+G+QQFo4U9SuKcgUFOJ8RbrZClIogj4\ni6waVQrcydeXE/aUU8P9nnfkqFrlgBpHjiqgouy1FS65XYzy9a2qKnieRyKRAMMw8Hq9iMfjBSUS\nDHjqISQ7TOgBAEJSwrTWj+O+//eQFkKl3/WXFQk9vT3w+NwAVMj6NlmRIevVl2S9+pMoCYjGo3C5\nOf1Yo42lnSpDkgXwIg9ZkRAVejP2p5ZlRYKsV4kKdb2Lj7nGpz0P1kXjlcOvYNUfbjTHQBEUrhz5\nbfz2vccQkWMpUERkwyBjmQABkqC0UuFWWKSHqGRBJAtwsrbJPMfKzasw5cKGNLg05cIGPLRjPVY9\nsL70N0aBkiJ9AEGC8tZUpP9C4dJQaTDupdGjx0AUxeEZ+ACyg0t/+uURLLl7i9lGK13vBkOX1zmV\nKVVVTIBjwp3MdVkA8Ps0sARo0EMUE1BUGVBVaN9sqv4dp6/py8ZerZ2+TYV9O327tpzq1xivWvtY\nhwAAIABJREFUsVf7p1j6zT5/X/QIuIzvGc5Fo6tvD17Z9YMB8kJZ8zGl50ZKAZ3cOZpy5WPK2mZp\nq6oENj28A/91nhtvx/6N6f5zsZt8Bed9Ddi4ZQnuvPOuHOe0bhuefEy5Ensvm7cKPj4MbkQr2IaR\nVTlpzAWYKiG6pgGJA3vgGlmZ/gdUBRw1hChCKbbqUxmBUSkVn4DqAHeOHDkaPjmgxpGjDA02R00p\nxwwHeClURqltA8r4/X7QNA1FUQrue/bMubjp7qsx9Yta+JOQlLDrjz3YsGSlOU6aoAFS+0rysCJq\n3LlDqQwpioL+/n7U1dUNOAajEpXH4ylozLv+2GMLl04f+9+4+6sPAdBeG0VVEDuyH9+pHw1q1FgT\nAikW6KOFnMgmCOJ5Lb8JQRnOBCkFqtQUiJJVCZLIp0Eqxbo/4xyHe/ZilCs92SvrovH60Vfxo5dW\nw8fVwu+qg99Vq//VwcfVwccFQJahHKvQdRhcU/lKclezButekiRpCEebroEAkx1cuvP224fFLUEQ\nJGiKA01xyHc/uiEQtHUBNdVOwtTx1VtO+ekGe/fS6Kbp+PzH7h7weC3pd3p4W2YibvscTakwuWLy\nMcmyiFiyG5wriG7xKF4O/wln+j8LDxXAn8O/wJt7fzlAXqhUOF6ufEw5czTZwiYDTtkl86agyCoo\nigbDcNi0aUea68rjcuH2q76O5LH9YCa3QWASICPteSBWKs/TUEKmXICpUg4myu2DKkuQ+cSgQ1hL\nAkyVyFGju2OKqlLlhD5VXNXmgHXkqNrkgBpHjsqsSldxKgW+FJN8WFEUc90ANADg8XjAMKl8EaH2\nEJZtWI6IGEGDtwHzb5iXszpTMBjEsvkb8P3/3YbORB8CnmZsWLIyZ3tjzOVU5nMbSLNnzjVz1NjB\nJaNPiqDgbWlFz84X4QUL0jWwvVoQBKiqCq7MVug3f38MQrIrCy4FG0/Fx8adj0iyF5FkHzrCB7G3\ncyeiyT5Ekr2IC1F4WJ8Gbly18OtAx+eqhYvywe+qRb2/GW4mO1G0ITkZh5yIwROcUtbnVI1SFBnr\nt622dS+t3boCixc9kMMlVd4ytqWoUMCUCZei0egwjDalgeBSoTmMqk0zrrkFix64wQx/KnbcQ52P\nSRRFNNe9Aj4ZA+eiEZP78VL/HzDNew5mfula/NcZ3wJRIPQtKB9TRk4mWwiVBZuMRxGCxAOSioSg\nIJroAefSSjz7qFpM830KYbkHK3+yGd++6rM5E4LbQSwAA4Ml2+TfeRJ9kxQUGaApBjTNmO03bXzU\nNqxvw5bFuOOO+TbnzJUsnDKdTPlEEARoPfyJahpd8nulZMAkSeXPUSNJRYGS0P4Qntj0K0TfoeG9\ndRWumP1NBMcFSz9/OaHPSagPws0dR45KkQNqHDkaZlXaUVOKBiq1HQqFcPVd18D7GT8ojkaEP4ir\n7roajy97LCd8aW1txZplG0EVUPWgGpLyBoNBbFjyCDY8vBqd8d68cImkGbB1zeC7DsM9anx2Z0Ok\nm2bcak7CDbj0zh+6tUl4czDncYoiI8qHEeV7TZgTSfbhUO/76I93I8r3Icb3Q5B5+Lga05Xj4+pM\nZ05zggHj90GQeXBkcXdhCwkhKpdUVQEvJZAUE/pjHLwUBy8mwEtxbbsYR1JKmNu1NqntkiJi99Fd\n+LgrPS8R66Kxt+Mt/ODfy0znk+F4khUZBJAe2maGt6XC2jLzJ2mPlB46Z5OziTByMuUOtbOGzq3b\ntN4WMK3ZulwDTOY5UucjicG7rQajQuCS1QVkVH2qhpwphbiX7rx9DX74ox1lT+xdKX3vyhuxbPUc\nEx5EElEs/dnjWH7zvYjtfQue4GkFlbMeinxMyWTSrFo3ov5l8Mkwxtechkme6dgTfxX7+/egoeYU\nfObMuUX1q6qKbSJuxQJ28ib4zoJNWh+8mgQASDJv9hFP9oLLuAHAuWj0hvfi3fY/Fp5UXN9mVPQj\nM4ARYXEO1ZFNGEmNxfsHf5IBltLBjyIDNM2AphgTNhl9bN74mC1g2v7IhrwhrKWGCeVVEflmQvtD\nWHzNE5gQ+Sya5G4k3pmIxdc8gXsfvbxkWFNqGJXjOHHk6IMtB9Q4cmSjwQCASic0rWT/RpiTIAg5\nS20DwMotq0xIAwAUR8Nzrh8rt6zClpWb8/Y/XCrldQsGg1i34qGC2nJNoxHd8zpcza0ghqmCQVtb\nEGsWbcVDO9ajI9ELv7sRaxY9MOCkjyQpBNx1CLizQ8is7h9JFjSIw/eZbpxIshfd4SMYQU7Crw//\nFp1vHwcBQnPm6BDHz9Xq63UW104NaIotKoRIVVUIctIWnvBSHLFkBLyYgKQKJoAxgAyvtxekJFja\nBY52g2M84Gg3XBmPbtaHWm8TONoDl97O2McxbrCUC/NfuM3WvXTaqI/glvPW2r7OiqqYuZQEiUci\nmQDLMalwNgvUsa5nhs4pGfsNGCSJ8YzQuezlo/0HMFp3FRhiXTT2dbyNH76wIq2tbJnspYBRJkTK\nlV+p8NxK6ZAqHRBRJI0NGzfZwqXVW5bh3rvvM0FVQ0stFt5zP2RRBkXScLsqmz9nIBX63h47trWq\nci8B+QFTa2tbVmjcPQvuQWNrG/jjBxB9/w14g1NAub3D+yQydN01N2PfK3/G2OYReDn8DHpiPSW7\nrgiCBEWRoFBeoGDkgLNWwWmpf9E2PG5U4+k454zbiupfC5VTLG6lFNhJy38kSyAPHMGUUV+ESqo2\noXQa/EnyCZAUASAFriQlCUWUEOf7wbnSExJzLhrxZF/+QVYg9IkQhILhzw82PInx9LlgpR6oigKW\nYjEe5+IHG57MXX57IA0i9Mlxmzhy9MGVA2ocOSqzqik5sLX/fD/2iqIgkUiA53mQJIna2tq87btj\n3SakMURxNLrjPXnHUeyYC1U1JNwjWQ50oB5891G4WsYO2zja2oIVSxxMUyzqvM2o8zanbU8ePwiF\nT+B705doMEVKIsIbzpxeRJN96It34WDPe4jy+ja+HyzF4R+/eBvTLhyTNQmft2Imvnz5ORbYEgcv\nJUGTjC084WgPWJIDQ7lQ524ER3vAMW64aI8FyLjB0u6yhCDldi89kPMYkiBBUixoCqBJFpTKFpwz\nqVx69an2nIDp5s+tsT1GVRWEI2EtabeqQFalnLDIAD3pyb3tciulgJQkC+CVeE4g1RE+jLGu1rQx\nsS4aoa5dePKVjfq55bTE4MY4CCDlWioQDGWCp3T4ZFSWy4ZP1iTi+QHTUiy6+z7tGIJCkudBsmqa\nA2qoE+1alQ8wjRqlhcLkSuztGtEGknMjtu8tuMdOBhMYOH9YucduBUwzrpiFcePGQ4pHUJ/ogftD\nH8Xmn/wQYdN1VV3uJbvfsnKG9RluGoDCQD65WG8CHrIBbH1zzjbxeBwcx9k6ZVvq/m0LmDyu2vwn\nrkR5br3PQq4V4u/JqOvuA8JhzYnT0QEWQO97cunnd0KfbFUN126OHFWzHFDjyNEJplJz1OTabpTa\nZllt0iiK4oA/nA3eBkT4g2mwRuYlNHhGFjWuwaqafuBVVQXXPAaxvTu1hLp58jQUmy+nmqWqCoTu\no/CO03LTEAShgRPGjUbfqLzHxYUo3np6Rho0ALQJrYv24NOTv5bldsmX9FgURciyDJfLVZ4nl0el\nupeGW6UAJoIgQZE0WNo1LJ+5l379vi1cOnXkWZj1mRVZ7XmeB0EQYBhGqxpl41jKl+xbUdLhUr5k\n32KeZN+dOQHTbvzi1c0mXJJkEapZ4U7rS1XVDGBkCX0ryqmUHQo3UKW5jRsfsgVMqzY/gLsWLISq\nEvDInhznpcDWNYNkXYiHdkFpGQuuMff3QDllB5juWnELNs5bCZ+ShHv0BPhrG7F48RlDMp5i1d4e\nwvqtqxEXwgh46k0XU67KcZX+rmFqGiCGu/OCmnwqGTBJEuAusxtOFAsGJZ5JFASxNpW8vLkZgizA\nM6n0ENCKlBw/CSRJUpp7zJEjR+lyPh2OHJVZ1eioyVRmqe1AIACKoiAIQkF9zr9hHq6662p4ztXC\nn2ReQt9fujH/wY15x1Ho86zEa1Lp1xkASM4NyuOH0NMBrnFoodVwSezrBsm6Qbl9RR1HECS8XACN\n/pG2k/CWmlYEG08r93DLqkq6l0pRqD2EtdvWoSfeg3pPPeZcfxuCNhWoTjTAVApcAlLuARIUGGro\nJ0kvtOzJAZjOxMxzlwHQvotjsRh8vvTPj1aWXE5zL2U6hxQLIEoLlcu13QKqBJmHIkq25+iKHEWr\nK90VyLpoHOjeg6fefASyIkGFYkKsNACmh8mRBIVapgZfSJ6Lo+89g1fjb5t5TvLmVtJzn2TmVioE\nSG3auCUNMAU8Xiy89kJ0H94L+ozpECgBZOx41vk0h9TwJvu2QqYGmzC5XA6mSooO1CNxeB9URQGR\np1LSgQPt2PLoxqwwuVIBEyEIRZfSHkjFhD5dMfubWHzNE5gonQaWJCHIAvZJf8W9sy8vfQAlhj6d\n7I6TRCIxJDdWHDk6UeWAGkeObDQUk3qrKgl2rO1VVYUgCEgkEmmltovtOxgM4vFlj+GB9UsRESOQ\nVBIjL/gQWttaBzy23Kp0la1Cx2CMg2seg/iB3WAbRpzUF1iGhK4j4JrHlHx8qZNwR+kKtYcwY9F1\n8H4mAIqjcYA/jBmLrsOOJdttYc2JBJhORLgEDO69rZUlJ0GXOQdKIXqhebctYDpl5Jm45hP35XWt\nqapqOpQUVYIkCqg7HMJpmA65pQUyFJtwuezQuaxlIw+TLEJQErYuKStgGsWNwPn152JPfB82/XQH\nLmQ+meWG0uBUCmpZk33nz6NURLLvLAdTClBZYdHGjZtzhsnds/Be25A9o2+tklP5f2tImgHl8kKK\n9ucMYTtwoB13r7oNUy5stM3DVBJgqkR5bkvok6HQ/hB+sOFJhDtkBJops7JTcFwQ9z56OX54/RJ0\nHknCN+UM3Du79ETCAJzQpxzieb7s1S8dOTqZ5IAaR47KrFJAylDIWmrb6/WCGeQdq2AwiNX3rYLf\n7wdJkpj/h7vw3N6/4ryJn7Vt/0Fw1AAA7Q2ApDmIfV1g65oqfr5KK1+YlhQLQ5VE0IH6kvs/USfh\n1aa129aZkAbQckZ5PxPAyq2rsO6BtaB1x0C1wcNCAVO1wSVD+SDTifreHhxgInSYQAPgAMYLdWIN\nkof3QTp6DN5xU0CylbmD/u/mdyEmu/GJ5o9gqu80/KXn73ivL4TxTdNw3TkDj11RlYIdSTnD5Gxh\nkLZdEvmcQKozcgRjbVxMoa7d+OVrW7JcS+lhcsqA4WxW51Cma4nUgVJmCBtJ0hgp+8Ad7MBxl2Ab\nOrf+4U2YcmFjFmB6aMf60j+vlSjPbQl9IgjCrOw0nj4XTRQLoUtIq+wUHBfE4q+eCfLQIfArSkwg\nbBEhCE7ok42SyaQDahw5yiMH1DhyNMwailCpWCwGVVVtS20Ptm/juBkf+x6WPvcgPt723/AwQ1dt\nZajdT4WIaxmD5NF2MLWNtq91NY65FAmdR8A2jhz05L9aJ+EnknriPbYJvl888BKu+uW1kBQZiqqA\nJmnQJAWapEERNGjKsmzdpz8a26AALMOBoei07VofNGgi1Z6y7UNvT6afZ9mmZSckYAIKg0zV+t4u\nFTCJolj0uQiChGv0BAhdRxB97014xp0G2uMf+MAidfM1N+PoOy/AwzD46bFfozcexttPd2LV3YsL\nOt5I9j1glt0K6IXmgcPkcskIk8sPi7LzKBUCpfpVARMEN/YluzQwlXGO7tgxBG3yMHUkekt/MSqY\nTNiQWdkJFKjXXwd7xhlZlZ3KmlemREeNqqog84Sdnejied4JfXLkKI8cUOPIUZk1FJPwQvqXZRmJ\nRAKyLIPjOHg8ngEnO6H2EJZvXIt+PoZGXy0W3HQbgsFgzvbW5zq5cRKmj5qGn+/8Ba4487K8bQdS\npV7DoYIjtL8OOBqCFO0D4x/aqidDJUVIQor2wT124nAPJU0nAwArRfWeehzgD2cl+P5k8BPY+K0N\nAAzHgAxJkfQ/bVm2LBt/sipDkiVIqrY9noiBpEjIUFJtzONliIoEXoyntpnHp84lZ51Xxq7j72L0\nWekTvdyAKRskZQImmqRBgABD0mBoJjcwygBM1jaZ7c11gtKO0d0HSx9aaguZVj28GhuXbjgpAVOp\nny+CIMA1jdaSDO9/B+7RE8DUNpY0djvAJIZ7UBvvAjtxGtY+8X2EEzH43Y1YdsfdaG1tG7jjYVY5\nwuRQoTC5yLuv4jOtXwblyc5D9o+GtyEk+7IAk99d/P+tIUIUC84nU3CfGTlqEi/3oP7wXkBRgHgc\n5K5dcAHoiVkqVwpC+YCRE/pkq2Qy6YAaR47yyAE1jhyVWaU4ZIqpADRQ/0apbUEQ4HK5QNM0GIYZ\nGNKEQrhywS2gz24DxdWgkxdw2fwb8MOVW/LCGquuOPMy3PzUbTh/0nkY4R9R8HMajAiCwP7Qfqze\nugld0b6cgGkoJ0zGpITvOHTSghq+6yiYumYQVPX8jFTjpHioNOf623DlwqtRd16DmeA79nwYc5as\nNttojgESDFX85CMWi8Htdpf97u4tf5s9IGCSFcW8618IYEokE5BVBaCQAabsAZMgJmwAVXpb63mM\nbW8dextjzkyHABRH49/tL+CSn11esIOJAAGWZnPCqEIAU3bbygGmwXzOmJoGkCyH2P5dkPkEuOYx\nBfdnB5hm3TcTj9+xAm5FgKftVAR8NVhpAUyJRKLksZZbhbiY1m9djbgYRqCKwuToQD3EcLctqLn+\nqhvNHDVlyzFWqRw1LGvms/OMESD0sGAJGipNA243BEWEZ4yloEIZ4QrhgBpbGdepjhw5slf1XGE7\nclRFquYJXy6wo6oqEokEeJ4Hy7KoqakBSZKQJKmgflc8tE6HNNokjuIY4L/aMHPJfHzv5pna5IGi\nwJDGRT8FkRfgcbnBsSwYUpuEfKL1U9j0722Y8dHrzG00SUHgeXAKC4IiQVPUgBU2CoVd7QfaMWvJ\nHaDPDpYMmCohpq4JyWMHIMUjOW3+ofYQVm/dhO5YPxq8NZg76+asxK/VKFWWIfYch2/S9OEeygmr\ncrvGgm1BnHvxudj53E64aQb1nhbMWbK66t9Pc66/LW3ybQeYKJIEhcJDUozy3GyFJ0a3/MMeMn0q\n+ElsvGRDhoPJCnpS66IiIhaLgXExaQ6mTMAkZ4AiK2CS9WMqBZjSgBC0/CUMxdg4kAoHTB6CwdTj\ncSQ796LdpYDKDKnLhFY2gKnFV4frZ1yC0L7dOOu8i0Ax1ZvrolAX0wP3PFgRIDoYMYF69IfexfyN\na7Mg05gxY7H6nq3Y/MiG8uVhqlSOGkufl6+42cxRw1JsqrLTipvTj/F6y3P+EnPUDHehhErLqfrk\nyFF+OaDGkaMya6jLc+cqtV1s/13RPlBcTdo2imMQTsTQFe2DpGghD6IsmxOFJM9DJQBZVSDJ2jZR\nEnCM34WFv98AAh5zuyTrkwZZhqjIIAkCNEmDofQLfwsAIgkCDEmBoRk9JwapXbTr0IehaFAkCYak\n8dvv/1iHNOmAadb9d+Cam2fqbSlQICAJIgJ+P2iK1s5nnpNMHwtlTBAoMBRlmTzkvtOc+ToTBAmu\naRT4jkOgg9llptvb2zHrfgMw1aKLF3H1XbPx2LINVTW5tnv/HN27C+2hA9i47ZETCjCdyBoI6vUm\n+rBfOYAfrH0cPra4UunDqWBbEDuWbLc4DU4MwAQMDJkKcTCpqooYl12eu5IaDGBK8BocIijSNkSu\nEAcTL8YRVmQcVTrxUaoFYwUSz/G7EVP4nCFymYDpLP8EXD7yHDzd9Roe/uGTmBj+NRRVzXIlkQQJ\nhmRM8MNQGvQpFTCVEiJHU3ROF9Oah9di47INVT0ZP9TVAzLSh+iHwkiQYhpkamxoLH8epgq4TzJD\nn4zKTj/Y8CQ69apPmZWdCEGAMsw5ak52OTlqHDnKLwfUOHJ0gsmYOFtLbVMUlVVq21D7gQNYt207\numMxNAf8uPOWW2ydJo2+WnTyggk8AEDmRUwbPRE3fOpi27FEIhFwHJd15/qfoX/jZ2/+HOu+vBQU\nqUGjeDwOgiDgdrv1Eq4KRFlOTQTkFAiKxKJQoYKkKUiKBnZkE/gYx8gQZQm/BdLGDH29Px7FsXCP\n2b8gaWBJm2DIGdBJn2woigmU0rbpbbU7zYaziDaXDYhjwB0D+rgoBjeMmIRH3n8VEVXRoY/mJvr9\n4z/LBkxnB3HL8rtxzU0zU33pLibKcl4DUlEkmQaVaCv0smwbyL1UjEKh/Ygd2ouf9OxG7+nVC5js\ndKI6mELtIVx91+y8UO/Z9/+Mj7eeXXWQppDXPNgWxMZlG4ZngIPQiQqZBgOYBE6Aqqplq9Siqir4\no+24tL8BnnFTQLk8Odve8o/ZOCocw6Wt5+JDvlasP/A77O0/YgImuxC5aDwKgiIAAnlzMFUyRE5S\nZLxxdCfGnhlMez4UR+MfoX/hGz/5jgUM6bDHBD35k3yXFTBlOJiM45dtWYGvfuEsnNUwEc/17jQh\n09pt67DsrqVleR9YRYiiFo5UJvX19uLlvj7wP/kJ+LFjccZXvoL6+noExwXNxMG2KmNSYyf0yV5O\njhpHjvLLATWOHJVZlXbUqKoKRVEQDocB5C+1HQqF8L0774I87QyQjS04KAi45NY5+Mn6tVmwZsFN\nt+GyeTcAeviTzIuQX2rHgpVbCh6boU+0/Td+t+tpPPv+X/CFyedn7ddKuFImxMlUnPPhwMGDWLdt\nMzrCkRRgGhfMavvLxrF4nxdtAdNNn/66uU1RFPT396OurvScMYqqmK4g66PhLhIlEZLuLjJgUjwW\nxlfdk3GQZU3oI0giVFUFnQMwHerrMNsa/Vj7tN1mBViKBFmHTpp7iUy5kkjSdC5ZQROdAXcMOEQR\nJEgQ4FgODEnh4PMv4ruf+zR2J/vNMePsIGYvvxvX3jzL0qeNUykLJlnHk+6aosqciLUQ2FGoVFWF\npMgQZAmiLEKQNRAoyCJEWXs0tmn75VQ741EUEecTUEki1U9aH6m2r/7iz6j/xKlZUO8bC2birIs/\nB4og0Sn+ByPcp2PB7x7WJ17Z/5eGO6xY0EeTNCRBgNftAUNbJ3Tp7x/r/zNJkGg/0F6217xadSJC\npmoCTARBwDUqCNLlRmzvTnhaTwHtr7VtO/fam9C3700cJfqweN9PEU3E0wCTXYicCxwYhrG9iTGU\nuuVf9i6mT4/7JNZfss6EOv2RfrAuzqyqlAZ+0kLcyg2Ysh1MxvrrR/6DtngDzq2biud6dwLQIFNP\nvCfX0x2cyhj61Nfbi/+sX4/zk0kQ8Tgi77+Pf23YgDNuvRW1A10LWEp6D1pO6JOtHEeNI0f55YAa\nR45sNJgfxnInB7ZKkiTwPA9ZluHz+QZMErx840YN0ugXCCTLQjr9w/jm7XPx+e9eBkbPF6NNskh8\n9qLL8Lff/xJJVYSX8+DCS2bgheMdeLmzW5vkUaQ5IWMoCqLAw8Vx8LhcoEnS3E6TJC467Zt46MX1\nmNp8BmpcfkiSBIog4CrgwqP9wAFcdccCbex1TXkB023X3YDr75sH6O6UwQCmgUQSJFiKBGtzp1kU\nRSiKgvYDB7Bh8xZ0RqJo8vswf9ZM1MU60NY6BSSj/T/IsozfN42xBUynj5qAGz9p72AqRaZ7SS/R\nKuaAO9qFuZLmIpJkGbwkghd4EDQFWZExbfp0PH18X9o5KI5BbzyC9t5jBfZvdUXJ+sRATnM4KaqS\n00lEZ4AfAxSkoBKb5m6iSRJPPf5TWwfTjMXzcOEV3zDBiD1oSYETUdKWKZIES2mhFCzFgKVpMBQD\nlqIt2y3baCatPUczcFE0vG6vfoy+n04ts5QW+jfnuZ2I20C91tpmLPjcZXj1yCt48WA3rpr+jSwn\n2EBQLyHyWa+9JGugTzQnezJ4UYAK5AGF1nMqUFQFh/70Gkaec3rWa37Vottw8bWXIsB5EXB5EXB5\n4Hd59HXt0cu5yuoEc5SuagNMbH0LSJZDvH03XCPacDTK44ePPQpJSIJiXbjk/12EZkoE3XoKHnl8\nBxBX0eoZPSBgqpYqcPlcTFYHk8zK8Lq9VTU5v+WF2XizZz+uHXUe3CSLhCJA5iXUe5orc8IyOlne\neuYZnEvT2CdORJtKgaUofIai8M9nnsEnL7kk77FEiXDFVo6jxlaOo8aRo/xyQI0jRyeAZFlGPB6H\nJElgWbbgRJkd4QjIuqa0bSTLwstxOPeUyfoETUlN0mpqMOLqmWA4DqKsuUeO9vVDlGVzXTKWZRlJ\nQYCsqpBVVZ+0KXpbbVnmPJjx4/vBR4Lmdi2PgAXq6LDIADwMReHN3/0WrulnZgGmb82dhwsuvSwN\nCCmyjPO+ehn++affIikm4eU8+NJ3rsf/dXbh9Z5eE0JRJAkhmURtwIBO2rkZKjXhzwRXZrs8uWms\nCrW344p5d0CeNh1kQwsOCQIum38nfr7kTrBdR+AaGTTbzr5mFmbdf0caYJJeDGFumSdPA7mXMse/\navMOEzLNu/FGjGkdDVEU4Xa7ISdjOBqOY8UrrwBsqj+ZFzFt1ETc9Mmv5+m9OGnuJSUNGuSCDoaD\niBcFJPgkaIbOBlOyBMYGdsgAPjRiXBZoYSlGhyYZ4ISmTWg0qOenV2fzFpCsclRNE3bbQL1RNU0Y\nVz8SO159Gd+Y+jVMHTl+UGPKp2KrPsmKgu++eS36bV5zmmExrn4kwnwcHdFevN91CGE+hnAyjkgy\njjAfQ0IU4Ofc8HMeBFxe/TG1XOPywu/yIqBvN5aZMlYhO1FD5U5U0b5aeCdOw+5//gWPPfkrzLnq\nO/C4XYgnkliz5SHcOOcOjJt8atGAqRqgRzW5mIqVAZl2Bw9jqq8VL3a+i9jzYdy2eBWA8r++RBlB\njRDqRk8njWNyI7huGgpFgGFoCEx3AQeXuTy3J3dY3wdVyWQSgUBguIfhyFHVygE1jhzMYspCAAAg\nAElEQVSVWeUMfcoste3z+SBJUsElR5sDfhwUBBN4AIAiCJg8ogXnT51ie0xPTw/q6uoKuviKxWKg\nKCrnHZHeRB9u/M1srL70EtSz9ZBlGW6PnmDYAnYkWYYkK+a2G/7xN/RngCiSZeFhWHx8woQ0IBRP\nJKD4/fjOjJv0vjT3SKir2+zTmKzHE0kQFGV7XuPcmWPTIJaih+gYbiIyFVZiLJMk3vjdU2A/nO5g\nkqdNx40bH8ZDMy7Fj97cA5AUKIIAVBUXfO0K/O2Pv0FSTMLHefDF71yH//T24+3+tyzuJf28JGlC\nI4aiQFGklnA5bdkYl1FZq/AL6FB7Oy6fOz8NMl0+dz4ef3A5Ro7QSq0LnUdB1zUj+cI+051SKcCU\nci8N/DOlASYtTK7e48Gds29BsC29qs2zLb+yhR2nNLfiglM/Vtaxl1tzZ92Mq++abQv19vXsR3e8\nGx8dfdZwDzNNFEmiyVeHHrvXvHEMvvKhT+Q9XlJkRPk4wsk4wskYwrz2aICco+FuRPg4+pMxRPT9\nkWQcLM3Az7o1p47ba7p0DOBjwJ4A59VdPB64GS7r+66coXJDrRMZMFGcG7/8+0smpAEAj9uF26++\nFN//8Y+w8L4lwzzC0lVtLqZCZUCmv//5NzirbgKO7IlgzpLVaGttQywWK/8Jy5ijhg02oF7sQbfa\nj5EjAYlQAEoCG2woaBzlLM9dtsTEJ5F4nofb7R7uYThyVLVyQI0jR1UoRVGQTCbB8zw4jjNLbRer\nO2+5Bd+efasZ/qQIAuidb+DO9WtzHmOAo3LcJatz1+LrU7+GR19+HPM+MQcAQBIEWJoGm+dCrK2h\nAf+xAUynjGzBhdOmprWNRqNgGGbA5JaqqqK3txf19fVFPw8tJ0kK7hiOIqsjiRcE3Pq35xG2AUx9\nsQQiFItzW1zYL9MQJAkJnkfNqFG4+MrrdceHgoQiY8+xDrMylqS7mFLnU2yBkjmmjO0UQaS5lQyw\nRFkcQ5QOfl79za9BTZueBZm+d8+9uPDSS1HLcbi4nsYfowS+ePGVeP4Pv0FSTMDHefDlS2firb4I\n3o28Y4KkTNBkAqaMEDnD1WSMr1inSiZgOqwDpidWr0yDNflgR7Ur2BbEY8s26JPvPm3yrQODzS8+\njAsmfr4gx9RQazCvOU1SqHX7Ueu2L21vJ1VVEReT6Ar3ISokEJd4E+CE+RgO9nWYy+Gk5uAJ8zEo\nigKfxbET4Lz4209+axsqN2/lYsy+/TZLzp/0XD75ckEZuXsq6fA4kQEToI3/3XffgefiC9K2e9wu\nSAI/TKNyFGwLovWKmYjufh3n/b8rQRBk5cLKypijZuoFF+Cvu3ahWVVB0BQEgce/VRXTL7hgwGOr\nIfTpg5CjplyJyR05OhnlgBpHjsqswThqVFVFMplEMpm0LbUNAO3tB3D/qvXojSbQUhfAwrn2VZwA\nIBgM4n9WPohVm7egNx5HU8CPO23yvJSqQp7rV0/7Mp7Z8yzePL4TUxqyy1Tbad4Ns3DF/DsKAkyF\nvt7GxU4pFz4EQZhwIZckSUKwMQdgGtGC8ZM/hNi+d3DGtA9DBQoOeylVqqpqIUNmyFoG5LFskxUF\nC579EyI2kIkigGB9PSYwCo4rCkCRqG9uwVcvv9Y8tl+W0XX4sH4+4xwDQSWbMckyCIJIg0qmQ8gG\n+tAUhf/71S9tAdO8FQ9ixZLFaAkE4OO4vLDjRFCwLYiHVqxJ2xbmw3jp0MvY9OV1wzSq/Brq15wg\nCHhZN2g/WXB4KAAIkphy7OgunX/gdyBtwrYO93Xi+fdfh6RIWeF1Rj6frG1yKkdTdu4lSnePMTlz\nL9kn/k7BIGt/P9n2uC1gmr9qMW6de3sqz5NdnzZV5YxE30ORL8iATAQpIp5Imo4aAIgnkqDZ6p7Q\nnchOpkJEMhxI1gU5Fgbts0/4XBaVMZ9LbV0dPnzzzfjnur+ju64WvM+Hj1x00cCJhI1xlDP0yXHU\nZMlx1DhylF8OqHHkyEaDvYNR7J0mVVXB8/yApbZDoRC+PfN2hJtOB0k3Y1e3gNdm3Ipf7FifG9a0\ntWHj8mUFQ4FiQdNAYigGV3/kSjz+2hOYOXEGNj38GI73hvNCpmAwiO8vX4aNjzyCzqORsgOmSkkD\nTAs0d4cOmKg3/4N5q1eCcvtAuT0QeztA11UoCaNFhO6moSkKwMAXm+ObG/G6DWQ6deQIfOX0D0Fu\nfxvecVNxuruy5Z9lJcM9pDuFrGFoomyUbFfw3jN/QNQGMO3t6MC9v/k9jkfCIEGgJRBAS8CPts98\nAR/Tl/tIGkf7+9Hk8+mv09CpHJ+zv+x9Hh8dcxZqXNUb428HmKpNLM2gka5Bo7fG3Da5udU2VO7M\nsafgnvOvLPlc1txLmltOQjgaAedyZSVqNnIviXpi71SyZ7sKcFqbSDIGFzcy7ZwUx+BQbwee3f2y\nXhUuGyANlAuKJEizGphZLcwWJlldRfaVx4yqZJmVx57Y+ijos4OQYknM2/IwVt0w08xRs+LxH+PC\nq6/AW8f2p8BUnspmQwWXDJXDyTSciY8LhUx0TT3E/h7QvtqKuT0IUYRaJCDp6+3F7q9/HeJ55wHj\nx2PqBReYMKbG7cEpNIVJN96IWCwGn6/A37Ayhz4V+5w+CHKSCTtylF8OqHHkqMwq5sJFVVVIkmSC\nmnyltgFg6eqNOqTR3QM0i3Dj6Zh5x2LcfOvtoGk9hwqt/dEUCVWRQRGA3+c1t2v7KLB6e5omS0qO\nWuhk8+yxH8PPXv05btxyN471TgNJ1w0ImdpaW7Ftbe4QrVJUalhXKBTC0tUbcwImI5dQS0sLdjyw\nBOu2b0d3NIpGnw+3Lb0fI1pawPM8iNoWJI+3g/VqF7mCIKSNLXOsudaLaVuo5t14YyqEyAKZ5q5c\nATXSC5Jzg6owpAH0ErskiULvnU9obrIFTP89YTw2X3sVVFVFlOdxPBzB8XDYfHxhb5e53hOLoc7j\nQUvAbwId62NzwI+Ay1VVFnRZkfHH957F/E/NGe6hnJSqVKhcZu4lVVXBqVThk0ekcjJZk34bYX4v\nj3w2B2A6FfdecHVJY1ZVFbKqwZx4MglRlkAxNCTZ4irSwY61cpi1CphRRc4OAsVFHrIiozcehodr\n0ZxLU4DLNq1HDcOh83AHPnzZl/BcxzuQju20qTxmU7FOkaGqMEMtc5WUt1tnKN1NZAlls5aepzLh\nEEnh8S3bbZ1Md61ZigULFmSUsU93LRmV7EiChKIqQ/49UwxkYgINiIXegWvUuMoNSJKAInLURJ5+\nGv9etRrx/3Si99ibqOFeQHjbdnxq3lz4v/hFqEkBJFUCtCunC0YQgBJCfJzQJ0eOPthyQI0jR2VW\nofBCFEUkEgmzlLff7x/wB/l4bxgknW7ZJWkWBw/24s+v7tacBpLmSBAkGaIkQxQlCJIMWVEhSnq4\niSTrbVOPBAgT7hgwh9WBDkPr2ygqDQaRhAqaouDm2KxjNRBEmm3f+NVh+D7Jgn6BhCKlINNNdz6A\n2+fPTzuOUBWoiozaQCLtfEb/xrhIkii7AyhToVAIX59xK8KNp2cBpra2trRQNYZhMHHCBGx+8EEA\nNndIvQFIJAUl2ge4fGn7M0u6W/dl9jPQulUHDhzEui070NEfRXOND7ffdB1aW1vN/cZ7rqW5GTse\nWIL127ejKxpDo8+LOcsewIiWFogHdoFpHJ0GlqzHFrJeCbgE5AZM81avNPv1u1zwu1yY2Nxk24ek\nKOiKRk1w0xGOINTdjZf2h0ywIykyWvzZEMd4bPL78uZdyqV8k+58euXwq2jw1GFCfeUqPQ2kUsd+\nIqhaQ+VyJf02cjJVAjARBAGa0CADoX9NVWJy9cav/2pCJq7eD5xzKiK8iA+PCOKhKxcU3Z+sKIjE\nIqBoGioBEyqluZVkycadlGPZAoREWUJS5M39nZE+eLl0pyTFMXi/6xCeePWZLPdSzvMpMiiCzAZI\nA4W95XEXpUEhKhtYPbJpqy1kWr11U5YbjnR5ABVQknEQXIVCVoqs+vTPo8fwEv05eOAGMeIj6GRI\n8MrfQBw9hgsBqLwAgiKLhh6EIJQtqbET+mQvx1HjyFF+OaDGkaMhllFqW5ZluN1uMAyDvr6+gi4g\nWuoC2NUtmI4aAFAkAR+fOh7rbrIvjczzPARBgN+fOymnls9EK7Hd3dMLhnVBJQgN5Oh/kg6BrIAn\nHk+CF0RQDJNqa4Agy3HRBI/uw0kEOhvRMP4YOveMAaDBmvfbO/Djv7yaAkiSDEGSIIiSVvbbes6M\n/imSSMEj488ASxnOIoamAFWBx8WltU13Iem5UUwYROJ/H31YhzTpLqab7rwfN8+eDY5h4Pd5QJEk\nSEIFyzBaol4DclnBF0uDbBmLZMchYOSEikx2rNAmFGrH9+YuQqRpGki2Be9HBOy87R78aPNKtLW1\nZrWfOGECNq1YkdafFO0HZBmUvy4LCFUKLgHa5PDAgYNYu3k7OvujaKr14fYbNchk/ay0NDfjkaX3\nY922beiKRlHv8WLusgcwcsQIzcVUICBq9HjQ6PFg6siRtm1jPI/OaFQHNxq8eaW93VzvikYRcLnQ\nHPCjxW8Pc2o97rQ+Dxw8iOsX3Zdz0p1Pf3jvGVw4aeCEmJXSQMCg2lUIZBqKsC1VVSFIEsLJJARR\nAi9JSEoieH2ZlyTLsohtmzaZUBJI5WS6b906bFu1akgAU6Xu8JcbMhmAwsW6snK9laJ875ldv/uX\nrZPpY61TsOorNxTUv6qqiEajcHs9ae4g0epUssAde/dSfthkuJes4Ohofxf8XGP6a8cx6I71ZY2R\nIAgwNfUQwz1gm0YP4tXMoyJBzSvPHYQrfjYo5R0osRgoloVL+Qheee5FXHgNAEEESnHUOKFPFRfP\n8w6oceQojxxQ48iRjQZzIZrL4WFXajszkfBA51049xa8du2tZviTIgkIdO3EwuXrB/VctHwmBGiK\nhN/jMgHSQEomk5BluaD8N+/8/bf41243xn1qL/oPNUKIu6BIAs6dNhHb534nra0gCOB5viC4FI5E\nIUoSGJbTIU+2a8hY7+3rB8Nx2e6iTMAky4jERYiygs6+CMhRGTlQaBZ79h/Hj55/A4relyBJlnMq\ntu4lSVbA0iR+8N0zsPHJV7GrM56CSnoImgmQ0txJZJarSGtHpjmerGCIpij8z/bNGqSxQKZI8zTc\n8cBqLLzrrjz9pWCX0tcJsrapovZku89LKBTC1XPuQaRZg0zvhVOQqbV1bFrb8ePGYdOKFWY4mTWJ\nbLngEgmg2e1Gs9uN01uycwzJioL+RBId0Sg6IhG8d+gQnnj+b4ipKggXB8bnhSAraPL70Oz3o9nv\nw9+f/JntpPuOFQ/iljlzICsKZEWBolcd09ZVdCeO472uEE7xsfjx8Ze17aq2TzbbablQZFVNW1f0\nNlJmO9t1VU+Iq0Fc6zj2/flPqP/Y2Vlj//Itt+K0C7+kVxWjzepiRvJnu+3W/azx3if196H10Xhv\nmsmm7f8UWQJL0yD1KmLZpbfzQyZJUcCLYhooSeqgxIQpVpAiSentLfsFSUIyY5+1rSDJoAgCHMOA\no2lwDK09Gn/Gdv2vOxIBZZOT6f/2hXDhhs3gGAZNPh8az/w4pvl8aPL78J/efhwW96LJ70OTz48a\njxtkFYZTVAoylQMsDZWTSUumrjldyq1coGnfH16yhUwNlpxNVjGBBiSPtVcM1BBFlueOoBZ1hPZ6\nqcb/NUEhAi3hsZoUQDAlvJ5lBDUQxaJDn4zfn5M59Mlx1DhylF8OqHHkqMIaqNR2MT/CwWAQP9+x\nHvcuX4W+GI+WhgAWLs+dSNhQpUKDigk7WnDrTfj29XPQvXc0miYfwsFXWnNCpkL6NeCSm2PgYml4\nPJ4Bx9DX1we/31/U3dW9//4D/mrjYjpn+mQ8cufl5jZZliGKYt4y6qqqQpIV8N1HsbylAcrISXrl\nIyXlWLJxL+UCT+ayvj2eFM2qSqIk40hXH8ixE9PGQNIs3tp3BKt++lyqPymVrNdYliQF9R4K2785\nDZc88RoklUhBHSoT8FjXLZNoOh0k5QNQmevbN2/UIE0GZFq4fC0W37vIBEnW/iiCgKwoYBim4he3\ndu9Pv8+HMU2NaG9vx5I7HzfHr0gC/B3/wPfXL4Wrrg4d4TCOhSP4I5+eWwfQJt27jx3HT195DTRJ\ngiIJUAQJkiT0dRJH+P9DHTURB7t7QRGEmdeHIkmtJDtJgmNoUISxndCXLW0JUuuP0pepVB+dHR34\n8ZO/Qm84hnq/F9df+m2MHjUSLE3r7SjMfOsNdNqMfUJTI1ZdfJGWADqjspf5HpO1feb7Xm8nyNp7\nPymKGnBVdPCqJ5g29ovmnxa6YlQRSztGliEqChRFyYBAFPY++wz8Z300CzJ94aZb0PLpc7W8MRZg\n4qIZc52labjMfelwxcUwqHG74dLhCqvvc5nQReuLtfTNUhSSiUTBOWoO/fUvtjmZPnPqZDx0+2z0\nJxLojEbRFYmiMxJFZzSK3ceO41+Rveb2mCCgwetFk9+HRh3mGI9Nfr8GenxecMPgAKiW5NOKqiIu\nCIgmeUT4JBatXGULVW9bugw33TYHDEXi5uvn4smf/RBRvhuNbj+uvvUeSG4vQl3d5ndVZvU6LTdN\n5Sfi+UBTsZCJ8gUg83HIQrZjsSzKUZ67r7cXb/385wDPA42NZsLgUz97Kv71YwaTASAQgEyS2Cvx\n+MRnTwUAqDxfeuhTuT4DPO+EPtlIEASn6pMjR3nkgBpHjsosAzJYS22zLJsFaOyOKeQiYty4INav\nuB91dXUFu2VKGX+5NW5cEI+vX4qNO76P462d+O/xnVhSAGQaSARBZLkk8rUt9rnddfstePXa2WmT\n7kDXTtydx8WU7/wMTYFuGgW58xACDEDXVK5iz7HXn8M/em0g04cn4eGFl+c5UlPiyH6oioKnl50F\nlnOZziMpIxTNXNcBkRX8iJICydgny6llSYYgyognBctxqXb7j3aBbEtPWEnSLF7bcxB3bHsqDVBl\nAitZUW1AkAUaGe6OYgBSDsdR1nE0hYfWr7OFTMvXbcaKB5ZgXEMzJjWPwFMtLXjXZtL9iUkTsPE7\n37L9P4kJMcx66ldY8cXVqHMXUGLWIrv3fqajqL39AG5eskYffz329wjYc89yPLb2fowdP878DhtR\nU4PjNmMfWVODkTWBvO6kcoTF2S0b66FQuyVkzo/Zs67FiNGjtApiqoJZr72MXhvINGXkCPzw5lmg\ndRfOUORcKvb7KF9OJoIgUOvxoNbjwaTm3JXleElCdzSGzmgkC+h0RrX17mgULoZBo8+HRr8PTTrI\nqXW50OT3YWRdbUnunKHKayRIEmK8gI7ePkh9/Rp04QVE+SQiSR4xfdnYFk3y5nKMFxAXBLgZBl6O\ng9/FYdeRo/C1ZnwfsSwO9/bir7v36BW8FLR88jw06FDyR++8C2nn2yaoNCB6alnbriUuJvWExYTp\nODNgDmPkp6GMZdJ0ndEWB5odCDK+6/532zZb0LRq82ZsXrmyKCcTQZBg/HWQI72AtwJlum1Cn8yE\nwa8dR++YD6OGjZgJg78xksBe5W/Y728BeewYZJrAiNqd+MbIj2oHCxIIengdNYQgQHVATZYcR40j\nR/nlgBpHjiogVVXR398PiqIQCATKEh9fqiqZbLfYvtvaWvHIpnV4+dArePTl/8GY1jFl6bdSEkUR\n9fV1+J/1S7H+4UfQ2Rct2MWUTwRJgqhpAt95CHTbqQUdE2oPYe22deiJ96DeU4851982YEjAgltv\nxH9umGuGPymSAH/nm1iwePWA51NlGWLPcXgmToMiKfC4hvYiM/zO/2fvzMObKNc2fs+SpFm774UG\nBFRARBBEEQRBNnePC4oCKu4LiILrpwc9ooIIuOEBQQ4IB/cdxBUFPSqKKGqFsrRAoXRfss76/ZEm\nTdsskzTTpOX9XVcvmpl33nkyDZOZe57nfrYFFJlGn9YHrzxxY8BtvOWFer2hhXjUJlupdUaS4Jfp\n4Vf+1iZzSRDh5gXYnO4WWU+tt9t9sBx0j5Y3nzSrxQ9/HsANC9b7trPXW1D7+xbkjhnlu+k++tUW\nCD3OxOjZL7bIYPIKQUgrBXQpuP/lzwIIS35Ckq8ELkwJXavXi55dFFBkmr/kZSx+5knotFpoWBpz\n7rgd0wK0on/g2QUxLZNTIi75U1JSiuvveQSN2ac2l8zd+yhef+FpFBZ2hyzLyE9NQXUAkSnLbAKN\n5vK4jhKXAMDpdAIASksP4rmXlqOizoasFBPuvfMWWK3Nn6W83FyseupJLHrlFVQ32pBhNuPep+cj\nPy8PPM8rEpc0NI0cixm5rURi/7GyLAfMztlTUYHv9x9Atd2BKlsjHBwfPjvHbIKOZRX7GsmyDCfH\no7FJNGlsElK8v9vdHGyu4CJLo9sNSZJg0ulg0Gpg0es9gotOB6NOB1OS5/eC1FSYknQw6fx+ml4b\ntNoWnRDv+PvPgJlMQ3pY8cQlF4b824dCbipN5EUJnMDDZndAo9M1izkBstKEpgyytgJQy7GCKMLm\ndkOQJFTbApfMVTbaAESeycRa0sHVVoBWQ6gJYLzbbBicBOSegUoGzYbBN96A+848E5s3/4GaGiAt\nDRg//m5YUptKn9zu6ISaWBoARyH6dPWOT4DHo4Zk1BAIwaHCXFzE/06JQIgDrVsnK92G53k4HA5I\nkgSLxQJWYZ11pCU5tbW1ITN0/BEEAXa7HcnJgevNW2Oz2aDRaBTdbCnxkvEiiiIaGxuRkuJpTf3P\nL57AoPzTcHHfthe5kcQciU9OfX09DAZDWP8dSZLgcDggCAL0ej20Wm3YCyYlpU/+NNbXQz5UBFOf\ngaC1oZ8olZSW4KZHb4ZxtAWMjoXoFmD/ugErHl8eVqwpKSnF00teQmVdIzJTzHhg1h0tbvyC4a46\nCsFWB0PhSbDb7RG1D44FJSWlmBxAZNrw8rNB4/cKNUo+C2py66y52FprbiMyjUhtxCtLPN2oZFmG\n3W5HVXW1J8OgoRFpJhPuvulm5OUXBBSM3DyPF357GpO6X4HspG5tPJaCCVJhBSi/krrvP3kT7AnD\n2rwn+9/bkD/4PN/2nCBAsNfDeWQXaAagZQoZvU6HOSUzhEAUyEspSDldUGGpVVZTq/meePJJ7HRl\ntjn2w5PrsWzJAjA03VIw8BOZYmWEHIm45M28TEpKQmnpQUy58/5WJXO/Y+3zTwU0/w73Wg1xybuN\n9zzHCQKq7fYm4caOKrsNVTY7qm12VNntqLLZUG13QK/R4Og3X0N/2uA2Yofw+070nXg+7JwbNrcb\nDjcHLcv6hBOjVgtTUhJMOi1MuqQmMaXpdz9xxf93HcuCoijY7Xbo9XrF5+VgqP2ZAdQ9f90xdy5+\nNae2OfanNdbipQULIp5PFgU0/LUdbI/+MJrCf/8rRhRhSkuDrb6+xeInpswDWzIMml1/QuzXD2BZ\nSJIAwfoD/m/dYyGndH35P1TMXor8HevgdrsVlUkDgKmwELYdO4D09KjfjhfD0KFwrV4NqW9fxdsk\nyveZmkycOBHffPNNXB9mEggJQNAbDJJRQyAEINKnGN5W27Isw2AwwGazRfzFkyhZL5ESzdwUReHG\nIdfjwU//D6N6noPkpLZPdpXOG+nYUPiXq3n9hJR+FiL9zNAsCzotG+6KMugLTgg59rl/L/aJNADA\n6FgYR1tw8c1XYuDQIZh82UUoyCuAjtFCy2ihZbVNv+uQW5CLZYufiSg+WZbBVR2BvqBX+MFhiCYT\nCACs1kJsePnZZpEp1YwH5gUXaYDEycSKJJOpsHt3xTdKvxz5FakGMy4dPFK1J623lv0aMJNpzKDe\neGXxHS3Oa6LUVghqLfy0KIkLJyT5bedwcW3m4wWp1XZts5p+KypFUu+WJqc0q8Wn24tw2s0LQcFT\nfigZTkTj1z+AomXQYJB34hmY9ernfp3glGQitcxgUrody7ZcznNuJJvNePzZFwJmMy16ablP4FML\npeISx3Eef7CmhxAajQZGvR7d0j3rS0sPYuGGZahoEoafuutWdOtWgHqnCzfs/AX1AbI60gx6zB4z\nCiadFgaNFkZdczZLNOKSyHlKl7znA5fL5VvXemyw163X5efl4bWn52PRsldQZbMhw2TCfc88hYL8\nfAiCoEpZXCwJVTIXDRTDgtYbAacNiKVQE6Tjk79hMLyim59hcDBqa+uw6Ysi1DWmIeWNbRg9+kTF\nQg0pfeoYunrWEIHQHohQQyC0A0EQ4HQ6fa22lWReBCJRfGQinTsiY75W83ZP6YaRPc7Gup3/xe3D\nbok4zvZSUlKCx59civKKBuRkWfDAnNuRlZXZoeVqmvRcOIp3QpfTHTQbPMunxlHjE2m8MDoWvA44\nyLjx/KcrccqAXqA1lKczjegGJ3LgRA5ugYMgCdAwGo+Iw2ihY7XNvzM6aFkttIzG8zujRXc2Bf2Z\ndHx64AtoWC0oETAbzE3jm8Ug3/ZNc+tYz/ZMU8eS1plAB91luOnRm/HYzY9hzevvo7yyETmZZjx0\n/50BBRirtVD1G1Q1UCIyRXOe2LRnMyb2Ga/qhW0wkeneB59oM5ahaTDa9mUqxJpb64oCCk2TzuiH\nZYvn+jpY+QSeQEJSABNvQfAXjPwFpGaPJru/uBRSuGpbTifJMnbv2gvDiWe3eD80q8UnP/yBIbct\nCmm+HVgI8opHfkJS66wkb2ZTm3GButHRkCUJWpaBPknnF4dn3dGyMkyf9aBHbNJkYk89h99nPujL\ngrNmZgQsHzohOwsDmjKGIkGJuOR0OqHRaEDTtKIMo9LSg1j4wiuoqGtEVooJ9915KwoLu0OSJBTk\n52Pxv55osS3P8zHNXJJlGQ6HI+C6cK9DrcvPy8PqZ57Csy8v8wlNcxY8jW4FBRBFMSpxiTWnQrDV\nAZm5Id9fGxoaQB85AumkAGW/QYSavt0ZfPfLMfRKTgZdVwdRlrFf4jF8RPDv6eR1x1sAACAASURB\nVNraOixZshPaQ8OhBYvK4hH488+vcN99SUhNVVCyRUqfOoTj4T0SCNFCSp8IhCBwHBf0AksURTid\nTvA8D71eD51O1+LLJpLSJABoaGhQ3BIb8JTwGI1GRaVVkiShvr4eqanKjEftdjsYhlFk8OYt9VJS\nohQojkZ3I257/y78a9w8WFMLQ44NRiTlV42NjdDpdDhy5AguumImyh19QdFayBKH7KQ/8O6GxejZ\ns2ezIa4gguebzHCFIP/yXmNcAS4nB0mWg4xtuczhcAMUjXN6M2h0Svh2t8s3zn//giDhj7/Wofu1\nuhZijegW8MvSGoAeA1niYJT/wIChlzSXhGgYaDQMtBoGDEtBowUYjez5YWXQGgmURgLDygAjgWJE\nUIwEmRZxQV4udtvrsNtZAwkCeMkNigUk8BBlASIECDIPQebBS94fDrzEwy26QYOGjtVi9wdFyBtT\n0CbuP1/cB6d4UfOxN/yJd9YtUlSSFQpvOZGaZVolJaWY/8yLYUUmJdhsNhiNRkUXqkcaj+Lhzx/D\nKxe9CB2r7pPZQOVyWVmeFu2JnqJeUlKKybfdi8asUxWXzMUTb5mlyWQKWjJ3dkoDliyYr0BAajap\nbRaQAnswhTLjbh7nzVbym0do6rLlnw3VtN2RX76EsffQNvG79v8M6xnjIDrqUbZ7KzJHjfRldVR/\nuxWnDZ2E1MzswMJT2NI5/+VthSyB42A2GaBhWb8udYFNwUtLD0ZcbhkpocQlb4dIf8+OSESgkpJS\nPPviK83+Rnfc4iuZaz02FuKSxLkgHS4G2/MURYKR5rvvoPnuOzCHDkHz3XdwX301AIAfPhzC2Wej\nvq4Of73/PvSPPALn4sXoN24cUpquAerq6rFwwU+oLu0Gxi1A1LFItx7C/fefgZSUlID7feON71Bc\nfDY0fxeD+fFHuKdPB8+7cPLJP2Dy5JaCaBtkGebkZDTW1TVn8LQDY69ecGzbBjknR/E2oihGVKrV\nGZk4cSK2bt1KxBrC8Q4pfSIQYkHrVtspKSlBv2ASoQQjGjoqowYAzDozJg+4Eq9ufw1PnPdYh31Z\nP/7kUp9IAwAUrcUxV3+cfs4t0GeMaH4SzdJgNX43AsH+1XjbG1Mt1vnmCLCdVut5Kn2wkcaYXiJq\nZAvoprKLluNpbN67F59//D4KL7D6PGr+XFMKSRwFmvbEn5dpweybR4LjRfB8840b7/+alzzrmzos\ncY4mocm3XkSKnkJKXhK+/IyBy50GjhfhcvGQZPjGeMUp79ycT7TyCFY0A2j1NEDtQ7cAmUCCzgjK\n5XfsHf1w4ZVzcNaoq9oITRpN82st63nNsjS0fuu13mOloSGKPCxmU8sxrEewar2d/99VyWevpKQU\n/5hyL445+oGiUyDv4/DLlHtjIjKF49M9n2FMz9HtFmmUCE2BMpn8n/InMlZrIdY+/zQWvrAM1Q0O\nRSVz8cb72QuWzfTgvGeh12mgR8e3y26N2+1pyawNkB1w2bTdKG71+aRZLfoVZmP5Q9eBFyUcKDkf\nK/6zCjV2OyxJejzw0P8hMzs3jGDUsrucw8UrNgXneB6ihJailt/vgl/pXP1fW2Huc0bL0rPMAZhw\n/Sz0HXmhghK4tkKS0hI4DUuDBgXIIkxGQ/juck3LvJ+dkpJSTJ31kOezo8lCcT2H3+95OCYiUzBx\nSdBo4GQ10Eg8GIM56HifEDVyJNwjR4L96ivQ5eVw3n+/b119bS1+f/FFjHY6kUxRqNuzB1uKitD/\njjuQnJICo9GAmbMG4osvinyGwWPGnAqdTucz4m693yO7bZDLj0GqrAQtipDKysAAOELb2pzP2ohL\nggATw8DV5FXYntI2iqIAjoPIskCrDpWJUBYXLzrrNTKB0JEQoYZAUEAkrbYB9UuZ1PJwiYb2zj3x\nxPHYuHszfjy0HcO6DwWgrkeNJEk4Ul4Him6Zak/RWgwfYsXGD/4ZUfxeJEkCx3GKs6gcDocvQ8Fe\nUoTLu1ugy8xvM+5/h35EQ2051j61GstfW4Hvf92O8qNayNIo0JqmrhYSh949MnDm6e2/GXUcKgat\n1WHZM+f4lkWS+SHLMkRRBi+ImP1/R3DUXd4mo0ZwaAG/w0TRWqRYknDRuL4+oUkQJHB+YpBXdHK6\neHDejCNebDmeF+F0uSHLVAuRiuObM5Raj+d5z02aV8jxZiFp2JYikUbDYO8fn8CO/i0FPkc/XHbN\nAzh3wpSA2Uw+cSjAfKLAwWQyQusby0CjaSkwaTQMRHDYcuBbzBv5OFxuAVoNA5qO/II+nkJTR1JY\n2B0vLJwf0+5THUE0vkyJRFaqGbsDlJ3lZaQgJ83jQ9YtMwUjhyztsJiUnrtkWcZl0w5gbwChqTAn\nHU/NuKBtaZyf8NMig6lVSR3He8SlwBlRLQUnrqkMrtnLKXjpnCjJPuGm6vct0J8wpI3I9PSSl9pd\nPhro2FEUBZqmQZuSITbWQmtW3v2JkWXQGk0LsW/P119jrFYLnSBAphkYdDqMFUVs++YbnD15MgBA\nr9dj6lRlZVayLCPvRBOK6WzQDgdQXQ0qLw+C4EZuH2Obc0MbccntBrRaXwZhqGwkqZX4EnAsx8Et\ny5CaRKVgcwXCbrcDiFIgimJse7eNlONJnCIQIoUINQRCCGRZhtvt9tW5K/UuSRRTU3+U1jurlVET\nLA6GZjBjyPV45cflGJx/GjSMek+NvRlRNgcHWeJ8N9yAR+zIybKE2Do80V5w6LIK4Cj5G9qMXFBU\ns4JxqP4wlm9fiUdGPYAT0nri+flL/W62Db64sw1/4qH7lbdWDYYk8ODrq2A+cXDUc3iMRj2ZRfff\neV+bblUH3jwAkR8Bxu86WZY4nNw7G+ePPbld8Udb+iRJsucmKIDw48scEkTMmv0t9la2vJGjaC2S\ndAxOP7XAl23kEYY82zhdPBoa3Z4MJj/RyZOp5IYkU76sJkEQ/cSp5qylpJ5l0OZqMen59b4YaZoC\nywbPMvI+3fcXjH776T3U8v3aCE3znnwer61o/+eH0H46qy8TEJmJdqJBURSy0yzYE0Bo6p6dhhO7\nZakeQ6SlLpLPb0nE1Tf9jQMBRKbKukY1QgXgOd8yplQIx0qAvB6Kt6MEoY0PDVdSjZpKFnwNjSPC\nMBSWUwBYcJrqqGKjKArjx/dHUdEW0EIOaIaBJPEQxS2YMOH08NdxsgxotYrL0cPGw3HQJyeH9Klp\nfd0lCAJ4nveVoUdartZucUnh69YEE3lmzJiB2tpaaJuOq0ajQUVFBaZNmwZNk3Dn/Wn9+swzz8RZ\nZ50FAJg3bx5WrFiBrCzP/8n58+djwoQJAICnnnoKq1atAsuyWLp0KcaNGwcA2LFjB6ZPnw6Xy4VJ\nkyZhyZIlId8DgZAoEKGGQAiC2+2Gw+EATdMwm82KW21HSklJCR6dtwgVVTbk5aTg0Ydnwmq1htxG\nbTElEmIRx6D8gShILsBHRZ/gsv6XtJhbaRZHKLyeQoIg4I0P/0Qd3wMZut9Q5e7v80nJMfyFRx/u\nuCe9/rAGM2hdEvjaKmjTPBcfds6BBVsXYerAKTghradvrNVaiHfWLcL8Z17EscpjyM4046H7Y5MR\nwVWXQ5OcAVoTGw8Ua6EVKx5f7uv6VG6vwdU3TsZbz/2IY47kFh41sRCaooWmKWhpj6ARqhFq7x6Z\nKD7WVuDrf1IuJl88MOL9KnnaL8syZm2cgxmn34lTHurnW+bNWvKKSBzf7KfkLwj5l6g9WrQJdXVt\nhaaNX/6FIROfR+8eGejdIwO9eqSjV48M9OmZgfTUrtsatqMJVHbWvXu3eIelmJKSUjwxfykqqmzI\nzU5uUzbX2TOCOpvQ5DlvsdBqWORlpGBfAJEpMzWGHZkCQCUZIIsiRLcTjE4ffgMA4HnIrQQQrTUd\naXwNZJmDi21ATo4MThShtUbfGjs1NQWzZg3EF/NWoPZgCfJ7GTFmzCmKjIQpQWgTY9RIUkBxqs0+\nA2SqeDOXEhWl3eIAYObMmbDZbOB5HjzPg+M4LFu2DOeeey44jgPHcb7l3t8bGhrAcRzq6upa7GP2\n7NmYPXt2i2VFRUV48803UVRUhMOHD2Ps2LEoLi4GRVG47bbbsHLlSgwZMgSTJk3C5s2bMX78+Bgf\nDQIh9hChhkAIgiiKMBqNUT1RUSqklJSU+JnapmHHfg4/XzETH761NKxYoxbe8iClYyOdO5j4cuPp\n0zFn04MYfcIopOqVp1GHu8n1b7e9/v1dWP3GDmz8772Q+HqPV01lBXIyLXj04fYf8/Z0adBlFsB1\n9AA0qZmQIeOFH17GgJxTMLrnOW3GWq2FWL5sYbtibY0sSeCqjsLYs19M57UWWvH8fI8AVm47hvs3\nP4xVrz2GlxavibnQ5D32anXLeOj+O/GLr3SoY0SmP479CYoC+mf19S3zz1rSJyk/P53cJxsHfmgr\nNF00vj8e/b+p2HugCnv2V+H3v47i3Y1/oHh/FWiaQq8eGejRLRkn9spGn56Z6N0jA3k5FpKyHgHB\nys7efn2h78lwItMy/nT8eiBw2VwiZwSF82fqzEJTvEQmiqKgSU6DUF8NJqtA2UY8D7R68NV//Hhs\nKSrCMI4HKAqcKGKLIGBgO2+mU1NTcPXpuWDd+9F45VkQRVHZhjHu+CRrtUAXPF8GK4sLxBlnnNHi\nNc/zWLVqFaZNmxbxfgNdX3/wwQeYPHkyWJaF1WpF79698dNPP6GwsBCNjY0YMmQIAGDq1Kl4//33\niVBD6BQQoYZACILRaFQsWERLIFPbckdfPP7kUqxasTjodtF62qhxYxWrEq/85DyMOWEUXv91Pe46\n6/Z2x+TtSOVtt71i3Y9Y/cYv2PDKFHTPTwWQGvIYR4r36Vck4/2PHWtOAY5SEBpr8cHBLWhwN+De\n4bNiFl84+Poq0El6MHr1MihyTNkY03M0ttV8F3OhqSNQM5spGBuLN2Ni79i05A4mND18/yJ0y0tB\nt7wUjB7eyzdelmVUVtux90AV/tx9BCWHa/HVtn0oPlAFu4PDCdb0Flk4vXtkoHt+Klg2cZ8AdxQc\nL6KyyoaKKhvKKxux6NkFvuMONJedXTL5AZw+/DJoNRqA8mRJUKA8/1IATVEARXlMwykKNNW0nKaA\nVuMoigLV6rVnPgReTvk/tfeM8yz3/jS//u/aVwLGf/usJ3HX3bPblNn5vJ68ZXlsq7K8Jg+maLyW\nokGpP1MiC02hiKfIxFrS4K44DJ1SoSZAdklKaioGzpqFH5augJ39G4d698bA8eN9XZ/aA8VxkLXa\nyK6BYinUNPndREpXb8/tcrkUdRcNxIsvvoi1a9fi9NNPx6JFi5CcnIyysjKceeaZvjH5+fkoKysD\ny7IoKGj+bBYUFKCsrKzd8RMIHQERaggEFVAqpJRXNICiW/qiULQW5ZUVaoUWlo7wqAnGVadeidve\nuwv7qvcjjU5VdKHSOl5BEOBwOCDLsi8jatl/vsPqN37Gf5ddg/yc9vnQqAVFUdBlFaD68N/YvPdz\nPDPuSWiYjjlFy7IMrvIIdNmByzBiKfT9o9+luPuTe7C/5gB6pin3NUgU1MhmCkaFvRJ/VRTh7mF3\nxGS+SIUmiqKQlWFCVoYJA/tltWjPXd/owt4DVSg+UIXi/VVY/+5B7C2pxrEqG6wFqc3iTc9M9O6R\njh7d05Gk6/yXHG5OQGW1HccqG1FRZcOxykYcaxJkjlU2/9vQ6EJ6mgHZmWZkZZhQXtnYIpMJ8Jzr\nzSYtJp3bB1qtDpIsQ5YBWZIhQ4YkeV57lsue5X6vJUmGDAReLnv+X/uP8yz3/gCiKEOWpVb7bR7n\nv7yiOnD8+0ur8c4nu1p4OvkbdfsbgXtL9PyNwFmGDuyv1KpbmyLhp0UHOM94SRJgNOix/vXAQtPj\n85/HquWJ4c/U3nNsR4tM3nhZUwocpbshCTxoNnSGX8mBEry+9ms0ljbCOGshps68AtYeVs98oHBE\nyEYdbUUaCtAveMfayOC4Nhk84aAClGdFCrN1K5itWwG3G3J2NrTz5wMAxBEjII4Y0a65uwJutzuo\nUHPeeefh2LFjvtfez9qTTz6J22+/HY8++igoisIjjzyCe++9F6+++mpHhU0gdCid/6qJQEhAlIod\nOVkWyHvbliKYjKGfMqjZJSoaIjEqDoVJa8Q1A6/Ciu2rMHfo7JBjWyNJEpxOJziOg16vh06nA0VR\neHHVNrz+zi94b9V0pFji3942FDWMAIejHvcPuR1phrQO26/oaIQsCmAt6u/TqDXgqlOuwGs71uDx\nMY926SeG4QgngG0u/gyjeoyEXhPdU8dAxEpoSjYnYfCAAgwe0PIputPJY19ptUfAOVCFjz//C3sP\nVONgWS1ysy3o1ZSB4/PCsWbAbIp/dyaXW0CFT3BpEmFaiC+eZTa7GxnpRmRlmJCd4RFhsjNNOH1A\ngU+UycowIT3VAIZpziy6+bbt2Big7OzkPtmYMLoPjMbE9gI6uv/zgPGPHNYTyxddEdWcsixDEKSg\nHd4CCT+Cnx+TV/zx+jBxrTya7A4OTqcbgANHy+tB0T1b7J+itfjk879w0oiFyEw3IjPdhKx0IzIz\nTMhs+htnpXt+z8wwISPVSLLFAkDRNFhzCoSGGmjTsoOOKzlQgnk3rkWv6vHI4N1w/dUd825ci8dW\nXofklBQsWbIT2oNnQStLqC4+G0VFWzBr1kBFnjIhEYTIM1pikFHjL8hw8+a1a66uiLckPRCff/65\nojluuukmXHjhhQA8GTSHDh3yrTt8+DDy8/ODLicQOgNEqCEQgtARN5CPPjwTP/s8ajylCEbpV+zc\nNwjfbT+A4UM6PuMg3kbF43qPxcbdn+Ln8h0Ykzxa0TaSJKG+vr5N6/Slr36LDe/vxHurrkdutgVO\npzPhunF5cQkuPLNtMa6zjseJXPjOYrHEXVkGbUZeh4kmY3qOxqY9m/Hj4e0Y1m1oh+yzs+EWOHy1\nfwvmn/dEvEPxoeT/jl6vQf+TctD/pJwWy3leRMnhWp8Pzrc/7Meq//6EvSXVSE3Ro5c1o00WTiAj\n43A+I61xung/ASaAENP02uHgkJnuEV084otHdDljUDdkZzQvS0sxRFWuE9TfaG7nKAFUw5+Joihf\nJowesRfR/bvAVZR+GVBounhCfzz37N2eLKkqGyqrbKistqOi2obtOw95fm9aXlPnRLIlCVkZpjai\nTma6CdkZzaKOxaQ7rkRojSUNfBihZs3St9CTHQWt5MkY1jJa9MQorFn6FvoMOxMsOwoM9gMMA4bR\nAhiFzZu3YfLks9sVG8VxkDWayL77Y1n6FCVdvfSJ47ioSp/Ky8uRk+P5fnn33XfRv39/AMBFF12E\nKVOm4J577kFZWRn27t2LoUOHgqIoJCcn46effsKQIUOwZs0a3H333TF9LwSCWhChhkBQAaVih9Vq\nxYdvLcVjjz+HY5XlTV2fXsHBcgk33/cW7r/zXEy94vSo5492vFooiYOhGdw05AYs3vYCRvQaDj0T\nvJMEz/Ow2+0A0KYz1+Ll3+Dtj37He6umt7vtthr4HwtZlvHKTyvQM9WKgSeeA9vfv0B0OcAkKWvT\n2h4kzgXRVg9Dt96q78sLQzOYftp1+PfPr2Jwnrot2Tsr20q/Q6/0Xsg154Qf3AnQaBhfJs3Ec5uX\nS5KMw0frfQLOb38dxTuf7ELx/iowDI3ePb0CTgZMOheeXrAEVdwpPp+R7y+fiXtm3gmZsfhEmPKK\nBhyrsqGq2gGni/dlufiLMD2t6cjKMCGnaVlqcnQCjFKClZ11794NTqdTtf3GCm/8T8xfispqO3Ky\nLKr7M8WSUEKTyaiDyahDj+6hMwpFUUJNnaONqFN2tB6//nHEI+hU21BZZQfHC8hMb87MyUw3ISvD\n2Lws3fO5y0g3QqdVfikeqVDZUbCWNDjL9kOWJFBBuhQ5ikWkVtcB9fWeLJeKCmgB1BaLqM7hwFbW\ngKqtBWQZVHk5WADVGq79wfG8zxNHsfDhtw1BHaL1qJk7dy527twJmqZhtVrx73//GwDQt29fXHnl\nlejbty80Gg1efvll39/7pZdeatGe29vOm0BIdIhQQyDEGavVin+/vMDXZcqzDPhozY247s71+Htv\nBR6fMx4s2zLLIlLhJZIsmXgbFQ/IPQXW5O744K+PMHnglW3W+7fb1uv1sNvtLUSaRcu24L1Nu/Du\nqunIzlS3PWks+GT3JhxuKMOTYx8HzbDQZuTCXXEYhu59VN+3u+ooNGlZoDrID8fLqbkDUGDJx8Y9\nn+Liky+M6dyd/UmkLMvYtGczppw6Od6h+FDreNI0he75Keien4Jzz25pZFxRZcPeA9UoPlCJ4gPV\neO/NV9Egn9LCZ6ROPBXPv7QCF112A7IzTOhlTUd6ahIy043olpeGlGR9wnwWApWdqW1YH0us1kK8\n9Px8MAwTVTfEeBILI3CGoZuEFhP69gmeOQJ4SgAra+yorLb5Mroqq+3YVXQUFd4snWobqqrtMBi0\nyEo3ISPNgPRUPXKzU1qIOl6Rp6GuEpdfe19YQ+R4QLMaMHojBFsdNEFKaA29GXB8CpI4ziPUZGWB\nEzkYejNIt2pRw6dBY7MBycmQc3IgihzSrTHIaokiO8ZrQExQj2iFmjVr1gRd9+CDD+LBBx9ss3zw\n4MHYtWtXxPsiEOINEWoIBBWIRQZLz8J0bFw3AzfPeQtT7liH5QuvQLJFH9X8kd6oqJV9E0nc1/S/\nCo99+y+c12cs0pv8Wlq3205OTgYAX1YNACx8+Wt89NmfeHfVdGRltBRpImk93lH8WfEX3iv6AE+d\n9wR0rOfCUJueC9vfv0Di3KC16vl3yKIIvuYYTL0HhhynVkbW1NOuxSOf/xOjeoxEclJyTOZMlJvy\n9rC7ag9cogun5g6Idyhxg6IoZGeakZ1pxvChVgDArp/fx68H2hraWgtS8cTc5larHMdBluWg/geE\n45OONALX6zU+ATIUkiSjvsGJimo7jh6rR3lFPeoaeFRW2/DXnopmoafajooDX0CTMqSNIfL8Z16M\nSxe91oK4xpIGoaEmqFAzdeYVHo8asRe0FANO5LBf2ILHZno8aoqKtoAW8kHTNESRgyBswfjxob+b\nFMHzQKQeUBwX94yazv7AIRzt6fpEIBwvEKGGQFCJWJQmJVv0WPfSFDz27GZMmvIq1r54DXoWpkcs\nOKjpO6PWTXyWMQtjTzgXa3asw6zhd/rabbMsC4vF4us+4923JElY+PIWfPJlEd5ZOR2Z6aaYxxRr\nqh01WPz9C7h72B3IMmX5ltOsBprULLiryqDP6xlihvbB1R4DY0oGrYvPxVKBJR8jrMOxYddbuGXI\njLjEkIhs3PMpJvQeB5oixqX+5GSaIe9r6zPSGbLmCIRA0DSF1BQDUlMM6Nk9BaIoBr15vfCy3dhZ\n2laoPFZ5LOD4joZNTod97y4k5QcWGKw9rHhs5XVYN/VBVDqSYOrbF4/NvM7X9WnWrIH44snVqNX9\nDUvvNIwfHwMjYXg6OElN7bnpIGVZbUgAoaarE6rrE4FA8ECuAgmEILTnSUYsxQ6WZfDkA5Nwy9Qz\nceHUlfjmf/uijiveRCoYXd7vUvx6ZCd2HvwNTqcTRqMRJpPJJ9L4z/n0C19h01d/490QIk2iePUA\ngCAJeO5/SzGpz4SAmRO6zHzwNRWQBUGV/ftacmfkqTK/Uq7sfzl+OPQTDtYdCj/4OKDGUYOdR3/H\n6B6j4h1KwvHQ/Xci2/AnZMnjW9HsM3JnnCMjENQnN9vi++x7SSShktHpQTEMRKct6BhrDyvmjT0J\nSyb3xaNL5vhEGgBITU3B1UPzcPdJbkyefHZMRBoA0bfnJqVPqkIyagiE8BChhkBQATUEgalXnI7l\nz16BOx58F2vf3qGamXCiGBXLsgxKpHDZiRdj3a4NMJvNAX0RZFnG4hXf4/Nv9+CdldOQkZbYbW69\n/Oe315FuSMelJ18UcD2t1YG1pMFdfVSV/QuNtZ7uGsb4Gi2bdSb8o98lWP3r2oQR0eLJZ/u+xNmF\nZ8GoVd9IurPh9RmZNMyBQT0OYdIwR0L4cxAIHUFnECrZpvKnUFChjHrVMPGNpj03z5OuTypDMmoI\nhPAQoYZASACUih3Dh/TAx2tvxOo3f8ajCz8Hz4uqxBNPjxqvD40oet7bBf0nQZAFbC35LuDYJxZ/\njm0/leKtFVMDtvONdP8dwRf7vsLu6j24dfCMkBdiuqwCcFVHIEux/ztzlUegy8xXfCGo5nGb0Hsc\nKu2V2HF0p2r76AzwooDP936JiX3Ghx98nOL1Gfng7VewfNlCItIQgpII5/pY0hmESk1yOvj60EIN\neB5ykAwXShAgx1ioicoYmJQ+qQ7JqCEQwkOEGgJBBdQUBKzd0vDBa9Nx+Gg9rr7tddTWO2IaTzw9\nanieR0NDAziOA8uy0Ol0YBkWNw29Aat3rIVLcPvGyrKMeYs+w7c/7Mdri/+BtJTOkYFQXL0X637b\ngHuG3Q29JnjrcQBgkgxgDGZwNRUxjUF02iG6HNAkZygar/ZTPZZmMe20a7F6x1oIkjqlXomK//+d\n/x36Ad2SC9AtuSCOEREIwelsT/k7U6xKSCShMtBngTGYIQscJM4VfMOOzqhpKn2K5LNLSp/Uhwg1\nBEJ4iFBDIAShIy/wIhU7LOYkLF9wKfqdmI1JU15F8f7KmMbT0Rk1oiiisbERdrsder0eZrMZNE37\nxvbL7ouTMvvgvT/e98X36IJP8f32Ery9YhpSk/UJ8fQ03Gem3lWPZ7ctxq1Db0KeOVdRzLqsAnCV\nZTF9f+6qI9Bm5IBSaqzYAQzOG4QMQzo+2/tFu+ZJlKwpJbT+vGzasxmT+kyIUzQEAiGedJbzVigo\nigJrTg2dVSMIwcUYNTJZoil9iqKlNyEy3G439PrQD6sIhOOdxLlKJxC6EGrfLFIUBZqmMG/OBNx1\n49m4+PrXsOX7vTGJpyMzamRZhsPhQENDA1iWRXJyMrRabcAYrh88FR/9VwGlsAAAIABJREFU/Qkq\nbJV45JlN+GnnIby1YipSkpV/0cfzJl6URCz6binO6TESZxQMUXycWaMFFKsFX1cVkzgkgQdfXwVt\nem5M5osVFEVh+qDr8NYf78LGBTej7Krsrd6HWlctBucNincoBAIhTnSFDCBNcmifGorng5c3hRJx\noiUa8ScBSp86W/ZapLjdbuh0uniHQSAkNKQ9N4GgAh1pyHvNpYPQs3s6brrvTdw9YwRmXHNGTL7c\n1bhI8L5PWZbBcRycTmebdtutx3rJMmVhYp8JmL12Aap39cGb/74OyRZ9wLHxItQxW7tzPbSMBlf1\nvyLieXVZ+XAdOwhNSka7/yZc9VFokjNAs4lXf1+Y0h1nFAzBW3+8g+sHTYt3OB3Kpj2bMaHXODAJ\nlOVEIBAIkcKaUuE4WAxZFEAxzbcZzNatYLZuBV1cDNbpBH3I0+lPHDEC4ogRAMKIOFFCcVzkcyaA\nmXBXx+VyEaGGQAgDEWoIhE5Ia2Fi2OBCfLx2BqbetR5/F1fgqYcnQathg44PN3d7YgmHJElobGyE\nLMswGo0BOzkF2+73TXrUZRzGv55sFmnUjDUaAok1W0u+w09l2/HMuPlR3YizljTgaCkEWx005tTo\nY5MkcFXlMPbsF/UcajP5lCswa+McjO91HvIs8W0d3lHUu+qxvewXTB90XbxDIRAIBEUEezBBMQxY\nowV8Qy20qZm+5V5BhnvooeCT8nzErbSD4RWGpG7dwHz3HQw//giWZSGNHOkThoIRlQExISJI6ROB\nEB7y6I5AUIGOyKhpPb6wIBWfvD4DFdU2XHXzWtTUhTcZjmT+9iJJEnieh9vthlarhcViCSnS+B8T\nSZIw94mPUfR3NW4/+was++N1SLIU0/jaC0VRAS9aS2pLsWrHasw9+16Ydaao59Zl5cNdcbhdMfL1\nVaCT9GD0kbUw78iMpRR9Ci45+UL8Z+e6DtlfIvDFvq9wRrchMOvM8Q6FQCAQ2o0mOT1sm+6AxNBM\n2CsKuZcsAffEE7DPmQPXAw+EFWkAkNKnDoCYCRMI4SFCDYEQhEQ2Ew4Wm8mow+olkzHolHxMuHo5\ndu+riOn8wcaGmtvbbru+vh4AoNPpkJSUpHgfkiThvsc/wp79ldjwynWY0HcMKIrClv3fKI4hXtg4\nGxZuew7XD5oGa2rL7hyRxqxJyYTkdkF0ROffIssy3JVl0GXmR7V9R3L+iRNxsO4Qfi/fFe9QVEeU\nRGze+zkxESYQCF0G1pIGobEWcqQPVNTwqIkGNbpPEVrgdruJUEMghIEINQSCCnSEmXCw+RmGxv/N\nHod7bx2FS29YjS++3RPT+SPBv9222WyOuB5ZFCXM/ueH2HegGv9ddi1MRh1oisZNQ2/Amh3r4OSd\nEc3XkYKOJEtY+r+XMDhvEEZaz273fBRNQ5eZF3VWjWhvAEQRbDtKpzoKDaPB1IHXYPWvayFKkWdO\nJaJoF4ztZT8jy5iJHqnWeIdCIHQpOtN5AOh88YaC1mhB65Ig2hoi2k4Nj5poIKVP6kOEGgIhPESo\nIRBUIFpBIJYXalddPBD/WToZs//5IV5d9xOkKG54lRDovQZqt81GWHcuSTLu/9dGlB6qxfplU2A0\nNIs8J2WeiFNy+uPtXe8FjSHevPnHO3DxLkw9bUrM5tSm5UCw1UF0RyZQAU0tuTPzOk0q9bBuZ8Cg\nMeCr/V9HtF1neX9ePt37OSb2Hh/vMAiELklnOx90tnhDwVrSwTdUR7aRiiVHEZUSkdIn1SFCDYEQ\nHiLUEAgJgFoGvkMGdscnr8/AOxt34cH5m8HxQkznb00k7bZDIYoS5jz+CcrKG/D6Sy1FGi/TBl2L\nTXs+xTFbheJ5O0rQ2X74Z3y1/2vcO3wmWDp2nu0Uw0Cbnguusiyi7STOBdFWD21qdsxiURuKonD9\noKnYsOstOPj2+S0lKgfrD+GorRxndBsa71AUkWhiKIFAiB/hhASNJQ18Q01k541EKX0SBNL1SWWI\nmTCBEB4i1BAIQWjPk4xoBAG1RIRueSl4d+U01DU4ccWMNaiqscd0foqiIEkS3G436uvrIUkSkpOT\nodfr2xxDJe9REETc9fB7OFZlx6uL/gGjIfDFUoYxAxeefAFe+3lNQmXUHGk4ipd/Wo77ht+DFH1K\n0HHRxqzNyAVfVwWJ5xRv4646Ck1aNqhWLdCVEq/je0JaTwzMHYB3/ny/w/fdEXxx4Cuc1/PcmIp5\natGVn+wSCITYQycZAACSKwKhPUG8YUjpk/oQM2ECITxEqCEQVEStm1vvTZPS+Y0GLV6afzHOGNwd\nE65Zjr/2HAs7v9K5JUkCx3FwuVwwGo0wmUygo2hBDXhEmjsfeg9VNXaseu5y6JNCX7Bd2u9i7Kna\ng6LKvxXNr7bg4BLcWLBtEa4ecCX6ZPRWZR+0RgtNSia4qiOKxsuiAL7mGHQZuarEozZTBkzGF/u+\niihzqjNg42z48ch2jOk5Ot6hEAgEQsyhKAoaSzr4CLo/UTwPOUbtuVtDSp8SC5JRQyCEhwg1BIIK\nRPPlqqaI4GkdDTx091g8cOcYXD5jNTZv2d2uOSVJgt1uh9vtBsMwYdtte+MI9h4FQcTtD76LugYn\n/vP81dDrtWGPRxKrw/TBU7Hm93VRmc7GElmW8eIPL+PEjD4474Qxqu5Ll5kPrrocshi+lI2rrQBj\nSgat7ZxPrtIMaTj/xIlYu3N9vEOJKV/t24KB2QOQkhQ864pASDQSJXOR0DnQWNIg1EfgU5MgGTXg\neVL6pDJutxtacowJhJAQoYZACEGit+iOZLx37OUXDMDaF6dg7hMf4cVV2wLOEWru1u229Xo9GIZp\n17HieRG3zn0bNpsbq5dODptJ488I63DoGB2+PrBF8TZq3Gy89+cHqLRX4cbB01X/3NC6JLDmFHDV\n5SHHybIMrvIIdBl5qsajNhefdAH2VCvPnEp0REnCpuLPMK7n2HiHQogznVH46MpP+eNJZ8ugUBIv\nY7JA4lyQeLeySRPFo4bjiFDTAXSmzzuBEA+IUEMgqEQi+aa0/jIcPKAAm9bdhA8+/QN3P/Ie3Jwy\nk2H/dtsWiwVGoxE0TSt+n4GOCc+LuGXuW3C6Bby2dDKSdJqgY4PNOfXUKR7TWS50LbxaFwW/Hf0d\n7//1IeaOmA0to+zirr2fD11mAdyVRyCHyCQSGmtBMSwYoyXq/SQCOlaHa0+9Bq/tWANJDp05lUj/\n74Lx69FfYdGZ0SvthHiHQkgAyM0KoatCUTRYcyqEhlplGyRIRg3FcXFtE57o32Gxgpz7CITQEKGG\nQFAJtTNkIhkfaGxeTjLeX30DnE4el92wGhVVtqDjA7XbZqI0pvWH4wXcdN+b4AUJqxZfBZ02utr0\nXuknYGDOALy56+12xxQpFbYKLNq6BPeNuAcZxowO2y9jMIFJMoCvDe7d4q4si0lL7kQQP84uPAs0\nReHbkm1xjSMWbNyzGRP7kJbcBIKaxPucRfDAWtLAKyx/onheFYEk4s9CgpQ+ESGDQDi+IUINgdBJ\nicXNs9GgxfJnr8A5Z/bEpCkr8OfulqU0StptRysYcbyAGbPfhCwDK5+7so1IE+n7u/qUK/FZ8Zc4\n2hC6HCiWooNbcGP+1wtwWb9LMCD3lJjMGQm67AK4K8sCvh/RaYfkckKT3HHikZrQFI3pp03Fut82\nwCW44h1O1JQ1HEFJbSmGdz8z3qEQCF0ecqMbfzSWVAj2BsiiGH6wyhk1EZkJJ4BQ01UhIiqBoAwi\n1BAIIejIFt1qmwkHm5umacy941w8cs95uOKm/2Djl0UAPGVO4dptR4ubE3DDPW+AYWisWHQFtJr2\ndXmgKAopSSm4tN9FWPXz6pjEGA5ZlrHsh+XIt+Th4r4X+uLoSBhjMiiagdDQ9mmlu+oItBm5oKLs\nwJWInJR5Ik7OPBEfFH0U71CiZtOezRjb61xomPin9xMIBILaUAwL1mCGYFNQ/pRIpU8qdZ8iNEOE\nVAIhNF3nCp5AOM6ItbBzyYT+WL/sWjz01Ea8sOo78DyvqN12pBk1LhePG2ZtgE7LYvnC0CJNpO/v\n4r4X4kBtKX47uivkuFgct027N2Nv9T7cddbtcevyRVEUdFkFcFe0zKqReA58fRW06Tntmj8RuXbg\nNdi4ZzOqHRF0EkkQHLwDW0u3YXwvYiJMIBA6N5GYH7PJaeDrw7fpphJEqIl36VNnM5aOBpJVQyCE\nhwg1BIJKdJaMGi+SJKG3NQUbXr4SX27dh/uf/AyCgkzlSHC5Bdzx8Ecw6LV45ZnLodEE97mJ9CJF\nlmVoGS2uP30qXt2+CqIUOPhYXPwUVfyN9b9twEOj70eSpmXb646++GCT0yELPER7g28ZV1MOTXIG\naDYBLnhjTJYxE+N6jcW63zbEO5SI2XLgW5yS3R/phnTfMnKxSiAQgK59c66xpEFoqAl/vuN5VTJZ\nIj62CeJRQyAQjm+IUEMgqEikN2HxEHb8221TFIU+vfLx5r+nQJJlXHrDazhW2RiTOJwuHtfPegNm\nkxbLnvlHSJEmUvwvwM7qPgwmrQmfFX8Rs/n9qXHU4JlvnsXM4Xciz5Kryj4ioTmr5jAAQJYkcFVH\nY9qSOxHMhP25rO/F+P3YHyiu3hvvUBQjyRI27fkME/tM8C3rqjdlBAKB4A+tTQKl0bV4oBCQBGnP\nTXEcZCLUqIYkSSEztQkEggfyv4RAUIlIb8I64qat9c02x3Gor68Hz/OwWCwwGAygaRp6vRZLH78Q\n40b2wcRrVuD3v460a78OJ4epd61HWqoBCx6eAJYNL9JEKw5QFIWbht6A9TvfgI2zx2xeAOBFHk9/\n8yzG9xmHIQWnRzWHGmhSs1Cybx/+9X8PYd7992Lxa+txuKIq3mGphl6jx+RTrsDqHWvb/C0TTVTy\nsqv8D7A0i76ZJ8U7FAKBQOhwNMlp4BvClD8lUulTnNtzd2Uh3+12Q6fTxTsMAiHhIUINgRCCRDYT\njtQbxh9vu22HwwGDwQCTydSm3TZFUZh96yg8PncCJt+6Fh999mdUcTicHK67az2yMkx48clLwTCx\nv/hoHUPPtB44o9sQbPjtzZjuZ9XP/4FZa8JVAy5v91yxFBQOHjqE1e9+iBvPPxf3XPsP3Hz5hXhp\n4VMoLS2NyfyJyOgeo+AW3Pj+0A/xDkURG4s3Y1Kf8V364ptAIBwfRPPdpbGkQwgj1KjVnjtiSNcn\nVXG5XEhKSgo/kEA4ziFCDYFwnEBRFCRJCttu2zvWeyF2wXl98ca/p+KxhZuxaNmWiC7Q7A4OU+5Y\nh7xsC57/16VgGOWnnPYKGdeedjW+2rcFh+vLYjLvV/u2YMeRXzF7xEzQVOD3Ea+b8NdfW4nZ0ybD\noPdc+Bj0Sbjn2svx+msr4xJPR8DQNKYNug6v71wPTuTiHU5Iym3HsLtyD0Zaz453KATCcUMiZtZ1\nNSL5zqP1RsiiCNHlCD5IpUyWiDNUiFCjKiSjhkBQBhFqCASVSKSMGlmWIcsyGhoaomq3fcrJudi0\n/iZ8sa0YN895Cw5n841xsDjsDjem3P46uuelYMnjl7QQaWJ9AR0ohhR9Ci7vfylWxqBd977q/Vi5\nfTUeGjUXRq2x3fPFGoFz+UQaLwZ9EgTOHaeIOoZTsvvBmmrFx7s3xTuUkGwu/hyje54DHUsuTAmE\njqQzZbB1dWGJoihoktNCZ9UkSOlTvDN7unrpE8moIRCUQYQaAqGLIwgCGho8Bn7eMqdwJm6BhI/s\nTDPeWzUdGpbBpde/hqPHgpsC2uxuXHPbOvQoTMfixy/2iTSRXHjEojTowpPPR1n9Eewo+zXqORpc\njZi/ZQFuHXYTClML2xWPWrDaJDicrhbLHE4XWG1shIFE9X0BgKkDp+DDoo9R66yLdygBcQtufL3/\nG4zvfV68QyEQ2k2inge6Cl355hwAWEt6aJ+aBBFqSEaNurjdbiLUEAgKIEINgRCCzuxRI0kSbDYb\nGhsbkZSUBIqiwLaz7WWSToOXnroM54/ti0lTVuDXP8raxNFoc+Hq215Hrx4ZWPTYhQFFoY7IqAEA\nDaPBjadPw6vbX4MgCSHHBkKURCz89jmcXXgWRliHxzRmL7E4FtdefyMWv/62T6xxOF1Y/PrbuPb6\nG9s9d6KTa87BqB4jsWFXbP2IYsW3JdtwYmYf5Jiy4x1Ku0hksY7QsXR1MYGgHqwpGaLTDkngA66n\nBEG19twRQYQaVSEZNQSCMmJ/NiQQCAA65sYm0Pzedtsulws6nQ4pKSmgKAoulysi8+FgYymKwt0z\nRqB3z0xMuWMd7p5+KrZu+RC1dS5kpBlxqDYfQwb3xdMPnx9QpIn0Ir+9KcBDuw3Bx39vxKbdm3Hh\nyedHtO26nf+FDBlTB02Jev/BiOXNTmFhIe6Y8yBWvbYSAucGq9XhjjkPorAwMTOAYs0V/S/DXR/P\nxsTepcg15CSMoCDLMjYVb8a0gdfGOxQCgUCIGdF+L1M0DdacAqGhBtq0AOK1ihk1iuMVRc+/TPju\nlGpBSp8IBAJAhBoCIWGIRUYNx3FwOBxgGAYWi6VNJ6dYMvHck8DIjZg87T6wyWeBojMg7+WgcX+B\nVYuvDFteFY5oBJ1g88wYcj0e/uwxnNNjJGiFx/n70h/wzf6teO6CBWBoZccxnhdWhYWFePifj8dt\n//HEqDXiiv6XYfWva/Dg8LnxDsfHX5V/gxcFnJLTP96hEAgEQkKgsXjadHe0UKMYjot/DF0cUvpE\nICiDlD4RCCrRkaVPrdttm83mgO22Y5FR48+GDeubRBpPijBFa8HrTscT859v99xelIwNJ5AUphZi\neOFZ+O9vGxTt81DdYbz0v1fwwKg5SE5KVrQNIb6M6zUWtc56/HI0ej+iWLNpz6eY2GdcyC5hiZL9\nQyAQCB0Ba0mD0FgHWZJarpDlxBFqSNmTqpCMGgJBGUSoIRA6MbIsB2y3HWp8LCmvaPCJNF4oWovy\nyuBGw5EQywyVawZOxtaS73C4oSzkcXBwDjz59TO4fvB16J3RK2b7D0RnuVHvDHEyNIPpp12Ldb//\n1+dHFE+q7FXYdewPjO5xTrxDIRAIhISBZjVg9EYItlYG8KIIUJQqJUeRlBJRghDXjk9A1y99Ihk1\nBIIyiFBDIIQgUc2EZVmGIAjgOE5xu201Oi7lZFkgS1yLZbLEISfT0u65I0HJnMlJFlx5yuVY+/v6\noGMlWcJz257HgNz+GNt7TExjJKjPaXkDkW3Kxmf7voh3KPhs35cYUXg29Bp9vEMhEI5LEl1cbk1X\nvzn3R2NJg1DfqvtTImTTACSjpgMgGTUEgjKIUEMgqIRaWQjedtuCIIBlWUXttr3EOp5HH56JHMNf\nPrFGljjkGP7Cow/PjMn8sT6Gk06agCpHNXYEKY95e9e7qHfV46YhN0Q1vyzLkCSpxY8syy1+COpy\n7YCr8cHuj9HoboxbDJzI4Yu9X2Fin/Fxi4FAIJAOVWrRXlGJTfa06W7xnUiEmuMGItQQCMogZsIE\nQoKgpN22w+EAz/MwGAwAPObBkcwfq1i8WK1WfPjWUjzyz2dRU+tETpYFjz68FFartd1zR4LSOVma\nxdRTp+A/v67F0MIh0DDNF4W/lO3AJ39/iucuWNBiuVJ4nofdbm8TR7i4nE6n73f/v1Hrv1c066Kd\nI9DrzkKBJR/DCobijV1vY8bp18clhu8P/gBraiHyLXlx2T+BQCAkMoxOD4phIDptYA1mz0KeB1Ro\nzR0xxExYddxuNyyW4JnXBALBQwKcEQmErkmsBIlg7bYjEWliGU9rrFYrXlzyL5hMJrAxvshSI+bT\nck/F5r2f4ZO/N+GSfhcBAMoby7Fk2wt4YNQcpBvSIprPK6AJggC9Xu+LOxCt34vD4YBWqwXDMCEF\nnnDr/F9LfgaN0c4ZDLvdHhMBKVbrgr3+x0mXYO6XD2FC73EoSM4P+n7UYtOezbii/2Udvl8CgUDo\nLGgs6RDqa3xCjZreMBF51PA85Dhn1HT1MjjiUUMgKIMINQRCCDryizLSdttqGrxG46+j1tyxnpOi\nKFx36hTMeesBfLLqI9Q561FadxC33HAL+mX3VbxPWZZ9fx+tVovk5GTfsmDHI5CoQNN0u1uZx5rW\nx1IURbhcLuj1+nYJSv6oISh5Pwd6Ro8Lek3Cyp9XY86Z9/jW+Y9rvV2s1hVX70WDuwEDcwb6Yu3K\nF9wEAoEQDWxyGpyH9yIpt9CzgJQ+HTd4HzwSCITQEKGGQFCJ9rbbdjgcEEURBoMhZCcnteKJFDXm\nVs3np47Hwa8PgJvAgdGxyHBnYc3KNRjTa3TIsi0voij6ypzMZnOLTKKucFPe+j3QNO0TlRKJQCKO\nIAgQRREXnDQJX5VuQVHtbgzMGRBzQSnYHB8XbcS5haPg8itpa433+HqfmgqC0GZdqN87Yh2BQOhY\nOpOHWSwyPhiDGbLAQ3K7QOuSEkuoSYQ4ujDeBz8EAiE0RKghEBIIb7ttb1qoyWQKmZ1BMmpaovTi\ncfHyJciZkA9G5zkFMjoWxtFmLHh5IV5e8FLI+b1laHq9HjqdjtzcxpFAQoP3R6fVYepp12LNznUY\nOHEAWEb9r7s6Zx1+q9iFW4bNgElr8i0PJvB4yxc1TTcF0WYW+YtJkWwXLjup9XZeUSnRyt8IhK7C\n8fTZpigKrCUNfEMNdJl5gCAkhECSKKVPifZgJJZwHEdKnwgEBRChhkBQiUjbbXtv2rzttpV8SavV\n/tt/fqUXjvHMqIlUKKpx1vhEGi+MjkW1oybIVs1mwYHK0PyJ9EljZ3qK2tkYmn86Nu7+FJ/v+xIT\neo9TfX+f7/sSZ3Y/o4VIA4TPkgn2WepIwpWY+YtKiSwo+Z8zWu+PCErtp6t7ZxA6Fo0lDe6qI9Bl\n5oHiuITwqCEZNerjdrtJ6ROBoAAi1BAIKqFUZBAEAXa73ffaZDKFGN1yfrWIdO5EyahRSpo+DYfd\nR1uINaJbQLoht81YSZLgdDp93bY0Gk3I9xvpsSCoB0VRmD7oOvxry1MYUTgcRq1RtX0JkoDP9n6B\nR0Y9GFF88f6/4EWJSTNFUXEXlcIJSrIsg+d5yLLsy1QKNLajDblDCTrec0ygdfEw5O4qJMr/LUJw\nWFMKHAf3QBYFUvp0HEFKnwgEZRChhkAIgZoXsK3bbWu1WtTW1ip+8hNNeVLrJ8yJTqQmwUqOHUVR\nmDnjbtzxrzthGGUGo2MhugU4tjRi7vw5vnGBzIK76g1NOBJJUFCCf6w9Uq0YnDcIb//5Lqaddp1q\n+/zx8HbkmnNQmNJdtX0QlIkMoihCluWYd6GLBKUCjyiK4Hk+qvK3jhaUvK9dLlenEZSO13N2Z4Fi\nGLCmZPANtWASpD13opQ+deXPLun6RCAoI/5nRAKhixLs5tbf58S/3XaioVT48B8bybzxpLB7IVbP\nfw0LXl6IakcN0g25mDt/js9IOJRZMCGxCfR5vXrAVZi18T6M63Uecs05qux3055PcX6fiarMTeh8\nRCIyCIIQt3NMpAKPKIoBY01EQUmSpDYPKGItKB0v2UmtiaWQoLGkQWioBqWiR01E8fI86fqkMiSj\nhkBQBrn7IBDC0F5hwXuB4E3JD9Zu239famXUxFsgUZNI/GxkWYbVam1jHOwvoiUlJSEpKem4ufDu\nyqTqU3DRSedj7c51mDvi3pjPf6C2BBX2SgwtGBLzuROJrnz+OF6JRmhgWbbDz4tKBR7/33meB9Ds\nARWtSBRL/6Rg67y43e7jzj+JtaTBefQAZLdbNY8aIIL3TEqfVIdk1BAIyiBCDYGgEv4XBf7tto1G\nYxvvhERErSyZRM2+8YpoNE2HNAuOJV1dPEskLjjxfMzcOBt/HPsT/bP7xXTujXs+xfhe54Gh428K\nrBbks0qIJ9EIDaIogqKoDvu+VeKfFGqdKIpgGCYhDLnDrZMkCbIsw+12R7RdwHUMC1qrh9h4DGDZ\nFrHGRVBKgIyarl765H0YRiAQQkOEGgJBZRwOBziOg16vD9luG4hcxACUf6GTG63AeI08OY7zeQV1\n5Quk4xUdq8V1p16D1b+uxTPj5oOJUevTRncjfjy0HS9c8FxM5iMcX5BzctehPVkrXtGjI0Sl9gpK\nXrylZcHWRTSn3gyuthpamm7RXMGf9mQTeX2rWscc6Hfa5YLEshBFUdH8gV4TQkMyaggEZRChhkBQ\nAa8Rrfd3pe22E0lMUTPzJZ7ZN95x3lI0u93uMwtW8jc6nunsT/nO6n4mPtnzKbYc+AZjThgdkzm/\n3Pc1huQPRnJSckzmIxAIBDWJhcjg/S7QxijzRGRyINoawDQ90PLuo/U+A/2udJ3/wy0vgfyTWKcT\nIsO0yBaKtX9SuHXe6xOvWBRPQ241kGWZXG8RCAogQg2BEGO8JTQU5Wlnq9frVftCUtPTRi0S4UJB\nlmXYbDZIkgSTydQpStHiSSL8zWIBRVG4ftBUPLN1Ec7qPgx6TfvMDEVJwqd7P8N9w++JUYQEAoFw\n/EHrDJBEETKtjq+O1wRbiWm3BgBtMMBgMCiaW03vI++/8TLkjsW63377DY2NjdBqtdBqtdBoNGAY\nBiUlJdDpdL7l3h//svO3334b//znP1FUVITt27dj0KBBvnVPPfUUVq1aBZZlsXTpUowbNw4AsGPH\nDkyfPh0ulwuTJk3CkiVLAAAcx2Hq1Kn45ZdfkJGRgTfeeAPdu5MujYTEhgg1BEIYlAocgdpt19fX\nq7KvjiARvGRiHYM300kURWi1WlXMgiOZL5H+3l2FcMe0d3ovnJLVD+8VfYhrBlzVrn39cmQHUpNS\n0Sv9hHbNQzi+6SpCaKLR2TMAjycoigKj0UGSpfCD1Y6F4yJqz61G1grP89BqtTF9yNcRZtqBBKV3\n3nkHv/zyC3ieB8dx4DgOVVVVGDNmjO+198drpu0VcC6//HK89957uOWWW1rsp6ioCG+++SaKiopw\n+PBhjB07FsXFxaAoCrfddhtWrlyJIUOGYNKkSdi8eTPGjx+PlSvSRmvCAAAgAElEQVRXIi0tDcXF\nxXjjjTcwd+5cbNiwIZJDSCB0OESoIRDaiX+noNbtttW+EU8EMSVS4hWHIAi+2neapklryOOYKadO\nxr2fPoDzThiDTGNG1PNs3PMpJvYZH/X2FEW18XIgEAixo7MINURUAlhNEiRZDD9Qbbpo16d4lUHN\nnz+/xWtZljFx4kRs27atTQxeU22vcMMwDMxmc5trxg8++ACTJ08Gy7KwWq3o3bs3fvrpJxQWFqKx\nsRFDhng6ME6dOhXvv/8+xo8fjw8++ADz5s0DAFx++eW48847VXzXBEJsIAWCBEKUeLMz6uvrIQgC\nLBYLDAZDwC8epUQqYqgpeiSCCBSLGGRZht1uR2NjI5KSkmA0GmMdJqGTkWHMwMQ+4/H6b+ujnuNQ\n/WEcqj+MM7sNi2FkBAKBcHxC0yxkmoLEu8MPjpCIhDCe75JCTaIR6O9BURRYloXBYEBKSgrMZnPA\nbcvKytCtWzff6/z8fJSVlaGsrAwFBQW+5QUFBSgrK2uzDcMwSElJQU1NTSzfEoEQc4hQQyBEgSiK\naGxshNPphNFohNlsDtjOOZGekB2PGTVeIc1r6KzT6VT/myTCMVaDRPn8xIr/Z+/d4ySnyvz/T5K6\n93Q3jFyUGS6i8MMRFIQBV4WfygLCl4UBRl1dBHEABR1c0RURlUUUcN0VlVXWVWBBlsuCiiAMCC6C\nyPJFBUFBFxGGmzgyDPS1qnL9/tE86VPpSlWSyklyqp/36zWvrkmlktPVyck5n/N5nmfVa/4GD//l\n9/jfjY8k+vzNf/gxDnj1/igbbExlGGZxIcMBpNk2tPoIrIl8J8+aaXJ57gw54IAD8LrXvc7/t9tu\nu+F1r3sdbrjhBqnnHabxDDO88AiTYfogPizFUs71er3vxF+2Q0am60VlR43rupiZmYHjOBgZGck8\nWbBqCZ4XI7VSDe993btxyX2X4ZwDPg9di75uMWPO4q4nfo7zD/6yxBYyTLEIVs9hmFSxLOiNJbAn\nN6G6xSvya4dpwmNHTWbceuutsT+zbNkyPPXUU/7/n376aSxbtix0u/iZbbbZBo7jYHJyEkuXLh38\nF2AYibCjhmEiQHloKDnw+Pi4tES0wz5xl/k7in8nwzAwPj6+QKSRHS7Gkxh1+P9fuS9cz8XPn7g7\n1uduf/yneP3LX4elDR7kMQzDpIJlQVuyBPbMJDwnx1w1lpWro2bYx4BJS3OL38thhx2Gq666CqZp\n4vHHH8ejjz6KvffeGy9/+csxPj6Oe++9F57n4bLLLsPhhx/uf+bSSy8FAFxzzTV4+9vfns4vxDAS\nYaGGYfpgWRYmJydhmiZGR0cxMjIS+SGjcjLhIjhq4uC6LlqtFkzTDM0XxDAiuqbj/Xu8D5c/cCXa\ndrS8CK7n4uY//BiH7PwOya1jBiHv/ohhmHholgVUqig1RmFPvZDqsWPnqMk59AkYXueaZVmRXc7X\nXXcdtt12W9xzzz049NBDcfDBBwMAVqxYgXe9611YsWIFDjnkEHzzm9/0v69vfOMbWLNmDXbeeWfs\ntNNOeMc75p7Va9aswcaNG7HTTjvhq1/9Ks477zw5vyDDpAiHPjFMHxzHQa1WQ6VSkf7gLILgIRsZ\nJbebzSZs20alUsHIyEiq4WiMWsT9+67Y6jXY6WWvxg2/vxGrdz2y7/4PPPsgakYN/98WOw/STEYi\nwzrBYYaXxZSTJBTLAkollMaXwprchPJmySvyDYLGoU9SaTabqNVqkfZdtWoVVq1a1fW9008/Haef\nfvqC7XvuuSd+85vfLNherVbxX//1X/EayzA5w44ahulDrVZLnIS2SI4aQoZLJi/xg5IFu66LSqWC\nUqmk1GBXJdFIpbbG5ejd34sb/vcmbJrtn8Typj/cgoN3Pkip64xhFiPD2l8VASnC0kvVlspjL4M9\nuSm/v18BkgkPM+12O7JQwzCLHRZqGKYPgw5GilRuOy5FShAs4roupqenMTs7i5GRESxZsgS6rsdq\nLw/iGeLlS7bGX7/qbbjiwat77vfs1J/x6PN/xFu2f3NGLWMYZhBYUFUI2wbKZeiVKrRKFc7MZD7t\nKECOmmG+blutFgs1DBMRFmoYRiJFC5WKK5LIakdSxGTBuq53TRbcj2EeADHJOeq1R+D+Z3+NxzY9\nHrrPzX/4Md6+41tRLaUziB9mlxLDMEwcNMvyQ47KY3PhT2kRR/zg0Ce5tNttVKvVvJvBMErAQg3D\nSER2eW7ZFMlRY9s2pqam/KTOwWTBRfnuWAhSk0a5gXfv9k5cct9lXa+jptXCTx+/EwftdEAOrWMY\nhhlyXspRA2Au/Gni+Xye6Rz6JBV21DBMdFioYZgCIVvYUdFR43keZmdnMTU1hUqlgtHRUZRKg+VB\nL5KoU4R2MHPsv+PbMW3O4P8+/YsF7925/mdYsdVrsNXIljm0rBjwtcowclDt3pISnmPbvkCi10fg\neS7cdjPdc0SBQ5+kwkINw0SHhRqG6cMgD8wsJuIyj5+3o8a2bbiuC9d1MT4+jlqt1vPvodpgVzVU\n+H4HuecMXcf73/A+XPbry2E5lr/d8zys+8MtOGSng9JqpnIM88SBYYrAYr/HNNMEXgo50jTNTyo8\nKHGfB2I7mPRhoYZhosNCDcMUiCQOGVnHz9NRQ8mCm80mNE3zkwX3a0NU2MkSn8UyiXj9y3fD8rHl\nuPGRm/1tv/3Lw/A8YNetX5tjyxiGYYYYy4InuGVLY0thTaSXpybyM0zIlcOkD1d9YpjosFDDMBIp\nYnnuOGR9bM/z0G63/WTBY2Nj0s7PMGEcu8fRuO7h6zHRmgAArHvkZhy884GLRqxiGIbJnJfKcxOl\nJeNwWjNwLTPbduSco2bYF5HYUcMw0Rks0QPDMD0pWjLhIjhqwo7rOA5mZmbgeZ6fh8Z1XSltKAoq\ntnkxsGxsG7y2+hr87Uffi9HKKB574XEcdtaheTeLYZgYcN+qGC+V5yY0XUd5dHPYUy+gsnTr7NqR\nc44aYLgdrFz1iWGiw44ahlEYlR014jlmZ2cxOTmJSqWCsbGxjmTBeefJSXJsRm3WP7EeN//XOlT/\nqg7nTcDyv94Waz+/FuufWJ930xgmN1RMcqpae1VBxrWgBRw1AFAaXwpr4vmBjhu3rVyeWy4c+sQw\n0WGhhmH6kGUyYVUdNUTcstuWZWFiYgKO43RNFsx5Z4rFYvl+v/Kt8zH69nEY1TnB0KiWMPK2MXzl\nW+fn3DKGYYaRxdK39iSQowYASqObw56egOc6mbYjb0fNMNNsNlmoYZiIcOgTwyhMURw1cUUdctHY\nto1Go4FKn0FR2qt3WXxvw7aSO2y/Ty82zW7yRRrCqJawaTa9xJYET9AYhgEWVx/blUDoEwDopTKM\n+hLY0xMojy3Nph0FyFEzzNeCaZqo1+t5N4NhlIAdNQwjkcXkqIlybEoWTIyPj/cUaZIIQCrBLqD0\noWtmkO91aWMpnLbdsc1p21jaSHeiMMyDcYZhmFiECCTlFMKf4qBx1SepcI4ahokOCzUMIxHZQo3s\nXCtpHttxHExNTaHVakHTNNTr9dRdMnH2ZYGECePUD34MM7dP+mKN07Yxc/skTv3gx3JuGcMwTP7I\nylHTTSApjS2FPbkp8TM7dltzdtQMO61Wix01DBMRFmoYZhGRh7DjeR6azSYmJydRLpcxNjYGXddj\n57NRBXZJqM8O2++Ab3/+37HdI8tQ/79lbPfIMnz78/+OHbbfIe+mMQzDDCe2DZQWZmQwqnVoRhnO\n7HQ27eDQJ6lwMmGGiQ7nqGGYPhQ5mXASESNLgcSyLMzMzMAwDIyNjcEwjIGO1wvVBB0VWUzf7w7b\n74Cvn/O1vJvBMAyzOOhS9Yl4dqqJqz7/j/AMA6VKDUcftwbbb799+m1wXWghghGTDizUMEx0uCdi\nmAISZ0UlrrAjC1EocV0XzWYTpmliZGQE5XJ5QTWnPB01RRF1itKOKAzzCh/DMMOHKn0roFZbpREi\n1DzxxBP49n9cilPf9y406jXMNls4/8vn4sP/cHr6Yg2FX/HzThqtVouFGoaJCIc+MUwEkk5Skzhk\n4h5fVnuSunXa7TYmJiYAzCcLzmKSr5LowTAMw8hHJYFZpbbKQOtSnhsALr/kIl+kAYBGvYaPHb0a\nl19yUaTjxgolKkB+Gg59YhiGYEcNw2SArAdv0cSJ2dlZAMCSJUtQ7lE1gR01jGyGfbDLMAyTF1L6\n1y7luQHANlu+SEM06jXYZnvBvgPTI/yKSQdOJsww0WFHDcNIJMlApmiVnKKU3G42m3Acx89F00uk\nkQWLLwzBAg3DMIxihLhZSpW5cCeR2WYLpUr6JZ41y4LHFZ+kwo4ahokOCzUMIxmZCYJlizr9sG0b\nk5OTsCwLpVIpcphT3HbL+B3z/N6CqCIwqdJOhmEYRjFC3CxHH7cG519+rS/WzDZbOP/ya3H0cWsi\nHTbWc4tDn6TDOWoYJjoc+sQwERgWt0ZS0Sg4aBCTBTcaDVQqFUxPyymdGWfAEkfgKgJFaUcUhuUe\nKBIqfacqtVUlVPpOVWorox6abXfNUbP99tvjw/9wOi6+5CLYZhulSjV2IuFYOWo49Ekq7KhhmOiw\nUMMwkpHtqAGyW4HxPM8vuV2pVDA+Pg5d1/225J13hmEYRjVU6rtUaqtKqCSCSWtrj/ww22+/Pc74\nx8/LOa8Ahz7Jx3EclLj8OcNEgu8UhllEDCIaOY6D2dlZOI7TN1lwmiQJk+o3mWB3AMMwDFMkVBPB\nUm9vERL5cuiTdIb992OYNGGhhmEkI9NRI+4v68HneR5arRaazSZqtRqWLFnS9Vx5553hBz/DMAxD\nsBivEJ4HTZJQE7s8d95iEcMwzEuwUMMwkimSeyNJW6anp6HrOsbGxmAYRmrtiLOvDFHHdd1Ujxk8\nfpx9eYUpXYp0zzEMkx/cryqC48DTdUDPt8YJhz4xDFMkWKhhmAhkOdiT7cCJgud5mJ2dheu6qNVq\nqNfrkb6DvCs5seghD9niFsMwDLNIKULYE1CI0KdhhsZ9PEZjmGhweW6GkYzs1f20k/iapomJiQl4\nngfDMFAulyOX3I5K3o4a2ajWXoZhGIaJgpTFkKIIJKYJ5JzodtgXm4b5d2OYtGFHDcMUjLyECdd1\nMTMzA8dxMDIygnK5jMnJyVjHKIqjJq39kraBByIMwzBMVDzP8ysoLkY025YmkMQSPiyrGIIRwzAM\nWKhhGOlklUw46b6e56HdbqPZbKJarXYkC5ZVcjuukDHMDhUVHUMMwzAMkxqWBa8AoU+co0Y+vJDF\nMNFhoYZhMqCoE3HbtjEzMwNN01JJFizLJSPjmDL/JsNuXWbSgUU6hmEYFCtHTc7tGObxg23bqRWl\nYJjFAAs1DBOBQR6acT+bhaPG8zw0m020223U63VUq9WBS27LzDsjw9Ujk2EcZBXlu42KSm1lGIZZ\n1BRJqGFHjTSazSZqtVrezWAYZWChhmEkU7QJrud5mJiYQKlUwvj4eKpx8XnmnYlLkf4mTLoMo1DG\nMEx0uH+XhwzHh+wcNVHHORz6JJd2u81CDcPEgIUahikYshw1lCwYABqNBioRBiNFcdSkfdwiTeSL\nJuQxDMMMC0Xq65keFCRHDYc+yaXVaqFarebdDIZRhsWbYp5hMiLJRDzNibvneWi1WpiYmPBjg8uS\nBiIqOWoYhmGY6HA/LI9hnpxHoighR0Vpx5DCjhqGiQc7ahgmAlkOoNLMaWPbNmZnZ+F5HkZHR1Eq\nldBqtVI5drd9ZRw3DkUozz3M8HfGMIubRS0mMPKwLKmhT1Hh0Ce5tFotFmoYJgYs1DCMZDRNg+u6\nsfYfdELcK1kwHV/GgJsdNXPfgeM4Hd83EfzOVZr0qNRWhmEYRg6yctTIDH2K3N6cQ59UGuskgR01\nDBMPFmoYRjKyhYbg8S3LwszMDAzDGDhZsGqOmrzP7zgOZmZmOoQa8Txh5xT3p/bFeS17P4YBhn8S\nwTBMThSp6lMB2jGsz1+u+sQw8WChhmEKRtKcNq7rYnZ2FrZt90wWLFMkydtRk5cA5Hke2u22Pwip\nVCp9BTJqZ6vVgmEYKAm27zBxp5foI/5fdHBF/Uyv700UnWZnZxdsD77u9V4WIlLeQuAwMqwTB4Zh\nCoDE0KfY7RgZybsVQ0u73eZkwgwTgwL0igwz3GQxabRtGxMTE6hWqxgfH09tUhUnbEslR02akIvG\n8zyMjY1B13VYltX3c2JolKZpqZZJH5RuAo7jODBNs2OQVSQRSXztui4cx4Ft2wv2CftMr9dJ94vK\nok8kyjCLnGF5HiZGoqMmTv+qmSZczlEjDc5RwzDxYKGGYSKQdTLhqIM2mjyLyYLTPH5RSNtRk1Ye\nINM0MTs7i1qthlqtpuR3241uQgQNdqlyWF5EEXRM04Su635bo4pAWYhIwdfA3OC1X04jDmdjmOio\n2A8v5vtTdo6ayOQcgjXsoj3nqGGYeLBQwzCSkTF5p5LbrVYLpVIJmqZFEmniIivsSFbum6xwXRcz\nMzNwXberQBZrBU8Rcaco7YwiPpBDScY90Yuogo742nEcGIbRN6dRmIgU9TxB4oo7FF7ZbrcjfybJ\neRgmLfiakoMUMcGyilEWuyjtGFLYUcMw8WChhmEyIFZ5yD4TYjFZ8NjYGGzbjhRqE/X4RaQojpow\nF00Qx3H849PPsNAm13WhaRocx+nazjCKFCrFzJFUfCiXy9InlUnEnaBQRNdqt/cGOU+QQZ1Eruv6\n9yqLSAyjAKZZiBw1mmlyeW6JtNttLFmyJO9mMIwy5N8rMsyQE3eQHyYihCULpjwcMmBHzTxRXDQ0\nmTUMo++klMp4O46Dcrm8QNjpRzdhJy60Mhr2HQcnv+J5wz7DAlIxGVR8oGs1LEn5ICQVd/q9R/dk\n3GMnEZF6vdfrM9RnWJbFIhKzeLHtQuSoybvq02IIfdpyyy3zbgbDKAMLNQwTgUEenIM6WEQXR6VS\nWZAsOO7xVXTUxCHO7xZlUCR+/9VqFUuWLFnwGVGkiRKGRqIbACxZsiR23pewBM9hglDYe922i9+J\n+FlybfVLLp2GgCS2I4qI1G07/T1EWEQqJmmLD5ZlwXGcVKqLpC0iBV+T+0d04cU5j4jMcDRy/VFb\n456HYXqhWVZxctSwo0YaXPWJYeLBQg3DFAxRSBErCkVNFiyrLWnuS0QRSmS0IeoEop+LBpj7G5Eg\n0EtYAOYFj1arhUqlgmq1mmgyk6XgYFkWms1mpPb2EnHiikhh26LkcrFtG6VSqWNCOchKZbfz9vob\nxBWXyIXFFA/ZDhYSlZLkbZAtIgU/E8xT1O8zIrJFpODrbo6qIotIKi2eSGlrQcpzc+iTXFqtFur1\net7NYBhlyL9XZJghJ6mA0Ww2/cRrYblQkhxftqMmqvhSdNrtdiwXTb/fyXVdNJtNuK6LkZGRwk/M\nPW8uYbVlWWg0GpFEwjwdK57nod1u+6GB5cDqbFYiEk0Qe+1LkJBk23Zfp1IYskWkfsdj8iPrMCjK\nhxZloiVTRAL6V2ijn81mM/QYxKAiUtT9en1GbLcKz8fU25hztSUfDn2SCld9Yph4sFDDMBHJKmRI\nnLyNjY3lOqEvQi6ZuG2IOuGl43bLGUG5gMJcNGKoQhSRRnSlNBqNwg/EHMfB7OwsDMPA6Oho4dsb\nDCXrJiwUSWwgEcx13a4iYHDfbtuihrL1+lyU+0p094khcFFgEWm4iDOJzDuXDgnjIyMjC96Lm+so\nyuugqy/O5wlRVCKycB8l/UyqFCVHDYc+SYWrPjFMPFioYRjJRBUaaGBpmiYAYGRkJNIkpGiOmrjt\nKJIIYJomZmZmUK1WF+QCAha6aPr9fcgZ5ThOZFdKnpArxTRN1Go1KYlj0yZOaFYREEWwfiJNEXAc\nB61WC57nodFodFzzYZPaLEWkbvdoXFGpn4jU62/EIlIxiRqG1e3/eTAzM4N6vQ5d11MPYRtURAr7\nLoOFDAYRhIxWC56u+/dsEhGp2/9jk7OjZthhRw3DxKPYswaGGSLCRAnPW5gs+MUXX4w14JAlvCQV\ngdIc+MrKkyPuK7polixZsiBsBkjuoimXy0pMyKO4UopEktCsvKFrolqtolKpFP6aKLqo1K2iE4lK\ntVqtq6iUVESKuq+I2F+EYdt2T1GpX3+TREQq+r2dFkW7XqNS9Fw6pmnC87wOIX9gEck04Qp5xZII\nVCLB7010K/USkWrtNkwAdruduvsoyt+yaItXacNCDcPEo/gjW4ZRnF4PXZoIUQhEN4FgkOOH7V8k\nR03ekIumW0UtIJmLRjUBwTRNtFotpQSEZrMJTdNYVJKECqKS+HcnR6Ku64UQlfpVZqMJr+u6vqgU\nVUTqFlIWtm83klZmo8TpJColFZHYhaQu9LfTdT3Vv5XheUC9PtAkPixXETkuu90zwdfaS6FPwfFJ\nFCdSv/dEwgQdMbdSliFsWfWXHPrEMPEo/miRYQrCIMJC0GlCE7ewZMFxnClJ2iXDoZK0LVGJ8n3E\nPf/s7Cwcx0nNRWPbNprNpjK5XcTQLBUSHAPqiUrkVNI0TZlrgsLfVBGVbNv2HYlFCX/rNYml+44E\n+qzFiX4iUrdtlmXBtm1Uq1W/nwjbv9+EOIxB3ATBz1K/HQzPIYomIhVh0SJPNMuCNzo62DG6CBH0\nvUbtxzTbRnlkBEbKYb9RxR3btuE4DsrlcuTPZCUixRWE7rnnHvz+979HpVLx++Zms4l77rkH69ev\n95/h3X7efPPNOPfcc/G73/0Ov/jFL/CGN7wBAPDEE0/gNa95DXbZZRcAwBvf+EZ885vfBADcd999\neP/7349Wq4VDDjkEX/3qVwHMjRmOOeYY/OpXv8IWW2yBq6++Gtttt13o788wRaL4IzCGGTJs28bM\nzAw0TQtNFixT8CiKkyVqO2RMumgl2zCM1F009Xo9kTMqa2hyq0poloqikgquFBHP8zA7OwvP85Rw\nKgHzwp0q9x0Jd7quY2RkJJdrIu7ftd1uw7IsLFmyROp9l1ZlNtd14TgODMNY4Fag505eIlK3fEi0\njRxLRRORMsGy4OV4/xo/+xmMn/0M9qGHovT97wMvfdfOvvvC2XffgY8fNZyNrtssBfKkYWv9Pv+X\nv/wFDz74ICzL8vuQxx9/HN/5znfguq6/ICD+pNdvetOb8IMf/AAf/OAHF7T31a9+Ne67774F2086\n6SRcdNFFWLlyJQ455BDccsstOOigg3DRRRdh6dKl+MMf/oCrr74an/zkJ3HVVVfF/JYYJh9YqGGY\nDNA0rSNZcKPRSG3ilsT1Ercykqy2yKDX+cVcNLquo16vdxVpkrpoVJjcio4JVSa3Rc+VEkRFVwp9\nx6VSaYHDr4iI4qgqwp3jOH6YZVGcP72g75hyd8nu29I4PoVFVqtVVKvVFFo1RxIRKUooG7l+xGdN\nkUSkIDR+0HW9I4RuYBHJsoAc+8m0BBkVkRUGtXr1aqxevbpj22GHHYYbb7wxcvhTL4FW5M9//jOm\npqawcuVKAMAxxxyD6667DgcddBB++MMf4qyzzvLb9JGPfCTur8IwuVH80SPDDAlTU1Mol8sYHx/v\nO3ApguAhIiPBXZLEv1FCn8KwLAszMzP+32BqamrBgJhcNED/waU4Ga/VaiiXy4WfeNFknPJ4qCAq\n0UqbKlWoxFAnFb5jYN6Vosp3rKLzh9xVKn3HFJ4VtQJh3pBLUMZ3LOP3JyEMGCyBexwRKcyFFLZ/\n8D3HceA4DnRdj7TgE3XsUDZNOELVp+Bn+4lIYeegz4W1VYXrepgwTXPgxaH169fjDW94A8bHx3H2\n2WfjLW95C5555hksX77c32f58uV45plnAADPPPMMtt12WwCAYRjYbLPNsGnTJixdunSgdjBMFrBQ\nwzASEZMFNxoNKUnUxIFM2jltVE1ULEKTOlp5TyMXjcqChyphOORA8zxPGceEaqXCRceEKt+xas4f\nAL6lXxV3led5fnhuXuFZcSGRRhWXYFAIG+Q7zur547oupqenUa/XIwthUfMhGY4Dr1pFqVRKLCJ1\n+z+5lcISaEepzNaPfuOGXscOvkffl4zFsaJAv9cBBxyADRs2+Nvpd/7iF7+Iv/mbv+n62W222QZP\nPvkkNt98c9x3331YtWoVHn744VjnL9oYlWF6UfwRA8MUhDgPTXJb0Aoq5UOJc65hf5gkcdTE3Y9c\nNKVSCWNjYx0DWtqX8gLQtl5/56DDQwUXDQkeNCFQYTIu5s9RYTKuYqhT0PlT9O8YUNOVopoQ5rqu\n32eqcO8B89eFKvdemiJNVpBIQ0J/VKKKSJplQa/VUv37US66sMWUJEm10xSRuu1HOWrCkmDHJU0R\niRhEGBTbcuutt8b+fLlcxuabbw4AeMMb3oBXvepVeOSRR7Bs2TI89dRT/n5PP/00li1bBgD+e9ts\nsw0cx8Hk5CS7aRhlKP4TjWEUo1uyYNHOKwOZVaJkV6CSAbloTNPEyMhI6MDSsizfxt3t9xRfk+AB\nDGZTzxLR4dFoNAo/IVAxfw4JHoA610URqyT1QhRIVZqMi+FZRf+OgfkcOqq47oD5sD1VhDBVRRq6\nLtLM+9OBbQMp9veWZfnXRVifXKS+mkRdwzDQaDRiCUK9RKSwbXnkQ3JdFxs2bIh9DLFdGzduxNKl\nS6HrOh577DE8+uij2HHHHbHZZpthfHwc9957L1auXInLLrsMp5xyCoC5vDiXXnop9tlnH1xzzTV4\n+9vfnqj9DJMHxR/tMIwi0ACMJhPiQFfFpLyykeWocV0XExMTKJVKXfMBUS4aXdc7Vq26DXa6nVPT\nNExPT/uvxZ9pbUv6GbHdlGhVlYmtirldVBY8VBHCqF91HEeZ64ImtjTpKvp1AcjN7yILCilTSaQh\nUVc1kYb6OGlYVmpCDSX4bzQaSlwXwNy1TEm7o4Rf58UgldkuuOACvOlNb4rUh1933XVYu3YtNm7c\niEMPPRS777471q1bhzvvvBOf+9znUKlUoOs6vvWtb2GzzVxvwU4AACAASURBVDYDAHzjG9/oKM/9\njne8AwCwZs0avO9978NOO+2El73sZVzxiVEKrc/kZ7hnigwTA9u2Q+OcTdP08yY0Go0FD6KpqalY\nluHp6WmUy+XIA6MXX3wRo6OjkQYl5LIYGxtL/dhx2h1n34mJCYyMjPQUHCivgmmaWLJkSdfvWgx1\nIhdNL8SwoXq9DsMwugo5eW0TCZahBdDxO6YtDCURkMJQsYy1as4fUfBQZQIjlrLuVqGtiKjoSlEx\ndIjK/aqS6FgUaVQR7zITaQBUV6+GfdxxcP7P/xnoOCQ4qnItA/OuMFWE6CTceOONuOKKK3Dttdcq\n8exhmIwJfSCo0YsxTAHoNrCigYzjOKGJaumzMh0yso8v49hpOmooFw0NAIIiTbeKTv1y0ZB1Ohg2\nVKQBtviduK4L0zRhmiaq1SrK5XIiEUhMZtjtPFGPI9JLyHFdF57nwTAMv7RuUQSkbohJjlUZWIsO\nD1XCcFRzKwHzgocq4h2gpitFzPujwv2nskgTZ8FoICwL3oDCCiUar9fryog0UUK0VOfhhx/G+eef\nj5tvvlmJPoZhioQaPRnDFAwxWXC1Wu07+SlS6FOStsg69qCI4WbkuJmYmFiwD4k0USzFKiXfFcUO\nyp8zOjqa+4Avisjjui5arRZ0XfedB71EINFyPYgbSSSuuON5Hmzbhq7rKJVKME0z1dA1GRM41dxK\nwPwKsyqCh6o5dEjwUEVwFPO7qCI4ktNTJVeYKNLIqFTZlQFDn6jNlORfBWhhQhWHYxKef/55nHzy\nybjiiisiu7gZhpmn+KMJhikYYrLg0dHRQgzKVXTUxDlut9/Ptm1MT0935KIJTuZFF00UkYYmteVy\nWZmVT5rUFmki3kt8EN1KeVTOShpqZlkWbNtGuVzuCIErgoAU9j6Fa9J1YVlWYQSkbgTdEipMXlQX\nPFRZyVcxv4vqIk0mThpigGTCYrJjVfIrUZtVcv/ExbIsrFmzBueeey5e/epX590chlGS4ewdGEYC\nNFCkRJxx7PjsqBnsuCLBpM3iYJLOH9dFE8zfocLASWyzSpPavNscV4Cg+97zPKlupTTzF5GwBMAP\ngaPE2WnlQUo7JA2YC8MBgGq16ueTCvtst89njSgeqObw0DSNBQ+JUJsNw1CmzDldz6VSKftwQ8sC\nEogs9D1nLiwNALWZQpSHEc/z8KlPfQqHHXYY9t9//7ybwzDKUvzZCMMUhHa7Ddd1u1YSyhtVHTVx\nITeTruuhFZ0AoNVqAZj7XsR9uk0QKa69XC4rM9mi/B3cZrnQtVEqlaRPttISIMTrOY02D5rsOkoe\nJBKS6H41TTPSsYmschqJ71O4IU1qxbYV9dqmVfwsrue0ULXNs7Ozyok0eQpLmmnGdtRQm8V7sOio\n2OYkXHLJJXBdFx/+8IfzbgrDKA0LNQwTkUEsquyoGbwdVL4yWPqcICcB5Q4RtwdfdzuvaZq+CyGL\niV+cCaLYfhWrDanYZso5olK5YgqDS7PNWeXQqdfrsdocRxiKuq1XIu3gNnLr2bYN27YHEpD6vZ/G\nNhLwihQi2Y9gGI5KbVZJWMpbpAEQO5kwuX90XVfqe6Zk+Znl/smBu+66Cz/84Q9x4403KvF3YZgi\nw0INw2REXOGlKMJO3LakDU2EerloxFw0UXLL2LaNZrPZYaXPY7LX631C/F3ofV3X/WotMtwCUT/T\nDxUrJBUhPCsulCeFyhWr0uZBEvDmFQJFYlij0egqOmYtIMU5DjDnNmy1WrkL0P3+fmKZc1WcByoK\nS2JYWa6CR4wcNdRHA1AmFA6Yu/coL5QqbY7L+vXr8ZnPfAY/+tGPlFngYJgiw0INw2RA3Iey7BAL\nmY4aMbnqIPvS5LPVasEwDJTL5Z4ijaZFy0UT5u4o0sApOEmzLAvtdhuVSmXB5FCWgDRouAn9bXRd\nh2EYUieHwddJIdeBSmWsKcxC0zQlxTCV2kx9Ry8xLC8BKQzRsURi2KAiMr2WmUjb8zxompaa03HQ\nz/Qjl0pJA1Ko3D8Rqz7RuEA1wYMcwSq1OS7T09M4/vjj8e1vfxtbbbVV3s1hmKGAhRqGyQDZoU9J\nHDU0EI66b5YEc9GQXVjEdd2OZKP9fheahOu6XvjJIf0uVMKaytHm7ZSIMpkjp4TjOKhWqx3fcxEE\npLBtruvCtm2USqWOPClFEJDCoLw/lUpFmRV8EpboPlShzSQs0X1Y5L5DhFx3QWGpaN+5eB+TsFSr\n1RYIS1kISMHXRL/73HEcv/IgJZgusjAthg7lLtIAkYWadrvtux1zb3NEaKFFpb4jLo7j4EMf+hBO\nPfVUvP71r8+7OQwzNLBQwzARUWVQ0A+Z7p44glG3fUUXjZiLRtx3EBdNHuWgk0ITlkqlUphS4VHC\nFcg5I7NCUpBBHAIkLHme15G7oygCUtg2CgmsVCowDMNPxhvls8HXWaGysKRpalVJUqlkOH2nlmWh\n1WphZGSkEJX3otznFN5ZLpf9Cmu9PjuI8yitfgWA31+USiUpIbSxiSDUtNttP7xThfsQmA+zHhkZ\nKfx9mBTP83Deeedh9913x1FHHZV3cxhmqMj/Scgwi4AiOmrikIWjhlw0mqZhbGysq3vE8+ZK9pIb\nKIqLhtw4KkxYgM58I6qUCgfmc3fkkaw06URBDHWSsaqcVo6S4ETPtm24rotSqQTXdSNXSYrrFEhz\nm2VZvlhaKpU6Jq15C0hhZFn1Ky1E949Kk0PqP4qUYymqMF2r1TLNo5OGMK1pWkcYbd7CdM2y0LRt\neAH3LL12HAeWZaFWq8FxnA43bZzzZQn1H/V6vTDXtAyuu+46PProo7jyyiuV6CMZRiXUmAEwDNOX\nJMJOlIeqbEeN6KKp1+uhK+22bfsrf7quLziP+BkajLbbbaWqnIjCwejoqBJtpomhSsl3gc4KSbJc\nVmlPFCgPxqA5dNIWkHqFmpDrwPM8P6SMHEzB44lkJSCFbaN7MetJ+CB4nueL3Sq5DsJCtIoMJTvO\noypc0n5FDHeS4dKMKhZ126aZJvRaDV7g2U4hzo7joFQqwXEc2LYd6xwiSdxBSbbRuKZSqUDXdTiO\nUxgBKU0eeOABXHjhhbjllluUEYUZRiVYqGGYDCiaoybu/rIcNZ7nYXJysq+LRtO0jlW0fpM8QtM0\nf2Ioe5I3iBU8GJ6lSrUEFZPvimEhKk0MKRQuDeExq4lCcGLYbyCfpYDUa5v4fxKRibwFpLC/napl\noSmcRRXHI5CvSJMUEtSBaJURkzBQv2LbqIyMAAFR1LIs//pI2lcPIiCFbesnTpMwQ+0PO45I3v1I\n3L/fhg0bcMopp+Caa67ByMhI3/0ZhokPCzUMkwFxhREgvkMmarWluMgQdTzPg2VZsG0bjUajq4uG\nVuJd10W5XO45IKbJFeU4qFarHbbuQQZiWVjBSYAqlUp+7pFu+6W1Lfg6LqJjSaXJCk1mVUtkSyKe\nSqFwScKG8l5pFoWDRqMBwzASTfL6vS+jWhJh2zamp6eluATSnOQFBVMWaeRBIo3neYXJd7aALjlq\nKL8L3YtJybJfIXG6XC73DadVpW/RNA3vec978MQTT6BSqfj/Nm7ciC233BJr1671847RQoL4eo89\n9sDq1atDvweGYcJRY8THMAUgy8GN7HPJctREaTcNdD3Pg2EYXUuZ0ooUuWn6HZcGokWpjkT0GwxR\nNYhyuYxSqdTxdwn7bLfVu17nSHsVD4AvJFGOlHa7nfh4WQ2i03SkZAUN+j3PU8pxIFbuUW0yG8zt\nkrd4FAbdz5SgmUL40nAJZCFOUzhcqVRK3bEU5f0k0Hddr9c7FgKKTPC6LtI17ON50Gy7Q6gR87uo\nIk6LrqUoOc+K2rcAC+/tb33rW5ienvYXwy699FK84hWvwH777ecvJFAIY/A1wzDJUaP3YxjFiSuM\nyN4/DnEHEGHtoNXqZrPpJ9ejMqbiPmJFpygTU8o1UqTqSETYQIwGdI7j5CIsJZ2IOY4D0zRhGEbH\n4DnvRJT9tlmW1VEuXIUKSaomsqVBukruH7FkeGEnswE0TetIwFuU7zqKC4Aqw9F1XSQBKWyb580l\n8aY8Kf1yjhShf1FCpAEA24ZnGIDgMJ2ZmVFKEAPmS4er4tbsRfD6XL58uf//f/u3f8Po6Ci+8IUv\nKP97MkzRKcaTnWEUQaYgkiVxfo+4+3aDBl4A/Fw05Mog4rpoXNdFq9WC4zhKTQppVbZcLuc2oEsS\npiCGhMgYPCexeffbJiaeJPdPMM9Rv+OJZBU2QomzKYSPnFR5i0e9CIqPqrh/qG9SqWQ4UNwEvL2u\nUc/z/NBDGVXWejFI/0IVh8rlcsd1XQQBqZ9A7bouarXagnDafp8NvpaKEPZE4ankEFMFyoU3DCJN\nL26//XbceuutuP7664f692SYoqDGzIZhFEdlR01cgoNRctHUarUOZwC1WXTRAIg0waPQijzFjrjQ\nd2GaplIrheQ2ACB1Ap72BIHKQQ8a6pSWaESv41RIokSUvcLXZItGUbZRiJZKuX8AdUO0KLeLSoJY\n3smOk/YvdA/Kci0NKvKEOZBs2/ZDy+h13GMTaYeeLdg2O4t6uew7xMrlMgzD6OgriyxSUziQSrmW\nkvDoo4/i85//PNatW6fM+IVhVIeFGobJgCIJKUA2jhoxF01YRSfaL6qLhlbtKQmxKi4aCmOhiawq\ngzmayKrkNhAFsTSukawmCCSIGYaBer3e8xqR4T5K6g4IHmNychJA7wlaXuKSCDlSVOpHgqEsqvQj\nJNKUy2Vl+hFgvv+TeY2k3b+IoWWDiKZpC9S0KNNtP21mBl65jGaz6TsKw8QlkSL0J9RvF83ZljYT\nExM48cQTcckll2Dp0qV5N4dhFg1qjE4YZpGhsqOGQpK6uWgI0UVDEzug/6CI8gKUSiWYpumXvZQx\nAEtr0Ew5O1RLYttqtfxQJ1UmsjRopkmKKhNZCoeLKogVZXWZVsDJIZal+yjONhGxr9R13Z8c0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ixR7x/2mU4d5uu+1wzz33+M/6n/zkJ1i5cuWgvyLDLCrUeRIwjOLQ4DfO/mk6aizL8icNo6Oj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\nHg4G651erSkAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "from mpl_toolkits.mplot3d import Axes3D\n",
+ "import numpy as np\n",
+ "%matplotlib inline\n",
+ "\n",
+ "import matplotlib.cm as cm\n",
+ "\n",
+ "fig = plt.figure()\n",
+ "ax = fig.add_subplot(111, projection='3d')\n",
+ "\n",
+ "c=1\n",
+ "m=1\n",
+ "\n",
+ "parameters = graphDAdvDMDN3.predict(data={'input':X_val[start:start+rang].squeeze()})['output']\n",
+ "\n",
+ "comp = np.reshape(parameters,[-1, c + 2, m])\n",
+ "mu_pred = comp[:, :c, :]\n",
+ "sigma_pred = comp[:, c, :]\n",
+ "alpha_pred = comp[:, c + 1, :]\n",
+ "y_pred = np.zeros((len(mu_pred)))\n",
+ "\n",
+ "thr_alpha = 0.5\n",
+ "\n",
+ "col = cm.autumn(np.linspace(0, 1, mu_pred.shape[-1]))\n",
+ "\n",
+ "cont = []\n",
+ "cont_const = []\n",
+ "const = 100\n",
+ "guany_sig = []\n",
+ "erSigConst = np.zeros((2,rang))\n",
+ "\n",
+ "\n",
+ "color = cm.gist_earth(np.linspace(0, 1, rang+2))\n",
+ "\n",
+ "for elem in xrange(rang):\n",
+ " ax.plot(xs=np.arange(12),ys=[elem]*12,zs=X_val_orig[start+elem,:,0].reshape(-1), c=color[elem+1], marker='o')\n",
+ "\n",
+ "zerror = 500\n",
+ "\n",
+ " \n",
+ "for mx in xrange(mu_pred.shape[-1]):\n",
+ " for i in xrange(len(mu_pred)):\n",
+ " if alpha_pred[i,mx] > thr_alpha:\n",
+ " ax.plot([12, 12], [i, i], [mu_pred[i,0,mx]-np.sqrt(2)*sigma_pred[i,mx],\n",
+ " mu_pred[i,0,mx]+np.sqrt(2)*sigma_pred[i,mx]], \n",
+ " marker=\"_\", c=col[mx], alpha=alpha_pred[i,mx])\n",
+ " y_pred[i] = mu_pred[i,0,mx]\n",
+ " #In order to avoid ERROR of 0.1 when approx 0, we add a margin of 0.1€\n",
+ " if mu_pred[i,0,mx]+np.sqrt(2)*sigma_pred[i,mx]+0.1y_val[start+i]:\n",
+ " cont += [i]\n",
+ " if mu_pred[i,0,mx]+consty_val[start+i]:\n",
+ " guany_sig += [const-np.sqrt(2)*sigma_pred[i,mx]-0.1]\n",
+ " cont_const += [i]\n",
+ " else:\n",
+ " guany_sig += [np.sqrt(2)*sigma_pred[i,mx]+0.1-const]\n",
+ " elif mu_pred[i,0,mx]+consty_val[start+i]:\n",
+ " cont_const += [i]\n",
+ " guany_sig += [const-np.sqrt(2)*sigma_pred[i,mx]-0.1]\n",
+ " \n",
+ " erSigConst[0,i] = np.sqrt(2)*sigma_pred[i,mx]+0.1 - np.abs(mu_pred[i,0,mx]-y_val[start+i])\n",
+ " erSigConst[1,i] = const - np.abs(mu_pred[i,0,mx]-y_val[start+i])\n",
+ " \n",
+ " tmp = alpha_pred[:,mx]>thr_alpha\n",
+ " if np.sum(tmp) > 0:\n",
+ " ax.plot([12]*rang,np.arange(rang)[tmp],\n",
+ " y_pred[tmp], color=col[mx],\n",
+ " linewidth=1, marker='o', linestyle=' ',\n",
+ " alpha=0.5, label='mixt_'+str(mx))\n",
+ " else:\n",
+ " print \"Distribution\",mx,\" has always alpha below\",thr_alpha\n",
+ " \n",
+ "for point in xrange(rang):\n",
+ " if point in cont:\n",
+ " ax.plot(xs=12, ys=point,zs=y_val[start+point], \n",
+ " color='green', linewidth=1, marker='p', \n",
+ " linestyle=' ',alpha=1)\n",
+ " else:\n",
+ " ax.plot(xs=12, ys=point,zs=y_val[start+point], \n",
+ " color='blue', linewidth=1, marker='o', \n",
+ " linestyle=' ',alpha=0.5)\n",
+ " ax.plot(xs=[12, 12], ys=[point, point],zs=[y_val[start+point],y_pred[point]],\n",
+ " marker=\"_\", alpha = 0.4, color = 'purple')\n",
+ "\n",
+ "\n",
+ "ax.xaxis.set_ticks(range(13))\n",
+ "ax.xaxis.set_ticklabels(['Jan','Feb','Mar','Apr',\n",
+ " 'May','June','July','Aug',\n",
+ " 'Sept','Oct','Nov','Des','Jan*'])\n",
+ "ax.xaxis.set_tick_params(labelsize='xx-large')\n",
+ "ax.yaxis.set_ticks(range(rang))\n",
+ "\n",
+ "\n",
+ "ax.set_xlim3d(0, 13)\n",
+ "ax.set_ylim3d(-1, rang+1)\n",
+ "ax.set_zlim3d(-1500, 800)\n",
+ "ax.view_init(15, -80)\n",
+ "\n",
+ "plt.gcf().set_size_inches((20,10))\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "********************************* Prediction ends *********************************\n",
+ "\n",
+ "Duration: 0:00:28.994437\n"
+ ]
+ }
+ ],
+ "source": [
+ "#y_pred = model.predict(X_val)['output']\n",
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "y_pred = graph.predict(data={'input':X_val.squeeze()})['output']\n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* Prediction ends *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "********************************* Prediction ends *********************************\n",
+ "\n",
+ "Duration: 0:00:03.392599\n",
+ "Elements below tolerance: 937641\n",
+ "Mean Absolute Error: 113.59279267\n",
+ "Mean Squared Error: 9055483.09071\n",
+ "Root Mean Squared Error: 3009.23297382\n",
+ "Maximum Total Error: [ 1459237.66311133](real: [-1459236.], predicted: [ 1.66311133])\n",
+ "AE 10% 0.516293676637 (829068)\n",
+ "\n",
+ "********************************* End *********************************\n",
+ "\n",
+ "Duration: 0:00:00.440633\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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DVfVyN/4Q+78udGh7Gb6WN2eZM2dV7a6qL3bjXweemiNDi0VnmLXPjwJ/XlU7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\nlmdJkiSp0f8H5At8sdoCW98AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "c=1\n",
+ "m=1\n",
+ "comp = np.reshape(y_pred,[-1, c + 2, m])\n",
+ "mu_pred = comp[:, :c, :]\n",
+ "sigma_pred = comp[:, c, :]\n",
+ "alpha_pred = comp[:, c + 1, :]\n",
+ "alpha_pred=alpha_pred.argmax(axis=1)\n",
+ "y_pred = np.array([mu_pred[i,:,alpha_pred[i]] for i in xrange(len(mu_pred))])\n",
+ "\n",
+ "\n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* Prediction ends *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))\n",
+ "\n",
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "tolerance = 1e-1\n",
+ "idx = np.where(np.abs(y_pred-y_val)>tolerance)[0]\n",
+ "\n",
+ "print 'Elements below tolerance: ' + str(len(idx))\n",
+ "\n",
+ "measure = np.mean(np.abs((y_pred-y_val*1.)))\n",
+ "print 'Mean Absolute Error: ' + str(measure) \n",
+ "measure = np.mean(np.power((y_pred-y_val*1.),2))\n",
+ "print 'Mean Squared Error: ' + str(measure) \n",
+ "measure = np.sqrt(measure)\n",
+ "print 'Root Mean Squared Error: ' + str(measure) \n",
+ "i = np.argmax(np.abs((y_pred-y_val*1.)))\n",
+ "measure = np.abs((y_pred[i]-y_val[i]*1.))\n",
+ "print 'Maximum Total Error: ' + str(measure) + '(real: ' + str(y_val[i]) + ', predicted: ' + str(y_pred[i]) + ')'\n",
+ "\n",
+ "measure = np.abs((y_pred[idx]-y_val[idx]))/(np.abs(y_val[idx])+1e-16)\n",
+ "MAE10 = np.where(measure>0.1)[0]\n",
+ "print 'AE 10% ' + str(1.*len(MAE10)/len(y_val)) + ' (' + str(len(MAE10)) + ')'\n",
+ "\n",
+ "bins = np.arange(0,1.0,0.05)-0.025\n",
+ "measure = np.abs(y_pred[idx]-y_val[idx])/(np.abs(y_val[idx])+1e-16)\n",
+ "[dat, bins] = np.histogram(measure,bins=bins)\n",
+ "plt.bar(np.arange(len(dat)),dat,width =1)\n",
+ "plt.xticks(np.arange(len(dat)),bins[:-1])\n",
+ "plt.gcf().set_size_inches((10,6))\n",
+ "\n",
+ "plt.gcf().set_size_inches((12,6))\n",
+ " \n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* End *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.11"
+ },
+ "widgets": {
+ "state": {},
+ "version": "1.1.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/MDN-DNN-Simple-Ensemble-Uncertainty.ipynb b/MDN-DNN-Simple-Ensemble-Uncertainty.ipynb
new file mode 100644
index 0000000..fe293d8
--- /dev/null
+++ b/MDN-DNN-Simple-Ensemble-Uncertainty.ipynb
@@ -0,0 +1,1081 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "
Mixture Density Networks (MDN) for distribution and uncertainty estimation
\n",
+ "\n",
+ "This material is copyright Axel Brando and made available under the Creative Commons Attribution-ShareAlike 4.0 International License (http://creativecommons.org/licenses/by-sa/4.0/). Code is also made available under the Apache Version 2.0 License (https://www.apache.org/licenses/LICENSE-2.0). \n",
+ "\n",
+ "Please, to use this material and code follow the instructions explained in the main repository [https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation](https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation#bibtex-reference-format-for-citation)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "
Trying to predict uncertainty by using Deep Ensemble
\n",
+ "Below we will show a implementation of the following points:\n",
+ "
\n",
+ " - The article of Lakshminarayanan et al. (DeepMind London) *Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles.*
\n",
+ " - With the other tricks described in the main page of the repository https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation
\n",
+ "
\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using TensorFlow backend.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import tensorflow as tf\n",
+ "tf.python.control_flow_ops = tf\n",
+ "\n",
+ "config = tf.ConfigProto()\n",
+ "config.gpu_options.allow_growth=True\n",
+ "sess = tf.Session(config=config)\n",
+ "\n",
+ "from keras.models import Sequential,Graph\n",
+ "from keras.layers.core import Dense, Dropout\n",
+ "from keras.callbacks import History\n",
+ "from keras.layers.recurrent import LSTM\n",
+ "from keras.models import model_from_json\n",
+ "from keras.regularizers import l2, activity_l2\n",
+ "\n",
+ "from keras import backend as K\n",
+ "\n",
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 274,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "c = 1\n",
+ "m = 1\n",
+ "\n",
+ "c = 1 #The number of outputs we want to predict\n",
+ "m = 1 #The number of distributions we want to use in the mixture\n",
+ "\n",
+ "#Note: The output size will be (c + 2) * m\n",
+ "\n",
+ "def log_sum_exp(x, axis=None):\n",
+ " \"\"\"Log-sum-exp trick implementation\"\"\"\n",
+ " x_max = K.max(x, axis=axis, keepdims=True)\n",
+ " return K.log(K.sum(K.exp(x - x_max), \n",
+ " axis=axis, keepdims=True))+x_max\n",
+ "\n",
+ "\n",
+ "def mean_log_Gaussian_like(y_true, parameters):\n",
+ " \"\"\"Mean Log Gaussian Likelihood distribution\n",
+ " Note: The 'c' variable is obtained as global variable\n",
+ " \"\"\"\n",
+ " components = K.reshape(parameters,[-1, c + 2, m])\n",
+ " mu = components[:, :c, :]\n",
+ " sigma = components[:, c, :]\n",
+ " alpha = components[:, c + 1, :]\n",
+ " alpha = K.softmax(K.clip(alpha,1e-8,1.))\n",
+ " \n",
+ " exponent = K.log(alpha) - .5 * float(c) * K.log(2 * np.pi) \\\n",
+ " - float(c) * K.log(sigma) \\\n",
+ " - K.sum((K.expand_dims(y_true,2) - mu)**2, axis=1)/(2*(sigma)**2)\n",
+ " \n",
+ " log_gauss = log_sum_exp(exponent, axis=1)\n",
+ " res = - K.mean(log_gauss)\n",
+ " return res\n",
+ "\n",
+ "\n",
+ "def mean_log_LaPlace_like(y_true, parameters):\n",
+ " \"\"\"Mean Log Laplace Likelihood distribution\n",
+ " Note: The 'c' variable is obtained as global variable\n",
+ " \"\"\"\n",
+ " components = K.reshape(parameters,[-1, c + 2, m])\n",
+ " mu = components[:, :c, :]\n",
+ " sigma = components[:, c, :]\n",
+ " alpha = components[:, c + 1, :]\n",
+ " alpha = K.softmax(K.clip(alpha,1e-2,1.))\n",
+ " \n",
+ " exponent = K.log(alpha) - float(c) * K.log(2 * sigma) \\\n",
+ " - K.sum(K.abs(K.expand_dims(y_true,2) - mu), axis=1)/(sigma)\n",
+ " \n",
+ " log_gauss = log_sum_exp(exponent, axis=1)\n",
+ " res = - K.mean(log_gauss)\n",
+ " return res\n",
+ "\n",
+ "\n",
+ "def scoring_rule_adv(y_true, y_pred):\n",
+ " \"\"\"Fast Gradient Sign Method (FSGM) to implement Adversarial Training\n",
+ " Note: The 'graphADV' pointer is obtained as global variable\n",
+ " \"\"\"\n",
+ " \n",
+ " # Compute loss \n",
+ " #Note: Replace with 'mean_log_Gaussian_like' if you want a Gaussian kernel.\n",
+ " error = mean_log_LaPlace_like(y_true, y_pred)\n",
+ " \n",
+ " # Craft adversarial examples using Fast Gradient Sign Method (FGSM)\n",
+ " # Define gradient of loss wrt input\n",
+ " grad_error = K.gradients(error,graphADV.input) #Minus is on error function\n",
+ " # Take sign of gradient, Multiply by constant epsilon, Add perturbation to original example to obtain adversarial example\n",
+ " #Sign add a new dimension we need to obviate\n",
+ " \n",
+ " epsilon = 0.08\n",
+ " \n",
+ " ##adversarial_X = K.stop_gradient(graphADV.input + epsilon * K.sign(grad_error)[0])\n",
+ " \n",
+ " # Note: If you want to test the variation of adversarial training \n",
+ " # proposed by XX, eliminate the following comment character \n",
+ " # and comment the previous one.\n",
+ " \n",
+ " adversarial_X = graphADV.input + epsilon * K.sign(grad_error)[0]\n",
+ " \n",
+ " adv_output = graphADV(adversarial_X)\n",
+ " \n",
+ " #Note: Replace with 'mean_log_Gaussian_like' if you want a Gaussian kernel.\n",
+ " adv_error = mean_log_LaPlace_like(y_true, adv_output)\n",
+ " return 0.3 * error + 0.7 * adv_error"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 256,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "graphN1 = Graph()\n",
+ "graphN1.add_input(name='input', input_shape=(1,), dtype='float32')\n",
+ "graphN1.add_node(Dense(output_dim=8, activation=\"relu\"), name='FC1', input='input')\n",
+ "graphN1.add_node(Dense(output_dim=c*m), name='FC_mus', input='FC1')\n",
+ "graphN1.add_node(Dense(output_dim=m, activation=K.exp, W_regularizer=l2(1e-3)), name='FC_sigmas', input='FC1')\n",
+ "graphN1.add_node(Dense(output_dim=m, activation='softmax'), name='FC_alphas', input='FC1')\n",
+ "graphN1.add_output(name='output', inputs=['FC_mus','FC_sigmas', 'FC_alphas'], merge_mode='concat',concat_axis=1)\n",
+ "graphADV = graphN1\n",
+ "graphN1.compile(optimizer='adam', loss={'output':scoring_rule_adv})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 279,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [],
+ "source": [
+ "graphN2 = Graph()\n",
+ "graphN2.add_input(name='input', input_shape=(1,), dtype='float32')\n",
+ "graphN2.add_node(Dense(output_dim=8, activation=\"relu\"), name='FC1', input='input')\n",
+ "graphN2.add_node(Dense(output_dim=c*m), name='FC_mus', input='FC1')\n",
+ "graphN2.add_node(Dense(output_dim=m, activation=K.exp), name='FC_sigmas', input='FC1')\n",
+ "graphN2.add_node(Dense(output_dim=m, activation='softmax'), name='FC_alphas', input='FC1')\n",
+ "graphN2.add_output(name='output', inputs=['FC_mus','FC_sigmas', 'FC_alphas'], merge_mode='concat',concat_axis=1)\n",
+ "graphADV = graphN2\n",
+ "graphN2.compile(optimizer='adam', loss={'output':scoring_rule_adv})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 280,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "graphN3 = Graph()\n",
+ "graphN3.add_input(name='input', input_shape=(1,), dtype='float32')\n",
+ "graphN3.add_node(Dense(output_dim=8, activation=\"relu\"), name='FC1', input='input')\n",
+ "graphN3.add_node(Dense(output_dim=c*m), name='FC_mus', input='FC1')\n",
+ "graphN3.add_node(Dense(output_dim=m, activation=K.exp), name='FC_sigmas', input='FC1')\n",
+ "graphN3.add_node(Dense(output_dim=m, activation='softmax'), name='FC_alphas', input='FC1')\n",
+ "graphN3.add_output(name='output', inputs=['FC_mus','FC_sigmas', 'FC_alphas'], merge_mode='concat',concat_axis=1)\n",
+ "graphADV = graphN3\n",
+ "graphN3.compile(optimizer='adam', loss={'output':scoring_rule_adv})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 281,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "graphN4 = Graph()\n",
+ "graphN4.add_input(name='input', input_shape=(1,), dtype='float32')\n",
+ "graphN4.add_node(Dense(output_dim=8, activation=\"relu\"), name='FC1', input='input')\n",
+ "graphN4.add_node(Dense(output_dim=c*m), name='FC_mus', input='FC1')\n",
+ "graphN4.add_node(Dense(output_dim=m, activation=K.exp), name='FC_sigmas', input='FC1')\n",
+ "graphN4.add_node(Dense(output_dim=m, activation='softmax'), name='FC_alphas', input='FC1')\n",
+ "graphN4.add_output(name='output', inputs=['FC_mus','FC_sigmas', 'FC_alphas'], merge_mode='concat',concat_axis=1)\n",
+ "graphADV = graphN4\n",
+ "graphN4.compile(optimizer='adam', loss={'output':scoring_rule_adv})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 282,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "graphN5 = Graph()\n",
+ "graphN5.add_input(name='input', input_shape=(1,), dtype='float32')\n",
+ "graphN5.add_node(Dense(output_dim=8, activation=\"relu\"), name='FC1', input='input')\n",
+ "graphN5.add_node(Dense(output_dim=c*m), name='FC_mus', input='FC1')\n",
+ "graphN5.add_node(Dense(output_dim=m, activation=K.exp), name='FC_sigmas', input='FC1')\n",
+ "graphN5.add_node(Dense(output_dim=m, activation='softmax'), name='FC_alphas', input='FC1')\n",
+ "graphN5.add_output(name='output', inputs=['FC_mus','FC_sigmas', 'FC_alphas'], merge_mode='concat',concat_axis=1)\n",
+ "graphADV = graphN5\n",
+ "graphN5.compile(optimizer='adam', loss={'output':scoring_rule_adv})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Data set with or without noise"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def unison_shuffled_copies(a, b):\n",
+ " assert len(a) == len(b)\n",
+ " p = np.random.permutation(len(a))\n",
+ " return a[p], b[p]\n",
+ "\n",
+ "NSAMPLE = 1000\n",
+ "X = np.concatenate((np.float32(np.random.uniform(-4, -1, (1, NSAMPLE/2))),\n",
+ " np.float32(np.random.uniform(1, 4, (1, NSAMPLE/2)))),\n",
+ " axis=1)\n",
+ "r_data = np.random.normal(scale=3.,size=X.shape)\n",
+ "y = np.float32(np.power(X,3)+r_data)\n",
+ "\n",
+ "X = X.T\n",
+ "y = y.T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def unison_shuffled_copies(a, b):\n",
+ " assert len(a) == len(b)\n",
+ " p = np.random.permutation(len(a))\n",
+ " return a[p], b[p]\n",
+ "\n",
+ "NSAMPLE = 1000\n",
+ "X = np.concatenate((np.float32(np.random.uniform(-4, -1, (1, NSAMPLE/2))),\n",
+ " np.float32(np.random.uniform(1, 4, (1, NSAMPLE/2)))),\n",
+ " axis=1)\n",
+ "r_data = np.random.normal(scale=3.,size=X.shape)\n",
+ "y = np.float32(np.power(X,3))\n",
+ "\n",
+ "X = X.T\n",
+ "y = y.T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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lMpyqVlkOhWhxu+kyTSrVKkWlCPn9NDc00Oj1ooVCfOahh3h47166m5vxNzRs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8cZDIcZu3aNkNuNxzS5qDWBZJJJ0yTr83G4rIwil4uwUsw1NLDvS19iuLHxludMf1DbUSGEEOJ2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\nkQaAKJEGgKgDkKBmwTov0QEAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "plt.figure(figsize=(8, 8))\n",
+ "plot_out = plt.plot(X,y,'ro',alpha=0.3)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 105,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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PPZab58+noHv3lI65JVNIi4i0EPNnz2bO2LEMi8fpi3ecqkPt17XAAGA9sN3n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\nAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "plt.figure(figsize=(8, 8))\n",
+ "plot_out = plt.plot(X,y,'ro',alpha=0.3)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 277,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "********************************* End *********************************\n",
+ "\n",
+ "Duration: 0:10:18.969904\n"
+ ]
+ }
+ ],
+ "source": [
+ "X,y = unison_shuffled_copies(X, y)\n",
+ "\n",
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "epoch=20000#abans 40\n",
+ "#8000\n",
+ "graphN1.fit(data={'input':X,'output':y}, batch_size=500, nb_epoch=epoch, \n",
+ " validation_split=0.1, verbose = 0) #[np.tile(y,(1,c*m)),y] \n",
+ "end_time = datetime.now()\n",
+ "a=0\n",
+ "print \n",
+ "print \"********************************* End *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 283,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "********************************* End *********************************\n",
+ "\n",
+ "Duration: 0:15:13.262165\n"
+ ]
+ }
+ ],
+ "source": [
+ "X,y = unison_shuffled_copies(X, y)\n",
+ "\n",
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "epoch=20000\n",
+ "graphN2.fit(data={'input':X,'output':y}, batch_size=500, nb_epoch=epoch, \n",
+ " validation_split=0.1, verbose = 0)\n",
+ "end_time = datetime.now()\n",
+ "a=0\n",
+ "print \n",
+ "print \"********************************* End *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 284,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "********************************* End *********************************\n",
+ "\n",
+ "Duration: 0:15:31.743126\n"
+ ]
+ }
+ ],
+ "source": [
+ "X,y = unison_shuffled_copies(X, y)\n",
+ "\n",
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "epoch=20000\n",
+ "graphN3.fit(data={'input':X,'output':y}, batch_size=500, nb_epoch=epoch, \n",
+ " validation_split=0.1, verbose = 0)\n",
+ "end_time = datetime.now()\n",
+ "a=0\n",
+ "print \n",
+ "print \"********************************* End *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 285,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "********************************* End *********************************\n",
+ "\n",
+ "Duration: 0:11:31.825447\n"
+ ]
+ }
+ ],
+ "source": [
+ "X,y = unison_shuffled_copies(X, y)\n",
+ "\n",
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "epoch=20000\n",
+ "graphN4.fit(data={'input':X,'output':y}, batch_size=500, nb_epoch=epoch, \n",
+ " validation_split=0.1, verbose = 0)\n",
+ "end_time = datetime.now()\n",
+ "a=0\n",
+ "print \n",
+ "print \"********************************* End *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 286,
+ "metadata": {
+ "collapsed": false,
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "********************************* End *********************************\n",
+ "\n",
+ "Duration: 0:03:31.885095\n"
+ ]
+ }
+ ],
+ "source": [
+ "X,y = unison_shuffled_copies(X, y)\n",
+ "\n",
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "epoch=4000\n",
+ "graphN5.fit(data={'input':X,'output':y}, batch_size=500, nb_epoch=epoch, \n",
+ " validation_split=0.1, verbose = 0)\n",
+ "end_time = datetime.now()\n",
+ "a=0\n",
+ "print \n",
+ "print \"********************************* End *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def unison_shuffled_copies(a, b):\n",
+ " assert len(a) == len(b)\n",
+ " p = np.random.permutation(len(a))\n",
+ " return a[p], b[p]\n",
+ "\n",
+ "NSAMPLE = 1000\n",
+ "X_val = np.float32(np.random.uniform(-10, 10, (1, NSAMPLE/2)))\n",
+ "\n",
+ "y_val = np.float32(np.power(X_val,3))\n",
+ "\n",
+ "X_val = X_val.T\n",
+ "y_val = y_val.T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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oi0QwenXsvKIT70RE5KycuSdv2zae59FdLNKh7n3WnOuJdwp5ERE5a6en64Ns\nFpNO06rufVYp5EVEZEaNZTK8+MADkMlAXR2bb7tNg3RzRCEvIiIzZiyTYe8XvsB18TiRUIiS6/JE\nPs/ln/ucgn4OnGvI68Q7ERH5d3bdey/No6Mc7e3l0PHjeL7PdfH4G529LBiarhcRkR8by2TYdd99\ndP/d31ERjbKhuZmKcpnuyUk6Vq16Y+leFgx18iIiAvxkif7S3bv5cCTC1qkpug4cYKpUotO2OTI4\nCFqqX1AU8iIiAsCu++6jc3SUiePHKSaTDLsu2yyLVwcG8IOArqkpNt9221yXKWdBy/UiIhe5vOPw\n6pNPcvArX6HR80jbNk3RKMdrajjpupxwXfbV17P6/e/X0N0Co5AXEbmI5R2Hg/ffT/D973OjMawq\nlej2PI5ls7TX13MilaJx7Vri115Lx7XXznW5cpa0XC8ichHr7+qidnCQJUFAR2srh32fTtumHI8z\nUCrxcKFA7Bd+QafZLVB6Tl5E5CJ0+sS6vieeoP7116kqFmkpFskXi/QdP86BcplwaytLP/MZtr73\nvXNd7kXvXJ+T13K9iMhFZiyTYc8999DmupDJkJiexsrl6LMs2qJROpcvx4/FGFq9mvU7dsx1uXIe\nFPIiIheBvOPQs3s3h595hu7vfY/1sRhuSwsba2vZPTHBKtsmlUoxYNu8Nj1Nob2da+68U0v0C5yW\n60VEFrmxTIZdd99NeN8+rN5eNufzlIBsOs300qW0dHSwJ58nawx1a9bQuGkTq7ZtU8DPI1quFxGR\nfyfvODz9N39Deu9eoidO0Do5ie951CYS2KUSselpxrNZOtraCN18M6uvuGKuS5YZpOl6EZFFrGf3\nbsIHDtAwPU2N51EBhD2PrOOA62K5LkXHoS8UonXjxrkuV2aYQl5EZBHKOw6Hdu1i/333YU1PU7Is\nfMsiFo3iG4NnWUzHYoyGQnSl02zV/vuipOV6EZFFJO849Dz3HEM//CGt6TTLCwUujcV4/ORJakMh\n3GKRFckkhz0Pv62NsfXree9/+k86yW6R0uCdiMgiMdDXx4++8AUaentpsG1aOzt5cXCQdsehPhTi\nScfBLZU4PjlJ7B3v4PI77mDV9u3q4BeAcx28U8iLiCwCA319fPNjH+PdpRJOLsfaaJS+IGDp1q10\nZTKsj0bp9n3a1q1jtKmJNR/4gMJ9AdF0vYjIRSrvOPzoC1/gllKJFcYw7vv0TkywrKqK40ePUn/Z\nZeQrKsgCIWnRAAAgAElEQVRHIhS2b2fNxo0K+IuEQl5EZAEa6OvjkbvvpvDaa4wWCmxwXUKhEF65\nTEUqRXOxyEA+jzc9TSiVItfeztU6f/6io5AXEVlgBvr6ePyTn+SGyUmWhsMcGB2ld2SE3NKl9MXj\ntIXDpGtreW1qilfjca7Ytk2H21yktCcvIrIAnJ6aH9m3j10PPsj7MhkuSSaJhkIcmpxkiePweC7H\nllWrMNEohUKBhyIRPvjVr9Lc1jbX5ct50p68iMgiNZbJsPvuu6l/7TVWJ5OY4WHciQmG83mW1tfT\nmkxytFSiorqaw1VVVKTT9CSTfPBzn1PAX+QU8iIi81DecXjlySfZ/73vcXL3btqNYcvSpdRYFvvK\nZTYYw8lSiez0NLXpNEsrK3kiFmPNu99NxbZt3KrhOkHL9SIi80recXhl5052f/nLmJde4hrLotLz\nsMtlXonH2b5uHYHr8sJLL5EGVlRXU1FZyYPFIg0f/zjbfuM3FO6LkJ6TFxFZ4A53d/ONT3+a0Asv\nkMjnuSUcxo7FcCyLGiBIJOhqbOT6FSvY5zjsHB+n6LpE29rY9OEPc/nNNyvgFyntyYuILFB5x2Hn\nN7/JI7/3e7RPTHAr0B0E1Ps+Y65LuqKCMWNIFAr4hQIl32coleLqD36QtbffrmCXt6SQFxGZI6cn\n5l9/8EEGvvENNkxNsYU3/o+5DFhBQI3vM5jPU1Fby4lIhJ50mpPxOKvvuIO1116rgJefSyEvInKB\n5R2Hnt27Ofnoo1SPj7PmyBHWlEo8HQS4QE0QsBx4DLgmCDDGMBgK8fzSpVz+uc+xXuEub5NCXkTk\nAjnzDXHB+DjXpFJkxsfJHj9OOBJhg2Ux5HnYlkWj72OA+4Dj8ThNV1/NLV/8oh6Jk7OikBcRmSV5\nx2HvD37Ay/fdRzAxQSgS4Z3r1vFu26Z7YoLM8DBeIkFVLIafTlOamiLj+7QFAc9ZFuPGMNjayjV/\n/udc/Yu/qO5dzpqm60VEZshYJsOTX/sax374Q4Z6e3Gmpkjlcry/sZG4bbO0VOJ53+ea972Poakp\n2nM5BqJR3PFxnJ4e2lIp/nVwkGO5HJOhEEtuvZUP/9mfqXsXPUInIjJXxjIZdn71qxz5p3+i4uhR\nlpdKhD2P6XKZkG1TkUgwmkxyQzrN0XKZUmMjne98J0d7ekhGIjS0tvJiJsP+oSFCS5fSeNllXHHH\nHdTU1c31/zSZJxTyIiIXyFgmw6777iPz4osMZjJUjoywtFCg4vBhLi8WGfZ9GoEez2PKGGKxGOOx\nGOsqKognErxg29xw663kSyWeyeUw1dUsuf56Vm3friV5eVN6Tl5E5AIYy2R49q676OjtpS2ToX5k\nhPWOw48chy3lMmFjaAFGgEuAfwsCWoAlxvCa53GZZWGvWMFAOs2hyUmWfuhDekOczBqFvIjIGU4/\n3jb88svkcjkGBwcpHTtGPpejbs0aiMW4bmwM13FYEwox7Lo02TZWuUwYcH2fsDEQBNi2TcnzcIOA\neCiEnUxybzLJO26/ncLKlVyl8+VllinkReSidTrQ+/bsIdPbi5PLcfzll4lks7SnUpSmp9mUzdIW\nDlOfTvPsiRMc9H1G6+pIRSLYxkAohOe61EcinCiVqAsCKj2P6SDgoG0zGYnQk0wyXFdH1bvfze2/\n//sapJMLRnvyIrLoDfT18eAXv8jwzp2M5vPEGxpo6ewk6jj4mQxLHIelU1OkhocplssE4TDdnsea\nYpFoJEJbNEoonSYUjfL16WlWxGJEKyrYkkgwWSqxu7+flaEQva5L0vfpzefJ2jaHQyGab7yRK3/1\nV1mnA2zkPCyawTtjzM3AlwAL+EoQBP/tTT6jkBcR4I2Xuvzrn/0Z9uAgGcuice1aSkNDhI3hyNGj\njPf0UDM1xSbfZ00sxlLgMc+jLhwmUVFB3LYJjGFFqYSTy2EHAZFwmOddlyrPYxXgxOPUV1aSSCZ5\n1PM47vusrK6myrJYZVnscRyOVlaSr6jAqahgeUcHqfZ2Nt92mybkZUYsipA3xljAIeDdwCCwF/hw\nEAQHf+ZzCnmRBej0o2b9Tz9NBEht3vzG9RdfJAIk1q8nPz5OcmKCMWD59u1M9vdTG48Tbmpi8223\nAfDiAw9AJsOJUonxb36TjyeTlCcmKExMcO/UFDe0tXHy+HEaHYfngfcChwEbqLAsOmybbwFXRiJM\nJZNUANFSiapSiSHPozIS4SXfJ1Eu0wnkYjEaa2oIRaM8m0jgbNjAcE0N1sQEk7kcy9avp+OqqzQd\nL7NmsUzXbwV6giDoBTDG3AfcChz8ub8lskjlHYcXHn6YF//xH5kcGGDKGOpra6murcVNp6mqqSGY\nnMRPJBh8/XUawmH8xkZWXHUVlbbNyPAw0Xicwy+9hD86Sj6fp7azk2hVFd7kJMdfeQUvnycfBOSz\nWVK+T7ypifUf+hANjY1Ey2VMOk3dqlVkeno4vn8/T913H+7QEBOFAkubm4lXVhKuqcEyBi+bJV1V\nRXrtWlovvZS0ZXF8bIzHv/Y1snv3UsxmiVoWH2hpoSOd5pGnniJhWXxq+XJcz2PfY48xUlPDpro6\nKoF/fPhhbnvHO4glEiRzOf7tj/8YC3hfbS2RUIgHH3iAFaOjZH2f6lKJsXKZ37IsvtLXx02lEi3G\nUAoCTgLLgQlg1PdZHQoR8n2sU/+cA8ADLNvG+D7ZIKA2EuFAEJAql/Ftm6XhME+7LtM1NdRdfTXX\nfeQjCnSZ9+ZbyDcD/Wf89+O8Efwii8aZ09vjY2Mce+014tksbirFki1buOTSSymGwxzt6uL4975H\nxcGD3BAOM+Z55KenaQmF8CsrOWzbRIF1K1dyZN8+OisqSFZVET14kGcfe4yO7dvZGInw9FNPcYVl\nEZ2aoqaqisf37KGcSBCfnuYXbZuBqSn8bJZp2+bSigqKJ0/y9IsvUn/TTbTfeCPl8XGe+Zd/YdWy\nZRTuvpuP5nL8aHqaziCgpq+PcjLJAcuizhguqarCSafZ/9JLFBobSV93Hce+9CXWjYyw3fep9X0O\nAa8fO0Z3Os0S12WHZVEcG2O0UGBbJMLg1BQHQyE6jOFT4TCPHzvGezZsYGJ0lNoTJ6g2hkhjIwAx\nx2FLKMQLk5PUhsMYzyNh28RKJcK80bnbgHvqP08r+j7hUAgnFCIWCjEMhEMh7KkpRqNRSokE45YF\nlZV8v62NcDTKD/J56tasYdMv/RLrd+xQwMuCMN9CXmRRyDsOrz75JId/8APGRkYYHh6msaKCkelp\nakMhqlyXba2tlB5/nA8VCrjJJDFg98svM3DVVZjhYar7+lg5OUlnqcSTjkPa97kmHGbc9+mZmGBr\nIkEhFuOVV1/lXeEw057H6xMTpJJJNgYBY11ddFdXs7FcprJQoDISoW98nGt8n8eHh2kLhYhaFvX5\nPAZYGgQcLZVwLYtfCId5fd8+MqtWMR0EXBeP88A//AM3+D69rss7gwAnCGgzhj2Ow6WhEOlwGNt1\nKU9OcmUiwVA+zw+//GU6s1kuAaJA2Bg2BgH4Pl25HE44TMQYCuUyplQibAwR18V2XQIgYduYQgHb\nGIJikVCxSPiMf85uMkkwPk7gefiRCIFt45RKFEIhyqUSHtAGfB9o541n1w3wjSBgbTTKWEsL6YoK\nBi2LcjzOj1yXciKBbVk0tLVxyS23cLsG5mQBm28hP8Ab/06e1nLq2r/z+c9//sc/79ixgx07dsxm\nXSJvaaCvjyf+9m+xh4eZqqjApNPkn3qKpf39rKmtZaKvj8uMYaRQ4PZUilcKBTa0t/PgK69wg23T\nEgoxMT2NE4vxvliMv3vmGXZUVZG2LDKFAlFjaANGfB87CLCDAAOEfJ8QECoWiaRSFIIA47oYzyNi\nDCHHIYjFiACW6xKKRqFcJmIMYdclbFkY3yd06m1nEWMwvg+eRzQSwcrnCRyHAIiEQkSyWcLGYPk+\nEcsi77qETv1OKAgI+/4bvx8EhIOAkO8TzeUInfq88X0C3pioDRtDCCgGASVjIBwm8DzK+Twl28YL\nhTDG4DgOQTKJFwSYaBQ3GqVsfrItufmaa3j8/vupiscpRCIkwmH+v0KBG9ra6D9+nJLjMGQM24OA\nbwGvAZlUivpVqyhs2kTT8uVkKytZ3dZGvKGBVj23LvPEzp072blz53nfZ76F/F5gpTFmGXAC+DBw\nx5t98MyQF7mQxjIZXnzgAYqDg7zU3U320UfZbNuEUik683n25PP8QirFyiDgpYMHaQmHKQYB6SDA\nLRS4zBgOnzzJJtdlsFRiWTKJ5XmYICBmWUSnpwklk4TDYcqWhet5hIzBCwJKvo93ahrctSxcwI1G\nf3L91F+lIMBNJLDjcUpAPBTCDQIIhymVSpRDIcqWRWBZuJaF8TxKQUBgWWDbFIMAPx7HJBKYIKA0\nPk4pnaY8OopvWZR8/40a4I17GEPZsrBP/X75VH3FVAp3aopSuUzcsrB9HycIKAcByXCYI7bN98Jh\nfqmmhgbPY/ehQ2/syVdVUQl8eXKS29rbGXNdkrW1jIbDjANrXZdIKERNVRXjN93EvnyeF8bGyLS1\n0bh2LY8PDRHeuJHvHz1K/tgxQq4LTU3c+Ad/wLs/9CEFucx7P9u8/umf/uk53WdehXwQBJ4x5v8B\nHuEnj9C9NsdlibxxVvm993L4qacYevZZLqusZMrzSPb28lu+T2UqxcjgII/6PldGo0xOThKurKQq\nCGgsl3nNGKosC4KAUCgEpRKhWAxnagovCPBPBXfB9ykmk7jhMFWRCMdqa+keHqYpCNhvWfzI81gW\nDlNbWcmeU3vy69et47l9+0jEYiSrqigbQ5fr0rFxI6sjEZ4eHGRtNEp2aora6moeHxmBxkb6pqdJ\n2zYjrotfLnPEGC6NRCjGYnzP99m8YQN1HR1Uui5PDA6y9dd+jd13301nKMRTxtAJ9Pk+tckk+0/v\nyYdChNNpngWq4nGu/9SnePpLX2JkZISrjSERCvFKuczeUIjJ9naab7sNPxLhy6en69/7XvLj4zx9\nerr+N36DJ8+Yrr/u1HT9U6em66mr4+b/8l/0mJrIW5hXj9C9XXqETmZL3nHoee45RvbtY3RsjN7u\nbtKeR+bIEXakUhw5eJDbXZcXymWGPY+2YpHNkQjFUIjA9+l3Xbpsm3bbZktVFa9PTtLoeRwwhgrL\noi6ZJGIMu4HLly7lm729vCcc/smefBAQP7UnXzs+TueSJRw7eZLvHTvGeCSCWbKEhoaGhTld/8IL\nGN8nuWIF1//e73HlrbeqoxZ5mxbFc/Jvl0JeZtpYJsO//u3f0vv1r7Msm6Wqro5UNktVNEq2VGKt\n6/LtbJZNts22UIj9xSKHCgXWBAEtxmCFw2BZDJbLHA4Cpqqr2WZZVAcBBycmGIzFML7Pjvp69vk+\nVevW8ezEBJEtWxjp73/T6fqBgwcpdHfjx2KsuukmTXSLXMQU8iJn6fQS/PG9exndu5elw8PcagwV\nwK5cjrQxdLS00DU1RasxONPTvOZ53BqPc9R12ZfPsyMIGPB9OmIx4tEovVNT/GMoxCeuuYbM9DT7\nBgcZrK9nKh6npbqasXyeNRs2UL1qlU5DE5G3TSEv8jbkHYf+ri5ef+klHv0f/4Nl+Twx16UznydR\nLNIRDlMdjTI4NUXJGNxUipFIhKWeh1su01su0xaJsDQI+FGpRLlcpi0cJlRRwXS5zA8TCdK33UZd\noYAdi7Hy5pt1ZrmInLfFcuKdyIw7ffjM4WeeIfPMMyxPJJh46il+0/MoAUOlEm4+TzwSAdelHAq9\nMSXu+/jlMhUNDQxls7QHAYeDgC3V1ezOZglXV/NsJMKBZcuosW1ia9fyid/9Xb1hTETmDXXysmid\nPpDm5b//e6a6u4lNTLAtkaA8Pk5iagrPGC6JRHiwVKK1UKApCDgZidBu28RDIXblckymUqxfvZrK\nmhr+sbcX1qwhPzFBc1sbjVu2cMUdd2jJXURmnTp5kTPkHYeD99+P9+CDbN+/n2W5HK/kciRKJQam\np6kNArJBQN51aQ+HOel5TBWLXBoOcyQe57Dr8lpTE/bGjeQqK7Gbm7nj7/5OXbqILCjq5GVROD1E\nl92/HzcSIdXezvpcjsJTT9F84gSJXA4nn2cwCPALBXJA5alDWSKJBDWWxd8VChQbGohVV1O/fTu/\n8Pu/r1AXkXlBnbxctMYyGXbddRebBgZYEongBQH/sns3JxsaqPB9LNvGN4ZUJEKuUKAuleLY1BS1\nlZX0eB7xcJjvWhZb/vIv2fHBD2pITkQWDYW8LEinj5Ylk2H/gQPcWiyyJBLBNgbbGC5LJDhx8iTJ\nykrKsRheoUCkVCIWj9Nr2/S1tvJiKERTayvx9nY++pnPqGsXkUVHIS8LykBfH9//q79i4tFHaais\n5PrLL6fc10dueJjS2rXEo1EAVtTU8FKhQEUiwWQsRkUsxguZDFPhMJNr1nD5xz7Gej3aJiKLnPbk\nZUHIOw47v/lN9vzJn9CSy9Fm22yoqOAxY7BaWnhXJkM+FmPZypUAlHyfH1RWYm/ahOU4ZI4fp7a5\nmZbLL2fV9u0KdxFZULQnL4tW3nHY98//TP6LX+SPPI9yucyRYpGucpl319Tw3elpno/FWOI4tAQB\nXhDwWKFA3Y03ctmv/IoCXUQuWgp5mbdO77sPvfACyYMH6SyViFoWfijE2nIZ2/PocRwqUik279jB\nP4+M0F1ZiRuJ0Pme92g5XkQuegp5mXdOH2Jz6P/8HzZWVNBQKFDOZChOT1OKx4kkEhQnJ7GAcrlM\nKR6ny7L42P/6XzqYRkTkDAp5mVfGMhlevOcerN27uXp6mnrL4qnRUarCYTqrqzmUybAmnSaUTjOV\nz/ND22bdrbdy+W/+pgJeRORnaPBO5oXTh9kc++53uT4UIpiaYhUw4Psk6+t5ZmSEpZkM1cZQNoaD\njsOh2lre99Wvsm7TprkuX0RkVuktdLIg5R2HFx5+mOf/+q8JRkdpy+e5MpWiN5djQ2Mj0XCYoXic\nTHU1DvDk6Cgtq1ZRvW6dzo0XkYuGputlwTk9NX/ynnu4amiIoFxmxHVxi0WWxuPsGxpiS0sLbqmE\nHYuRra3lN++5R8EuIvI2KeRlTgz09fH1//yfqXn6aaKTk1TFYiRDISp9nxdKJd4ZizFaWcmRdJqd\n5TItW7eqcxcROUsKebngDnd388hHP8qW119nS7lMyfPYNzXF0nicdChE2LLoBnoTCaquuooP3nmn\nwl1E5Bwo5OWCyTsOPbt3873PfpYPjoxQMIZIEIBtc7nv83y5TDoaJRMKkU0kiP/yL3Ptb/2WnnUX\nETlHCnm5IPKOw2vf+hYju3axZGiIouMQ8TxO2DaNvo8LFH2fl4KAoLmZlo9/nC2/8RsKeBGR86CQ\nl1mXdxwe/8pXCO3cSe3kJH44TIPvMxUEFI1hJB5nolDglUSC2IYNbPu932P9jh0KeBGR86SQl1lz\n+tn3yWeeYbyvj1+pq6PseYSSSfZOTLAlCBgPhQjFYjwTDtP04Q/znj/+Y+2/i4jMED0nL7NiLJNh\n5x//MRVdXdRMT3NkfJymcJhLLr2U0ugoJ4GBTIauQgGvpYVtv/u7XHXrrereRUTehA7DkXljLJPh\ny5/+NMt27WK177MmnWaoUOBENgttbWxdtYpXJyfxqqoYvvZa3vWJTyjcRUR+Dh2GI/PCqy+/zPc/\n+UkiBw+yMQhoiEbpzWRYUlODH4+za2yMVeEw/qpV2Fdeybs+8AEFvIjILFHIy4w53N3NA7/0S3y6\nVOJFz2O573PUdWmIRjmZy9GSTjNk27y8fDlLrr+eVdu3K+BFRGaRQl5mRN5xePgP/5CbXZdGy6Ix\nHGbQceiwbXp9nwKwOwiovP56rvnc5xTuIiIXgEJezkvecXhl5072fPWr5Pbs4VCpxOpIhNWpFK+5\nLoHvc8L3Ga2txVx1FbffdZcCXkTkAtHgnZyzsUyGp/76r7GfeILL8nkmRkaI5XK84HncVFlJyBhe\nnp7m4USCzX/wB1z7a7+mx+NERM6BBu/kgso7DnvuuYeql1/mylCIPBCNxxl2HLbYNt93XVbGYjxe\nU8Mvf/vbeue7iMgcUMjLWRvLZPj+X/0V0T17mB4cJKiuJplK4ReLNC5dyoFcjtFwmPENG/jIX/4l\nKzs757pkEZGLkpbr5awc7u7me3feSdPJk5Rcl6rp6f+/vXsPrvK6zz3+Xfu+ddlCQkIgIYG5CTAg\ngmPAdmOwTXxLGic4x3FuTnNy0oybTtObz6nbZJw2nsk5TU+nad140pNMmpvj0ySkNqceO3WCqJOY\niyFsjLkJLNBGAkkboev77vs6fyA72DWYi8SrvfV8ZjRsLWlLP2YjHq21fu96eUcuR+2cOfh9Pnoc\nh+T06bz8zndyr/bfRUTGhZbrZcIdOXSIH99zDxuHh7HGUBMK8ZTr8mtjWNXbS92sWSRjMRKLF3P3\nQw8p4EVEPKaZvFyUrs5OvvmBD/DB48ep9/mIBgKccF1qqqv5sbUMBINUNDXRcPfdrPvEJ9RgJyIy\njjSTlwnT1dnJ9z7yERo6OwllMkT9fkbzeWZHo5zIZKieNo3y97xHy/MiIpOMz+sCZHLbt2cP//Tu\nd7Nk716yIyPECgVOOg7lhQJuLoexlu4ZM7Q8LyIyCWkmL+fV1dnJsx/7GJ8eGSHm95PJZnnadXlv\nNEp3Pk/G72dzZSX3Pv64ludFRCYhhbyc15Z//Ec25PNEQyGi1kI2y/tCIX6YzxOMRulubuYjTzyh\nS+RERCYpLdfLefl7egiVlVEWCjHg8xGNxQhFIjQGg5xuaeFTP/mJAl5EZBLTTF7ewHUcEvE4dmiI\n3lyOmro6jg4PM7+8nKFMBtcYdlVU8LtPPEFjc7PX5YqIyAXoEjp5nes4dGzeTEs4jN/v52RPD08/\n9hj3zJxJf18fGcfheb+fO7/3PR1TKyJyFV3uJXQKeQHOHlX747/8S2bu2IE1htqlS1l+222MuC7/\n0tbGjECAfH09t3z2s5rBi4hcZQp5uWz9ySQv/PmfE21r4zafjwIQz+VIzZ9P6wMP0F1bS8sdd3hd\npojIlHW5Ia/GuynOdRye+vKXmb51K9OGh3EdBx/QGggQ7evj8C9+gYnFvC5TREQug0J+Cuvq7OR7\nn/0syR/+kEhvL4uspX14GGdw8Ow/jEyG9jNnaGpt9bpUERG5DOqun6L6k0l+/od/SGtHBx2ZDBWZ\nDIOZDNdUVnLMWrLpNPuqqqi96SadZCciUqQ0k5+itn7nO8w5dIia/n5WBQIc8vtJZzIMuy5zg0GO\n+f2UtbZyw4c/7HWpIiJymTSTn4K6OjuJf/3rbBwcZDSbZX4ohC0rY38oxO5MhrKKCqbddhv3ffGL\nOq5WRKSIKeSnmK7OTn78yU8yf2gIXy7HzFyOo6kU82MxKsvK6Gtq4p1//McsvvdeLdOLiBQ5hfwU\n0tXZyQ8++lFuPX6cgrWYTIYuv5/GSIT96TQHg0FiH/qQAl5EpEQo5KeI/mSSn/3pn3JnMsk8vx/j\n83EAiAQCbPf5GKisJHbvvdzxB3+ggBcRKRFqvJsidm/axDuMIVReTiAUouDzsaSiglw4TFltLcml\nS7lZ94QXESkpmslPAV2dnWz//vepP3GCmcBALsfK8nJymQy5fJ6XZ87kg489piY7EZESo2NtS9yR\nQ4f46cc/TuuJE1RnMriFAl0+HzNnzqQsn+fZ2lo+/P3v6zx6EZFJ7HKPtdVMvoT1J5M88clPcn8i\nQX0gQDafJwvYQoHt6TSV73gHH/6bv1HAi4iUKIV8iXIdhx2PP07DqVPMD5x9mUciEbCWGiAZDvPh\nr31NS/QiIiVMjXclKhGP05zLUQiHyRYK+IGKQICyaJRYdTXVS5cq4EVESpxCvkTZoSEC5eUsWbKE\n53I5UmNBn85keCafZ+X993tdooiITDAt15cY13FIxON07t/PnHSaUEMDVStW8FwigXEcjsditHz6\n01x/551elyoiIhNM3fUlxHUcOjZvpiUcJpPNcmT7dgKZDKPV1SQTCZL5PC3338+ydet0PbyISBG5\n3O56hXwJ2btlC8Ht2/Gl05holNqGBnpOnKA7EqFpzRqaWlsV7iIiRUiX0E1xruNw4plnWNbTgy+X\noxAIcLy7m8U33YQvFmPRDTd4XaKIiFxlCvkSsW/rVkJ79lBIpfCHw9TFYuSHhzlUU0PZu9/tdXki\nIuIBddeXANdxOPLkk7yrooLqXI4ZrkvfyZPMsJZ9R4/S1NrqdYkiIuIBhXwJSMTjNPj9BAIByhsb\ncaNRKgMBXs1kqL72Wu3Di4hMUVquL3L9ySQ7n3ySss5O/jWZZENzM1XTp5MvFOguFGi6/nqvSxQR\nEY8o5ItYV2cnLzz0EMuSSWbncoz4fDzT3s6ixYsJV1SQmjuXhWvWeF2miIh4RMv1Rcp1HJ7/0pd4\nfy7H4mnTGE6nKQ8EuK2ujhOZDKcWLeJdn/uclupFRKYwzeSLUH8yyTNf+QrO9u28FAqxrLGRWY2N\n9J45w3AggDN7Nrf/yZ8o4EVEpjjN5ItMfzLJzkcf5baODtYFg1w3PEx8/37cXI6mujqqp02j7rrr\nFPAiIqKQLzZbv/Mdag4e5NUjRxjIZNifybDa52NfVxepXI6txrBq40avyxQRkUlAy/VFpD+ZpOeH\nP+S9Ph++SITRkRH2AfvDYV7OZDhaU8OGL3xBt5AVERFAM/misnvTJlqrqrCA3++nvLaWZRUVDAWD\nVN51F/d99as0Njd7XaaIiEwSCvlikkxy7dKl7MpmyRQK+P1+IrEYh0Mh7n7oIe3Di4jIG2i5vgj0\nJ5Ps3rSJQ9u3U55O03Tddezp7MQ6DoVwmLoNG7RELyIi/4luNTvJvdZNf0s0Sjqd5sALL5C2ltab\nbyYSDrPFdbn+859XyIuIlDDdT75EPf9P/8TNHR2EAmcXXYZHRzm4fz/bo1EW33UXqzZuVMCLiJQ4\n3dXjcDQAABfJSURBVE++VCWTrwc8QGV5Oddffz2D0Sgbfvd3PSxMREQmOzXeTXa1tWRyuTcMZXI5\n0OxdRETehkJ+klu1cSNbXPf1oM/kcmxxXR14IyIib0t78pOU6zgk4nHs0BBDhQK9R44QHh6G2lrt\nw4uITDFqvCshruPQsXkzLeEwfr+ffD7PoXSaa377t3UtvIjIFHS5Ia/l+kkoEY+/HvBw9nS7lnCY\nRDzucWUiIlJM1F0/ybiOQ2L7dsoGBzFlZdRecw3hscC3Q0NelyciIkVEIT+JuI7DnieeILl1K+X9\n/ZiyMgYWLGDBunUEAgFMLOZ1iSIiUkQU8pPIy21tpJ56ivcEAvRnMszIZtm1bRvtVVX4li/nmtZW\nr0sUEZEioj35SaT9uee4KRKhIhSivqGB02VlNEQi/PzwYTXdiYjIJdNMfhLxpVL4zdnmyXAgQOP0\n6eStpa66WgEvIiKXTDP5SSS2fDnHMhnyY5cH5q3lWCZDbPlyjysTEZFipJm8x1zHoX3bNvr27iUz\nMsIWa7nZ76fMGHJ+Pwfq6rjx/vu9LlNERIqQDsPxkOs47H3iCQI/+xlN1kI4THs4zKFMhplz5xJq\naNDpdiIiorvQFaP2bdswP/sZK7JZgn4/edelMDRExapVRDdsYNENN3hdooiIFDHtyXvEdRxeeeop\ngt3dDJ05QzaXw28MdcEgw8eP6+AbERG5YprJe8B1HA786EeYI0ewg4OM5POkhoeZ0dSEz+cjXyjo\n4BsREblimsl7oH37doJ793JDVRUmFCKQTpMZHORUXx/7sllGGhtp0sE3IiJyhTST90DPnj3cGg5T\naG7GjIxwOhplYHCQX6dSzF2+nHd97nO6Ll5ERK6YQt4DvrEDb4LBILOWLqW8p4eZrktXfT23P/yw\nAl5ERMaFlus9ULdiBZ3pNPlCgWAwyLSGBkZnz+bae+5RwIuIyLjRTN4DC9eu5WBXF/7ubnypFIVI\nhIGGBhavXet1aSIiUkJ0GI5HXMchEY9jh4YwsRhNra2axYuIyFu63MNwFPJXSX8yye5NmyCZhNpa\nnWQnIiIX7XJDfsL25I0xjxhjThhjdo+93XnOxx42xrQbYw4YY24/Z3yVMWavMeawMebvJqq2q60/\nmWTno49yc0cHG1yXmzs62Pnoo/Qnk16XJiIiJWyiG+/+1lq7auztWQBjzBLgPmAJcBfwNWPMa7+d\nPA58ylq7CFhkjLljguu7KnZv2sQt0SihwNkWiFAgwC3R6NmZvYiIyASZ6JB/q6WFe4AnrbU5a+0x\noB1YbYyZCVRaa3eOfd53gPdPcH1XRzL5esC/JhQInF26FxERmSATHfK/b4zZY4z5hjGmamysEUic\n8zldY2ONwIlzxk+MjRW/2loyudwbhjK5HGhPXkREJtAVXUJnjPl3oP7cIcACfwF8Dfgra601xjwK\n/G/gv13J9zvXF7/4xdcfr1+/nvXr14/Xlx5X/ckkIyMjfKOtjWuqqlizZAkVkQhbXJfrN270ujwR\nEZmE2traaGtru+Kvc1W6640xc4DN1toVxpg/A6y19n+NfexZ4BHgOLDFWrtkbPx+YJ219sG3+HpF\n0V3f1dnJCw89xDprKfh8DJw+za9GRph+772s/53fUXe9iIhclMnYXT/znHc3AvvGHj8N3G+MCRlj\nrgEWADustaeAQWPM6rFGvAeApyaqvonmOg7Pf+lLvD+XY5YxzCwUmFFZyUdvuolYLKaAFxGRCTeR\nJ979tTFmJVAAjgGfAbDW7jfG/AuwH8gCv3fOtPyzwD8DEeCZ1zryi1EiHmeW4xAZa7jzG0NNIMDA\n6dNquBMRkatiwkLeWvvABT72ZeDLbzG+C1g+UTVdTXZoCN+0aWR6ewn5zi6Y+I0hMzqqhjsREbkq\ndIOaCWJiMVpXr2ZLKkWmUAAglcux1RhWqeFORESuAt2gZgL0J5O0v/QSg7/8JaGaGv7N76c8neZk\nLMaGL3xB+/EiInJVKOTHWVdnJ20PPcSN1uILhzGZDL9yXRo/8xnetW6dbkIjIiJXjUJ+HLmOw08f\neYR1PT0ErcX6/WQqKnjvwoVsSyQU8CIiclUp5MfRy1u3ktq1i1QuRy4QoCEaJek49MViMH261+WJ\niMgUo8a7ceI6Doe++11WpdPMGh6mbmiIV8+codZa+k6eVEe9iIhcdQr5cbJv61aqDx5knrWcdF3K\nMxlmj45yZHiYX2ez6qgXEZGrTiE/DlzH4dCTT7K0ooKAMTRXV3OkUKDPGH6Vy7H0j/5IHfUiInLV\naU9+HCTicWr9fmbW1tKTSlGdStEYDDIUiVC+eDHvvOMOr0sUEZEpSDP5cWCHhmiYN4+j1lI/ezaj\nM2YwUFvL9liMJR//uLrqRUTEE5rJXyHXcUgcP87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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "plt.figure(figsize=(8, 8))\n",
+ "plot_out = plt.plot(X_val,y_val,'ro',alpha=0.3)\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 287,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "parameters = graphN1.predict(data={'input':X_val})['output']\n",
+ "\n",
+ "comp = np.reshape(parameters,[-1, c + 2, m])\n",
+ "mu_pred1 = comp[:, :c, :]\n",
+ "sigma_pred1 = comp[:, c, :]\n",
+ "alpha_pred1 = comp[:, c + 1, :]\n",
+ "\n",
+ "parameters = graphN2.predict(data={'input':X_val})['output']\n",
+ "\n",
+ "comp = np.reshape(parameters,[-1, c + 2, m])\n",
+ "mu_pred2 = comp[:, :c, :]\n",
+ "sigma_pred2 = comp[:, c, :]\n",
+ "alpha_pred2 = comp[:, c + 1, :]\n",
+ "\n",
+ "parameters = graphN3.predict(data={'input':X_val})['output']\n",
+ "\n",
+ "comp = np.reshape(parameters,[-1, c + 2, m])\n",
+ "mu_pred3 = comp[:, :c, :]\n",
+ "sigma_pred3 = comp[:, c, :]\n",
+ "alpha_pred3 = comp[:, c + 1, :]\n",
+ "\n",
+ "parameters = graphN4.predict(data={'input':X_val})['output']\n",
+ "\n",
+ "comp = np.reshape(parameters,[-1, c + 2, m])\n",
+ "mu_pred4 = comp[:, :c, :]\n",
+ "sigma_pred4 = comp[:, c, :]\n",
+ "alpha_pred4 = comp[:, c + 1, :]\n",
+ "\n",
+ "parameters = graphN5.predict(data={'input':X_val})['output']\n",
+ "\n",
+ "comp = np.reshape(parameters,[-1, c + 2, m])\n",
+ "mu_pred5 = comp[:, :c, :]\n",
+ "sigma_pred5 = comp[:, c, :]\n",
+ "alpha_pred5 = comp[:, c + 1, :]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "mu_pred_s = (mu_pred1+mu_pred2+mu_pred3+mu_pred4+mu_pred5)/5.\n",
+ "\n",
+ "sigma_pred_s = np.sqrt((((sigma_pred1**2+mu_pred1[...,0]**2)+\\\n",
+ "(sigma_pred2**2+mu_pred2[...,0]**2)+\\\n",
+ "(sigma_pred3**2+mu_pred3[...,0]**2)+\\\n",
+ "(sigma_pred4**2+mu_pred4[...,0]**2)+\\\n",
+ "(sigma_pred5**2+mu_pred5[...,0]**2))/5.)-mu_pred_s[...,0]**2)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Test without weight normalisation in the $\\sigma$ output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 278,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Absolute error 67139.7\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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Fp5xyCvPmzeOWW25hxYoV/O1vf+OUU06hvr6e9957jzPOOIP29nbWrFnDu+++y2mnnUZd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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "import numpy as np\n",
+ "import matplotlib.pylab as pyplot\n",
+ "from matplotlib import lines\n",
+ "import matplotlib.cm as cm\n",
+ "\n",
+ "parameters = graphN2.predict(data={'input':X_val})['output']\n",
+ "\n",
+ "comp = np.reshape(parameters,[-1, c + 2, m])\n",
+ "mu_pred = comp[:, :c, :]\n",
+ "sigma_pred = comp[:, c, :]\n",
+ "alpha_pred = comp[:, c + 1, :]\n",
+ "y_pred = np.zeros((len(mu_pred)))\n",
+ "\n",
+ "col = cm.autumn(np.linspace(0, 1, mu_pred.shape[-1]))\n",
+ "\n",
+ "for mx in xrange(mu_pred.shape[-1]):\n",
+ " for i in xrange(len(mu_pred)):\n",
+ " pyplot.errorbar(X_val[i],mu_pred[i,0,mx],\n",
+ " yerr=sigma_pred[i,mx],\n",
+ " alpha=alpha_pred[i,mx], \n",
+ " color=col[mx])\n",
+ " y_pred[i] = mu_pred[i,0,mx]\n",
+ " pyplot.plot(X_val,y_pred, color=col[mx],linewidth=1, marker='o', \n",
+ " linestyle=' ',alpha=0.5, label='gaus_'+str(mx))\n",
+ "\n",
+ "knownP = (((X_val>-4) & (X_val<-1)) | ((X_val>1) & (X_val<4)))\n",
+ "\n",
+ "pyplot.plot(X_val[knownP],y_val[knownP], color='blue', \n",
+ " linewidth=1, marker='o', linestyle=' ',alpha=0.5, label='known')\n",
+ "\n",
+ "pyplot.plot(X_val[knownP==0],y_val[knownP==0], color='purple', \n",
+ " linewidth=1, marker='o', linestyle=' ',alpha=1, label='new')\n",
+ "\n",
+ "axes = pyplot.gca()\n",
+ "\n",
+ "from matplotlib import collections as mc\n",
+ "axes.set_ylim(-100,100)\n",
+ "axes.set_xlim(-5,5)\n",
+ "pyplot.gcf().set_size_inches((20,10))\n",
+ "pyplot.legend()\n",
+ "print 'Absolute error', np.min(np.abs(np.expand_dims(y_val,axis=2)-mu_pred),axis=2).sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 240,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Absolute error 65882.1\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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IBwElDOINMtmWlUFT1UpiNPIJy+hFSG9+wxOZZVRshWHHHsv6DRtYBtCyvf3d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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "import numpy as np\n",
+ "import matplotlib.pylab as pyplot\n",
+ "from matplotlib import lines\n",
+ "import matplotlib.cm as cm\n",
+ "#v_rang = 100\n",
+ "\n",
+ "parameters = graphN1.predict(data={'input':X_val})['output']\n",
+ "\n",
+ "comp = np.reshape(parameters,[-1, c + 2, m])\n",
+ "mu_pred = comp[:, :c, :]\n",
+ "sigma_pred = comp[:, c, :]\n",
+ "alpha_pred = comp[:, c + 1, :]\n",
+ "y_pred = np.zeros((len(mu_pred)))\n",
+ "\n",
+ "#col = ['#A96E6E', '#D93636', '#E3701A', '#D04545', \n",
+ "# '#F70000', '#FC1501', '#FF5333', '#BF6666',\n",
+ "# '#FF3300', '#CD3700', '#8B2500', '#5E2605',\n",
+ "# '#E69898']\n",
+ "\n",
+ "#ax.set_color_cycle(col[:mu_pred.shape[-1]])#\n",
+ "col = cm.autumn(np.linspace(0, 1, mu_pred.shape[-1]))\n",
+ "\n",
+ "for mx in xrange(mu_pred.shape[-1]):\n",
+ " for i in xrange(len(mu_pred)):\n",
+ " pyplot.errorbar(X_val[i],mu_pred[i,0,mx],\n",
+ " yerr=sigma_pred[i,mx],\n",
+ " alpha=alpha_pred[i,mx], \n",
+ " color=col[mx])\n",
+ " y_pred[i] = mu_pred[i,0,mx]\n",
+ " pyplot.plot(X_val,y_pred, color=col[mx],linewidth=1, marker='o', \n",
+ " linestyle=' ',alpha=0.5, label='gaus_'+str(mx))\n",
+ "\n",
+ "knownP = (((X_val>-4) & (X_val<-1)) | ((X_val>1) & (X_val<4)))\n",
+ "\n",
+ "pyplot.plot(X_val[knownP],y_val[knownP], color='blue', \n",
+ " linewidth=1, marker='o', linestyle=' ',alpha=0.5, label='known')\n",
+ "\n",
+ "pyplot.plot(X_val[knownP==0],y_val[knownP==0], color='purple', \n",
+ " linewidth=1, marker='o', linestyle=' ',alpha=1, label='new')\n",
+ "\n",
+ "axes = pyplot.gca()\n",
+ "#origins = zip(np.arange(rang)*1.,y_val)\n",
+ "#endings = zip(np.arange(rang)*1.,y_pred)\n",
+ "#lines_vals = [[origins[i],endings[i]] for i in xrange(len(origins))]\n",
+ "\n",
+ "from matplotlib import collections as mc\n",
+ "#lc = mc.LineCollection(lines_vals, linewidths=1, alpha = 0.4, color = 'purple')\n",
+ "#axes.add_collection(lc)\n",
+ "axes.set_ylim(-100,100)\n",
+ "axes.set_xlim(-5,5)\n",
+ "pyplot.gcf().set_size_inches((20,10))\n",
+ "pyplot.legend()\n",
+ "print 'Absolute error', np.min(np.abs(np.expand_dims(y_val,axis=2)-mu_pred),axis=2).sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 250,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "parameters = graphN1.predict(data={'input':X_val})['output']\n",
+ "\n",
+ "comp = np.reshape(parameters,[-1, c + 2, m])\n",
+ "mu_pred1 = comp[:, :c, :]\n",
+ "sigma_pred1 = comp[:, c, :]\n",
+ "alpha_pred1 = comp[:, c + 1, :]\n",
+ "\n",
+ "parameters = graphN2.predict(data={'input':X_val})['output']\n",
+ "\n",
+ "comp = np.reshape(parameters,[-1, c + 2, m])\n",
+ "mu_pred2 = comp[:, :c, :]\n",
+ "sigma_pred2 = comp[:, c, :]\n",
+ "alpha_pred2 = comp[:, c + 1, :]\n",
+ "\n",
+ "parameters = graphN3.predict(data={'input':X_val})['output']\n",
+ "\n",
+ "comp = np.reshape(parameters,[-1, c + 2, m])\n",
+ "mu_pred3 = comp[:, :c, :]\n",
+ "sigma_pred3 = comp[:, c, :]\n",
+ "alpha_pred3 = comp[:, c + 1, :]\n",
+ "\n",
+ "parameters = graphN4.predict(data={'input':X_val})['output']\n",
+ "\n",
+ "comp = np.reshape(parameters,[-1, c + 2, m])\n",
+ "mu_pred4 = comp[:, :c, :]\n",
+ "sigma_pred4 = comp[:, c, :]\n",
+ "alpha_pred4 = comp[:, c + 1, :]\n",
+ "\n",
+ "parameters = graphN5.predict(data={'input':X_val})['output']\n",
+ "\n",
+ "comp = np.reshape(parameters,[-1, c + 2, m])\n",
+ "mu_pred5 = comp[:, :c, :]\n",
+ "sigma_pred5 = comp[:, c, :]\n",
+ "alpha_pred5 = comp[:, c + 1, :]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 251,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "mu_pred_s = (mu_pred1+mu_pred2+mu_pred3+mu_pred4+mu_pred5)/5.\n",
+ "\n",
+ "sigma_pred_s = np.sqrt((((sigma_pred1**2+mu_pred1[...,0]**2)+\\\n",
+ "(sigma_pred2**2+mu_pred2[...,0]**2)+\\\n",
+ "(sigma_pred3**2+mu_pred3[...,0]**2)+\\\n",
+ "(sigma_pred4**2+mu_pred4[...,0]**2)+\\\n",
+ "(sigma_pred5**2+mu_pred5[...,0]**2))/5.)-mu_pred_s[...,0]**2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 289,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Absolute error 70645.3\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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UsG/fPsrKypg2bVprfGMMhYWFuFwuzjvvPLKysqioqIiYa15eHlOmTGHEiBH8+7//O+vX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9pUvtNOo2z3bkzgs7v+hdXrBvOh7xqaOZ4gCRhUUk8yTobhN7ZhM1JJxUFGQimD0jcxJ8fBbLsT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\nSZIkqTcwQFKPdGir2oXNzfQhb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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "mu_pred = mu_pred_s\n",
+ "sigma_pred = sigma_pred_s\n",
+ "alpha_pred = alpha_pred5\n",
+ "y_pred = np.zeros((len(mu_pred)))\n",
+ "\n",
+ "col = cm.autumn(np.linspace(0, 1, mu_pred.shape[-1]))\n",
+ "\n",
+ "for mx in xrange(mu_pred.shape[-1]):\n",
+ " for i in xrange(len(mu_pred)):\n",
+ " pyplot.errorbar(X_val[i],mu_pred[i,0,mx],\n",
+ " yerr=sigma_pred[i,mx],\n",
+ " alpha=alpha_pred[i,mx], \n",
+ " color=col[mx])\n",
+ " y_pred[i] = mu_pred[i,0,mx]\n",
+ " pyplot.plot(X_val,y_pred, color=col[mx],linewidth=1, marker='o', \n",
+ " linestyle=' ',alpha=0.5, label='gaus_'+str(mx))\n",
+ "\n",
+ "knownP = (((X_val>-4) & (X_val<-1)) | ((X_val>1) & (X_val<4)))\n",
+ "\n",
+ "pyplot.plot(X_val[knownP],y_val[knownP], color='blue', \n",
+ " linewidth=1, marker='o', linestyle=' ',alpha=0.5, label='known points')\n",
+ "\n",
+ "pyplot.plot(X_val[knownP==0],y_val[knownP==0], color='green', \n",
+ " linewidth=1, marker='o', linestyle=' ',alpha=1, label='unknown points')\n",
+ "\n",
+ "axes = pyplot.gca()\n",
+ "#origins = zip(np.arange(rang)*1.,y_val)\n",
+ "#endings = zip(np.arange(rang)*1.,y_pred)\n",
+ "#lines_vals = [[origins[i],endings[i]] for i in xrange(len(origins))]\n",
+ "\n",
+ "from matplotlib import collections as mc\n",
+ "#lc = mc.LineCollection(lines_vals, linewidths=1, alpha = 0.4, color = 'purple')\n",
+ "#axes.add_collection(lc)\n",
+ "axes.set_ylim(-200,300)\n",
+ "axes.set_xlim(-10,10)\n",
+ "pyplot.gcf().set_size_inches((20,10))\n",
+ "pyplot.legend()\n",
+ "print 'Absolute error', np.min(np.abs(np.expand_dims(y_val,axis=2)-mu_pred),axis=2).sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 290,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Absolute error 70645.3\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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IpL8ajqPR9HE02nTcQfkFBUwrLWVlPE4IBFOmMC0WI7+goKuz1SAUhGHYOwMF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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "mu_pred = mu_pred_s\n",
+ "sigma_pred = sigma_pred_s\n",
+ "alpha_pred = alpha_pred5\n",
+ "y_pred = np.zeros((len(mu_pred)))\n",
+ "\n",
+ "col = cm.autumn(np.linspace(0, 1, mu_pred.shape[-1]))\n",
+ "\n",
+ "for mx in xrange(mu_pred.shape[-1]):\n",
+ " for i in xrange(len(mu_pred)):\n",
+ " pyplot.errorbar(X_val[i],mu_pred[i,0,mx],\n",
+ " yerr=sigma_pred[i,mx],\n",
+ " alpha=alpha_pred[i,mx], \n",
+ " color=col[mx])\n",
+ " y_pred[i] = mu_pred[i,0,mx]\n",
+ " pyplot.plot(X_val,y_pred, color=col[mx],linewidth=1, marker='o', \n",
+ " linestyle=' ',alpha=0.5, label='gaus_'+str(mx))\n",
+ "\n",
+ "knownP = (((X_val>-4) & (X_val<-1)) | ((X_val>1) & (X_val<4)))\n",
+ "\n",
+ "pyplot.plot(X_val[knownP],y_val[knownP], color='blue', \n",
+ " linewidth=1, marker='o', linestyle=' ',alpha=0.5, label='known points')\n",
+ "\n",
+ "pyplot.plot(X_val[knownP==0],y_val[knownP==0], color='green', \n",
+ " linewidth=1, marker='o', linestyle=' ',alpha=1, label='unknown points')\n",
+ "\n",
+ "axes = pyplot.gca()\n",
+ "\n",
+ "from matplotlib import collections as mc\n",
+ "axes.set_ylim(-5,5)\n",
+ "axes.set_xlim(-3,2)\n",
+ "pyplot.gcf().set_size_inches((20,10))\n",
+ "pyplot.legend()\n",
+ "print 'Absolute error', np.min(np.abs(np.expand_dims(y_val,axis=2)-mu_pred),axis=2).sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 235,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Absolute error 71076.6\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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AEhERERERkX7LXmNnWhZUj1wBtwOuGiyvwMpdqHgkEoY8wFVVRfmnn5K7eHG3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8/A32DFgQPTYsUXjKPdSMRhi6Nmr4+f/r9vstEsp9Y1ioK6fWytfg/DXsPUWXE8coemoImIiIjI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\nEA/O7qoJShqSqEaSJEmS1JUBko5YmXSa3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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "mu_pred = mu_pred_s\n",
+ "sigma_pred = sigma_pred_s\n",
+ "alpha_pred = alpha_pred5\n",
+ "y_pred = np.zeros((len(mu_pred)))\n",
+ "\n",
+ "col = cm.autumn(np.linspace(0, 1, mu_pred.shape[-1]))\n",
+ "\n",
+ "for mx in xrange(mu_pred.shape[-1]):\n",
+ " for i in xrange(len(mu_pred)):\n",
+ " pyplot.errorbar(X_val[i],mu_pred[i,0,mx],\n",
+ " yerr=sigma_pred[i,mx],\n",
+ " alpha=alpha_pred[i,mx], \n",
+ " color=col[mx])\n",
+ " y_pred[i] = mu_pred[i,0,mx]\n",
+ " pyplot.plot(X_val,y_pred, color=col[mx],linewidth=1, marker='o', \n",
+ " linestyle=' ',alpha=0.5, label='gaus_'+str(mx))\n",
+ "\n",
+ "knownP = (((X_val>-4) & (X_val<-1)) | ((X_val>1) & (X_val<4)))\n",
+ "\n",
+ "pyplot.plot(X_val[knownP],y_val[knownP], color='blue', \n",
+ " linewidth=1, marker='o', linestyle=' ',alpha=0.5, label='known points')\n",
+ "\n",
+ "pyplot.plot(X_val[knownP==0],y_val[knownP==0], color='green', \n",
+ " linewidth=1, marker='o', linestyle=' ',alpha=1, label='unknown points')\n",
+ "\n",
+ "axes = pyplot.gca()\n",
+ "#origins = zip(np.arange(rang)*1.,y_val)\n",
+ "#endings = zip(np.arange(rang)*1.,y_pred)\n",
+ "#lines_vals = [[origins[i],endings[i]] for i in xrange(len(origins))]\n",
+ "\n",
+ "from matplotlib import collections as mc\n",
+ "#lc = mc.LineCollection(lines_vals, linewidths=1, alpha = 0.4, color = 'purple')\n",
+ "#axes.add_collection(lc)\n",
+ "axes.set_ylim(-200,300)\n",
+ "axes.set_xlim(-10,10)\n",
+ "pyplot.gcf().set_size_inches((20,10))\n",
+ "pyplot.legend()\n",
+ "print 'Absolute error', np.min(np.abs(np.expand_dims(y_val,axis=2)-mu_pred),axis=2).sum()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.11"
+ },
+ "widgets": {
+ "state": {},
+ "version": "1.1.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/MDN-Introduction.ipynb b/MDN-Introduction.ipynb
new file mode 100644
index 0000000..fe8a89c
--- /dev/null
+++ b/MDN-Introduction.ipynb
@@ -0,0 +1,429 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "
Mixture Density Networks (MDN) for distribution and uncertainty estimation
\n",
+ "\n",
+ "This material is copyright Axel Brando and made available under the Creative Commons Attribution-ShareAlike 4.0 International License (http://creativecommons.org/licenses/by-sa/4.0/). Code is also made available under the Apache Version 2.0 License (https://www.apache.org/licenses/LICENSE-2.0). \n",
+ "\n",
+ "Please, to use this material and code follow the instructions explained in the main repository [https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation](https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation#bibtex-reference-format-for-citation)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "
Introduction
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As **Bishop** [1] explains, if we assume that the conditional distribution of the target data is, in fact, Gaussian, then we can obtain the least-squares formalism using maximum likelihood. This motivates the idea of replacing the Gaussian distribution in the conditional density of the complete target vector with a mixture model (**McLachlan et al.** [2]), which has the flexibility to completely model a general distribution functions. The probability density of the target data is then represented as a linear combination of kernel functions in the form\n",
+ "\n",
+ "$$\n",
+ "p(\\boldsymbol{y} | \\boldsymbol{x} ) = \\sum_{i=1}^m \\alpha_i (\\boldsymbol{x}) \\phi_i (\\boldsymbol{y} | \\boldsymbol{x} )$$\n",
+ "\n",
+ "where $m$ is the number of components in the mixture and $\\alpha_i(\\boldsymbol{x})$ is called *mixing coefficients*.\n",
+ "\n",
+ "In his paper, **Bishop** [1] selected the kernel functions which are Gaussian of the form:\n",
+ "\n",
+ "$$\n",
+ "\\phi_i(\\boldsymbol{y} | \\boldsymbol{x}) = \\frac{1}{(2 \\pi )^{c/2} \\sigma_i (\\boldsymbol{x})^c} \\exp\\left\\{ - \\frac{ \\parallel \\boldsymbol{y} - \\boldsymbol{\\mu}_i (\\boldsymbol{x})\\parallel^2}{2 \\sigma_i (\\boldsymbol{x})^2} \\right\\}$$\n",
+ "\n",
+ "where $\\boldsymbol{\\mu}_i$ represents the centre of the $i^{th}$ kernel. The author assumed that the components of the output vector are statically independent within each component of the distribution, and it can be described by a common variance $\\sigma_i(\\boldsymbol{x})$. As **Bishop** [1] explains, to be more formal, the assumption of independence can be relaxed by introducing a full covariance matrices for each Gaussian kernel. However, according to **McLachlan et al.** [2] and **Bishop** [1], a Gaussian mixture model with this simplified kernel can approximate any given density function to arbitrary accuracy, provided the mixing coefficients and the Gaussian parameters (means and variances) are correctly chosen. Note that this assumption simplifies the calculation of the inverse of the covariance matrix $\\boldsymbol{\\Sigma}_i$ since we will have a diagonal matrix with the same variance $\\sigma_i$ across all dimensions\n",
+ "\n",
+ "$$\\boldsymbol{\\Sigma}_i = \\begin{bmatrix}\\sigma_{i} & 0 & \\cdots & 0\\\\\n",
+ "0 & \\sigma_{i} & & 0\\\\\n",
+ "\\vdots & & \\ddots & \\vdots\\\\\n",
+ "0 & \\cdots & 0 & \\sigma_{i}\n",
+ "\\end{bmatrix} $$\n",
+ "\n",
+ "Which simplifies the $\\mid \\boldsymbol{\\Sigma}_i \\mid^{-1}$ calculation of the first equation to the $\\sigma_i^{-c}$ of the equation second one.\n",
+ "\n",
+ "As we can see in next graph, given a input vector $\\boldsymbol{x}$, the Mixture Density Network model provides a general formalism for modelling an arbitrary conditional density function $p(\\boldsymbol{y} \\mid \\boldsymbol{x})$. This union between the traditional neural network and the mixture model part is achieved by using the log-likelihood of the linear combination of kernel functions as a loss function of the neural network. According to **Bishop** [1], by choosing a mixture model with a sufficient number of kernel functions, and a neural network with a sufficient number of hidden units, the Mixture Density Network can approximate any conditional density $p(\\boldsymbol{y} \\mid \\boldsymbol{x})$ as closely as desired.\n",
+ "\n",
+ "The representation graph of the Mixture Density Network model is as follows:\n",
+ "\n",
+ "
\n",
+ "
\n",
+ "
\n",
+ "The output of the feed-forward neural network determine the parameters in a mixture density model. Therefore, the mixture density model represents the conditional probability density function of the target variables conditioned on the input vector of the neural network.\n",
+ "
\n",
+ "\n",
+ "Building this Mixture Density Network increases the number of parameters from $c$ output parameters to $(c+2)\\times m$ parameters, where $c$ remains to be the dimension of the output and $m$ is the number of mixtures we are using in the model.\n",
+ "\n",
+ "There are some restrictions that **Bishop** [1] proposes in his article to the different parameters to satisfy:\n",
+ "\n",
+ " 1. As required for probabilities, it is important that the mixing coefficients $\\alpha_i$ satisfy the constraint $\\sum^m_{i=1}\\alpha_{i}=1$. To achieve this restriction, in principle, it is enough to have a *softmax* activation function in the nodes corresponding to $\\alpha_i$.\n",
+ " 2. Since variance $\\sigma_i$ represents scale parameters, **Bishop**[1] recommends to represent them in terms of the exponential of the corresponding network output $z_i^{\\sigma}$\n",
+ " \n",
+ " $$\\sigma_i = exp(z_{i}^{\\sigma})$$\n",
+ " 3. The centres parameters $\\mu_i$ represent location parameters. Taking into account the notion of an uninformative prior it suggests that these would be represented directly by the network outputs, i.e. $$\\mu_{i,k} = z_{i,k}^{\\mu}$$ which, in a Bayesian framework, would correspond to the choice of an un-informative Bayesian prior, assuming that the corresponding network outputs $z_i^{\\sigma}$ had uniform probability distribution (**Nowlan et al.** [3], **Jacobs et al.** [4] and **Bishop** [1]). According to **Bishop** [1], the use of this representation avoids pathological configurations in which one or more of the variances goes to zero.\n",
+ "\n",
+ "\n",
+ "To define an error function, to use as a loss function, the standard approach is the maximum likelihood method, which requires maximisation of the log-likelihood function or, equivalently, minimisation of the negative logarithm of the likelihood. Therefore, the error function for the Mixture Density Network is:\n",
+ "\n",
+ "$$ \\log \\mathcal{L}(\\boldsymbol{y} \\mid \\boldsymbol{x}) = - \\log \\left( p(\\boldsymbol{y} \\mid \\boldsymbol{x}) \\right) = - \\log \\left(\\overset{m}{\\underset{i=0}{\\sum}} \\alpha_i (\\boldsymbol{x}) \\phi_i (\\boldsymbol{y} | \\boldsymbol{x} ) \\right)\n",
+ "$$\n",
+ "\n",
+ "Where $\\phi_i (\\boldsymbol{y} | \\boldsymbol{x} )$ is the same of the Gaussian Kernel Equation. As it is explained in **Bishop** [1], the term $\\sum p(\\boldsymbol{x})$ has been dropped as it is independent from the parameters of the mixture model, and hence it is independent from the network weights. Thus, the aim of Mixture Density Networks is to model the complete conditional probability density of the output variables. From this density function, any desired statistic involving the output variables can, in principle, be computed.\n",
+ "\n",
+ "### How to minimise the error function with respect to the weights in the neural network\n",
+ "\n",
+ "Once our neural network architecture is decided, we need a way to minimise the error function to modify the weights in order to obtain an expected result. In order to do this we need to calculate the derivatives of the loss function with respect to the weights in the neural network. According to **Bishop** [1], one method to solve this problem is by using the standard \\textit{back-propagation} procedure, provided we obtain suitable expressions for the derivatives of the error with respect to the activations of the output units of the neural network. Since the loss function is a composition of a sum of terms, one for each pattern, we can consider the derivatives $\\delta_k = \\frac{\\partial \\mathcal{L}(\\boldsymbol{y} \\mid \\boldsymbol{x})}{\\partial z_k}$ for a particular pattern and then we can find the derivatives of $\\mathcal{L}$ by summing over all patterns. The derivatives $\\delta_k$ act as \\textit{errors} which can be back-propagated through the network to find the derivatives with respect to the network weights. There is a lot of bibliography about this process of optimisation like **Nielsen** [5], **Goodfellow et al.** [6] and **Bishop** [7]. As **Bishop** [1] notes, standard optimisation algorithms, such as conjugate gradients or quasi-Newton methods, can then be used to find a minimum of $\\mathcal{L}$. Alternatively, if an optimisation algorithm such as stochastic gradient descent is to be used, the weight updates can be applied after presentation of each pattern separately. In recent years, many new gradient descend optimisation algorithms have been developed, such as [Nesterov accelerated gradient, Adagrad, Adadelta, RMSprop or Adam](http://sebastianruder.com/optimizing-gradient-descent/).\n",
+ "\n",
+ "Nowadays, this differentiation process is implemented in the most Deep Learning relevant libraries in the way it can automatically differentiate native code. As it is used in [Autograd Library](https://github.com/HIPS/autograd), most part of the libraries use reverse-mode differentiation (also called *reverse accumulation* (Process well explained on the [Automatic\\_differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation\\#Reverse_accumulation) Wikipedia website or on page $7$ of [Ilya Sutskever's PhD thesis](http://www.cs.utoronto.ca/~ilya/pubs/ilya_sutskever_phd_thesis.pdf)), which means by using this libraries we can efficiently take gradients of scalar-valued functions with respect to array-valued arguments. Thus, to use this libraries simplifies the gradient-based optimisation problem and this allows us to focus on other problems.\n",
+ "\n",
+ "\n",
+ "### REFERENCES\n",
+ "\n",
+ " [1]: [Bishop, C. M. (1994)](http://eprints.aston.ac.uk/373/). Mixture density networks.\n",
+ "\n",
+ " [2]: [McLachlan, G. J., & Basford, K. E. (1988)](https://espace.library.uq.edu.au/view/UQ:308790). Mixture models: Inference and applications to clustering (Vol. 84). Marcel Dekker.\n",
+ " \n",
+ " [3]: [Nowlan, S. J., & Hinton, G. E. (1992)](http://www.mitpressjournals.org/doi/abs/10.1162/neco.1992.4.4.473). Simplifying neural networks by soft weight-sharing. Neural computation, 4(4), 473-493.\n",
+ " \n",
+ " [4]: [Jacobs, R. A., Jordan, M. I., Nowlan, S. J., & Hinton, G. E. (1991)](http://www.mitpressjournals.org/doi/abs/10.1162/neco.1991.3.1.79). Adaptive mixtures of local experts. Neural computation, 3(1), 79-87.\n",
+ "\n",
+ " [5]: [Nielsen, M. A. (2015). Neural networks and deep learning](http://neuralnetworksanddeeplearning.com/)\n",
+ " \n",
+ " [6]: [Bengio, Y., Goodfellow, I. J., & Courville, A. (2015)](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.672.7118&rep=rep1&type=pdf). Deep learning. Nature, 521, 436-444.\n",
+ " \n",
+ " [7]: [Bishop, C. M. (2006)](http://www.academia.edu/download/30428242/bg0137.pdf). Pattern recognition. Machine Learning, 128, 1-58."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "
Implementation
\n",
+ "Below we will show a generic implementation of the MDN in the following points view:\n",
+ "
\n",
+ " - Prepared to use as many distributions in the mixture as defined in $m$ variable.
\n",
+ " - Prepared to have as many outputs as defined in $c$ variable.
\n",
+ " - Prepared to use the desired likelihood (Gaussian or Laplace) function.
\n",
+ " - Prepared to use adversarial training (with variation of modifying weights twice) during MDN training or not.
\n",
+ " - With the other tricks described in the main page of the repository https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation
\n",
+ "
\n",
+ "
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#Import of the TensorFlow and definition of the control_flow_ops variable\n",
+ "import tensorflow as tf\n",
+ "tf.python.control_flow_ops = tf\n",
+ "\n",
+ "#GPU Memory allocation on demand (Remove comments if necessary)\n",
+ "##config = tf.ConfigProto()\n",
+ "##config.gpu_options.allow_growth=True\n",
+ "##sess = tf.Session(config=config)\n",
+ "\n",
+ "#Import of TensorFlow backend of Keras\n",
+ "from keras import backend as K\n",
+ "\n",
+ "#Some other imports\n",
+ "import os\n",
+ "import numpy as np\n",
+ "from pandas.io.parsers import read_csv\n",
+ "from sklearn.utils import shuffle\n",
+ "import random\n",
+ "from datetime import datetime"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#Imports of the Keras library parts we will need\n",
+ "from keras.models import Sequential,Graph\n",
+ "from keras.layers.core import Dense, Dropout\n",
+ "from keras.callbacks import History\n",
+ "from keras.layers.recurrent import LSTM\n",
+ "from keras.models import model_from_json\n",
+ "from keras.regularizers import l2, activity_l2\n",
+ "\n",
+ "from keras.objectives import mean_absolute_error\n",
+ "\n",
+ "#Definition of the ELU+1 function\n",
+ "#With some margin to avoid problems of instability\n",
+ "from keras.layers.advanced_activations import ELU\n",
+ "\n",
+ "def elu_modif(x, a=1.):\n",
+ " e=1e-15\n",
+ " return ELU(alpha=a)(x)+1.+e\n",
+ "\n",
+ "\n",
+ "c = 1 #The number of outputs we want to predict\n",
+ "m = 1 #The number of distributions we want to use in the mixture\n",
+ "\n",
+ "#Note: The output size will be (c + 2) * m\n",
+ "\n",
+ "def log_sum_exp(x, axis=None):\n",
+ " \"\"\"Log-sum-exp trick implementation\"\"\"\n",
+ " x_max = K.max(x, axis=axis, keepdims=True)\n",
+ " return K.log(K.sum(K.exp(x - x_max), \n",
+ " axis=axis, keepdims=True))+x_max\n",
+ "\n",
+ "\n",
+ "def mean_log_Gaussian_like(y_true, parameters):\n",
+ " \"\"\"Mean Log Gaussian Likelihood distribution\n",
+ " Note: The 'c' variable is obtained as global variable\n",
+ " \"\"\"\n",
+ " components = K.reshape(parameters,[-1, c + 2, m])\n",
+ " mu = components[:, :c, :]\n",
+ " sigma = components[:, c, :]\n",
+ " alpha = components[:, c + 1, :]\n",
+ " alpha = K.softmax(K.clip(alpha,1e-8,1.))\n",
+ " \n",
+ " exponent = K.log(alpha) - .5 * float(c) * K.log(2 * np.pi) \\\n",
+ " - float(c) * K.log(sigma) \\\n",
+ " - K.sum((K.expand_dims(y_true,2) - mu)**2, axis=1)/(2*(sigma)**2)\n",
+ " \n",
+ " log_gauss = log_sum_exp(exponent, axis=1)\n",
+ " res = - K.mean(log_gauss)\n",
+ " return res\n",
+ "\n",
+ "\n",
+ "def mean_log_LaPlace_like(y_true, parameters):\n",
+ " \"\"\"Mean Log Laplace Likelihood distribution\n",
+ " Note: The 'c' variable is obtained as global variable\n",
+ " \"\"\"\n",
+ " components = K.reshape(parameters,[-1, c + 2, m])\n",
+ " mu = components[:, :c, :]\n",
+ " sigma = components[:, c, :]\n",
+ " alpha = components[:, c + 1, :]\n",
+ " alpha = K.softmax(K.clip(alpha,1e-2,1.))\n",
+ " \n",
+ " exponent = K.log(alpha) - float(c) * K.log(2 * sigma) \\\n",
+ " - K.sum(K.abs(K.expand_dims(y_true,2) - mu), axis=1)/(sigma)\n",
+ " \n",
+ " log_gauss = log_sum_exp(exponent, axis=1)\n",
+ " res = - K.mean(log_gauss)\n",
+ " return res\n",
+ "\n",
+ "\n",
+ "def scoring_rule_adv(y_true, y_pred):\n",
+ " \"\"\"Fast Gradient Sign Method (FSGM) to implement Adversarial Training\n",
+ " Note: The 'graphADV' pointer is obtained as global variable\n",
+ " \"\"\"\n",
+ " \n",
+ " # Compute loss \n",
+ " #Note: Replace with 'mean_log_Gaussian_like' if you want a Gaussian kernel.\n",
+ " error = mean_log_LaPlace_like(y_true, y_pred)\n",
+ " \n",
+ " # Craft adversarial examples using Fast Gradient Sign Method (FGSM)\n",
+ " # Define gradient of loss wrt input\n",
+ " grad_error = K.gradients(error,graphADV.input) #Minus is on error function\n",
+ " # Take sign of gradient, Multiply by constant epsilon, Add perturbation to original example to obtain adversarial example\n",
+ " #Sign add a new dimension we need to obviate\n",
+ " \n",
+ " epsilon = 0.08\n",
+ " \n",
+ " adversarial_X = K.stop_gradient(graphADV.input + epsilon * K.sign(grad_error)[0])\n",
+ " \n",
+ " # Note: If you want to test the variation of adversarial training \n",
+ " # proposed by XX, eliminate the following comment character \n",
+ " # and comment the previous one.\n",
+ " \n",
+ " ##adversarial_X = graphADV.input + epsilon * K.sign(grad_error)[0]\n",
+ " \n",
+ " adv_output = graphADV(adversarial_X)\n",
+ " \n",
+ " #Note: Replace with 'mean_log_Gaussian_like' if you want a Gaussian kernel.\n",
+ " adv_error = mean_log_LaPlace_like(y_true, adv_output)\n",
+ " return 0.3 * error + 0.7 * adv_error\n",
+ "\n",
+ "#Definition of 3 stacked dense layers followed by Mixture Density block.\n",
+ "# This initial feed-forward neural network could be as you want.\n",
+ "graph = Graph()\n",
+ "graph.add_input(name='input', input_shape=(12,))\n",
+ "graph.add_node(Dense(500, activation='relu'), name='dense1_1', input='input')\n",
+ "graph.add_node(Dropout(0.25), name='drop1_1', input='dense1_1')\n",
+ "\n",
+ "graph.add_node(Dense(500, activation='relu'), name='dense2_1', input='drop1_1')\n",
+ "graph.add_node(Dropout(0.25), name='drop2_1', input='dense2_1')\n",
+ "\n",
+ "graph.add_node(Dense(500, activation='relu'), name='dense3_1', input='drop2_1')\n",
+ "graph.add_node(Dropout(0.25), name='drop3_1', input='dense3_1')\n",
+ "\n",
+ "\n",
+ "graph.add_node(Dense(output_dim=500, activation=\"relu\"), name='FC1', input='drop3_1')\n",
+ "graph.add_node(Dense(output_dim=c*m), name='FC_mus', input='FC1')\n",
+ "graph.add_node(Dense(output_dim=m, activation=elu_modif), name='FC_sigmas', input='FC1') #K.exp, W_regularizer=l2(1e-3)\n",
+ "graph.add_node(Dense(output_dim=m, activation='softmax'), name='FC_alphas', input='FC1')\n",
+ "graph.add_output(name='output', inputs=['FC_mus','FC_sigmas', 'FC_alphas'], merge_mode='concat',concat_axis=1)\n",
+ "graphADV = graph\n",
+ "\n",
+ "#Note 1: 'scoring_rule_adv' by 'mean_log_Gaussian_like' or\n",
+ "# 'mean_log_LaPlace_like' depending on your needs.\n",
+ "#Note 2: Replace 'rmsprop' by 'adam' depending on your needs.\n",
+ "graph.compile('rmsprop', {'output':scoring_rule_adv})"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Training of the neural network"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from keras.callbacks import Callback, ModelCheckpoint\n",
+ "class LossHistory(Callback):\n",
+ " def on_train_begin(self, logs={}):\n",
+ " self.losses = []\n",
+ "\n",
+ " def on_batch_end(self, batch, logs={}):\n",
+ " self.losses.append(logs.get('loss'))\n",
+ "lossHistory = LossHistory()\n",
+ "\n",
+ "# checkpoint\n",
+ "filepath=\"MDN--{epoch:02d}-{val_loss:.2f}.hdf5\"\n",
+ "checkpoint = ModelCheckpoint(filepath, monitor='val_loss', verbose=1, save_best_only=True, mode='min')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "epoch=300\n",
+ "graph.fit(data={'input':X,'output':y}, batch_size=40000, nb_epoch=epoch, \n",
+ " validation_split=0.1,callbacks=[lossHistory, checkpoint])\n",
+ "end_time = datetime.now()\n",
+ "a=0\n",
+ "print \n",
+ "print \"********************************* End *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "plt.plot(np.arange(len(lossHistory.losses)), lossHistory.losses)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Predict by using the neural network"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#Depending on your needs you will load the weights\n",
+ "##graph.load_weights('MDN-Weights.hdf5')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#y_pred = model.predict(X_val)['output']\n",
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "y_pred = graph.predict(data={'input':X_val})['output']\n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* Prediction ends *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### How to obtain the parameters"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "comp = np.reshape(y_pred,[-1, c + 2, m])\n",
+ "mu_pred = comp[:, :c, :]\n",
+ "sigma_pred = comp[:, c, :]\n",
+ "alpha_pred = comp[:, c + 1, :]"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.11"
+ },
+ "widgets": {
+ "state": {},
+ "version": "1.1.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/MDN-LSTM-Regression.ipynb b/MDN-LSTM-Regression.ipynb
new file mode 100644
index 0000000..b8fe992
--- /dev/null
+++ b/MDN-LSTM-Regression.ipynb
@@ -0,0 +1,1816 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "
Mixture Density Networks (MDN) for distribution and uncertainty estimation
\n",
+ "\n",
+ "This material is copyright Axel Brando and made available under the Creative Commons Attribution-ShareAlike 4.0 International License (http://creativecommons.org/licenses/by-sa/4.0/). Code is also made available under the Apache Version 2.0 License (https://www.apache.org/licenses/LICENSE-2.0). \n",
+ "\n",
+ "Please, to use this material and code follow the instructions explained in the main repository [https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation](https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation#bibtex-reference-format-for-citation)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "
\n",
+ "
Time series regression problem by using LSTM
"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Using TensorFlow backend.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import tensorflow as tf\n",
+ "tf.python.control_flow_ops = tf\n",
+ "\n",
+ "config = tf.ConfigProto()\n",
+ "config.gpu_options.allow_growth=True\n",
+ "sess = tf.Session(config=config)\n",
+ "\n",
+ "from keras import backend as K\n",
+ "\n",
+ "import os\n",
+ "import numpy as np\n",
+ "from pandas.io.parsers import read_csv\n",
+ "from sklearn.utils import shuffle\n",
+ "import random\n",
+ "from datetime import datetime"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### We load our dataset\n",
+ "Because our dataset is private we will only expose a dummy code"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "X = np.load('Normalised-X_train.npy')\n",
+ "y = np.load('y_train.npy')\n",
+ "X_val = np.load('Normalised-X_val.npy')\n",
+ "y_val = np.load('y_val.npy')\n",
+ "X_orig = np.load('Original-X_train.npy')\n",
+ "X_val_orig = np.load('Original-X_val.npy')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Comparison between different LSTM models and Mixture Density LSTM Models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from keras.models import Sequential,Graph\n",
+ "from keras.layers.core import Dense, Dropout\n",
+ "from keras.callbacks import History\n",
+ "from keras.layers.recurrent import LSTM\n",
+ "from keras.models import model_from_json\n",
+ "from keras.regularizers import l2, activity_l2\n",
+ "\n",
+ "from keras import backend as K\n",
+ "\n",
+ "ograph = Graph()\n",
+ "ograph.add_input(name='input', input_shape=(12,1,), dtype='float32')\n",
+ "ograph.add_node(LSTM(output_dim=128, return_sequences=True), name='LSTM1_1', input='input')\n",
+ "ograph.add_node(Dropout(0.5), name='Dropout1', input='LSTM1_1')\n",
+ "ograph.add_node(LSTM(output_dim=128, return_sequences=False), name='LSTM2_1', input='Dropout1')\n",
+ "ograph.add_node(Dropout(0.5), name='Dropout2', input='LSTM2_1')\n",
+ "ograph.add_node(Dense(output_dim=128, activation=\"relu\"), name='FC1', input='Dropout2')\n",
+ "ograph.add_node(Dense(output_dim=1, activation=\"linear\"), name='FC2', input='FC1')\n",
+ "ograph.add_output(name='output', input='FC2')\n",
+ "ograph.compile(optimizer='rmsprop', loss={'output':'mean_absolute_error'})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#dummy code\n",
+ "ograph.load_weights('MDN-weights.hdf5')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "********************************* Prediction ends *********************************\n",
+ "\n",
+ "Duration: 0:03:17.080772\n"
+ ]
+ }
+ ],
+ "source": [
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "y_pred = ograph.predict(data={'input':X_val})['output']\n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* Prediction ends *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Elements below tolerance: 635365\n",
+ "Mean Absolute Error: 110.400910833\n",
+ "Mean Squared Error: 9053896.50934\n",
+ "Root Mean Squared Error: 3008.96934337\n",
+ "Maximum Total Error: [ 1459235.99922457](real: [-1459236.], predicted: [-0.00077543])\n",
+ "AE 10% 0.305856183215 (491146)\n",
+ "\n",
+ "********************************* End *********************************\n",
+ "\n",
+ "Duration: 0:00:01.233166\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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TXNeNn5zkgSTPJbk/yUm9fTYn2Z7k2SQX9cbXJHkqyfNJtvTGj0myrdvnkSRnLvUTlSRJ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\nJPOw2BxLeVwcibd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+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "start_time = datetime.now()\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "tolerance = 1e-1\n",
+ "idx = np.where(np.abs(y_pred-y_val)>tolerance)[0]\n",
+ "\n",
+ "print 'Elements below tolerance: ' + str(len(idx))\n",
+ "\n",
+ "measure = np.mean(np.abs((y_pred-y_val*1.)))\n",
+ "print 'Mean Absolute Error: ' + str(measure) \n",
+ "measure = np.mean(np.power((y_pred-y_val*1.),2))\n",
+ "print 'Mean Squared Error: ' + str(measure) \n",
+ "measure = np.sqrt(measure)\n",
+ "print 'Root Mean Squared Error: ' + str(measure) \n",
+ "i = np.argmax(np.abs((y_pred-y_val*1.)))\n",
+ "measure = np.abs((y_pred[i]-y_val[i]*1.))\n",
+ "print 'Maximum Total Error: ' + str(measure) + '(real: ' + str(y_val[i]) + ', predicted: ' + str(y_pred[i]) + ')'\n",
+ "\n",
+ "measure = np.abs((y_pred[idx]-y_val[idx]))/(np.abs(y_val[idx])+1e-16)\n",
+ "MAE10 = np.where(measure>0.1)[0]\n",
+ "print 'AE 10% ' + str(1.*len(MAE10)/len(y_val)) + ' (' + str(len(MAE10)) + ')'\n",
+ "\n",
+ "bins = np.arange(0,1.0,0.05)-0.025\n",
+ "measure = np.abs(y_pred[idx]-y_val[idx])/(np.abs(y_val[idx])+1e-16)\n",
+ "[dat, bins] = np.histogram(measure,bins=bins)\n",
+ "plt.bar(np.arange(len(dat)),dat,width =1)\n",
+ "plt.xticks(np.arange(len(dat)),bins[:-1])\n",
+ "plt.gcf().set_size_inches((10,6))\n",
+ "\n",
+ "plt.gcf().set_size_inches((12,6))\n",
+ " \n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* End *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "____________________________________________________________________________________________________\n",
+ "Layer (type) Output Shape Param # Connected to \n",
+ "====================================================================================================\n",
+ "input (InputLayer) (None, 12, 1) 0 \n",
+ "____________________________________________________________________________________________________\n",
+ "LSTM1_1 (LSTM) (None, 12, 128) 66560 input[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "Dropout1 (Dropout) (None, 12, 128) 0 LSTM1_1[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "LSTM2_1 (LSTM) (None, 128) 131584 Dropout1[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "Dropout2 (Dropout) (None, 128) 0 LSTM2_1[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "FC1 (Dense) (None, 128) 16512 Dropout2[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "output (Dense) (None, 1) 129 FC1[0][0] \n",
+ "====================================================================================================\n",
+ "Total params: 214785\n",
+ "____________________________________________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "ograph.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from keras.models import Sequential,Graph\n",
+ "from keras.layers.core import Dense, Dropout\n",
+ "from keras.callbacks import History\n",
+ "from keras.layers.recurrent import LSTM\n",
+ "from keras.models import model_from_json\n",
+ "from keras.regularizers import l2, activity_l2\n",
+ "\n",
+ "from keras import backend as K\n",
+ "\n",
+ "ograph2 = Graph()\n",
+ "ograph2.add_input(name='input', input_shape=(12,1,), dtype='float32')\n",
+ "ograph2.add_node(LSTM(output_dim=128, return_sequences=True), name='LSTM1_1', input='input')\n",
+ "ograph2.add_node(Dropout(0.5), name='Dropout1', input='LSTM1_1')\n",
+ "ograph2.add_node(LSTM(output_dim=128, return_sequences=True), name='LSTM2_1', input='Dropout1')\n",
+ "ograph2.add_node(Dropout(0.5), name='Dropout2', input='LSTM2_1')\n",
+ "ograph2.add_node(LSTM(output_dim=128, return_sequences=False), name='LSTM3_1', input='Dropout2')\n",
+ "ograph2.add_node(Dropout(0.5), name='Dropout3', input='LSTM3_1')\n",
+ "ograph2.add_node(Dense(output_dim=128, activation=\"relu\"), name='FC1', input='Dropout3')\n",
+ "ograph2.add_node(Dense(output_dim=1, activation=\"linear\"), name='FC2', input='FC1')\n",
+ "ograph2.add_output(name='output', input='FC2')\n",
+ "ograph2.compile(optimizer='rmsprop', loss={'output':'mean_absolute_error'})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#dummy code\n",
+ "ograph2.load_weights('MDN-weights.hdf5')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#y_pred = model.predict(X_val)['output']\n",
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "y_pred = ograph2.predict(data={'input':X_val})['output']\n",
+ "end_time = datetime.now()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "********************************* Prediction ends *********************************\n",
+ "\n",
+ "Duration: 0:04:44.474199\n",
+ "Elements below tolerance: 621976\n",
+ "Mean Absolute Error: 109.989235589\n",
+ "Mean Squared Error: 9053640.58208\n",
+ "Root Mean Squared Error: 3008.92681567\n",
+ "Maximum Total Error: [ 1459235.99927701](real: [-1459236.], predicted: [-0.00072299])\n",
+ "AE 10% 0.295548593324 (474594)\n",
+ "\n",
+ "********************************* End *********************************\n",
+ "\n",
+ "Duration: 0:00:00.615621\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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bjc0iY4jTYgg5hpABhpHDDIcMIccQMsAwcgwhAwwjxxAywDByDCEDDCPHEDLA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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "print \"********************************* Prediction ends *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))\n",
+ "\n",
+ "\n",
+ "start_time = datetime.now()\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "tolerance = 1e-1\n",
+ "idx = np.where(np.abs(y_pred-y_val)>tolerance)[0]\n",
+ "\n",
+ "print 'Elements below tolerance: ' + str(len(idx))\n",
+ "\n",
+ "measure = np.mean(np.abs((y_pred-y_val*1.)))\n",
+ "print 'Mean Absolute Error: ' + str(measure) \n",
+ "measure = np.mean(np.power((y_pred-y_val*1.),2))\n",
+ "print 'Mean Squared Error: ' + str(measure) \n",
+ "measure = np.sqrt(measure)\n",
+ "print 'Root Mean Squared Error: ' + str(measure) \n",
+ "i = np.argmax(np.abs((y_pred-y_val*1.)))\n",
+ "measure = np.abs((y_pred[i]-y_val[i]*1.))\n",
+ "print 'Maximum Total Error: ' + str(measure) + '(real: ' + str(y_val[i]) + ', predicted: ' + str(y_pred[i]) + ')'\n",
+ "\n",
+ "measure = np.abs((y_pred[idx]-y_val[idx]))/(np.abs(y_val[idx])+1e-16)\n",
+ "MAE10 = np.where(measure>0.1)[0]\n",
+ "print 'AE 10% ' + str(1.*len(MAE10)/len(y_val)) + ' (' + str(len(MAE10)) + ')'\n",
+ "\n",
+ "bins = np.arange(0,1.0,0.05)-0.025\n",
+ "measure = np.abs(y_pred[idx]-y_val[idx])/(np.abs(y_val[idx])+1e-16)\n",
+ "[dat, bins] = np.histogram(measure,bins=bins)\n",
+ "plt.bar(np.arange(len(dat)),dat,width =1)\n",
+ "plt.xticks(np.arange(len(dat)),bins[:-1])\n",
+ "plt.gcf().set_size_inches((10,6))\n",
+ "\n",
+ "plt.gcf().set_size_inches((12,6))\n",
+ " \n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* End *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "____________________________________________________________________________________________________\n",
+ "Layer (type) Output Shape Param # Connected to \n",
+ "====================================================================================================\n",
+ "input (InputLayer) (None, 12, 1) 0 \n",
+ "____________________________________________________________________________________________________\n",
+ "LSTM1_1 (LSTM) (None, 12, 128) 66560 input[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "Dropout1 (Dropout) (None, 12, 128) 0 LSTM1_1[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "LSTM2_1 (LSTM) (None, 12, 128) 131584 Dropout1[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "Dropout2 (Dropout) (None, 12, 128) 0 LSTM2_1[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "LSTM3_1 (LSTM) (None, 128) 131584 Dropout2[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "Dropout3 (Dropout) (None, 128) 0 LSTM3_1[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "FC1 (Dense) (None, 128) 16512 Dropout3[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "output (Dense) (None, 1) 129 FC1[0][0] \n",
+ "====================================================================================================\n",
+ "Total params: 346369\n",
+ "____________________________________________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "ograph2.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from keras.models import Sequential,Graph\n",
+ "from keras.layers.core import Dense, Dropout\n",
+ "from keras.callbacks import History\n",
+ "from keras.layers.recurrent import LSTM\n",
+ "from keras.models import model_from_json\n",
+ "from keras.regularizers import l2, activity_l2\n",
+ "\n",
+ "from keras import backend as K\n",
+ "\n",
+ "ograph3 = Graph()\n",
+ "ograph3.add_input(name='input', input_shape=(12,1,), dtype='float32')\n",
+ "ograph3.add_node(LSTM(output_dim=256, return_sequences=True), name='LSTM1_1', input='input')\n",
+ "ograph3.add_node(Dropout(0.5), name='Dropout1', input='LSTM1_1')\n",
+ "ograph3.add_node(LSTM(output_dim=256, return_sequences=True), name='LSTM2_1', input='Dropout1')\n",
+ "ograph3.add_node(Dropout(0.5), name='Dropout2', input='LSTM2_1')\n",
+ "ograph3.add_node(LSTM(output_dim=256, return_sequences=True), name='LSTM3_1', input='Dropout2')\n",
+ "ograph3.add_node(Dropout(0.5), name='Dropout3', input='LSTM3_1')\n",
+ "ograph3.add_node(LSTM(output_dim=256, return_sequences=False), name='LSTM4_1', input='Dropout3')\n",
+ "ograph3.add_node(Dropout(0.5), name='Dropout4', input='LSTM4_1')\n",
+ "ograph3.add_node(Dense(output_dim=256, activation=\"relu\"), name='FC1', input='Dropout4')\n",
+ "ograph3.add_node(Dense(output_dim=1, activation=\"linear\"), name='FC2', input='FC1')\n",
+ "ograph3.add_output(name='output', input='FC2')\n",
+ "ograph3.compile(optimizer='rmsprop', loss={'output':'mean_absolute_error'})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#dummy code\n",
+ "ograph3.load_weights('MDN-weights.hdf5')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "********************************* Prediction ends *********************************\n",
+ "\n",
+ "Duration: 0:06:14.859437\n"
+ ]
+ }
+ ],
+ "source": [
+ "#y_pred = model.predict(X_val)['output']\n",
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "y_pred = ograph3.predict(data={'input':X_val})['output']\n",
+ "end_time = datetime.now()\n",
+ "print \"********************************* Prediction ends *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Elements below tolerance: 619679\n",
+ "Mean Absolute Error: 109.934169164\n",
+ "Mean Squared Error: 9053751.38505\n",
+ "Root Mean Squared Error: 3008.94522799\n",
+ "Maximum Total Error: [ 1459236.00022386](real: [-1459236.], predicted: [ 0.00022386])\n",
+ "AE 10% 0.28995016213 (465604)\n",
+ "\n",
+ "********************************* End *********************************\n",
+ "\n",
+ "Duration: 0:00:00.646956\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "start_time = datetime.now()\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "tolerance = 1e-1\n",
+ "idx = np.where(np.abs(y_pred-y_val)>tolerance)[0]\n",
+ "\n",
+ "print 'Elements below tolerance: ' + str(len(idx))\n",
+ "\n",
+ "measure = np.mean(np.abs((y_pred-y_val*1.)))\n",
+ "print 'Mean Absolute Error: ' + str(measure) \n",
+ "measure = np.mean(np.power((y_pred-y_val*1.),2))\n",
+ "print 'Mean Squared Error: ' + str(measure) \n",
+ "measure = np.sqrt(measure)\n",
+ "print 'Root Mean Squared Error: ' + str(measure) \n",
+ "i = np.argmax(np.abs((y_pred-y_val*1.)))\n",
+ "measure = np.abs((y_pred[i]-y_val[i]*1.))\n",
+ "print 'Maximum Total Error: ' + str(measure) + '(real: ' + str(y_val[i]) + ', predicted: ' + str(y_pred[i]) + ')'\n",
+ "\n",
+ "measure = np.abs((y_pred[idx]-y_val[idx]))/(np.abs(y_val[idx])+1e-16)\n",
+ "MAE10 = np.where(measure>0.1)[0]\n",
+ "print 'AE 10% ' + str(1.*len(MAE10)/len(y_val)) + ' (' + str(len(MAE10)) + ')'\n",
+ "\n",
+ "bins = np.arange(0,1.0,0.05)-0.025\n",
+ "measure = np.abs(y_pred[idx]-y_val[idx])/(np.abs(y_val[idx])+1e-16)\n",
+ "[dat, bins] = np.histogram(measure,bins=bins)\n",
+ "plt.bar(np.arange(len(dat)),dat,width =1)\n",
+ "plt.xticks(np.arange(len(dat)),bins[:-1])\n",
+ "plt.gcf().set_size_inches((10,6))\n",
+ "\n",
+ "plt.gcf().set_size_inches((12,6))\n",
+ " \n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* End *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "____________________________________________________________________________________________________\n",
+ "Layer (type) Output Shape Param # Connected to \n",
+ "====================================================================================================\n",
+ "input (InputLayer) (None, 12, 1) 0 \n",
+ "____________________________________________________________________________________________________\n",
+ "LSTM1_1 (LSTM) (None, 12, 256) 264192 input[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "Dropout1 (Dropout) (None, 12, 256) 0 LSTM1_1[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "LSTM2_1 (LSTM) (None, 12, 256) 525312 Dropout1[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "Dropout2 (Dropout) (None, 12, 256) 0 LSTM2_1[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "LSTM3_1 (LSTM) (None, 12, 256) 525312 Dropout2[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "Dropout3 (Dropout) (None, 12, 256) 0 LSTM3_1[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "LSTM4_1 (LSTM) (None, 256) 525312 Dropout3[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "Dropout4 (Dropout) (None, 256) 0 LSTM4_1[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "FC1 (Dense) (None, 256) 65792 Dropout4[0][0] \n",
+ "____________________________________________________________________________________________________\n",
+ "output (Dense) (None, 1) 257 FC1[0][0] \n",
+ "====================================================================================================\n",
+ "Total params: 1906177\n",
+ "____________________________________________________________________________________________________\n"
+ ]
+ }
+ ],
+ "source": [
+ "ograph3.summary()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "********************************* Prediction ends *********************************\n",
+ "\n",
+ "Duration: 0:00:00.002236\n",
+ "Elements below tolerance: 570186\n",
+ "Mean Absolute Error: 160.146857001\n",
+ "Mean Squared Error: 9104905.15196\n",
+ "Root Mean Squared Error: 3017.43353729\n",
+ "Maximum Total Error: [ 1459236.](real: [-1459236.], predicted: [ 0.])\n",
+ "AE 10% 0.355077540452 (570186)\n",
+ "\n",
+ "********************************* End *********************************\n",
+ "\n",
+ "Duration: 0:00:00.592520\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "##y_predg2 = graphG2.predict(data={'input':X_val})['output']\n",
+ "c=1\n",
+ "m=1\n",
+ "y_pred = np.zeros_like(y_pred)\n",
+ "\n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* Prediction ends *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))\n",
+ "#y_pred = y_predg2\n",
+ "\n",
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "tolerance = 1e-1\n",
+ "idx = np.where(np.abs(y_pred-y_val)>tolerance)[0]\n",
+ "\n",
+ "print 'Elements below tolerance: ' + str(len(idx))\n",
+ "\n",
+ "measure = np.mean(np.abs((y_pred-y_val*1.)))\n",
+ "print 'Mean Absolute Error: ' + str(measure) \n",
+ "measure = np.mean(np.power((y_pred-y_val*1.),2))\n",
+ "print 'Mean Squared Error: ' + str(measure) \n",
+ "measure = np.sqrt(measure)\n",
+ "print 'Root Mean Squared Error: ' + str(measure) \n",
+ "i = np.argmax(np.abs((y_pred-y_val*1.)))\n",
+ "measure = np.abs((y_pred[i]-y_val[i]*1.))\n",
+ "print 'Maximum Total Error: ' + str(measure) + '(real: ' + str(y_val[i]) + ', predicted: ' + str(y_pred[i]) + ')'\n",
+ "\n",
+ "measure = np.abs((y_pred[idx]-y_val[idx]))/(np.abs(y_val[idx])+1e-16)\n",
+ "MAE10 = np.where(measure>0.1)[0]\n",
+ "print 'AE 10% ' + str(1.*len(MAE10)/len(y_val)) + ' (' + str(len(MAE10)) + ')'\n",
+ "\n",
+ "bins = np.arange(0,1.0,0.05)-0.025\n",
+ "measure = np.abs(y_pred[idx]-y_val[idx])/(np.abs(y_val[idx])+1e-16)\n",
+ "[dat, bins] = np.histogram(measure,bins=bins)\n",
+ "plt.bar(np.arange(len(dat)),dat,width =1)\n",
+ "plt.xticks(np.arange(len(dat)),bins[:-1])\n",
+ "plt.gcf().set_size_inches((10,6))\n",
+ "\n",
+ "plt.gcf().set_size_inches((12,6))\n",
+ " \n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* End *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from keras.models import Sequential,Graph\n",
+ "from keras.layers.core import Dense, Dropout\n",
+ "from keras.callbacks import History\n",
+ "from keras.layers.recurrent import LSTM\n",
+ "from keras.models import model_from_json\n",
+ "from keras.regularizers import l2, activity_l2\n",
+ "\n",
+ "from keras import backend as K\n",
+ "\n",
+ "import numpy as np\n",
+ "\n",
+ "c = 1 #The number of outputs we want to predict\n",
+ "m = 1 #The number of distributions we want to use in the mixture\n",
+ "\n",
+ "#Note: The output size will be (c + 2) * m\n",
+ "\n",
+ "def log_sum_exp(x, axis=None):\n",
+ " \"\"\"Log-sum-exp trick implementation\"\"\"\n",
+ " x_max = K.max(x, axis=axis, keepdims=True)\n",
+ " return K.log(K.sum(K.exp(x - x_max), \n",
+ " axis=axis, keepdims=True))+x_max\n",
+ "\n",
+ "\n",
+ "def mean_log_Gaussian_like(y_true, parameters):\n",
+ " \"\"\"Mean Log Gaussian Likelihood distribution\n",
+ " Note: The 'c' variable is obtained as global variable\n",
+ " \"\"\"\n",
+ " components = K.reshape(parameters,[-1, c + 2, m])\n",
+ " mu = components[:, :c, :]\n",
+ " sigma = components[:, c, :]\n",
+ " alpha = components[:, c + 1, :]\n",
+ " alpha = K.softmax(K.clip(alpha,1e-8,1.))\n",
+ " \n",
+ " exponent = K.log(alpha) - .5 * float(c) * K.log(2 * np.pi) \\\n",
+ " - float(c) * K.log(sigma) \\\n",
+ " - K.sum((K.expand_dims(y_true,2) - mu)**2, axis=1)/(2*(sigma)**2)\n",
+ " \n",
+ " log_gauss = log_sum_exp(exponent, axis=1)\n",
+ " res = - K.mean(log_gauss)\n",
+ " return res\n",
+ "\n",
+ "\n",
+ "def mean_log_LaPlace_like(y_true, parameters):\n",
+ " \"\"\"Mean Log Laplace Likelihood distribution\n",
+ " Note: The 'c' variable is obtained as global variable\n",
+ " \"\"\"\n",
+ " components = K.reshape(parameters,[-1, c + 2, m])\n",
+ " mu = components[:, :c, :]\n",
+ " sigma = components[:, c, :]\n",
+ " alpha = components[:, c + 1, :]\n",
+ " alpha = K.softmax(K.clip(alpha,1e-2,1.))\n",
+ " \n",
+ " exponent = K.log(alpha) - float(c) * K.log(2 * sigma) \\\n",
+ " - K.sum(K.abs(K.expand_dims(y_true,2) - mu), axis=1)/(sigma)\n",
+ " \n",
+ " log_gauss = log_sum_exp(exponent, axis=1)\n",
+ " res = - K.mean(log_gauss)\n",
+ " return res\n",
+ "\n",
+ "graphG = Graph()\n",
+ "graphG.add_input(name='input', input_shape=(12,1,), dtype='float32')\n",
+ "graphG.add_node(LSTM(output_dim=128, return_sequences=True), name='LSTM1_1', input='input')\n",
+ "graphG.add_node(Dropout(0.5), name='Dropout1', input='LSTM1_1')\n",
+ "graphG.add_node(LSTM(output_dim=128, return_sequences=True), name='LSTM2_1', input='Dropout1')\n",
+ "graphG.add_node(Dropout(0.5), name='Dropout2', input='LSTM2_1')\n",
+ "graphG.add_node(LSTM(output_dim=128, return_sequences=False), name='LSTM3_1', input='Dropout2')\n",
+ "graphG.add_node(Dropout(0.5), name='Dropout3', input='LSTM3_1')\n",
+ "graphG.add_node(Dense(output_dim=128, activation=\"relu\"), name='FC1', input='Dropout3')\n",
+ "graphG.add_node(Dense(output_dim=c*m), name='FC_mus', input='FC1')\n",
+ "graphG.add_node(Dense(output_dim=m, activation=K.exp, W_regularizer=l2(1e-3)), name='FC_sigmas', input='FC1')\n",
+ "graphG.add_node(Dense(output_dim=m, activation='softmax'), name='FC_alphas', input='FC1')\n",
+ "graphG.add_output(name='output', inputs=['FC_mus','FC_sigmas', 'FC_alphas'], merge_mode='concat',concat_axis=1)\n",
+ "graphG.compile(optimizer='rmsprop', loss={'output':mean_log_likeG})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#dummy code\n",
+ "graphG.load_weights('MDN-weights.hdf5')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "********************************* Prediction ends *********************************\n",
+ "\n",
+ "Duration: 0:04:51.455047\n"
+ ]
+ }
+ ],
+ "source": [
+ "#y_pred = model.predict(X_val)['output']\n",
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "y_pred = graphG.predict(data={'input':X_val})['output']\n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* Prediction ends *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "********************************* Prediction ends *********************************\n",
+ "\n",
+ "Duration: 0:00:02.429238\n",
+ "Elements below tolerance: 1604993\n",
+ "Mean Absolute Error: 174.063290169\n",
+ "Mean Squared Error: 9102559.05139\n",
+ "Root Mean Squared Error: 3017.04475462\n",
+ "Maximum Total Error: [ 1459199.92052078](real: [-1459236.], predicted: [-36.07947922])\n",
+ "AE 10% 0.983303099314 (1578995)\n",
+ "\n",
+ "********************************* End *********************************\n",
+ "\n",
+ "Duration: 0:00:00.570887\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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6NtO/IAtGF4ZJOlietiFJkiQ1WrY8Jzktyd1JvpzkwSRXDuMnJ7kzyVeS3JHkpLFtrk6y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\n/66+pQoNQ+zkAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "c=1\n",
+ "m=1\n",
+ "comp = np.reshape(y_pred,[-1, c + 2, m])\n",
+ "mu_pred = comp[:, :c, :]\n",
+ "sigma_pred = comp[:, c, :]\n",
+ "alpha_pred = comp[:, c + 1, :]\n",
+ "alpha_pred=alpha_pred.argmax(axis=1)\n",
+ "y_pred = np.array([mu_pred[i,:,alpha_pred[i]] for i in xrange(len(mu_pred))])\n",
+ "\n",
+ "\n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* Prediction ends *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))\n",
+ "\n",
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "tolerance = 1e-1\n",
+ "idx = np.where(np.abs(y_pred-y_val)>tolerance)[0]\n",
+ "\n",
+ "print 'Elements below tolerance: ' + str(len(idx))\n",
+ "\n",
+ "measure = np.mean(np.abs((y_pred-y_val*1.)))\n",
+ "print 'Mean Absolute Error: ' + str(measure) \n",
+ "measure = np.mean(np.power((y_pred-y_val*1.),2))\n",
+ "print 'Mean Squared Error: ' + str(measure) \n",
+ "measure = np.sqrt(measure)\n",
+ "print 'Root Mean Squared Error: ' + str(measure) \n",
+ "i = np.argmax(np.abs((y_pred-y_val*1.)))\n",
+ "measure = np.abs((y_pred[i]-y_val[i]*1.))\n",
+ "print 'Maximum Total Error: ' + str(measure) + '(real: ' + str(y_val[i]) + ', predicted: ' + str(y_pred[i]) + ')'\n",
+ "\n",
+ "measure = np.abs((y_pred[idx]-y_val[idx]))/(np.abs(y_val[idx])+1e-16)\n",
+ "MAE10 = np.where(measure>0.1)[0]\n",
+ "print 'AE 10% ' + str(1.*len(MAE10)/len(y_val)) + ' (' + str(len(MAE10)) + ')'\n",
+ "\n",
+ "bins = np.arange(0,1.0,0.05)-0.025\n",
+ "measure = np.abs(y_pred[idx]-y_val[idx])/(np.abs(y_val[idx])+1e-16)\n",
+ "[dat, bins] = np.histogram(measure,bins=bins)\n",
+ "plt.bar(np.arange(len(dat)),dat,width =1)\n",
+ "plt.xticks(np.arange(len(dat)),bins[:-1])\n",
+ "plt.gcf().set_size_inches((10,6))\n",
+ "\n",
+ "plt.gcf().set_size_inches((12,6))\n",
+ " \n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* End *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "from keras.models import Sequential,Graph\n",
+ "from keras.layers.core import Dense, Dropout\n",
+ "from keras.callbacks import History\n",
+ "from keras.layers.recurrent import LSTM\n",
+ "from keras.models import model_from_json\n",
+ "from keras.regularizers import l2, activity_l2\n",
+ "\n",
+ "from keras import backend as K\n",
+ "\n",
+ "import numpy as np\n",
+ "\n",
+ "c = 1 #The number of outputs we want to predict\n",
+ "m = 1 #The number of distributions we want to use in the mixture\n",
+ "\n",
+ "#Note: The output size will be (c + 2) * m\n",
+ "\n",
+ "def log_sum_exp(x, axis=None):\n",
+ " \"\"\"Log-sum-exp trick implementation\"\"\"\n",
+ " x_max = K.max(x, axis=axis, keepdims=True)\n",
+ " return K.log(K.sum(K.exp(x - x_max), \n",
+ " axis=axis, keepdims=True))+x_max\n",
+ "\n",
+ "\n",
+ "def mean_log_Gaussian_like(y_true, parameters):\n",
+ " \"\"\"Mean Log Gaussian Likelihood distribution\n",
+ " Note: The 'c' variable is obtained as global variable\n",
+ " \"\"\"\n",
+ " components = K.reshape(parameters,[-1, c + 2, m])\n",
+ " mu = components[:, :c, :]\n",
+ " sigma = components[:, c, :]\n",
+ " alpha = components[:, c + 1, :]\n",
+ " alpha = K.softmax(K.clip(alpha,1e-8,1.))\n",
+ " \n",
+ " exponent = K.log(alpha) - .5 * float(c) * K.log(2 * np.pi) \\\n",
+ " - float(c) * K.log(sigma) \\\n",
+ " - K.sum((K.expand_dims(y_true,2) - mu)**2, axis=1)/(2*(sigma)**2)\n",
+ " \n",
+ " log_gauss = log_sum_exp(exponent, axis=1)\n",
+ " res = - K.mean(log_gauss)\n",
+ " return res\n",
+ "\n",
+ "\n",
+ "def mean_log_LaPlace_like(y_true, parameters):\n",
+ " \"\"\"Mean Log Laplace Likelihood distribution\n",
+ " Note: The 'c' variable is obtained as global variable\n",
+ " \"\"\"\n",
+ " components = K.reshape(parameters,[-1, c + 2, m])\n",
+ " mu = components[:, :c, :]\n",
+ " sigma = components[:, c, :]\n",
+ " alpha = components[:, c + 1, :]\n",
+ " alpha = K.softmax(K.clip(alpha,1e-2,1.))\n",
+ " \n",
+ " exponent = K.log(alpha) - float(c) * K.log(2 * sigma) \\\n",
+ " - K.sum(K.abs(K.expand_dims(y_true,2) - mu), axis=1)/(sigma)\n",
+ " \n",
+ " log_gauss = log_sum_exp(exponent, axis=1)\n",
+ " res = - K.mean(log_gauss)\n",
+ " return res\n",
+ "\n",
+ "\n",
+ "graphLP = Graph()\n",
+ "graphLP.add_input(name='input', input_shape=(12,1,), dtype='float32')\n",
+ "graphLP.add_node(LSTM(output_dim=128, return_sequences=True), name='LSTM1_1', input='input')\n",
+ "graphLP.add_node(Dropout(0.5), name='Dropout1', input='LSTM1_1')\n",
+ "graphLP.add_node(LSTM(output_dim=128, return_sequences=True), name='LSTM2_1', input='Dropout1')\n",
+ "graphLP.add_node(Dropout(0.5), name='Dropout2', input='LSTM2_1')\n",
+ "graphLP.add_node(LSTM(output_dim=128, return_sequences=False), name='LSTM3_1', input='Dropout2')\n",
+ "graphLP.add_node(Dropout(0.5), name='Dropout3', input='LSTM3_1')\n",
+ "graphLP.add_node(Dense(output_dim=128, activation=\"relu\"), name='FC1', input='Dropout3')\n",
+ "graphLP.add_node(Dense(output_dim=c*m), name='FC_mus', input='FC1')\n",
+ "graphLP.add_node(Dense(output_dim=m, activation=K.exp, W_regularizer=l2(1e-3)), name='FC_sigmas', input='FC1')\n",
+ "graphLP.add_node(Dense(output_dim=m, activation='softmax'), name='FC_alphas', input='FC1')\n",
+ "graphLP.add_output(name='output', inputs=['FC_mus','FC_sigmas', 'FC_alphas'], merge_mode='concat',concat_axis=1)\n",
+ "graphLP.compile(optimizer='rmsprop', loss={'output':mean_log_LaPlace_like})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#dummy code\n",
+ "graphLP.load_weights('MDN-weights.hdf5')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "********************************* Prediction ends *********************************\n",
+ "\n",
+ "Duration: 0:04:50.729723\n"
+ ]
+ }
+ ],
+ "source": [
+ "#y_pred = model.predict(X_val)['output']\n",
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "y_pred = graphLP.predict(data={'input':X_val})['output']\n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* Prediction ends *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "********************************* Prediction ends *********************************\n",
+ "\n",
+ "Duration: 0:00:02.218120\n",
+ "Elements below tolerance: 661977\n",
+ "Mean Absolute Error: 114.283108227\n",
+ "Mean Squared Error: 9056219.58286\n",
+ "Root Mean Squared Error: 3009.3553434\n",
+ "Maximum Total Error: [ 1459236.50285828](real: [-1459236.], predicted: [ 0.50285828])\n",
+ "AE 10% 0.346161151371 (555868)\n",
+ "\n",
+ "********************************* End *********************************\n",
+ "\n",
+ "Duration: 0:00:00.397627\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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AwCDlGQAABinPAAAwSHkGAIBByjMAAAxSngEAYJDyDAAAg5RnAAAYpDwDAMAg5RkAAAYp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\ngg==\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "c=1\n",
+ "m=1\n",
+ "comp = np.reshape(y_pred,[-1, c + 2, m])\n",
+ "mu_pred = comp[:, :c, :]\n",
+ "sigma_pred = comp[:, c, :]\n",
+ "alpha_pred = comp[:, c + 1, :]\n",
+ "alpha_pred=alpha_pred.argmax(axis=1)\n",
+ "y_pred = np.array([mu_pred[i,:,alpha_pred[i]] for i in xrange(len(mu_pred))])\n",
+ "\n",
+ "\n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* Prediction ends *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))\n",
+ "\n",
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "tolerance = 1e-1\n",
+ "idx = np.where(np.abs(y_pred-y_val)>tolerance)[0]\n",
+ "\n",
+ "print 'Elements below tolerance: ' + str(len(idx))\n",
+ "\n",
+ "measure = np.mean(np.abs((y_pred-y_val*1.)))\n",
+ "print 'Mean Absolute Error: ' + str(measure) \n",
+ "measure = np.mean(np.power((y_pred-y_val*1.),2))\n",
+ "print 'Mean Squared Error: ' + str(measure) \n",
+ "measure = np.sqrt(measure)\n",
+ "print 'Root Mean Squared Error: ' + str(measure) \n",
+ "i = np.argmax(np.abs((y_pred-y_val*1.)))\n",
+ "measure = np.abs((y_pred[i]-y_val[i]*1.))\n",
+ "print 'Maximum Total Error: ' + str(measure) + '(real: ' + str(y_val[i]) + ', predicted: ' + str(y_pred[i]) + ')'\n",
+ "\n",
+ "measure = np.abs((y_pred[idx]-y_val[idx]))/(np.abs(y_val[idx])+1e-16)\n",
+ "MAE10 = np.where(measure>0.1)[0]\n",
+ "print 'AE 10% ' + str(1.*len(MAE10)/len(y_val)) + ' (' + str(len(MAE10)) + ')'\n",
+ "\n",
+ "bins = np.arange(0,1.0,0.05)-0.025\n",
+ "measure = np.abs(y_pred[idx]-y_val[idx])/(np.abs(y_val[idx])+1e-16)\n",
+ "[dat, bins] = np.histogram(measure,bins=bins)\n",
+ "plt.bar(np.arange(len(dat)),dat,width =1)\n",
+ "plt.xticks(np.arange(len(dat)),bins[:-1])\n",
+ "plt.gcf().set_size_inches((10,6))\n",
+ "\n",
+ "plt.gcf().set_size_inches((12,6))\n",
+ " \n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* End *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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xpdfWIxfUAIBUmOf0JxpYID11XBeCZQUW1LAPUPRomgZZlgfa9uGHH8ZVV12F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AhLl15JFWl8MDSLk7qdgR4dj8UHG5/BH9XfY0LfS9HOUhC0XiQT70zJWsoEnluxNIKR9924cdY1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8mtPgiJ1CMxoxI7HwDIfDuN3uZBRNtnbDTZwm2ncmG6lpXAkBSkwAi8NoIlcS37+hytoXktFG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iIiIvrAbdy4EcePH0dsbCwGDBgACwuL0iuGR3MTEWnFpU9ERERERB84W1tbGBkZoXv37ggL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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "from mpl_toolkits.mplot3d import Axes3D\n",
+ "import numpy as np\n",
+ "%matplotlib inline\n",
+ "\n",
+ "import matplotlib.cm as cm\n",
+ "\n",
+ "fig = plt.figure()\n",
+ "ax = fig.add_subplot(111, projection='3d')\n",
+ "\n",
+ "rang = 10\n",
+ "start = np.random.randint(0,y_val.shape[0]-rang)\n",
+ "v_rang = 1000\n",
+ "\n",
+ "c=1\n",
+ "m=1\n",
+ "\n",
+ "parameters = graphLP.predict(data={'input':X_val[start:start+rang]})['output']\n",
+ "\n",
+ "comp = np.reshape(parameters,[-1, c + 2, m])\n",
+ "mu_pred = comp[:, :c, :]\n",
+ "sigma_pred = comp[:, c, :]\n",
+ "alpha_pred = comp[:, c + 1, :]\n",
+ "y_pred = np.zeros((len(mu_pred)))\n",
+ "\n",
+ "thr_alpha = 0.5\n",
+ "\n",
+ "col = cm.autumn(np.linspace(0, 1, mu_pred.shape[-1]))\n",
+ "\n",
+ "cont = []\n",
+ "cont_const = []\n",
+ "const = 100\n",
+ "guany_sig = []\n",
+ "erSigConst = np.zeros((2,rang))\n",
+ "\n",
+ "\n",
+ "color = cm.gist_earth(np.linspace(0, 1, rang+2))\n",
+ "\n",
+ "for elem in xrange(rang):\n",
+ " ax.plot(xs=np.arange(12),ys=[elem]*12,zs=X_val_orig[start+elem,:,0].reshape(-1), c=color[elem+1], marker='o')\n",
+ "\n",
+ "zerror = 500\n",
+ "\n",
+ " \n",
+ "for mx in xrange(mu_pred.shape[-1]):\n",
+ " for i in xrange(len(mu_pred)):\n",
+ " if alpha_pred[i,mx] > thr_alpha:\n",
+ " ax.plot([12, 12], [i, i], [mu_pred[i,0,mx]-np.sqrt(2)*sigma_pred[i,mx],\n",
+ " mu_pred[i,0,mx]+np.sqrt(2)*sigma_pred[i,mx]], \n",
+ " marker=\"_\", c=col[mx], alpha=alpha_pred[i,mx])\n",
+ " y_pred[i] = mu_pred[i,0,mx]\n",
+ " #In order to avoid ERROR of 0.1 when approx 0, we add a margin of 0.1€\n",
+ " if mu_pred[i,0,mx]+np.sqrt(2)*sigma_pred[i,mx]+0.1y_val[start+i]:\n",
+ " cont += [i]\n",
+ " if mu_pred[i,0,mx]+consty_val[start+i]:\n",
+ " guany_sig += [const-np.sqrt(2)*sigma_pred[i,mx]-0.1]\n",
+ " cont_const += [i]\n",
+ " else:\n",
+ " guany_sig += [np.sqrt(2)*sigma_pred[i,mx]+0.1-const]\n",
+ " elif mu_pred[i,0,mx]+consty_val[start+i]:\n",
+ " cont_const += [i]\n",
+ " guany_sig += [const-np.sqrt(2)*sigma_pred[i,mx]-0.1]\n",
+ " \n",
+ " erSigConst[0,i] = np.sqrt(2)*sigma_pred[i,mx]+0.1 - np.abs(mu_pred[i,0,mx]-y_val[start+i])\n",
+ " erSigConst[1,i] = const - np.abs(mu_pred[i,0,mx]-y_val[start+i])\n",
+ " \n",
+ " tmp = alpha_pred[:,mx]>thr_alpha\n",
+ " if np.sum(tmp) > 0:\n",
+ " ax.plot([12]*rang,np.arange(rang)[tmp],\n",
+ " y_pred[tmp], color=col[mx],\n",
+ " linewidth=1, marker='o', linestyle=' ',\n",
+ " alpha=0.5, label='mixt_'+str(mx))\n",
+ " else:\n",
+ " print \"Distribution\",mx,\" has always alpha below\",thr_alpha\n",
+ " \n",
+ "for point in xrange(rang):\n",
+ " if point in cont:\n",
+ " ax.plot(xs=12, ys=point,zs=y_val[start+point], \n",
+ " color='green', linewidth=1, marker='p', \n",
+ " linestyle=' ',alpha=1)\n",
+ " else:\n",
+ " ax.plot(xs=12, ys=point,zs=y_val[start+point], \n",
+ " color='blue', linewidth=1, marker='o', \n",
+ " linestyle=' ',alpha=0.5)\n",
+ " ax.plot(xs=[12, 12], ys=[point, point],zs=[y_val[start+point],y_pred[point]],\n",
+ " marker=\"_\", alpha = 0.4, color = 'purple')\n",
+ "\n",
+ "\n",
+ "ax.xaxis.set_ticks(range(13))\n",
+ "ax.xaxis.set_ticklabels(['Jan','Feb','Mar','Apr',\n",
+ " 'May','June','July','Aug',\n",
+ " 'Sept','Oct','Nov','Des','Jan*'])\n",
+ "ax.xaxis.set_tick_params(labelsize='xx-large')\n",
+ "ax.yaxis.set_ticks(range(rang))\n",
+ "\n",
+ "\n",
+ "ax.set_xlim3d(0, 13)\n",
+ "ax.set_ylim3d(-1, rang+1)\n",
+ "ax.set_zlim3d(-800, 800)\n",
+ "ax.view_init(15, -80)\n",
+ "\n",
+ "plt.gcf().set_size_inches((20,10))\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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uGcSK85yFYQjXdXdea8xjdcqM6va5FEIgDAKsLC1CVuvZVEEQAHglaJdkZtQg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\nIrbPzuG7Wx5kWdsSVpWsYEnRYmymiUs70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vxte2jf8TdM9oy4cGPLKk2o7Da4OluTKPKV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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "from mpl_toolkits.mplot3d import Axes3D\n",
+ "import numpy as np\n",
+ "%matplotlib inline\n",
+ "\n",
+ "import matplotlib.cm as cm\n",
+ "\n",
+ "fig = plt.figure()\n",
+ "ax = fig.add_subplot(111, projection='3d')\n",
+ "\n",
+ "rang = 10\n",
+ "start = np.random.randint(0,y_val.shape[0]-rang)\n",
+ "v_rang = 1000\n",
+ "\n",
+ "c=1\n",
+ "m=1\n",
+ "\n",
+ "parameters = graphLP.predict(data={'input':X_val[start:start+rang]})['output']\n",
+ "\n",
+ "comp = np.reshape(parameters,[-1, c + 2, m])\n",
+ "mu_pred = comp[:, :c, :]\n",
+ "sigma_pred = comp[:, c, :]\n",
+ "alpha_pred = comp[:, c + 1, :]\n",
+ "y_pred = np.zeros((len(mu_pred)))\n",
+ "\n",
+ "thr_alpha = 0.5\n",
+ "\n",
+ "col = cm.autumn(np.linspace(0, 1, mu_pred.shape[-1]))\n",
+ "\n",
+ "cont = []\n",
+ "cont_const = []\n",
+ "const = 100\n",
+ "guany_sig = []\n",
+ "erSigConst = np.zeros((2,rang))\n",
+ "\n",
+ "\n",
+ "color = cm.gist_earth(np.linspace(0, 1, rang+2))\n",
+ "\n",
+ "for elem in xrange(rang):\n",
+ " ax.plot(xs=np.arange(12),ys=[elem]*12,zs=X_val_orig[start+elem,:,0].reshape(-1), c=color[elem+1], marker='o')\n",
+ "\n",
+ "zerror = 500\n",
+ " \n",
+ "for mx in xrange(mu_pred.shape[-1]):\n",
+ " for i in xrange(len(mu_pred)):\n",
+ " if alpha_pred[i,mx] > thr_alpha:\n",
+ " ax.plot([12, 12], [i, i], [mu_pred[i,0,mx]-np.sqrt(2)*sigma_pred[i,mx],\n",
+ " mu_pred[i,0,mx]+np.sqrt(2)*sigma_pred[i,mx]], \n",
+ " marker=\"_\", c=col[mx], alpha=alpha_pred[i,mx])\n",
+ " y_pred[i] = mu_pred[i,0,mx]\n",
+ " #In order to avoid ERROR of 0.1 when approx 0, we add a margin of 0.1€\n",
+ " if mu_pred[i,0,mx]+np.sqrt(2)*sigma_pred[i,mx]+0.1y_val[start+i]:\n",
+ " cont += [i]\n",
+ " if mu_pred[i,0,mx]+consty_val[start+i]:\n",
+ " guany_sig += [const-np.sqrt(2)*sigma_pred[i,mx]-0.1]\n",
+ " cont_const += [i]\n",
+ " else:\n",
+ " guany_sig += [np.sqrt(2)*sigma_pred[i,mx]+0.1-const]\n",
+ " elif mu_pred[i,0,mx]+consty_val[start+i]:\n",
+ " cont_const += [i]\n",
+ " guany_sig += [const-np.sqrt(2)*sigma_pred[i,mx]-0.1]\n",
+ " \n",
+ " erSigConst[0,i] = np.sqrt(2)*sigma_pred[i,mx]+0.1 - np.abs(mu_pred[i,0,mx]-y_val[start+i])\n",
+ " erSigConst[1,i] = const - np.abs(mu_pred[i,0,mx]-y_val[start+i])\n",
+ " \n",
+ " tmp = alpha_pred[:,mx]>thr_alpha\n",
+ " if np.sum(tmp) > 0:\n",
+ " ax.plot([12]*rang,np.arange(rang)[tmp],\n",
+ " y_pred[tmp], color=col[mx],\n",
+ " linewidth=1, marker='o', linestyle=' ',\n",
+ " alpha=0.5, label='mixt_'+str(mx))\n",
+ " else:\n",
+ " print \"Distribution\",mx,\" has always alpha below\",thr_alpha\n",
+ " \n",
+ "for point in xrange(rang):\n",
+ " if point in cont:\n",
+ " ax.plot(xs=12, ys=point,zs=y_val[start+point], \n",
+ " color='green', linewidth=1, marker='p', \n",
+ " linestyle=' ',alpha=1)\n",
+ " else:\n",
+ " ax.plot(xs=12, ys=point,zs=y_val[start+point], \n",
+ " color='blue', linewidth=1, marker='o', \n",
+ " linestyle=' ',alpha=0.5)\n",
+ " ax.plot(xs=[12, 12], ys=[point, point],zs=[y_val[start+point],y_pred[point]],\n",
+ " marker=\"_\", alpha = 0.4, color = 'purple')\n",
+ "\n",
+ "ax.xaxis.set_ticks(range(13))\n",
+ "ax.xaxis.set_ticklabels(['Jan','Feb','Mar','Apr',\n",
+ " 'May','June','July','Aug',\n",
+ " 'Sept','Oct','Nov','Des','Jan*'])\n",
+ "ax.xaxis.set_tick_params(labelsize='xx-large')\n",
+ "ax.yaxis.set_ticks(range(rang))\n",
+ "\n",
+ "ax.set_xlim3d(0, 13)\n",
+ "ax.set_ylim3d(-1, rang+1)\n",
+ "ax.set_zlim3d(-800, 800)\n",
+ "ax.view_init(15, -80)\n",
+ "\n",
+ "plt.gcf().set_size_inches((20,10))\n",
+ "plt.legend()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Absolute error 9366.40744946 €\n",
+ "Absolut error from zero 18621.0 €\n",
+ "% Outside prediction noise: 20 0.9\n",
+ "% Outside const (100) noise: 20 0.9\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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uncp1x6/Ldddd1+/SAAAAukZoBHAA7/23702S3Peu+3Lrm27tczUAAADd5/I0AAAAAFoY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\n0DnHgHOM1AuGRpJ6w3BIkkZeoTDB/PwsabpEjccoTieUSrO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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "%matplotlib inline\n",
+ "import numpy as np\n",
+ "import matplotlib.pylab as pyplot\n",
+ "from matplotlib import lines\n",
+ "\n",
+ "import matplotlib.cm as cm\n",
+ "v_rang = 100\n",
+ "rang = 200\n",
+ "\n",
+ "c=1\n",
+ "m=1\n",
+ "\n",
+ "parameters = graphLP.predict(data={'input':X_val[start:start+rang]})['output']\n",
+ "\n",
+ "comp = np.reshape(parameters,[-1, c + 2, m])\n",
+ "mu_pred = comp[:, :c, :]\n",
+ "sigma_pred = comp[:, c, :]\n",
+ "alpha_pred = comp[:, c + 1, :]\n",
+ "y_pred = np.zeros((len(mu_pred)))\n",
+ "\n",
+ "thr_alpha = 0.5\n",
+ "\n",
+ "col = cm.autumn(np.linspace(0, 1, mu_pred.shape[-1]))\n",
+ "\n",
+ "cont = []\n",
+ "cont_const = []\n",
+ "const = 100\n",
+ "guany_sig = []\n",
+ "erSigConst = np.zeros((2,rang))\n",
+ "\n",
+ "for mx in xrange(mu_pred.shape[-1]):\n",
+ " for i in xrange(len(mu_pred)):\n",
+ " if alpha_pred[i,mx] > thr_alpha:\n",
+ " pyplot.errorbar(i,mu_pred[i,0,mx],\n",
+ " yerr=np.sqrt(2)*sigma_pred[i,mx],\n",
+ " alpha=alpha_pred[i,mx], \n",
+ " color=col[mx])\n",
+ " y_pred[i] = mu_pred[i,0,mx]\n",
+ " #In order to avoid ERROR of 0.1 when approx 0, we add a margin of 0.1€\n",
+ " if mu_pred[i,0,mx]+np.sqrt(2)*sigma_pred[i,mx]+0.1y_val[start+i]:\n",
+ " cont += [i]\n",
+ " if mu_pred[i,0,mx]+consty_val[start+i]:\n",
+ " guany_sig += [const-np.sqrt(2)*sigma_pred[i,mx]-0.1]\n",
+ " cont_const += [i]\n",
+ " else:\n",
+ " guany_sig += [np.sqrt(2)*sigma_pred[i,mx]+0.1-const]\n",
+ " elif mu_pred[i,0,mx]+consty_val[start+i]:\n",
+ " cont_const += [i]\n",
+ " guany_sig += [const-np.sqrt(2)*sigma_pred[i,mx]-0.1]\n",
+ " \n",
+ " erSigConst[0,i] = np.sqrt(2)*sigma_pred[i,mx]+0.1 - np.abs(mu_pred[i,0,mx]-y_val[start+i])\n",
+ " erSigConst[1,i] = const - np.abs(mu_pred[i,0,mx]-y_val[start+i])\n",
+ " \n",
+ " tmp = alpha_pred[:,mx]>thr_alpha\n",
+ " if np.sum(tmp) > 0:\n",
+ " pyplot.plot(np.arange(len(mu_pred))[tmp],\n",
+ " y_pred[tmp], color=col[mx],\n",
+ " linewidth=1, marker='o', linestyle=' ',\n",
+ " alpha=0.5, label='mixt_'+str(mx))\n",
+ " else:\n",
+ " print \"Distribution\",mx,\" has always alpha below\",thr_alpha\n",
+ "\n",
+ "for point in xrange(rang):\n",
+ " if point in cont:\n",
+ " pyplot.plot(point,y_val[start+point], \n",
+ " color='green', linewidth=1, marker='p', \n",
+ " linestyle=' ',alpha=1)\n",
+ " else:\n",
+ " pyplot.plot(point,y_val[start+point], \n",
+ " color='blue', linewidth=1, marker='o', \n",
+ " linestyle=' ',alpha=0.5)\n",
+ "\n",
+ "axes = pyplot.gca()\n",
+ "origins = zip(np.arange(rang)*1.,y_val[start:start+rang])\n",
+ "endings = zip(np.arange(rang)*1.,y_pred)\n",
+ "lines_vals = [[origins[i],endings[i]] for i in xrange(len(origins))]\n",
+ "\n",
+ "from matplotlib import collections as mc\n",
+ "lc = mc.LineCollection(lines_vals, linewidths=1, alpha = 0.4, color = 'purple')\n",
+ "axes.add_collection(lc)\n",
+ "axes.set_ylim(-v_rang,v_rang)\n",
+ "axes.set_xlim(-1,rang+1)\n",
+ "pyplot.gcf().set_size_inches((20,10))\n",
+ "pyplot.legend()\n",
+ "print 'Absolute error', np.abs(y_val[start:start+rang].squeeze() - y_pred).sum(), '€'\n",
+ "print 'Absolut error from zero', np.abs(y_val[start:start+rang]).sum(), '€'\n",
+ "print '% Outside prediction noise: '+str(len(cont))+\" \"+str(1-len(cont)/float(rang))\n",
+ "print '% Outside const ('+str(const)+') noise: '+str(len(cont_const))+\" \"+str(1-len(cont_const)/float(rang))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [
+ {
+ "ename": "KeyboardInterrupt",
+ "evalue": "",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 64\u001b[0m \u001b[0mgraphLP\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_node\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mLSTM\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput_dim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m128\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_sequences\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'LSTM2_1'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Dropout1'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[0mgraphLP\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_node\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDropout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Dropout2'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'LSTM2_1'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 66\u001b[1;33m \u001b[0mgraphLP\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_node\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mLSTM\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput_dim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m128\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreturn_sequences\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'LSTM3_1'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Dropout2'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 67\u001b[0m \u001b[0mgraphLP\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_node\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDropout\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Dropout3'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'LSTM3_1'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 68\u001b[0m \u001b[0mgraphLP\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_node\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mDense\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutput_dim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m128\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"relu\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'FC1'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Dropout3'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m/usr/local/anaconda/lib/python2.7/site-packages/keras/legacy/models.pyc\u001b[0m in \u001b[0;36madd_node\u001b[1;34m(self, layer, name, input, inputs, merge_mode, concat_axis, dot_axes, create_output)\u001b[0m\n\u001b[0;32m 167\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Unknown node/input identifier: '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 168\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0minput\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph_nodes\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 169\u001b[1;33m \u001b[0mlayer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_inbound_node\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph_nodes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 170\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0minput\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph_inputs\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 171\u001b[0m \u001b[0mlayer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_inbound_node\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph_inputs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m/usr/local/anaconda/lib/python2.7/site-packages/keras/engine/topology.pyc\u001b[0m in \u001b[0;36madd_inbound_node\u001b[1;34m(self, inbound_layers, node_indices, tensor_indices)\u001b[0m\n\u001b[0;32m 564\u001b[0m \u001b[1;31m# call build()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 565\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_shapes\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 566\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minput_shapes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 567\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 568\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minput_shapes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m/usr/local/anaconda/lib/python2.7/site-packages/keras/layers/recurrent.pyc\u001b[0m in \u001b[0;36mbuild\u001b[1;34m(self, input_shape)\u001b[0m\n\u001b[0;32m 695\u001b[0m self.U_c = self.inner_init((self.output_dim, self.output_dim),\n\u001b[0;32m 696\u001b[0m name='{}_U_c'.format(self.name))\n\u001b[1;32m--> 697\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mb_c\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mK\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moutput_dim\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'{}_b_c'\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 698\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 699\u001b[0m self.W_o = self.init((self.input_dim, self.output_dim),\n",
+ "\u001b[1;32m/usr/local/anaconda/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36mzeros\u001b[1;34m(shape, dtype, name)\u001b[0m\n\u001b[0;32m 240\u001b[0m \u001b[0mshape\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 241\u001b[0m \u001b[0mtf_dtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_convert_string_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 242\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mvariable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconstant_initializer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtf_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 243\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 244\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m/usr/local/anaconda/lib/python2.7/site-packages/keras/backend/tensorflow_backend.pyc\u001b[0m in \u001b[0;36mvariable\u001b[1;34m(value, dtype, name)\u001b[0m\n\u001b[0;32m 152\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mget_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 153\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 154\u001b[1;33m \u001b[0mget_session\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minitializer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 155\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 156\u001b[0m warnings.warn('Could not automatically initialize variable, '\n",
+ "\u001b[1;32m/usr/local/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 715\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 716\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 717\u001b[1;33m run_metadata_ptr)\n\u001b[0m\u001b[0;32m 718\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 719\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m/usr/local/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 913\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 914\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m--> 915\u001b[1;33m feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[0;32m 916\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 917\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m/usr/local/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 963\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 964\u001b[0m return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[1;32m--> 965\u001b[1;33m target_list, options, run_metadata)\n\u001b[0m\u001b[0;32m 966\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 967\u001b[0m return self._do_call(_prun_fn, self._session, handle, feed_dict,\n",
+ "\u001b[1;32m/usr/local/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m 970\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 971\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 972\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 973\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 974\u001b[0m \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;32m/usr/local/anaconda/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m 952\u001b[0m return tf_session.TF_Run(session, options,\n\u001b[0;32m 953\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 954\u001b[1;33m status, run_metadata)\n\u001b[0m\u001b[0;32m 955\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 956\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msession\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+ ]
+ }
+ ],
+ "source": [
+ "import tensorflow as tf\n",
+ "tf.python.control_flow_ops = tf\n",
+ "\n",
+ "config = tf.ConfigProto()\n",
+ "config.gpu_options.allow_growth=True\n",
+ "sess = tf.Session(config=config)\n",
+ "\n",
+ "from keras.models import Sequential,Graph\n",
+ "from keras.layers.core import Dense, Dropout\n",
+ "from keras.callbacks import History\n",
+ "from keras.layers.recurrent import LSTM\n",
+ "from keras.models import model_from_json\n",
+ "from keras.regularizers import l2, activity_l2\n",
+ "\n",
+ "from keras import backend as K\n",
+ "\n",
+ "import numpy as np\n",
+ "\n",
+ "c = 1 #The number of outputs we want to predict\n",
+ "m = 3 #The number of distributions we want to use in the mixture\n",
+ "\n",
+ "#Note: The output size will be (c + 2) * m\n",
+ "\n",
+ "def log_sum_exp(x, axis=None):\n",
+ " \"\"\"Log-sum-exp trick implementation\"\"\"\n",
+ " x_max = K.max(x, axis=axis, keepdims=True)\n",
+ " return K.log(K.sum(K.exp(x - x_max), \n",
+ " axis=axis, keepdims=True))+x_max\n",
+ "\n",
+ "\n",
+ "def mean_log_Gaussian_like(y_true, parameters):\n",
+ " \"\"\"Mean Log Gaussian Likelihood distribution\n",
+ " Note: The 'c' variable is obtained as global variable\n",
+ " \"\"\"\n",
+ " components = K.reshape(parameters,[-1, c + 2, m])\n",
+ " mu = components[:, :c, :]\n",
+ " sigma = components[:, c, :]\n",
+ " alpha = components[:, c + 1, :]\n",
+ " alpha = K.softmax(K.clip(alpha,1e-8,1.))\n",
+ " \n",
+ " exponent = K.log(alpha) - .5 * float(c) * K.log(2 * np.pi) \\\n",
+ " - float(c) * K.log(sigma) \\\n",
+ " - K.sum((K.expand_dims(y_true,2) - mu)**2, axis=1)/(2*(sigma)**2)\n",
+ " \n",
+ " log_gauss = log_sum_exp(exponent, axis=1)\n",
+ " res = - K.mean(log_gauss)\n",
+ " return res\n",
+ "\n",
+ "\n",
+ "def mean_log_LaPlace_like(y_true, parameters):\n",
+ " \"\"\"Mean Log Laplace Likelihood distribution\n",
+ " Note: The 'c' variable is obtained as global variable\n",
+ " \"\"\"\n",
+ " components = K.reshape(parameters,[-1, c + 2, m])\n",
+ " mu = components[:, :c, :]\n",
+ " sigma = components[:, c, :]\n",
+ " alpha = components[:, c + 1, :]\n",
+ " alpha = K.softmax(K.clip(alpha,1e-2,1.))\n",
+ " \n",
+ " exponent = K.log(alpha) - float(c) * K.log(2 * sigma) \\\n",
+ " - K.sum(K.abs(K.expand_dims(y_true,2) - mu), axis=1)/(sigma)\n",
+ " \n",
+ " log_gauss = log_sum_exp(exponent, axis=1)\n",
+ " res = - K.mean(log_gauss)\n",
+ " return res\n",
+ "\n",
+ "\n",
+ "graphLP = Graph()\n",
+ "graphLP.add_input(name='input', input_shape=(12,1,), dtype='float32')\n",
+ "graphLP.add_node(LSTM(output_dim=128, return_sequences=True), name='LSTM1_1', input='input')\n",
+ "graphLP.add_node(Dropout(0.5), name='Dropout1', input='LSTM1_1')\n",
+ "graphLP.add_node(LSTM(output_dim=128, return_sequences=True), name='LSTM2_1', input='Dropout1')\n",
+ "graphLP.add_node(Dropout(0.5), name='Dropout2', input='LSTM2_1')\n",
+ "graphLP.add_node(LSTM(output_dim=128, return_sequences=False), name='LSTM3_1', input='Dropout2')\n",
+ "graphLP.add_node(Dropout(0.5), name='Dropout3', input='LSTM3_1')\n",
+ "graphLP.add_node(Dense(output_dim=128, activation=\"relu\"), name='FC1', input='Dropout3')\n",
+ "graphLP.add_node(Dense(output_dim=c*m), name='FC_mus', input='FC1')\n",
+ "graphLP.add_node(Dense(output_dim=m, activation=K.exp, W_regularizer=l2(1e-3)), name='FC_sigmas', input='FC1')\n",
+ "graphLP.add_node(Dense(output_dim=m, activation='softmax'), name='FC_alphas', input='FC1')\n",
+ "graphLP.add_output(name='output', inputs=['FC_mus','FC_sigmas', 'FC_alphas'], merge_mode='concat',concat_axis=1)\n",
+ "graphLP.compile(optimizer='rmsprop', loss={'output':mean_log_LaPlace_like})"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "#dummy code\n",
+ "graphLP.load_weights('MDN-weights.hdf5')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "********************************* Prediction ends *********************************\n",
+ "\n",
+ "Duration: 0:04:49.670360\n"
+ ]
+ }
+ ],
+ "source": [
+ "#y_pred = model.predict(X_val)['output']\n",
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "y_pred = graphLP.predict(data={'input':X_val})['output']\n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* Prediction ends *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "collapsed": false
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "********************************* Prediction ends *********************************\n",
+ "\n",
+ "Duration: 0:00:02.185090\n",
+ "Elements below tolerance: 654573\n",
+ "Mean Absolute Error: 134.436800312\n",
+ "Mean Squared Error: 9078183.87086\n",
+ "Root Mean Squared Error: 3013.00246778\n",
+ "Maximum Total Error: [ 1459203.33190918](real: [-1459236.], predicted: [-32.66809082])\n",
+ "AE 10% 0.384212424034 (616971)\n",
+ "\n",
+ "********************************* End *********************************\n",
+ "\n",
+ "Duration: 0:00:00.385110\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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zSVVNOgNJTgW+ClwKfA34EvDTVbVnosEkSZKknkEcea6q55K8F7iX0akkN1uc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\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "c=1\n",
+ "m=3\n",
+ "comp = np.reshape(y_pred,[-1, c + 2, m])\n",
+ "mu_pred = comp[:, :c, :]\n",
+ "sigma_pred = comp[:, c, :]\n",
+ "alpha_pred = comp[:, c + 1, :]\n",
+ "alpha_pred=alpha_pred.argmax(axis=1)\n",
+ "y_pred = np.array([mu_pred[i,:,alpha_pred[i]] for i in xrange(len(mu_pred))])\n",
+ "\n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* Prediction ends *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))\n",
+ "\n",
+ "\n",
+ "from datetime import datetime\n",
+ "start_time = datetime.now()\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib inline\n",
+ "tolerance = 1e-1\n",
+ "idx = np.where(np.abs(y_pred-y_val)>tolerance)[0]\n",
+ "\n",
+ "print 'Elements below tolerance: ' + str(len(idx))\n",
+ "\n",
+ "measure = np.mean(np.abs((y_pred-y_val*1.)))\n",
+ "print 'Mean Absolute Error: ' + str(measure) \n",
+ "measure = np.mean(np.power((y_pred-y_val*1.),2))\n",
+ "print 'Mean Squared Error: ' + str(measure) \n",
+ "measure = np.sqrt(measure)\n",
+ "print 'Root Mean Squared Error: ' + str(measure) \n",
+ "i = np.argmax(np.abs((y_pred-y_val*1.)))\n",
+ "measure = np.abs((y_pred[i]-y_val[i]*1.))\n",
+ "print 'Maximum Total Error: ' + str(measure) + '(real: ' + str(y_val[i]) + ', predicted: ' + str(y_pred[i]) + ')'\n",
+ "\n",
+ "measure = np.abs((y_pred[idx]-y_val[idx]))/(np.abs(y_val[idx])+1e-16)\n",
+ "MAE10 = np.where(measure>0.1)[0]\n",
+ "print 'AE 10% ' + str(1.*len(MAE10)/len(y_val)) + ' (' + str(len(MAE10)) + ')'\n",
+ "\n",
+ "bins = np.arange(0,1.0,0.05)-0.025\n",
+ "measure = np.abs(y_pred[idx]-y_val[idx])/(np.abs(y_val[idx])+1e-16)\n",
+ "[dat, bins] = np.histogram(measure,bins=bins)\n",
+ "plt.bar(np.arange(len(dat)),dat,width =1)\n",
+ "plt.xticks(np.arange(len(dat)),bins[:-1])\n",
+ "plt.gcf().set_size_inches((10,6))\n",
+ "\n",
+ "plt.gcf().set_size_inches((12,6))\n",
+ " \n",
+ "end_time = datetime.now()\n",
+ "print \n",
+ "print \"********************************* End *********************************\"\n",
+ "print\n",
+ "print('Duration: {}'.format(end_time - start_time))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 2",
+ "language": "python",
+ "name": "python2"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.11"
+ },
+ "widgets": {
+ "state": {},
+ "version": "1.1.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..5e3fd20
--- /dev/null
+++ b/README.md
@@ -0,0 +1,101 @@
+# Mixture Density Networks implementation for distribution and uncertainty estimation
+A generic Mixture Density Networks implementation for distribution and uncertainty estimation by using Keras (TensorFlow)
+
+This repository is a collection of [Jupyter](https://jupyter.org/) notebooks intended to solve a lot of problems in which we want to predict a probability distribution by using Mixture Density Network avoiding NaN's problem and other derived problems of the model proposed by [Bishop, C. M. (1994)](http://eprints.aston.ac.uk/373/). The second major objective of this repository is to look for ways to predict uncertainty by using artificial neural networks.
+
+The whole code, until 20.1.2017, is the result of a final Master's Thesis of the [Master's degree in Artificial Intelligence](http://www.upc.edu/master/fitxa_master.php?id_estudi=50&lang=esp).
+
+
+
+
+Representation of the Mixture Density Network model. The output of the feed-forward neural network determine the parameters in a mixture density model. Therefore, the mixture density model represents the conditional probability density function of the target variables conditioned on the input vector of the neural network.
+
+
+## Implemented tricks and techniques
+
+> - Log-sum-exp trick.
+> - ELU+1 representation function for variance scale parameter proposed by us in the Master's Thesis that I will link to when it has been published.
+> - Clipping of the mixing coefficient parameter value.
+> - mean log Gaussian likelihood proposed by [Bishop](http://eprints.aston.ac.uk/373/).
+> - mean log Laplace likelihood proposed by us in the Master's Thesis that I will link to when it has been published.
+> - Fast Gradient Sign Method to produce Adversarial Training proposed [by Goodfellow et al.](https://arxiv.org/abs/1412.6572).
+> - Modified version of Adversarial Training proposed by [Nokland](https://arxiv.org/abs/1510.04189).
+
+## Some Keras algorithms used
+
+> - RMSProp optimisation algorithm.
+> - Adam optimisation algorithm.
+> - Gradient Clipping
+> - Batch normalisation
+
+## Implemented visualisation functionalities
+
+> - Generic implementation to visualize mean and variance (as errorbar) of the distribution with maximum mixing coefficient of of the MDN.
+> - Generic implementation to visualize mean and variance (as errorbar) of all the distributions of of the MDN.
+> - Generic implementation to visualize all the probability density function as a *heat graphic* for 2D problems.
+> - Generic implementation to visualize the original 3D surface and visualise the mean of the distribution of the mixture through a sampling process.
+> - Adversarial data set visualisation by us in the Master's Thesis that I will link to when it has been published.
+
+
+
+## Notebooks
+(Currently tested on Keras (1.1.0) and TensorFlow (0.11.0rc2)
+
+#### [Introduction to MDN models and generic implementation of MDN](https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation/blob/master/MDN-Introduction.ipynb.ipynb)
+
+#### [MDN applied to a 2D regression problem](https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation/blob/master/MDN-2D-Regression.ipynb)
+
+#### [MDN applied to a 3D regression problem](https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation/blob/master/MDN-3D-Regression.ipynb)
+
+#### [MDN with LSTM neural network for a time series regression problem](https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation/blob/master/MDN-LSTM-Regression.ipynb)
+
+#### [MDN with completely dense neural network for a time series regression problem by using Adversarial Training](https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation/blob/master/MDN-DNN-Regression.ipynb)
+
+#### [Ensemble of MDNs with completely dense neural network for a simple regression problem for Predictive Uncertainty Estimation](https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation/blob/master/MDN-DNN-Simple-Ensemble-Uncertainty.ipynb)
+
+#### [Ensemble of MDNs with completely dense neural network for a complex regression problem for Predictive Uncertainty Estimation and Adversarial Data set test](https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation/blob/master/MDN-DNN-Complex-Ensemble-Uncertainty.ipynb)
+
+
+
+
+
+## Contributing
+
+Contributions are welcome! For bug reports or requests please [submit an issue](https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation/issues).
+
+## Contact
+
+Feel free to contact me to discuss any issues, questions, or comments.
+
+* GitHub: [axelbrando](https://github.com/axelbrando)
+* Website: [axelbrando.github.io](http://axelbrando.github.io)
+
+### BibTex reference format for citation
+```
+@misc{MDNABrando,
+title={Mixture Density Networks (MDN) for distribution and uncertainty estimation},
+url={https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation/},
+note={GitHub repository with a collection of Jupyter notebooks intended to solve a lot of problems related to MDN.},
+author={Axel Brando},
+ year={2017},
+}
+```
+
+## License
+
+The content developed by Axel Brando is distributed under the following license:
+
+ Copyright 2016 Axel Brando
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+
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