27 - tripletloss vs quadrupletloss MNIST-10-Mining.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import os.path\n",
    "from os import path\n",
    "\n",
    "import numpy as np\n",
    "np.random.seed(0)\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from pylab import *\n",
    "from sklearn.metrics import roc_curve,roc_auc_score\n",
    "from datetime import datetime,timedelta\n",
    "import wandb\n",
    "from GenericTools import *\n",
    "from TripletLossTools import *\n",
    "from QuadrupletLossTools import *\n",
    "\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "from tensorflow.keras.layers import BatchNormalization\n",
    "from tensorflow.keras.layers import MaxPooling2D\n",
    "from tensorflow.keras.layers import Concatenate\n",
    "from tensorflow.keras.layers import Lambda, Flatten, Dense\n",
    "from tensorflow.keras.initializers import glorot_uniform\n",
    "\n",
    "from tensorflow.keras.layers import Layer\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.utils import plot_model\n",
    "import tensorflow as tf\n",
    "mnist = tf.keras.datasets.mnist\n",
    "\n",
    "# Allow memory growth for the GPU\n",
    "physical_devices = tf.config.experimental.list_physical_devices('GPU')\n",
    "tf.config.experimental.set_memory_growth(physical_devices[0], True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "projectName = \"27_mnist_10_mining\"\n",
    "project_path = './{0}/'.format(projectName)\n",
    "model_path = '../azurenb_temp/{0}/'.format(projectName)\n",
    "if not path.exists(project_path):\n",
    "    os.mkdir(project_path)\n",
    "if not path.exists(model_path):\n",
    "    os.mkdir(model_path)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "nb_classes = 10\n",
    "img_rows, img_cols = 28, 28\n",
    "input_shape = (img_rows, img_cols, 1)\n",
    "\n",
    "def buildDataSet():\n",
    "    \"\"\"Build dataset for train and test\n",
    "    \n",
    "    \n",
    "    returns:\n",
    "        dataset : list of lengh 10 containing images for each classes of shape (?,28,28,1)\n",
    "    \"\"\"\n",
    "    (x_train_origin, y_train_origin), (x_test_origin, y_test_origin) = mnist.load_data()\n",
    "\n",
    "    assert K.image_data_format() == 'channels_last'\n",
    "    x_train_origin = x_train_origin.reshape(x_train_origin.shape[0], img_rows, img_cols, 1)\n",
    "    x_test_origin = x_test_origin.reshape(x_test_origin.shape[0], img_rows, img_cols, 1)\n",
    "    \n",
    "    dataset_train = []\n",
    "    dataset_test = []\n",
    "    \n",
    "    #Sorting images by classes and normalize values 0=>1\n",
    "    for n in range(nb_classes):\n",
    "        images_class_n = np.asarray([row for idx,row in enumerate(x_train_origin) if y_train_origin[idx]==n])\n",
    "        dataset_train.append(images_class_n/255)\n",
    "        \n",
    "        images_class_n = np.asarray([row for idx,row in enumerate(x_test_origin) if y_test_origin[idx]==n])\n",
    "        dataset_test.append(images_class_n/255)\n",
    "        \n",
    "    return dataset_train,dataset_test,x_train_origin,y_train_origin,x_test_origin,y_test_origin\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking shapes for class 0 (train) :  (5923, 28, 28, 1)\n",
      "Checking shapes for class 0 (test) :  (980, 28, 28, 1)\n",
      "Checking first samples\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x144 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x144 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_train,dataset_test,dataset_train_flat_X,dataset_train_flat_Y,dataset_test_flat_X,dataset_test_flat_Y = buildDataSet()\n",
    "print(\"Checking shapes for class 0 (train) : \",dataset_train[0].shape)\n",
    "print(\"Checking shapes for class 0 (test) : \",dataset_test[0].shape)\n",
    "print(\"Checking first samples\")\n",
    "for i in range(2):\n",
    "    DrawPics(dataset_train[i],5,template='Train {}',classnumber=i)\n",
    "    DrawPics(dataset_test[i],5,template='Test {}',classnumber=i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build Neural Network for computing triplet similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_network(input_shape, embeddingsize):\n",
    "    '''\n",
    "    Define the neural network to learn image similarity\n",
    "    Input : \n",
    "            input_shape : shape of input images\n",
    "            embeddingsize : vectorsize used to encode our picture   \n",
    "    '''\n",
    "\n",
    "    # Convolutional Neural Network\n",
    "    network = Sequential()\n",
    "    network.add(Conv2D(128, (7,7), activation='relu',padding='same',\n",
    "                     input_shape=input_shape,\n",
    "                     kernel_initializer='he_uniform',\n",
    "                     kernel_regularizer=l2(2e-4)))\n",
    "    network.add(MaxPooling2D())\n",
    "    network.add(Conv2D(128, (3,3), activation='relu', kernel_initializer='he_uniform',padding='same',\n",
    "                     kernel_regularizer=l2(2e-4)))\n",
    "    network.add(MaxPooling2D())\n",
    "    network.add(Conv2D(256, (3,3), activation='relu', kernel_initializer='he_uniform',padding='same',\n",
    "                     kernel_regularizer=l2(2e-4)))\n",
    "    network.add(Flatten())\n",
    "    network.add(Dense(4096, activation='relu',\n",
    "                   kernel_regularizer=l2(1e-3),\n",
    "                   kernel_initializer='he_uniform'))\n",
    "    \n",
    "    \n",
    "    network.add(Dense(embeddingsize, activation=None,\n",
    "                   kernel_regularizer=l2(1e-3),\n",
    "                   kernel_initializer='he_uniform'))\n",
    "    \n",
    "    #Force the encoding to live on the d-dimentional hypershpere\n",
    "    network.add(Lambda(lambda x: K.l2_normalize(x,axis=-1)))\n",
    "    \n",
    "    return network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha1=1\n",
    "alpha2=0.5\n",
    "embeddingsize=10\n",
    "nb_test_class=10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Output 3xLoss missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to 3xLoss.\n",
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "anchor_input (InputLayer)       [(None, 28, 28, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "positive_input (InputLayer)     [(None, 28, 28, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "negative_input (InputLayer)     [(None, 28, 28, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "sequential (Sequential)         (None, 10)           51874442    anchor_input[0][0]               \n",
      "                                                                 positive_input[0][0]             \n",
      "                                                                 negative_input[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "3xLoss (TripletLossLayer)       ()                   0           sequential[1][0]                 \n",
      "                                                                 sequential[2][0]                 \n",
      "                                                                 sequential[3][0]                 \n",
      "==================================================================================================\n",
      "Total params: 51,874,442\n",
      "Trainable params: 51,874,442\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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P6YknnlBhYaHTocAhe/fu1dKlS9Xc3JywPgzDUHNzs7Zs2RL1/tLU88no7f39/aqqqrLnXqqtrdWOHTtiajfSNmtOqPb2dkmX56EKXVl0uuey6cRH3iPvITLyHlIl78V6Lu7r69M999yj1tZWbdq0yb49OudN97OjRP6bSuzJFH+65D+rfnLw4MGYHzvb7N27V5J4nzCLdXd3a+/evZH+1h4fU7KcyshJa5i09e2ERlVDR9+OtM36NmOiOUBiaSf00jlrv4mGg8dqKs9xvD6jiS3atqYSezLFP9nzsvqZ7DVnmuHfqLa3t9tzpozua/TccdHGMhmGrUdP4vK22S5VLm+ztoee86LNOxZrLiNp4kujoj1XW+fDY8eOmQMDA2Z7e3vEx4x3rosWeS8x8ZP3ZifyHlIl7413Lpsop7S3t9sj3ELn+bPE47NjaDvkv+hjT6b40yX/cVl39HifgIku647Lat2bN2+WJC1dunTKbbhcLklSfX29gsGgJGlwcFCVlZUxtWN9M/jOO+/Y26z2Nm7cOOX4EsVa8WzDhg0ORzI1Mxm/3+9XSUmJpOhec3l5eaqoqNDmzZvV2NiogoKCsPsbGhokSU1NTfZrxFrFDQAiiXTOizbvGIahYDCovLw87du3T729vaqqqpp2TOvWrZMkHThwQEePHlVxcXHY/cl2riPvRY+8ByDZTHYe8Xq9Ki4u1o4dO2Saptrb27Vp06awNuLx2VEi/8008h+ARIpLcdIqLA4ODtonLenyH3wgELBvWycC619rH0l65JFH5HK5VF9fr6ysLBmGoaefflo/+9nPwvYL/T1SO+vXr5fL5dKePXvsbc8995wqKirsBBba1lSMjiXa52hpa2uz92lqapLL5bKPoSRVVFRI+joB+P1++z6rWGvtH3pS9Xg88ng8E8YeGlfoiTkZ4p/o/8Xv92vNmjVauXJl2OMjveZCbd26NWz/UI888ogk6amnnrJfczk5Odq4ceO0XyMA0sdk57xo8o6ltrZWg4ODkqTrrrtOtbW1kiLnuNHnyUj7SFJ2draqq6tVX1+vDz74QJmZmWF9TnSuixZ5j7wHIL3F8plNGj+nuN1ue3voT2Vl5Zj8Nt75jPx3GfmP/AfMKjEMsxyXNYy6urraHBoaslfUGhgYsIdIK2SodKRtpmnaj7XaCh12HWs7DQ0N9vbRiw+EPiZ0sYJoje432tis33t7e+3LABsaGsYsjDAwMGDfb12eYF0SYV02OPqYm+bllcwmWn1usridjD/a2Ky+JnrNjeZyucYdwj8wMGC/5kIfP93XiGkybD0WEpe3zXbJfHnbZOc805w871jtDQ0N2Ze6RbqkO/ScO9l5MpS1b6znuliPRTLlDdMk75H3Uhd5D8mW96I9L5rmxDkl9Hw5+se6THey8xn5j/yXSvmPy7qjx/sETHRZd9wWxMHk4jUps1NSMf5gMDhmQuSZUlpaKkkJneg8XcQ6YTvSz0z8vcRrQRxEL9WPYSrGT95LDeQ9JGPei4f+/n5deeWVYy7B7e/vV25ubkqdT6cjFfNHqFSM36n8R/0kerxPwAR/L4/H5bJuIFkdPHgwKecaBQAgEch7AJzS1tamFStWRJwbMCcnR62trQ5EhdmC/AekNoqTM2S8uVJSRSrF7/F47PltBgcHx8z5htQ3eh6jSJjkOv7q6urC5iUKFc3/SSpJpXNeskr1Y5hK8ZP30h95zxmzKe/FQ0tLixobG+25lS39/f06ePDgmIVx0lUq5Y9IUil+8l/6I/85w4n8R3EyxOiDPN7PVOTk5ET8PVWkUvzWt7UNDQ2qqalxOBrnBIPBhL5ZTnT70TBNM+IlFIFAQLt27QqbELutrU1ut3vMpOyxGBwcVGVlpd1GR0dHxP28Xq/dl9vtticTT/b+gsGg/H6/Ghsb5Xa7x9z/8MMPq6ysLOKxG+//IlWl0jlvOsh740ul+Ml7l5H3yHuxIu/FV1NTkxYuXKinn37azh8ej0fvv/++tm3b5nR4Ych/40ul+Ml/syP3SeS/ePeXlPkvhgkqAcTA6Ql/29vbE/q3HM/2FePCAIowIbpleHjYdLlc5rFjx+xtDQ0N5uHDh+3bra2tpsvlMnt7e6Puc3h42J7oe3h42GxtbQ2b/NtiLXZitW1N4h26+Eky9meaX0+uPtHxPXbsmOlyuSIuCmOaE//fTCTZFgYAEDvyXvTIe873Z5rkPQDT53T9JJVy31TOe+S/+Pdnms7lv4kWxKE4CSSIkx/SrBN1ov6W491+PD+k1dbWjlm9ULq8evLobbGsyDc6OYwXx3jbYl39b6b7m6zNUBUVFeMmQT6kAbMXeS965D3n+5uszVDkPQDjcbJ+kmq5L97FSfJf6uW/iYqTXNYNJJlgMKi2tjb7cpLGxsaw4dSRLjUZva22tlZerzfsvkAgYA8Dl6TGxkZ72Hh/f/+025cuz/vi8XgScViiEggEVFVVpYceeihse0NDg1paWsbsv3jx4qjbDr1UIFRFRUXY7draWkmS3++XJHvepVgvNZnp/mKxceNGVVVVJf08RABSA3lv6sh75D0AqYncNz3kv/TLfxQngSRTVlams2fPyjRNDQ0Nyev1qry83J6QdmhoaMxjBgYGwm6HnqDM/5sTIicnR263W16vV36/X9u2bdPw8LAkKTc3105WU20/GRw/flyStHz58rDt27ZtU3t7u33beq6jT/ixsP4/NmzYELZ9x44dqq6u1po1a+T3+3X06FENDQ0pLy9vyn050d9ErONrHW8AmA7y3tSR98h7AFITuW96yH/pl/8oTgJJpKOjQ16vV4888ogkKTs7Wzt37pTX69Vzzz1nbxvNmgx6IqHJpKCgQJKUmZlpn6itb8Wm2r50OYE5ORl1T0+PpMnjbWpqUm9v77RO5C+99JJcLpeKi4vH3FdTU6OKigqtWbNGJ0+e1Pz586fcj1P9TSQzM1OSwr59BYCpIO9ND3mPvAcg9ZD7po/8l375j+IkkEQOHTokKTxZrFy5UpIiDk+PB+tEXVVVlZD2Z9JTTz016T4dHR169NFHp/0N029/+1vt3LnTPmGHqqurU0lJif0tZVlZmf0NWKr0NxErhnR4zQBwFnlvesh75D0AqYfcN33kv/TLfxQngSRSX18/Zpt1QrC+5cL0XHXVVdNOUG1tbXK5XPa3kaPvq6qq0vr165WZmamysjJ5vV4dPHgwZfoDgJlC3ks88h4AJBdy38wg/6UWipNAErEmw4004ex05smIRqLbTwZtbW0RT/Sx6Ovr08mTJ7Vt27aI92/evFnS128wcnJyJEnbt29Pif4AYCaR9xKLvAcAyYfcl3jkv9RDcRJIIlu2bJEkvfPOO/Y2a5j2xo0bE9KnNX/E6Al3U5G1gtl4Q9s3bdo0rfYDgYCef/75sDlW+vr6VFlZad8evdqalTzGW4UtmfqLVXV1dcL7AJDeyHvTQ94j7wFIPeS+6SP/pV/+ozgJJJH169fL5XJpz5499jdpzz33nCoqKrRu3Tp7P+sbLyvJ+P1++z7rBBb6jVxdXV1YP21tbZIun8ybmprkcrnCTmpTbd/j8cjj8Uz9AEzTihUrJI2fpMaLr66uToZhqK+vb9y2A4GAysvLVVVVJcMw7J977rknLMn/9Kc/lfT1MbaOnbU9WfuzhB678Y7j4OCgJCk/P3/S9gBgIuS96SHvkfcApB5y3/SR/9Iv/1GcBJJIZmam9u/fL5fLpZycHBmGIUl65plnwvZ78skn5XK5lJubK6/Xq4KCArlcLrW2tmr37t2SZH/r8uyzz6qsrCzs8StXrpTb7VZWVpaWLl2qpqamuLbvlNWrV0uSTp8+HdPjhoeHVVFRMWGS3bVr17hzwOTm5tq/r1u3TocPH5bP55NhGDpw4IAOHz4c9kYjGfuTJMMwlJWVZd/OysqyX4OhrONrHW8AmCry3vSQ98h7AFIPuW/6yH/pl/8MM3SteV1eHaq0tFSjNgOIUWlpqSSpubnZ4Ui+Zp1wku3v2zAMNTc325c4RLO/FPl5WN/o7dixI+Y43G632tvbY37cVKVqfx6PR1lZWRGP8VRfYzPx9xLr6wxAbMh70SPvpVZ/5D0A40nG+kmy5r6pnPfIf872F+/8N8Hfy+OMnASQVsrLy+Xz+cIuS4iG3+/Xzp07ExRV+vTX19envr4+lZeXxyEqAMB0kfcS2x95DwCSE/kvsf3NdP6jOAnMEqGrwUVaGS5dWJdJ7NmzJ6q5NiSpo6NDixYtmvaKbtFK1f76+/tVX1+v/fv32xMwA0CyIu+NL1Xz0Ez3R94DkGpmS+6TyH+J7M+J/JcxI70AcFxOTk7Y78k2zH8qxhtKnp2draamJu3fv195eXmTthM6T8dMSNX+vF6vdu/erezs7DH3RZqjBACcRN4bX6rmoZnuj7wHINWkY+6TyH8z3Z8T+Y/iJDBLpEtikqJ7LpmZmVOafwTjm+h4ptPrC0B6SKfzEnnPGeQ9AKkm3c5N5D9nOJH/uKwbAAAAAAAAgCMoTgIAAAAAAABwBMVJAAAAAAAAAI6gOAkAAAAAAADAEeMuiHPo0KGZjANIO4ODg5L4W4rW8ePHNW/ePKfDmNQHH3ygm266SXPnznU6lLRy6NAhbdy4cUb6SYXXGZCKyHuxSZW8h8Qg76WHs2fP6uLFi7ruuuucDgUOOX78uCRyXzR4n4CJ/u8Nc9RSOz09PVq9enXCgwIAINQvf/lLPfXUUwlrf/78+frqq68S1j4AALEg7wEAZpsrrrhC58+fH7358THFSQBAZKZp6vXXX1dnZ6d8Pp86Ozt1+vRpLViwQAUFBSopKVFxcbEKCgq0YMECp8MFgLhraWlRaWmpePsIYDZ4++231d3dLZ/Pp66uLr311lvKyMjQt771LRUXF6u4uFgPPPCAFi1a5HSoAJDKKE4CwHS89dZb6urq0pEjR9TZ2an33ntPV1xxhe6//34VFxerpKREa9eu1cKFC50OFQCmjeIkgHRlmqZee+01dXV1qaurSz6fTx988IGuvPJK5efnq6SkREVFRVqzZo2uueYap8MFgHRCcRIA4ukvf/mLfD6f/Q37qVOnlJGRoVWrVtnFysLCQuYmApCSKE4CSBcjIyPq7e21C5Hd3d36+OOPtXDhQq1du1bFxcUqKipSfn6+5s+f73S4AJDOKE4CQCKdOXNGnZ2d9ujKkydPyjAM3XXXXSopKbG/hc/OznY6VACYFMVJAKnq/PnzOnHihP2+rLu7W2fPntX111+vwsJC+wvkVatWKSNj3HVjAQDxR3ESAGbSJ598Ys9d5PP51NfXp5GREa1cudKeu6ikpESLFy92OlQAGIPiJIBUce7cOfn9fnuu8J6eHn3xxRe65ZZb7FGRxcXFuvPOO2UYhtPhAsBsRnESAJz02Wefqbu7276k6MSJE7pw4YK+8Y1vqKioyB5deeuttzodKgBQnASQtIaHh9Xd3W2PjDxx4oQuXrxov6eyCpLLly93OlQAQDiKkwCQTEK/5T9y5Ih6enr05ZdfasmSJfZq4MXFxbr99tudDhXALERxEkCyGBoaUldXlz0y8s9//rNM09Qdd9yh4uJi+1JtrkYBgKRHcRIAktn58+fV09NjL7Dzwgsv6Ny5c8rJybFHATz44IO68847NWfOHKfDBZDmKE4CcMrAwIA6OzvtkZGnTp3S3LlztWrVqrA5I2+44QanQwUAxOZxZvoFgCQ2f/58FRUVqaioSJJ08eJFnThxwh4p4PF4FAwGtWjRIvuNeXFxsVatWqW5c+c6HD0AAMDUvPHGG/b7na6uLg0MDGj+/Pm6//779YMf/EBFRUUqLCzUwoULnQ4VADBNjJwEgBQ2MjKiP/3pT/YlTV1dXfr444+1cOFCFRYW2qMr8/PzNW/ePKfDBZDiGDkJIBEuXbpkv5/p6upSV1eXhoaGdPXVV6ugoMBeMDA/P18LFixwOlwAQHxxWTcApBPTNPXaa6/Zlz35fD59+OGHuuqqq4qoRsQAACAASURBVMLe3K9evZo39wBiRnESQDxcuHDBvhKkq6tL3d3dGh4eVlZWlgoLC+3Fa+677z5lZHCxHwCkOYqTAJDu3nzzzbBiZehlUdYCOw888ICuueYap0MFkOQoTgKYii+++EJ+v9++TNvv94+ZQ7ukpER33XUXc2gDwOxDcRIAZpvQCeU7OzvV39+vjIwMfetb37KLlUVFRcrKynI6VABJhuIkgGh89tln6u7uVnd3tzo7O/Xiiy/qq6++0rJly8Lea+Tm5jodKgDAeRQnAWC2+/DDD+05no4cOaLXXntNhmHo7rvvDvsAkZ2d7XSoABxGcRJAJB999JFdiOzs7FRfX59GRkZ0++232yMji4uLtXTpUqdDBQAkH4qTAIBwH3/8sbq7u+Xz+ewPGJcuXdLKlSvtQuWDDz6oW265xelQAcwwipMAJOn999+3v9js7OzU66+/PuaLzcLCQuXk5DgdKgAg+VGcBABMLBgM2qMhurq69OKLL+rixYtavny5ioqKVFJSouLiYt16661OhwogwShOArPTm2++GfbF5bvvvqt58+bpvvvus0dGPvDAA0wJAwCYCoqTAIDYnDt3TkePHlVXV5d8Pp96enr05Zdf6u/+7u/sQmVRUZFuv/12p0MFEGcUJ4H0d+nSJb322mvy+Xz2yMgPP/xQCxYsUEFBgZ3nCwoKdPXVVzsdLgAg9VGcBABMz5dffqmenh77Q8zRo0d17tw53XTTTfYHmJKSEt15552swAmkOIqTQPq5ePGiXnnlFftLx+7ubn366ae69tprVVhYqKKiIhUVFen+++/XFVdc4XS4AID0Q3ESABBfFy5c0IkTJ+zRFt3d3QoGg7r++utVWFiokpISFRUVadWqVZo7d67T4QKIAcVJIPWdP39+zJeKf/vb33TjjTfahcji4mLl5eWRpwEAM4HiJAAgsUZGRtTX16fOzk57RMbHH38cNiKjuLhY999/v+bNm+d0uAAmQHESSD1/+9vfxkzHcv78eS1ZskQlJSUqLCxUcXGxVq5cKcMwnA4XADD7UJwEAMws0zR18uTJMXNZXXXVVVqzZo2Ki4tVUlKi/Px8LViwwOlwAYSgOAkkv08//dRevKa7u1svv/yyLl68qNtuu83+QpCF7AAASYTiJADAef39/ers7LR/BgYGNH/+fOXn59uXga9du1bXXHON06ECsxrFSSD5fPjhh+rs7LS/8Dt58qQk6Y477rBzaHFxsW6++WaHIwUAICKKkwCA5PPee++FFSvffPNNZWRk6N5777VHfBQWFiorK8vpUIFZheIk4Lx3333Xzo9dXV12jly1apW9CF1hYaEWLVrkdKgAAESD4iQAIPl9+OGH8vl89oex1157TYZhKC8vzy5WFhUV6cYbb3Q6VCCtUZwEZpZpmnr99dftBeZ8Pp/ef/99ri4AAKQTipMAgNTz8ccfh42s/NOf/qRLly5p5cqVKikpsQuWt9xyi9OhAmmF4iSQWKGLyHV1damrq0sfffSRrrnmGq1du9YeGZmfn6/58+c7HS4AAPFAcRIAkPqCwaA911ZXV5dOnDihixcvavny5WHFyr//+793OlQgpVGcBOLrq6++0osvvmgXIru7u/XZZ59p0aJFKiwstEdGrlq1ShkZGU6HCwBAIlCcBACkn3PnzumFF15QV1eXfD6fenp6dP78ef3d3/2dHnzwQfsy8NzcXKdDBVIKxUlges6dOye/329/oeb3+/XFF1/o5ptvtnNTSUmJ7rjjDs2ZM8fpcAEAmAkUJwEA6e/LL79UT0+Pjhw5Yn8YPHfunG666SZ7VEpJSYnuvPNOGYbhdLhA0qI4CcRmeHhYL7zwQtjI/gsXLujWW29VcXGxvXjNbbfd5nSoAAA4heIkAGD2uXDhgk6cOCGfzxd2Gd3111+vwsJCe3RlXl6e5s6d63S4QNKgOAlMbGhoSN3d3WFzIpumqZUrV9ojI4uLi7VkyRKnQwUAIFlQnAQAwFqAwOfzyefzqbu7W5988omuvfbasDm/7rvvPs2bN8/pcAHHUJwEwg0ODtqFyK6uLr3xxhuaO3eu8vLy7PmOCwsLdeONNzodKgAAyYriJAAAo5mmqZMnT+rIkSP2vGBnzpzR1VdfrYKCApWUlNirpV555ZVOhwvMGIqTmO1OnTpl54XOzk4NDAzoiiuu0P33328XIgsLC3Xttdc6HSoAAKmC4iQAANGwPpBaoyv/8pe/aP78+crPz9eDDz6ooqIirV27VldffbXToQIJQ3ESs8mlS5f05z//WT6fT52dneru7g77osq6TLugoEALFixwOlwAAFIVxUkAAKbivffeU2dnpz268q233lJGRobuu+8+e4GdwsJCZWZmOh0qEDcUJ5HOLl68qBMnTtgjI1944QX99a9/VVZWlh544AG7GMkUHwAAxBXFSQAA4uH06dPq7Oy0R9i8/vrrmjNnju6++24VFxfrwQcfVGFhoW644QanQwWmjOIk0skXX3yhnp4ee3G0Y8eO6dy5c8rJyVFhYaE9Z+Tdd9+tOXPmOB0uAADpiuIkAACJ8NFHH9mXgYeu2HrHHXeouLhYJSUlKi4u1s033+x0qMC4Dh48qHfffde+3dvbq7a2Nj3zzDNh+/3zP/+z7rrrrpkOD4jJ2bNn1d3dra6uLnV1denFF1/U+fPntXTpUrsQWVRUpNtvv93pUAEAmE0oTgIAMBOGh4fV3d1tj658+eWXdfHiRd12221hxcply5Y5HSpgMwxDkjR//vxx9zl//rx+/vOf69e//vVMhQVE5eOPP1Z3d7d8Pp+6u7v1yiuvaGRkRLm5uSoqKrILkpx3AQBwFMVJAACc8Le//U1Hjx61i5XWCJ5ly5bZH5hLSkp02223OR0qZrGf/OQnamho0FdffTXhfkeOHFFJSckMRQVE9sEHH9iFyM7OTr322msyDEN33XWX/QVQUVGRcnJynA4VAAB8jeIkAADJIHTuM5/PJ7/fr88//1w333xzWLHyjjvusEezAYn2wgsvqLCwcMJ9brrpJn3wwQfMyYcZ99Zbb9mL13R1dentt98OW5isuLhYhYWFysrKcjpUAAAwPoqTAAAkowsXLujFF19UZ2enOjs71d3drbNnz+qGG25QYWGhSkpKVFJSorvvvltz5851OlykKdM0tWTJEp0+fTri/VdccYV+8pOfqLa2doYjw2xjmqZOnjxpFyI7Ozt1+vRpLViwQPn5+fbIyIKCAl199dVOhwsAAKJHcRIAgFQwMjKiV155xV5kp6urS59++qkyMzPDVpW97777lJGR4XS4SCNPPvmk6urqdOHChYj3nzhxQvfee+8MR4V0d/HiRb3yyithc0Z+8sknWrhwoQoLC+2Rkffdd9+Ec6ICAICkR3ESAIBUdOnSJZ08edJeDbyzs1NDQ0O6+uqrtXbtWhUVFamkpESrV6+e1gf3//7v/9bLL7+sJ598kksjZ6lXX31Vd999d8T7br31Vr3zzjszHBGSybvvvqvf/OY3+vnPf64lS5ZMuZ3z58+rp6fHHhV59OjRMaPFi4qKdM899zBaHACA9EJxEgCAdPHGG2/YH+yPHDmi999/X1deeWXYJY9r1qyJ6ZLHBx98UD6fTzfeeKMaGhr0ve99L4HPAMlqxYoVevPNN8O2zZs3T9XV1fp//+//ORQVnDQyMqLf/e53+uUvf6kvv/xStbW12rFjR9SPP3funI4ePWqPBu/p6dGXX36pxYsX24XIoqIi5tkFACD9UZwEACBdvfvuu/Zq4J2dnXr77bc1b968sMUiioqKdO2110Z8/KVLl3Tdddfps88+05w5c3Tp0iX94Ac/0H/913+x2u0s86tf/Uq7d+8ec2l3f38/K8rPQq+++qp+/OMfq7e3V5cuXdKcOXO0YcMGeb3ecR/z17/+1V5Fu7OzUy+//LIuXryo5cuX2yO9CwsL9Y1vfGMGnwkAAEgCFCcBAJgtPvjgA3u+ys7OTr3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ORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#modele en 3xloss\n",
    "network3 = build_network(input_shape,embeddingsize=embeddingsize)\n",
    "network3_train = build_model3(input_shape,network3,margin=alpha1)\n",
    "optimizer3 = Adam(lr = 0.00006)\n",
    "network3_train.compile(loss=None,optimizer=optimizer3)\n",
    "network3_train.summary()\n",
    "plot_model(network3_train,show_shapes=True, show_layer_names=True, to_file=project_path+'model_summary_3x.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_metric_network(single_embedding_shape):\n",
    "    '''\n",
    "    Define the neural network to learn the metric\n",
    "    Input : \n",
    "            single_embedding_shape : shape of input embeddings or feature map. Must be an array\n",
    "            \n",
    "    '''\n",
    "    #compute shape for input\n",
    "    input_shape = single_embedding_shape\n",
    "    #the two input embeddings will be concatenated    \n",
    "    input_shape[0] = input_shape[0]*2\n",
    "    \n",
    "     # Neural Network\n",
    "    network = Sequential(name=\"learned_metric\")\n",
    "    network.add(Dense(10, activation='relu',\n",
    "                   input_shape=input_shape, \n",
    "                   kernel_regularizer=l2(1e-3),\n",
    "                   kernel_initializer='he_uniform'))\n",
    "    network.add(Dense(10, activation='relu',                   \n",
    "                   kernel_regularizer=l2(1e-3),\n",
    "                   kernel_initializer='he_uniform'))  \n",
    "    network.add(Dense(10, activation='relu',                   \n",
    "                   kernel_regularizer=l2(1e-3),\n",
    "                   kernel_initializer='he_uniform'))\n",
    "    \n",
    "    #Last layer : binary softmax\n",
    "    network.add(Dense(2, activation='softmax'))\n",
    "    \n",
    "    #Select only one output value from the softmax\n",
    "    network.add(Lambda(lambda x: x[:,0]))\n",
    "    \n",
    "    return network\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Output 4xLoss missing from loss dictionary. We assume this was done on purpose. The fit and evaluate APIs will not be expecting any data to be passed to 4xLoss.\n",
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "anchor_input (InputLayer)       [(None, 28, 28, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "positive_input (InputLayer)     [(None, 28, 28, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "negative_input (InputLayer)     [(None, 28, 28, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "negative2_input (InputLayer)    [(None, 28, 28, 1)]  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "sequential_1 (Sequential)       (None, 10)           51874442    anchor_input[0][0]               \n",
      "                                                                 positive_input[0][0]             \n",
      "                                                                 negative_input[0][0]             \n",
      "                                                                 negative2_input[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "Anchor-Positive (Concatenate)   (None, 20)           0           sequential_1[1][0]               \n",
      "                                                                 sequential_1[2][0]               \n",
      "__________________________________________________________________________________________________\n",
      "Anchor-Negative (Concatenate)   (None, 20)           0           sequential_1[1][0]               \n",
      "                                                                 sequential_1[3][0]               \n",
      "__________________________________________________________________________________________________\n",
      "Negative-Negative2 (Concatenate (None, 20)           0           sequential_1[3][0]               \n",
      "                                                                 sequential_1[4][0]               \n",
      "__________________________________________________________________________________________________\n",
      "learned_metric (Sequential)     (None,)              452         Anchor-Positive[0][0]            \n",
      "                                                                 Anchor-Negative[0][0]            \n",
      "                                                                 Negative-Negative2[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "4xLoss (QuadrupletLossLayer)    ()                   0           learned_metric[1][0]             \n",
      "                                                                 learned_metric[2][0]             \n",
      "                                                                 learned_metric[3][0]             \n",
      "==================================================================================================\n",
      "Total params: 51,874,894\n",
      "Trainable params: 51,874,894\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#modele en 4xloss\n",
    "network4 = build_network(input_shape,embeddingsize=10)\n",
    "network4.set_weights(network3.get_weights()) #copy weights to have identical networks\n",
    "metric_network4 = build_metric_network(single_embedding_shape=[10])\n",
    "network4_train = build_model4(input_shape,network4,metric_network4,margin=alpha1, margin2=alpha2)\n",
    "optimizer4 = Adam(lr = 0.00006)\n",
    "network4_train.compile(loss=None,optimizer=optimizer4)\n",
    "network4_train.summary()\n",
    "plot_model(network4_train,show_shapes=True, show_layer_names=True, to_file=project_path+'model_summary_4x.png')\n",
    "\n",
    "n_iteration=0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![alt text](25_mnist_10/model_summary_4x.png \" \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-0.07986476  0.16704349  0.580824    0.19283454  0.1799758  -0.3376034\n",
      "   0.4927724  -0.02770547  0.24476843 -0.37590575]]\n"
     ]
    }
   ],
   "source": [
    "#testing our NN with dummy image\n",
    "featured_img = network4.predict(np.ones((1,img_rows,img_cols,1)))\n",
    "print(featured_img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing batch for training\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking batch width, should be 4 :  4\n",
      "Shapes in the batch A:(2, 28, 28, 1) P:(2, 28, 28, 1) N:(2, 28, 28, 1) N2:(2, 28, 28, 1)\n",
      "Shapes in the hardbatch 4x A:(2, 28, 28, 1) P:(2, 28, 28, 1) N:(2, 28, 28, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "quadruplets = get_batch_random(2,dataset_train)\n",
    "print(\"Checking batch width, should be 4 : \",len(quadruplets))\n",
    "print(\"Shapes in the batch A:{0} P:{1} N:{2} N2:{3}\".format(quadruplets[0].shape, quadruplets[1].shape, quadruplets[2].shape, quadruplets[3].shape))\n",
    "drawQuadriplets(quadruplets)\n",
    "hardtriplets,hardquadruplets = get_batch_hard(50,1,1,network3,network4,metric_network4,dataset_train)\n",
    "print(\"Shapes in the hardbatch 4x A:{0} P:{1} N:{2}\".format(hardquadruplets[0].shape, hardquadruplets[1].shape, hardquadruplets[2].shape, hardquadruplets[3].shape))\n",
    "drawQuadriplets(hardquadruplets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validation / evaluation\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_roc_dual(fpr1, tpr1,thresholds1,auc1,fpr2, tpr2,thresholds2,auc2,n_iteration, name1, name2, namewandb, savewandb=False):\n",
    "    #find threshold\n",
    "    targetfpr=1e-3\n",
    "    _, idx1 = find_nearest(fpr1,targetfpr)\n",
    "    threshold1 = thresholds1[idx1]\n",
    "    recall1 = tpr1[idx1]\n",
    "    _, idx2 = find_nearest(fpr2,targetfpr)\n",
    "    threshold2 = thresholds2[idx2]\n",
    "    recall2 = tpr2[idx2]\n",
    "    \n",
    "    fig=plt.figure(figsize=(16,6))\n",
    "    \n",
    "    subplot = fig.add_subplot(1,2,1)   \n",
    "    # plot no skill\n",
    "    subplot.plot([0, 1], [0, 1], linestyle='--')\n",
    "    # plot the roc curve for the model\n",
    "    subplot.plot(fpr1, tpr1, marker='.')\n",
    "    subplot.title.set_text('{5}\\nAUC: {0:.3f} @ {4} iterations\\nSensitivity : {2:.1%} @FPR={1:.0e}\\nThreshold={3})'.format(auc1,targetfpr,recall1,abs(threshold1),n_iteration,name1 ))\n",
    "    \n",
    "    subplot = fig.add_subplot(1,2,2)   \n",
    "    # plot no skill\n",
    "    subplot.plot([0, 1], [0, 1], linestyle='--')\n",
    "    # plot the roc curve for the model\n",
    "    subplot.plot(fpr2, tpr2, marker='.')\n",
    "    subplot.title.set_text('{5}\\nAUC: {0:.3f} @ {4} iterations\\nSensitivity : {2:.1%} @FPR={1:.0e}\\nThreshold={3})'.format(auc2,targetfpr,recall2,abs(threshold2),n_iteration,name2 ))\n",
    "    \n",
    "    # show the plot\n",
    "    fig.show()\n",
    "    \n",
    "    if savewandb:\n",
    "        wandb.log({\"Evaluation @ {0} iterations ({1})\".format(n_iteration,namewandb):wandb.Image(fig)})\n",
    "        print(\"saved\")\n",
    "    \n",
    "def draw_embeddings(network):\n",
    "    basepredict = network.predict(basepicture)\n",
    "    plt.scatter(basepredict[:,0],basepredict[:,1])\n",
    "    plt.xlim(-1, 1)\n",
    "    plt.ylim(-1, 1)\n",
    "    for i in range(4):\n",
    "        plt.annotate(i, (basepredict[i,0], basepredict[i,1]))\n",
    "\n",
    "    for i in range(4):\n",
    "        preds = network.predict(dataset_test[i])\n",
    "        plt.scatter(preds[:,0],preds[:,1])\n",
    "        \n",
    "def draw_embeddings_dual(network1, network2, savewandb=False):\n",
    "    fig = plt.figure(figsize=(16,6))\n",
    "    \n",
    "    subplot = fig.add_subplot(1,2,1)   \n",
    "    subplot.set_xlim([-11, 11])\n",
    "    subplot.set_ylim([-11, 11])\n",
    "\n",
    "    for i in range(nb_classes):\n",
    "        preds = network1.predict(dataset_test[i])\n",
    "        subplot.scatter((i+1)*preds[:,0],(i+1)*preds[:,1])\n",
    "        \n",
    "    subplot = fig.add_subplot(1,2,2)   \n",
    "    subplot.set_xlim([-11, 11])\n",
    "    subplot.set_ylim([-11, 11])\n",
    "\n",
    "    for i in range(nb_classes):\n",
    "        preds = network2.predict(dataset_test[i])\n",
    "        subplot.scatter((i+1)*preds[:,0],(i+1)*preds[:,1])\n",
    "        \n",
    "    if savewandb:\n",
    "        wandb.log({\"Embeddings @{0} iterations\".format(n_iteration):wandb.Image(fig)})\n",
    "        \n",
    "def draw_eval(network,n_iteration):\n",
    "    probs,yprob = compute_probs(network, metricnetwork,dataset_test_flat_X,dataset_test_flat_Y)\n",
    "    fpr, tpr, thresholds,auc = compute_metrics(probs,yprob)\n",
    "    draw_roc(fpr, tpr,thresholds,auc,n_iteration)\n",
    "    draw_embeddings(network)\n",
    "    draw_interdist(network,n_iteration)\n",
    "    \n",
    "def draw_eval_dual(network1,network2,metricnetwork2,n_iteration,savewandb=False):\n",
    "    interdistnbclass = 10\n",
    "    interdistnbsample = 300\n",
    "    probs_nbsamples = interdistnbclass*interdistnbsample\n",
    "    \n",
    "    t_start=time.time()\n",
    "    #Computing probs for 3x\n",
    "    probs1,yprob1 = compute_cache(\"probs3x\", lambda :compute_probs(network1, dataset_test_flat_X[:probs_nbsamples,:,:,:],dataset_test_flat_Y[:probs_nbsamples]), \\\n",
    "                        parameter_dict={'n_iteration':n_iteration,'interdistnbclass':interdistnbclass,'interdistnbsample':interdistnbsample, 'dataset':'test'}, folder=project_path)\n",
    "    probs1train,yprob1train = compute_cache(\"probs3x\", lambda :compute_probs(network1, dataset_train_flat_X[:probs_nbsamples,:,:,:],dataset_train_flat_Y[:probs_nbsamples]), \\\n",
    "                        parameter_dict={'n_iteration':n_iteration,'interdistnbclass':interdistnbclass,'interdistnbsample':interdistnbsample, 'dataset':'train'}, folder=project_path)\n",
    "    \n",
    "    #Computing probs for 4x\n",
    "    probs2,yprob2 = compute_cache(\"probs4x\", lambda :compute_probs3iVectorized(network2, metricnetwork2,dataset_test_flat_X[:probs_nbsamples,:,:,:],dataset_test_flat_Y[:probs_nbsamples]), \\\n",
    "                        parameter_dict={'n_iteration':n_iteration,'interdistnbclass':interdistnbclass,'interdistnbsample':interdistnbsample, 'dataset':'test'}, folder=project_path)\n",
    "    probs2train,yprob2train = compute_cache(\"probs4x\", lambda :compute_probs3iVectorized(network2, metricnetwork2,dataset_train_flat_X[:probs_nbsamples,:,:,:],dataset_train_flat_Y[:probs_nbsamples]), \\\n",
    "                        parameter_dict={'n_iteration':n_iteration,'interdistnbclass':interdistnbclass,'interdistnbsample':interdistnbsample, 'dataset':'train'}, folder=project_path)\n",
    "\n",
    "    #Computing metrics for 3x\n",
    "    fpr1, tpr1, thresholds1,auc1 = compute_cache(\"metric3x\", lambda :compute_metrics(probs1,yprob1),\\\n",
    "                        parameter_dict={'n_iteration':n_iteration,'interdistnbclass':interdistnbclass,'interdistnbsample':interdistnbsample, 'dataset':'test'}, folder=project_path)\n",
    "    fpr1train, tpr1train, thresholds1train,auc1train = compute_cache(\"metric3x\", lambda :compute_metrics(probs1train,yprob1train), \\\n",
    "                        parameter_dict={'n_iteration':n_iteration,'interdistnbclass':interdistnbclass,'interdistnbsample':interdistnbsample, 'dataset':'train'}, folder=project_path)                                             \n",
    "    \n",
    "    #Computing metrics for 4x\n",
    "    fpr2, tpr2, thresholds2,auc2 = compute_cache(\"metric4x\", lambda :compute_metrics(probs2,yprob2),\\\n",
    "                        parameter_dict={'n_iteration':n_iteration,'interdistnbclass':interdistnbclass,'interdistnbsample':interdistnbsample, 'dataset':'test'}, folder=project_path)                         \n",
    "    fpr2train, tpr2train, thresholds2train,auc2train = compute_cache(\"metric4x\", lambda : compute_metrics(probs2train,yprob2train),\\\n",
    "                        parameter_dict={'n_iteration':n_iteration,'interdistnbclass':interdistnbclass,'interdistnbsample':interdistnbsample, 'dataset':'train'}, folder=project_path)                                                                                          \n",
    "        \n",
    "    draw_roc_dual(fpr1train, tpr1train,thresholds1train,auc1train,fpr2train, tpr2train,thresholds2train,auc2train,n_iteration, \"Triplet (Train)\", \"Quadruplet (Train)\", savewandb)\n",
    "    draw_roc_dual(fpr1, tpr1,thresholds1,auc1,fpr2, tpr2,thresholds2,auc2,n_iteration, \"Triplet (Test)\", \"Quadruplet (Test)\", savewandb)\n",
    "    #draw_embeddings_dual(network1,network2, savewandb)\n",
    "\n",
    "    draw_interdist(network1, n_iteration, dataset_train, savewandb, titleprefix=\"Triplet (Train)\",nbclasseval=interdistnbclass, nbsampleperclass=interdistnbsample, folder=project_path)\n",
    "    draw_interdist_learnedmetric(network2, metricnetwork2,n_iteration, dataset_train, savewandb, titleprefix=\"Quadruplet (Train)\",nbclasseval=interdistnbclass, nbsampleperclass=interdistnbsample, folder=project_path)\n",
    "    \n",
    "    draw_interdist(network1, n_iteration, dataset_test, savewandb, titleprefix=\"Triplet (Test)\",nbclasseval=interdistnbclass, nbsampleperclass=interdistnbsample, folder=project_path)\n",
    "    draw_interdist_learnedmetric(network2, metricnetwork2,n_iteration, dataset_test, savewandb, titleprefix=\"Quadruplet (Test)\",nbclasseval=interdistnbclass, nbsampleperclass=interdistnbsample, folder=project_path)\n",
    "    elapsed_minute = (time.time()-t_start)/60\n",
    "    \n",
    "def compute_CMC_scores():\n",
    "    #Compute CMC Scores\n",
    "    cmc3x = compute_cache(\"cmc3x\", lambda :ComputeCMCScoresL2(network3, dataset_test, nb_test_class=nb_test_class ), \\\n",
    "                            parameter_dict={'n_iteration':n_iteration,'nb_test_class':nb_test_class}, folder=project_path)\n",
    "    cmc4x = compute_cache(\"cmc4x\", lambda :ComputeCMCScoresLearnedMetric(network4, metric_network4, dataset_test, nb_test_class=nb_test_class ), \\\n",
    "                            parameter_dict={'n_iteration':n_iteration,'nb_test_class':nb_test_class}, folder=project_path)\n",
    "    cmc3xtrain = compute_cache(\"cmc3xtrain\", lambda :ComputeCMCScoresL2(network3, dataset_train, nb_test_class=nb_test_class ), \\\n",
    "                            parameter_dict={'n_iteration':n_iteration,'nb_test_class':nb_test_class}, folder=project_path)\n",
    "    cmc4xtrain = compute_cache(\"cmc4xtrain\", lambda :ComputeCMCScoresLearnedMetric(network4, metric_network4, dataset_train, nb_test_class=nb_test_class ), \\\n",
    "                            parameter_dict={'n_iteration':n_iteration,'nb_test_class':nb_test_class}, folder=project_path)\n",
    "\n",
    "    cmc_dict ={\n",
    "        '3x @ {0} iterations'.format(n_iteration): cmc3x ,\n",
    "        '4x @ {0} iterations'.format(n_iteration): cmc4x,\n",
    "        '3x @ {0} iterations (train)'.format(n_iteration): cmc3xtrain,\n",
    "        '4x @ {0} iterations (train)'.format(n_iteration): cmc4xtrain}\n",
    "\n",
    "    from CMC import CMC\n",
    "    cmc = CMC(cmc_dict)\n",
    "\n",
    "    #custimised color and marker\n",
    "    new_color = ['r','g','r','g','m','y','orange','brown']\n",
    "    new_marker = ['*','o','s','v','X','*','.','P']\n",
    "    cmc = CMC(cmc_dict,color=new_color,marker=new_marker)\n",
    "    #simple plot\n",
    "    cmc.plot(title = 'CMC on MNIST ({0} test IDs)'.format(nb_test_class))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x144 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "for i in range(5):\n",
    "    DrawTestImageWithRank(network3,np.expand_dims(dataset_test[i][1,:,:,:],axis=0),dataset_test,threshold=abs(0), classindicator=i,nb_test_class=nb_test_class)\n",
    "    DrawTestImageLearnedMetricWithRank(network4,metric_network4,np.expand_dims(dataset_test[i][1,:,:,:],axis=0),dataset_test,threshold=abs(0), classindicator=i,nb_test_class=nb_test_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reloading cmc3x for n_iteration=0, nb_test_class=10, \n",
      "Reloading cmc4x for n_iteration=0, nb_test_class=10, \n",
      "Reloading cmc3xtrain for n_iteration=0, nb_test_class=10, \n",
      "Reloading cmc4xtrain for n_iteration=0, nb_test_class=10, \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compute_CMC_scores()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reloadFromIteration(n):\n",
    "    global n_iteration\n",
    "    n_iteration = n\n",
    "    network3_train.load_weights('{1}3x-temp_weights_{0:08d}.h5'.format(n_iteration,model_path))\n",
    "    network4_train.load_weights('{1}4x-temp_weights_{0:08d}.h5'.format(n_iteration,model_path))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyper parameters\n",
    "evaluate_every = 1000 # interval for evaluating on one-shot tasks\n",
    "n_iter = 10000 # No. of training iterations\n",
    "log_every = 50\n",
    "sample_batch_size = 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "                Logging results to <a href=\"https://wandb.com\" target=\"_blank\">Weights & Biases</a> <a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">(Documentation)</a>.<br/>\n",
       "                Project page: <a href=\"https://app.wandb.ai/crimy/27_mnist_10_mining\" target=\"_blank\">https://app.wandb.ai/crimy/27_mnist_10_mining</a><br/>\n",
       "                Run page: <a href=\"https://app.wandb.ai/crimy/27_mnist_10_mining/runs/tuhnzb8a\" target=\"_blank\">https://app.wandb.ai/crimy/27_mnist_10_mining/runs/tuhnzb8a</a><br/>\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "wandb: Wandb version 0.8.35 is available!  To upgrade, please run:\n",
      "wandb:  $ pip install wandb --upgrade\n"
     ]
    }
   ],
   "source": [
    "import wandb\n",
    "wandb.init(project=projectName)\n",
    "wandb.config.alpha1 = alpha1\n",
    "wandb.config.alpha2 = alpha2\n",
    "wandb.config.sample_batch_size = sample_batch_size\n",
    "wandb.config.learningrate = K.eval(optimizer4.lr) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing probs for 3x\n",
      "Computing probs3x for n_iteration=0, interdistnbclass=10, interdistnbsample=600, dataset=test, \n",
      "Computing probs3x for n_iteration=0, interdistnbclass=10, interdistnbsample=600, dataset=train, \n",
      "Computing probs for 4x\n",
      "Computing probs4x for n_iteration=0, interdistnbclass=10, interdistnbsample=600, dataset=test, \n",
      "Computing probs4x for n_iteration=0, interdistnbclass=10, interdistnbsample=600, dataset=train, \n",
      "Computing metrics for 3x\n",
      "Computing metric3x for n_iteration=0, interdistnbclass=10, interdistnbsample=600, dataset=test, \n",
      "Computing metric3x for n_iteration=0, interdistnbclass=10, interdistnbsample=600, dataset=train, \n",
      "Computing metrics for 4x\n",
      "Computing metric4x for n_iteration=0, interdistnbclass=10, interdistnbsample=600, dataset=test, \n",
      "Computing metric4x for n_iteration=0, interdistnbclass=10, interdistnbsample=600, dataset=train, \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Crimy\\AppData\\Local\\Continuum\\anaconda3\\envs\\QuadLoss\\lib\\site-packages\\ipykernel_launcher.py:28: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "C:\\Users\\Crimy\\AppData\\Local\\Continuum\\anaconda3\\envs\\QuadLoss\\lib\\site-packages\\ipykernel_launcher.py:28: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing L2 distance done                                  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32400000,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing learned distance done                                  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing L2 distance done                                  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(32400000,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing learned distance done                                  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation time : 85.9430066784223 min\n"
     ]
    }
   ],
   "source": [
    "#Testing on an untrained network\n",
    "draw_eval_dual(network3,network4,metric_network4,n_iteration,savewandb=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting training process!\n",
      "-------------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "wandb: Wandb version 0.8.35 is available!  To upgrade, please run:\n",
      "wandb:  $ pip install wandb --upgrade\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11000] iteration 1000: 198.7 iter/min, Train Loss: 1.2947584390640259 6.960384845733643, eta : 2020-05-04 15:12:11\n",
      "[12000] iteration 2000: 199.4 iter/min, Train Loss: 0.855588436126709 4.276537895202637, eta : 2020-05-04 15:12:01\n",
      "[13000] iteration 3000: 199.7 iter/min, Train Loss: 0.8170614838600159 7.502551555633545, eta : 2020-05-04 15:11:56\n",
      "[14000] iteration 4000: 199.8 iter/min, Train Loss: 0.7734636068344116 6.724652290344238, eta : 2020-05-04 15:11:54\n",
      "[15000] iteration 5000: 199.8 iter/min, Train Loss: 0.7358054518699646 6.7211456298828125, eta : 2020-05-04 15:11:54\n",
      "[16000] iteration 6000: 199.8 iter/min, Train Loss: 0.690421462059021 2.7692666053771973, eta : 2020-05-04 15:11:54\n",
      "[17000] iteration 7000: 199.7 iter/min, Train Loss: 0.6540760397911072 4.177533149719238, eta : 2020-05-04 15:11:56\n",
      "[18000] iteration 8000: 199.7 iter/min, Train Loss: 0.6209302544593811 5.705739974975586, eta : 2020-05-04 15:11:55\n",
      "[19000] iteration 9000: 199.3 iter/min, Train Loss: 0.5925851464271545 8.186153411865234, eta : 2020-05-04 15:12:02\n",
      "[20000] iteration 10000: 199.0 iter/min, Train Loss: 0.564391553401947 3.6714181900024414, eta : 2020-05-04 15:12:06\n",
      "Done !\n"
     ]
    }
   ],
   "source": [
    "print(\"Starting training process!\")\n",
    "print(\"-------------------------------------\")\n",
    "t_start = time.time()\n",
    "for i in range(1, n_iter+1):\n",
    "    microtask_start =time.time() \n",
    "    triplets,quadruplets = get_batch_hardOptimized(100,16,16,network3,network4,metric_network4, dataset_train)\n",
    "    timetogetbatch = time.time()-microtask_start\n",
    "    microtask_start =time.time() \n",
    "    loss1 = network3_train.train_on_batch(triplets, None)\n",
    "    timebatch3 = time.time()-microtask_start\n",
    "    microtask_start =time.time() \n",
    "    loss2 = network4_train.train_on_batch(quadruplets, None)\n",
    "    timebatch4 = time.time()-microtask_start\n",
    "    n_iteration += 1\n",
    "    if i % log_every == 0:\n",
    "        wandb.log({'loss3x': loss1,'loss4x': loss2}, step=n_iteration)\n",
    "    if i % evaluate_every == 0:\n",
    "        elapsed_minutes = (time.time()-t_start)/60.0\n",
    "        rate = i/elapsed_minutes \n",
    "        eta = datetime.now()+timedelta(minutes=(n_iter-i)/rate)\n",
    "        eta = eta + timedelta(hours=0) #french time\n",
    "        print(\"[{4}] iteration {0}: {1:.1f} iter/min, Train Loss: {2} {3}, eta : {5}\".format(i, rate,loss1,loss2,n_iteration,eta.strftime(\"%Y-%m-%d %H:%M:%S\") ))\n",
    "        network3_train.save_weights('{1}3x-temp_weights_{0:08d}.h5'.format(n_iteration,model_path))\n",
    "        network4_train.save_weights('{1}4x-temp_weights_{0:08d}.h5'.format(n_iteration,model_path))      \n",
    "#Final save\n",
    "network3_train.save_weights('{1}3x-temp_weights_{0:08d}.h5'.format(n_iteration,model_path))\n",
    "network4_train.save_weights('{1}4x-temp_weights_{0:08d}.h5'.format(n_iteration,model_path))     \n",
    "print(\"Done !\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing probs for 3x\n",
      "Computing probs for 4x\n",
      "Computing metrics for 3x\n",
      "Computing metrics for 4x\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Crimy\\AppData\\Local\\Continuum\\anaconda3\\envs\\QuadLoss\\lib\\site-packages\\ipykernel_launcher.py:321: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing L2 distance done                                  \n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing learned distance done                                  \n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation time : {} min 60.33430322408676\n"
     ]
    }
   ],
   "source": [
    "#Full evaluation\n",
    "draw_eval_dual(network3,network4,metric_network4,n_iteration, savewandb=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def DisplayFeatureMaps(network,n, name):\n",
    "    # retrieve weights from the second hidden layer\n",
    "    filters, biases = network.layers[0].get_weights()\n",
    "    # normalize filter values to 0-1 so we can visualize them\n",
    "    f_min, f_max = filters.min(), filters.max()\n",
    "    filters = (filters - f_min) / (f_max - f_min)\n",
    "    # plot first few filters\n",
    "    n_filters, ix = n, 1\n",
    "    \n",
    "    for i in range(n_filters):\n",
    "        # get the filter\n",
    "        f = filters[:, :, :, i]\n",
    "        # plot each channel separately\n",
    "        for j in range(1):\n",
    "            # specify subplot and turn of axis\n",
    "            ax = plt.subplot(n_filters, 4, ix)\n",
    "            ax.set_xticks([])\n",
    "            ax.set_yticks([])\n",
    "            # plot filter channel in grayscale\n",
    "            plt.imshow(f[:, :, j], cmap='gray')\n",
    "            ix += 1\n",
    "    # show the figure\n",
    "    wandb.log({name:plt})\n",
    "    plt.show()\n",
    "DisplayFeatureMaps(network3,4, \"CNN Filters 3xLoss\")\n",
    "DisplayFeatureMaps(network4,4, \"CNN Filters 4xLoss\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reloading probs3x for n_iteration=20000, interdistnbclass=10, interdistnbsample=300, dataset=test, \n",
      "Reloading probs3x for n_iteration=20000, interdistnbclass=10, interdistnbsample=300, dataset=train, \n",
      "Reloading probs4x for n_iteration=20000, interdistnbclass=10, interdistnbsample=300, dataset=test, \n",
      "Reloading probs4x for n_iteration=20000, interdistnbclass=10, interdistnbsample=300, dataset=train, \n",
      "Reloading metric3x for n_iteration=20000, interdistnbclass=10, interdistnbsample=300, dataset=test, \n",
      "Reloading metric3x for n_iteration=20000, interdistnbclass=10, interdistnbsample=300, dataset=train, \n",
      "Reloading metric4x for n_iteration=20000, interdistnbclass=10, interdistnbsample=300, dataset=test, \n",
      "Reloading metric4x for n_iteration=20000, interdistnbclass=10, interdistnbsample=300, dataset=train, \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Crimy\\AppData\\Local\\Continuum\\anaconda3\\envs\\QuadLoss\\lib\\site-packages\\ipykernel_launcher.py:28: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n",
      "C:\\Users\\Crimy\\AppData\\Local\\Continuum\\anaconda3\\envs\\QuadLoss\\lib\\site-packages\\ipykernel_launcher.py:28: UserWarning: Matplotlib is currently using module://ipykernel.pylab.backend_inline, which is a non-GUI backend, so cannot show the figure.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reloading interdist_Triplet (Train) for n_iteration=20000, interdistnbclass=10, interdistnbsample=300, \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing interdist_Quadruplet (Train) for n_iteration=20000, interdistnbclass=10, interdistnbsample=300, \n",
      "Computing learned distance done                                  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing interdist_Triplet (Test) for n_iteration=20000, interdistnbclass=10, interdistnbsample=300, \n",
      "Computing L2 distance done                                                       \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 810x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing interdist_Quadruplet (Test) for n_iteration=20000, interdistnbclass=10, interdistnbsample=300, \n",
      "Computing learned distance of class 2 vs 9 51%              \r"
     ]
    }
   ],
   "source": [
    "draw_eval_dual(network3,network4,metric_network4,n_iteration, savewandb=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing cmc3x for n_iteration=20000, nb_test_class=10, \n",
      "Computing cmc4x for n_iteration=20000, nb_test_class=10, \n",
      "Computing cmc3xtrain for n_iteration=20000, nb_test_class=10, \n",
      "Computing cmc4xtrain for n_iteration=20000, nb_test_class=10, \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "compute_CMC_scores()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}