Tensore Board With Scalars.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true,
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   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import os\n",
    "from tensorflow.examples.tutorials.mnist import input_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting MNIST_data/train-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n",
      "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "mnist = input_data.read_data_sets('MNIST_data',one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "learning_rate = 0.001\n",
    "training_epochs = 15\n",
    "batch_size = 100\n",
    "display_step = 1\n",
    "tf.reset_default_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "logs_path = \"tmp/mnist/3\"\n",
    "if not os.path.exists(logs_path):\n",
    "    os.makedirs(logs_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Network Parameters\n",
    "n_hidden_1 = 256 # 1st layer number of features\n",
    "n_hidden_2 = 256 # 2nd layer number of features\n",
    "n_input = 784 # MNIST data input (img shape: 28*28)\n",
    "n_classes = 10 # MNIST total classes (0-9 digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "with tf.name_scope('Input'):\n",
    "    x = tf.placeholder(\"float\", [None, n_input],name=\"X-Input\")\n",
    "    y = tf.placeholder(\"float\", [None, n_classes],name=\"Y-Input\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def build_three_layered_network():\n",
    "    \n",
    "    with tf.name_scope(\"Weights\"):\n",
    "        weights = {\n",
    "            'h1': tf.Variable(tf.random_normal([n_input, n_hidden_1]),name=\"Hidden-Layer-1-Weights\"),\n",
    "            'h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2]),name=\"Hidden-Layer-2-Weights\"),\n",
    "            'out': tf.Variable(tf.random_normal([n_hidden_2, n_classes]),name=\"Output-Layer-Weights\")\n",
    "        }\n",
    "    \n",
    "    with tf.name_scope(\"Biases\"):\n",
    "        biases = {\n",
    "            'b1': tf.Variable(tf.random_normal([n_hidden_1]),name=\"Hidden-Layer-1-Biases\"),\n",
    "            'b2': tf.Variable(tf.random_normal([n_hidden_2]),name=\"Hidden-Layer-2-Biases\"),\n",
    "            'out': tf.Variable(tf.random_normal([n_classes]),name=\"Output-Layer-Biases\")\n",
    "        }\n",
    "    layer_1 = tf.add(tf.matmul(x, weights['h1'],name=\"Matmul-HL1\"), biases['b1'],name=\"Bias-Add-HL1\")\n",
    "    layer_1 = tf.nn.relu(layer_1,name=\"Relu-HL1\")\n",
    "        # Hidden layer with RELU activation\n",
    "    layer_2 = tf.add(tf.matmul(layer_1, weights['h2'],name=\"Matmul-HL2\"), biases['b2'],name=\"Bias-Add-HL2\")\n",
    "    layer_2 = tf.nn.relu(layer_2,name=\"Relu-HL2\")\n",
    "        # Output layer with linear activation\n",
    "    pred = tf.add(tf.matmul(layer_2, weights['out'],name=\"Matmul-Output\"), biases['out'],name=\"Bias-Output\")\n",
    "    return pred\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "pred = build_three_layered_network()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Define loss and optimizer\n",
    "\n",
    "cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y,name=\"Softmax-Cross-Entropt\"),name=\"Reduce-Mean-Softmax-Cross-Entropt\")\n",
    "optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate,name=\"Adam-Optimizer\").minimize(cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "init = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "session = tf.Session()\n",
    "session.run(init)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def optimize(training_epochs):\n",
    "    writer = tf.summary.FileWriter(logs_path)\n",
    "    writer.add_graph(tf.get_default_graph())\n",
    "    for epoch in range(training_epochs):\n",
    "        total_batch = int(mnist.train.num_examples/batch_size)\n",
    "        for i in range(total_batch):\n",
    "            batch_x, batch_y = mnist.train.next_batch(batch_size)\n",
    "            _, summary=session.run([optimizer,summary_op], feed_dict={x: batch_x,\n",
    "                                                          y: batch_y})\n",
    "            writer.add_summary(summary, epoch * total_batch + i)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"),name=\"Accuracy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "tf.summary.scalar(\"cost\", cost)\n",
    "tf.summary.scalar(\"accuracy\", accuracy)\n",
    "\n",
    "summary_op = tf.summary.merge_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def print_accuracy():\n",
    "    acc = session.run(accuracy,feed_dict={x: mnist.test.images,y: mnist.test.labels})\n",
    "    print(\"Accuracy on test-set: {0:.1%}\".format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on test-set: 85.3%\n"
     ]
    }
   ],
   "source": [
    "optimize(1)\n",
    "print_accuracy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#tensorboard --logdir=~/Documents/BlogDeepLearning/tmp/mnist/3 --port=6007"
   ]
  }
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