convnet1d_3clases.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline \n",
    "import tensorflow as tf\n",
    "\n",
    "from parser_3clases import CIFAR10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = tf.ConfigProto()\n",
    "#config.gpu_options.per_process_gpu_memory_fraction = 0.4\n",
    "tf.set_random_seed(1)\n",
    "sess = tf.InteractiveSession(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Load dataset\n",
    "batch_size = 64\n",
    "cifar10 = CIFAR10(batch_size=batch_size, validation_proportion=0.1, augment_data=False, file='data_3clases.csv')\n",
    "\n",
    "SUMMARIES_DIR = './summaries/convnet'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model blocks\n",
    "def conv_layer(input_tensor, kernel_shape, layer_name):\n",
    "    # input_tensor b01c\n",
    "    # kernel_shape 01-in-out\n",
    "    weights = tf.get_variable(\"weights\", kernel_shape,\n",
    "                               initializer = tf.contrib.layers.xavier_initializer_conv2d())\n",
    "    biases = tf.get_variable(\"biases\", [kernel_shape[3]],\n",
    "                             initializer=tf.constant_initializer(0.05))\n",
    "    \n",
    "    tf.summary.histogram(layer_name + \"/weights\", weights)\n",
    "    tf.summary.histogram(layer_name + \"/biases\", biases)\n",
    "    \n",
    "    # Other options are to use He et. al init. for weights and 0.01 \n",
    "    # to init. biases.\n",
    "    conv = tf.nn.conv2d(input_tensor, weights, \n",
    "                       strides = [1, 1, 1, 1], padding='SAME')\n",
    "    return tf.nn.relu(conv + biases)\n",
    "\n",
    "def fc_layer(input_tensor, weights_shape, layer_name):\n",
    "    # weights_shape in-out\n",
    "    weights = tf.get_variable(\"weights\", weights_shape,\n",
    "                              initializer = tf.contrib.layers.xavier_initializer())\n",
    "    biases = tf.get_variable(\"biases\", [weights_shape[1]],\n",
    "                             initializer=tf.constant_initializer(0.0))\n",
    "    tf.summary.histogram(layer_name + \"/weights\", weights)\n",
    "    tf.summary.histogram(layer_name + \"/biases\", biases)\n",
    "    mult_out = tf.matmul(input_tensor, weights)\n",
    "    return tf.nn.relu(mult_out+biases)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model\n",
    "model_input = tf.placeholder(tf.float32, name='model_input', \n",
    "                             shape=(batch_size,1,200,1))\n",
    "tf.summary.image('input', model_input, 10)\n",
    "\n",
    "keep_prob = tf.placeholder(tf.float32, name='dropout_prob', shape=())\n",
    "\n",
    "target = tf.placeholder(tf.float32, name='target', shape=(batch_size, 3))\n",
    "\n",
    "# First convolution layer\n",
    "layer_name = 'conv1'\n",
    "with tf.variable_scope(layer_name):\n",
    "    conv1_out = conv_layer(model_input, [1, 3, 1, 20], layer_name)\n",
    "# First pooling layer\n",
    "with tf.name_scope('pool1'):\n",
    "    pool1_out = tf.nn.avg_pool(conv1_out, ksize=[1, 2, 2, 1],\n",
    "                strides=[1, 2, 2, 1], padding='SAME',\n",
    "                name='pool1')\n",
    "    \n",
    "\n",
    "# Second convolution layer\n",
    "layer_name = 'conv2'\n",
    "with tf.variable_scope(layer_name):\n",
    "    conv2_out = conv_layer(pool1_out, [1, 3, 20, 20], layer_name)\n",
    "# Second pooling layer\n",
    "with tf.name_scope('pool2'):\n",
    "    pool2_out = tf.nn.avg_pool(conv2_out, ksize=[1, 2, 2, 1],\n",
    "                            strides=[1, 2, 2, 1], padding='SAME',\n",
    "                            name='pool2')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "_,_,dim3,dim4 = pool2_out.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pool2_out_flat = tf.reshape(pool2_out, [-1, 1*dim3.value*dim4.value], name='pool2_flat')\n",
    "# First fully connected layer\n",
    "layer_name = 'fc1'\n",
    "with tf.variable_scope(layer_name):\n",
    "    fc1_out = fc_layer(pool2_out_flat, [1*dim3.value*dim4.value, 500], layer_name)\n",
    "fc1_out_drop = tf.nn.dropout(fc1_out, keep_prob)\n",
    "\n",
    "# Second fully connected layer\n",
    "layer_name = 'fc2'\n",
    "with tf.variable_scope(layer_name):\n",
    "    fc2_out = fc_layer(fc1_out_drop, [500, 250], layer_name)\n",
    "fc2_out_drop = tf.nn.dropout(fc2_out, keep_prob)\n",
    "\n",
    "\n",
    "# Third fully connected layer\n",
    "layer_name = 'fc3'\n",
    "with tf.variable_scope(layer_name):\n",
    "    fc3_out = fc_layer(fc2_out_drop, [250,3], layer_name)\n",
    "model_output = fc3_out\n",
    "\n",
    "\n",
    "with tf.name_scope('loss_function'):\n",
    "    cross_entropy = tf.reduce_mean(\n",
    "        tf.nn.softmax_cross_entropy_with_logits(logits=model_output, labels=target,\n",
    "                                           name='cross_entropy'))\n",
    "    tf.summary.scalar('cross_entropy', cross_entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Optimization\n",
    "with tf.name_scope('optimizer'):\n",
    "    optimizer = tf.train.AdamOptimizer()\n",
    "    grads_vars = optimizer.compute_gradients(cross_entropy)\n",
    "    optimizer.apply_gradients(grads_vars)\n",
    "    train_step = optimizer.minimize(cross_entropy)\n",
    "\n",
    "# Metrics\n",
    "correct_prediction = tf.equal(tf.argmax(model_output, 1),\n",
    "                             tf.argmax(target, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='accuracy')\n",
    "tf.summary.scalar('accuracy', accuracy)\n",
    "\n",
    "# Useful training functions\n",
    "def validate():\n",
    "    batches = cifar10.getValidationSet(asBatches=True)\n",
    "    accs = []\n",
    "    for batch in batches:\n",
    "        data, labels = batch\n",
    "        acc = sess.run(accuracy,\n",
    "                       feed_dict={\n",
    "                model_input: data,\n",
    "                target: labels,\n",
    "                keep_prob: 1.0\n",
    "            })\n",
    "        accs.append(acc)\n",
    "    mean_acc = np.array(accs).mean()\n",
    "    summary = sess.run(merged,\n",
    "                       feed_dict={\n",
    "                model_input: data,\n",
    "                target: labels,\n",
    "                keep_prob: 1.0\n",
    "            })\n",
    "    return summary, mean_acc\n",
    "def test():\n",
    "    batches = cifar10.getTestSet(asBatches=True)\n",
    "    accs = []\n",
    "    for batch in batches:\n",
    "        data, labels = batch\n",
    "        acc = sess.run(accuracy,\n",
    "                       feed_dict={\n",
    "                model_input: data,\n",
    "                target: labels,\n",
    "                keep_prob: 1.0\n",
    "            })\n",
    "        accs.append(acc)\n",
    "    mean_acc = np.array(accs).mean()\n",
    "    return mean_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trainable variables\n",
      "conv1/weights:0\n",
      "conv1/biases:0\n",
      "conv2/weights:0\n",
      "conv2/biases:0\n",
      "fc1/weights:0\n",
      "fc1/biases:0\n",
      "fc2/weights:0\n",
      "fc2/biases:0\n",
      "fc3/weights:0\n",
      "fc3/biases:0\n",
      "Epoch 0, training loss 0.980170, accuracy 0.515625\n",
      "Validation accuracy 0.545000\n",
      "Time elapsed 0.010072370370229086 minutes\n",
      "Epoch 1, training loss 0.558841, accuracy 0.765625\n",
      "Validation accuracy 0.674375\n",
      "Time elapsed 0.0690394918123881 minutes\n",
      "Epoch 2, training loss 0.741123, accuracy 0.593750\n",
      "Validation accuracy 0.661250\n",
      "Time elapsed 0.13218886454900106 minutes\n",
      "Epoch 3, training loss 0.822196, accuracy 0.531250\n",
      "Validation accuracy 0.696250\n",
      "Time elapsed 0.19951963822046917 minutes\n",
      "Epoch 4, training loss 0.656545, accuracy 0.687500\n",
      "Validation accuracy 0.684375\n",
      "Time elapsed 0.2737091223398844 minutes\n",
      "Epoch 5, training loss 0.496562, accuracy 0.828125\n",
      "Validation accuracy 0.699375\n",
      "Time elapsed 0.34154731829961144 minutes\n",
      "Epoch 6, training loss 0.533943, accuracy 0.734375\n",
      "Validation accuracy 0.710625\n",
      "Time elapsed 0.39537219603856405 minutes\n",
      "Epoch 7, training loss 0.503644, accuracy 0.781250\n",
      "Validation accuracy 0.718125\n",
      "Time elapsed 0.4442654450734456 minutes\n",
      "Epoch 8, training loss 0.525365, accuracy 0.781250\n",
      "Validation accuracy 0.725625\n",
      "Time elapsed 0.5071732878684998 minutes\n",
      "Epoch 9, training loss 0.521501, accuracy 0.687500\n",
      "Validation accuracy 0.744375\n",
      "Time elapsed 0.5920101682345073 minutes\n",
      "Epoch 10, training loss 0.441170, accuracy 0.765625\n",
      "Validation accuracy 0.781875\n",
      "Time elapsed 0.6556394497553507 minutes\n",
      "Epoch 11, training loss 0.540508, accuracy 0.718750\n",
      "Validation accuracy 0.794375\n",
      "Time elapsed 0.7114203612009684 minutes\n",
      "Epoch 12, training loss 0.335459, accuracy 0.875000\n",
      "Validation accuracy 0.765000\n",
      "Time elapsed 0.7628295620282491 minutes\n",
      "Epoch 13, training loss 0.421042, accuracy 0.765625\n",
      "Validation accuracy 0.788750\n",
      "Time elapsed 0.8253428181012471 minutes\n",
      "Epoch 14, training loss 0.624505, accuracy 0.765625\n",
      "Validation accuracy 0.777500\n",
      "Time elapsed 0.8778985897699992 minutes\n",
      "Epoch 15, training loss 0.487801, accuracy 0.812500\n",
      "Validation accuracy 0.792500\n",
      "Time elapsed 0.9413080096244812 minutes\n",
      "Epoch 16, training loss 0.378122, accuracy 0.828125\n",
      "Validation accuracy 0.806875\n",
      "Time elapsed 0.9960011680920918 minutes\n",
      "Epoch 17, training loss 0.525151, accuracy 0.843750\n",
      "Validation accuracy 0.804375\n",
      "Time elapsed 1.0496988495190938 minutes\n",
      "Epoch 18, training loss 0.412884, accuracy 0.906250\n",
      "Validation accuracy 0.835000\n",
      "Time elapsed 1.1071121493975322 minutes\n",
      "Epoch 19, training loss 0.360920, accuracy 0.843750\n",
      "Validation accuracy 0.848125\n",
      "Time elapsed 1.1858884533246359 minutes\n",
      "Epoch 20, training loss 0.545661, accuracy 0.812500\n",
      "Validation accuracy 0.838125\n",
      "Time elapsed 1.250528903802236 minutes\n",
      "Epoch 21, training loss 0.441252, accuracy 0.843750\n",
      "Validation accuracy 0.841875\n",
      "Time elapsed 1.3028082688649496 minutes\n",
      "Epoch 22, training loss 0.589098, accuracy 0.750000\n",
      "Validation accuracy 0.848750\n",
      "Time elapsed 1.359104875723521 minutes\n",
      "Epoch 23, training loss 0.351319, accuracy 0.859375\n",
      "Validation accuracy 0.841875\n",
      "Time elapsed 1.4202916622161865 minutes\n",
      "Epoch 24, training loss 0.403263, accuracy 0.828125\n",
      "Validation accuracy 0.846875\n",
      "Time elapsed 1.4925399422645569 minutes\n",
      "Epoch 25, training loss 0.364505, accuracy 0.921875\n",
      "Validation accuracy 0.845000\n",
      "Time elapsed 1.5672203818957011 minutes\n",
      "Epoch 26, training loss 0.225234, accuracy 0.921875\n",
      "Validation accuracy 0.836250\n",
      "Time elapsed 1.6313096205393474 minutes\n",
      "Epoch 27, training loss 0.272699, accuracy 0.937500\n",
      "Validation accuracy 0.850000\n",
      "Time elapsed 1.6867164929707845 minutes\n",
      "Epoch 28, training loss 0.376389, accuracy 0.812500\n",
      "Validation accuracy 0.858750\n",
      "Time elapsed 1.743576963742574 minutes\n",
      "Epoch 29, training loss 0.315892, accuracy 0.875000\n",
      "Validation accuracy 0.853125\n",
      "Time elapsed 1.8110876083374023 minutes\n",
      "Epoch 30, training loss 0.427428, accuracy 0.828125\n",
      "Validation accuracy 0.847500\n",
      "Time elapsed 1.8856515169143677 minutes\n",
      "Epoch 31, training loss 0.356023, accuracy 0.890625\n",
      "Validation accuracy 0.858750\n",
      "Time elapsed 1.9514280835787454 minutes\n",
      "Epoch 32, training loss 0.229227, accuracy 0.921875\n",
      "Validation accuracy 0.863750\n",
      "Time elapsed 2.004162327448527 minutes\n",
      "Epoch 33, training loss 0.399774, accuracy 0.859375\n",
      "Validation accuracy 0.858125\n",
      "Time elapsed 2.0578909953435263 minutes\n",
      "Epoch 34, training loss 0.227792, accuracy 0.921875\n",
      "Validation accuracy 0.868125\n",
      "Time elapsed 2.1163516283035277 minutes\n",
      "Epoch 35, training loss 0.282809, accuracy 0.875000\n",
      "Validation accuracy 0.881250\n",
      "Time elapsed 2.194161319732666 minutes\n",
      "Epoch 36, training loss 0.456607, accuracy 0.796875\n",
      "Validation accuracy 0.863125\n",
      "Time elapsed 2.258034578959147 minutes\n",
      "Epoch 37, training loss 0.310868, accuracy 0.890625\n",
      "Validation accuracy 0.870625\n",
      "Time elapsed 2.333149834473928 minutes\n",
      "Epoch 38, training loss 0.314377, accuracy 0.843750\n",
      "Validation accuracy 0.871875\n",
      "Time elapsed 2.399438226222992 minutes\n",
      "Epoch 39, training loss 0.301095, accuracy 0.875000\n",
      "Validation accuracy 0.876250\n",
      "Time elapsed 2.4619465231895448 minutes\n",
      "Epoch 40, training loss 0.325036, accuracy 0.859375\n",
      "Validation accuracy 0.877500\n",
      "Time elapsed 2.520678186416626 minutes\n",
      "Epoch 41, training loss 0.248527, accuracy 0.890625\n",
      "Validation accuracy 0.875000\n",
      "Time elapsed 2.5767628073692324 minutes\n",
      "Epoch 42, training loss 0.245413, accuracy 0.937500\n",
      "Validation accuracy 0.873125\n",
      "Time elapsed 2.670619817574819 minutes\n",
      "Epoch 43, training loss 0.393122, accuracy 0.812500\n",
      "Validation accuracy 0.873750\n",
      "Time elapsed 2.7315677483876546 minutes\n",
      "Epoch 44, training loss 0.249207, accuracy 0.921875\n",
      "Validation accuracy 0.870625\n",
      "Time elapsed 2.805408243338267 minutes\n",
      "Epoch 45, training loss 0.205792, accuracy 0.937500\n",
      "Validation accuracy 0.886875\n",
      "Time elapsed 2.8647270878156026 minutes\n",
      "Epoch 46, training loss 0.235606, accuracy 0.921875\n",
      "Validation accuracy 0.879375\n",
      "Time elapsed 2.916892910003662 minutes\n",
      "Epoch 47, training loss 0.254367, accuracy 0.921875\n",
      "Validation accuracy 0.876250\n",
      "Time elapsed 2.9666683952013653 minutes\n",
      "Epoch 48, training loss 0.365377, accuracy 0.828125\n",
      "Validation accuracy 0.895625\n",
      "Time elapsed 3.0182009379069012 minutes\n",
      "Epoch 49, training loss 0.228493, accuracy 0.921875\n",
      "Validation accuracy 0.886875\n",
      "Time elapsed 3.0808252453804017 minutes\n",
      "Epoch 50, training loss 0.268214, accuracy 0.875000\n",
      "Validation accuracy 0.893750\n",
      "Time elapsed 3.1468119700749715 minutes\n",
      "Epoch 51, training loss 0.303850, accuracy 0.843750\n",
      "Validation accuracy 0.893750\n",
      "Time elapsed 3.20342853864034 minutes\n",
      "Epoch 52, training loss 0.390017, accuracy 0.875000\n",
      "Validation accuracy 0.883125\n",
      "Time elapsed 3.2578490138053895 minutes\n",
      "Epoch 53, training loss 0.261854, accuracy 0.906250\n",
      "Validation accuracy 0.898750\n",
      "Time elapsed 3.3166226506233216 minutes\n",
      "Epoch 54, training loss 0.364287, accuracy 0.828125\n",
      "Validation accuracy 0.898125\n",
      "Time elapsed 3.3692134380340577 minutes\n",
      "Epoch 55, training loss 0.204350, accuracy 0.937500\n",
      "Validation accuracy 0.896250\n",
      "Time elapsed 3.4255005995432537 minutes\n",
      "Epoch 56, training loss 0.207645, accuracy 0.921875\n",
      "Validation accuracy 0.899375\n",
      "Time elapsed 3.478791964054108 minutes\n",
      "Epoch 57, training loss 0.365605, accuracy 0.875000\n",
      "Validation accuracy 0.896875\n",
      "Time elapsed 3.529577390352885 minutes\n",
      "Epoch 58, training loss 0.303501, accuracy 0.921875\n",
      "Validation accuracy 0.890625\n",
      "Time elapsed 3.577399472395579 minutes\n",
      "Epoch 59, training loss 0.205840, accuracy 0.953125\n",
      "Validation accuracy 0.902500\n",
      "Time elapsed 3.6289462089538573 minutes\n",
      "Epoch 60, training loss 0.311635, accuracy 0.859375\n",
      "Validation accuracy 0.891250\n",
      "Time elapsed 3.7159771800041197 minutes\n",
      "Epoch 61, training loss 0.197521, accuracy 0.921875\n",
      "Validation accuracy 0.895000\n",
      "Time elapsed 3.7891589721043903 minutes\n",
      "Epoch 62, training loss 0.236989, accuracy 0.906250\n",
      "Validation accuracy 0.905625\n",
      "Time elapsed 3.8408659219741823 minutes\n",
      "Epoch 63, training loss 0.375318, accuracy 0.875000\n",
      "Validation accuracy 0.890625\n",
      "Time elapsed 3.8991506656010944 minutes\n",
      "Epoch 64, training loss 0.244704, accuracy 0.906250\n",
      "Validation accuracy 0.907500\n",
      "Time elapsed 3.952856481075287 minutes\n",
      "Epoch 65, training loss 0.258808, accuracy 0.921875\n",
      "Validation accuracy 0.897500\n",
      "Time elapsed 4.003615633646647 minutes\n",
      "Epoch 66, training loss 0.167354, accuracy 0.953125\n",
      "Validation accuracy 0.903750\n",
      "Time elapsed 4.053235205014547 minutes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 67, training loss 0.299833, accuracy 0.890625\n",
      "Validation accuracy 0.906250\n",
      "Time elapsed 4.104803144931793 minutes\n",
      "Epoch 68, training loss 0.375807, accuracy 0.875000\n",
      "Validation accuracy 0.903125\n",
      "Time elapsed 4.1778028090794885 minutes\n",
      "Epoch 69, training loss 0.369545, accuracy 0.859375\n",
      "Validation accuracy 0.900625\n",
      "Time elapsed 4.254761226971945 minutes\n",
      "Epoch 70, training loss 0.198646, accuracy 0.937500\n",
      "Validation accuracy 0.901875\n",
      "Time elapsed 4.331967616081238 minutes\n",
      "Epoch 71, training loss 0.195145, accuracy 0.937500\n",
      "Validation accuracy 0.893750\n",
      "Time elapsed 4.405069895585378 minutes\n",
      "Epoch 72, training loss 0.208758, accuracy 0.937500\n",
      "Validation accuracy 0.908750\n",
      "Time elapsed 4.475274622440338 minutes\n",
      "Epoch 73, training loss 0.198304, accuracy 0.921875\n",
      "Validation accuracy 0.908125\n",
      "Time elapsed 4.565276408195496 minutes\n",
      "Epoch 74, training loss 0.308459, accuracy 0.921875\n",
      "Validation accuracy 0.906875\n",
      "Time elapsed 4.653072261810303 minutes\n",
      "Epoch 75, training loss 0.323575, accuracy 0.890625\n",
      "Validation accuracy 0.910625\n",
      "Time elapsed 4.727624352773031 minutes\n",
      "Epoch 76, training loss 0.237209, accuracy 0.906250\n",
      "Validation accuracy 0.903125\n",
      "Time elapsed 4.804220529397329 minutes\n",
      "Epoch 77, training loss 0.226245, accuracy 0.875000\n",
      "Validation accuracy 0.907500\n",
      "Time elapsed 4.865924898783366 minutes\n",
      "Epoch 78, training loss 0.338366, accuracy 0.875000\n",
      "Validation accuracy 0.915000\n",
      "Time elapsed 4.93334550857544 minutes\n",
      "Epoch 79, training loss 0.239228, accuracy 0.906250\n",
      "Validation accuracy 0.913750\n",
      "Time elapsed 4.993176925182342 minutes\n",
      "Epoch 80, training loss 0.204495, accuracy 0.921875\n",
      "Validation accuracy 0.906875\n",
      "Time elapsed 5.082854239145915 minutes\n",
      "Epoch 81, training loss 0.205263, accuracy 0.906250\n",
      "Validation accuracy 0.898125\n",
      "Time elapsed 5.141890025138855 minutes\n",
      "Epoch 82, training loss 0.108899, accuracy 0.984375\n",
      "Validation accuracy 0.909375\n",
      "Time elapsed 5.1981507261594135 minutes\n",
      "Epoch 83, training loss 0.318515, accuracy 0.921875\n",
      "Validation accuracy 0.901250\n",
      "Time elapsed 5.256912501653035 minutes\n",
      "Epoch 84, training loss 0.231976, accuracy 0.921875\n",
      "Validation accuracy 0.913750\n",
      "Time elapsed 5.3087968826293945 minutes\n",
      "Epoch 85, training loss 0.246701, accuracy 0.875000\n",
      "Validation accuracy 0.913125\n",
      "Time elapsed 5.359840667247772 minutes\n",
      "Epoch 86, training loss 0.277602, accuracy 0.921875\n",
      "Validation accuracy 0.905000\n",
      "Time elapsed 5.410618249575297 minutes\n",
      "Epoch 87, training loss 0.197146, accuracy 0.921875\n",
      "Validation accuracy 0.903750\n",
      "Time elapsed 5.467148764928182 minutes\n",
      "Epoch 88, training loss 0.129490, accuracy 0.968750\n",
      "Validation accuracy 0.918125\n",
      "Time elapsed 5.555066597461701 minutes\n",
      "Epoch 89, training loss 0.170008, accuracy 0.953125\n",
      "Validation accuracy 0.918750\n",
      "Time elapsed 5.618832782904307 minutes\n",
      "Epoch 90, training loss 0.231311, accuracy 0.937500\n",
      "Validation accuracy 0.923750\n",
      "Time elapsed 5.700738636652629 minutes\n",
      "Epoch 91, training loss 0.229752, accuracy 0.906250\n",
      "Validation accuracy 0.924375\n",
      "Time elapsed 5.776722319920858 minutes\n",
      "Epoch 92, training loss 0.129019, accuracy 0.953125\n",
      "Validation accuracy 0.913750\n",
      "Time elapsed 5.894491183757782 minutes\n",
      "Epoch 93, training loss 0.305799, accuracy 0.875000\n",
      "Validation accuracy 0.915625\n",
      "Time elapsed 5.972881698608399 minutes\n",
      "Epoch 94, training loss 0.234001, accuracy 0.921875\n",
      "Validation accuracy 0.914375\n",
      "Time elapsed 6.045418719450633 minutes\n",
      "Epoch 95, training loss 0.215931, accuracy 0.937500\n",
      "Validation accuracy 0.911250\n",
      "Time elapsed 6.098806897799174 minutes\n",
      "Epoch 96, training loss 0.135011, accuracy 0.953125\n",
      "Validation accuracy 0.917500\n",
      "Time elapsed 6.157936831315358 minutes\n",
      "Epoch 97, training loss 0.250016, accuracy 0.906250\n",
      "Validation accuracy 0.917500\n",
      "Time elapsed 6.210141483942667 minutes\n",
      "Epoch 98, training loss 0.208781, accuracy 0.937500\n",
      "Validation accuracy 0.918750\n",
      "Time elapsed 6.264726535479228 minutes\n",
      "Epoch 99, training loss 0.175223, accuracy 0.937500\n",
      "Validation accuracy 0.909375\n",
      "Time elapsed 6.316046114762624 minutes\n",
      "Epoch 100, training loss 0.145547, accuracy 0.921875\n",
      "Validation accuracy 0.924375\n",
      "Time elapsed 6.374584702650706 minutes\n",
      "Epoch 101, training loss 0.301687, accuracy 0.859375\n",
      "Validation accuracy 0.920000\n",
      "Time elapsed 6.443470744291941 minutes\n",
      "Epoch 102, training loss 0.138559, accuracy 0.953125\n",
      "Validation accuracy 0.919375\n",
      "Time elapsed 6.505138818422953 minutes\n",
      "Epoch 103, training loss 0.233832, accuracy 0.937500\n",
      "Validation accuracy 0.916875\n",
      "Time elapsed 6.557949515183767 minutes\n",
      "Epoch 104, training loss 0.220929, accuracy 0.921875\n",
      "Validation accuracy 0.917500\n",
      "Time elapsed 6.613635472456614 minutes\n",
      "Epoch 105, training loss 0.166730, accuracy 0.937500\n",
      "Validation accuracy 0.920625\n",
      "Time elapsed 6.668753910064697 minutes\n",
      "Epoch 106, training loss 0.101864, accuracy 0.984375\n",
      "Validation accuracy 0.915625\n",
      "Time elapsed 6.7211169600486755 minutes\n",
      "Epoch 107, training loss 0.252043, accuracy 0.890625\n",
      "Validation accuracy 0.916250\n",
      "Time elapsed 6.780388748645782 minutes\n",
      "Epoch 108, training loss 0.176608, accuracy 0.921875\n",
      "Validation accuracy 0.917500\n",
      "Time elapsed 6.832398887475332 minutes\n",
      "Epoch 109, training loss 0.153697, accuracy 0.968750\n",
      "Validation accuracy 0.921250\n",
      "Time elapsed 6.887625849246978 minutes\n",
      "Epoch 110, training loss 0.094170, accuracy 0.984375\n",
      "Validation accuracy 0.912500\n",
      "Time elapsed 6.943309593200683 minutes\n",
      "Epoch 111, training loss 0.206339, accuracy 0.937500\n",
      "Validation accuracy 0.907500\n",
      "Time elapsed 6.996683526039123 minutes\n",
      "Epoch 112, training loss 0.166190, accuracy 0.953125\n",
      "Validation accuracy 0.920000\n",
      "Time elapsed 7.06070169210434 minutes\n",
      "Epoch 113, training loss 0.153678, accuracy 0.953125\n",
      "Validation accuracy 0.927500\n",
      "Time elapsed 7.11735448439916 minutes\n",
      "Epoch 114, training loss 0.192280, accuracy 0.921875\n",
      "Validation accuracy 0.921250\n",
      "Time elapsed 7.182292743523916 minutes\n",
      "Epoch 115, training loss 0.142899, accuracy 0.953125\n",
      "Validation accuracy 0.915000\n",
      "Time elapsed 7.238370831807455 minutes\n",
      "Epoch 116, training loss 0.294625, accuracy 0.875000\n",
      "Validation accuracy 0.919375\n",
      "Time elapsed 7.291255366802216 minutes\n",
      "Epoch 117, training loss 0.275666, accuracy 0.890625\n",
      "Validation accuracy 0.918750\n",
      "Time elapsed 7.342081240812937 minutes\n",
      "Epoch 118, training loss 0.205405, accuracy 0.906250\n",
      "Validation accuracy 0.915625\n",
      "Time elapsed 7.397328980763754 minutes\n",
      "Epoch 119, training loss 0.293441, accuracy 0.890625\n",
      "Validation accuracy 0.917500\n",
      "Time elapsed 7.450014913082123 minutes\n",
      "Epoch 120, training loss 0.206798, accuracy 0.921875\n",
      "Validation accuracy 0.917500\n",
      "Time elapsed 7.504194593429565 minutes\n",
      "Epoch 121, training loss 0.299174, accuracy 0.890625\n",
      "Validation accuracy 0.914375\n",
      "Time elapsed 7.575481220086416 minutes\n",
      "Epoch 122, training loss 0.261939, accuracy 0.890625\n",
      "Validation accuracy 0.921250\n",
      "Time elapsed 7.640464170773824 minutes\n",
      "Epoch 123, training loss 0.264318, accuracy 0.921875\n",
      "Validation accuracy 0.911250\n",
      "Time elapsed 7.705891994635264 minutes\n",
      "Epoch 124, training loss 0.332265, accuracy 0.859375\n",
      "Validation accuracy 0.923750\n",
      "Time elapsed 7.782332102457683 minutes\n",
      "Epoch 125, training loss 0.156563, accuracy 0.953125\n",
      "Validation accuracy 0.918125\n",
      "Time elapsed 7.887798706690471 minutes\n",
      "Epoch 126, training loss 0.170134, accuracy 0.937500\n",
      "Validation accuracy 0.926875\n",
      "Time elapsed 7.993697559833526 minutes\n",
      "Epoch 127, training loss 0.237407, accuracy 0.890625\n",
      "Validation accuracy 0.919375\n",
      "Time elapsed 8.059783244132996 minutes\n",
      "Epoch 128, training loss 0.224162, accuracy 0.906250\n",
      "Validation accuracy 0.928125\n",
      "Time elapsed 8.126891958713532 minutes\n",
      "Epoch 129, training loss 0.227982, accuracy 0.937500\n",
      "Validation accuracy 0.918750\n",
      "Time elapsed 8.190151278177897 minutes\n",
      "Epoch 130, training loss 0.223378, accuracy 0.921875\n",
      "Validation accuracy 0.934375\n",
      "Time elapsed 8.267099130153657 minutes\n",
      "Epoch 131, training loss 0.187678, accuracy 0.937500\n",
      "Validation accuracy 0.926250\n",
      "Time elapsed 8.321428565184275 minutes\n",
      "Epoch 132, training loss 0.157350, accuracy 0.921875\n",
      "Validation accuracy 0.925625\n",
      "Time elapsed 8.382214470704396 minutes\n",
      "Epoch 133, training loss 0.280100, accuracy 0.890625\n",
      "Validation accuracy 0.917500\n",
      "Time elapsed 8.463395889600118 minutes\n",
      "Epoch 134, training loss 0.218998, accuracy 0.906250\n",
      "Validation accuracy 0.914375\n",
      "Time elapsed 8.560709186395009 minutes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 135, training loss 0.241285, accuracy 0.937500\n",
      "Validation accuracy 0.920625\n",
      "Time elapsed 8.649202318986257 minutes\n",
      "Epoch 136, training loss 0.285088, accuracy 0.890625\n",
      "Validation accuracy 0.926875\n",
      "Time elapsed 8.724862464269002 minutes\n",
      "Epoch 137, training loss 0.149086, accuracy 0.921875\n",
      "Validation accuracy 0.915000\n",
      "Time elapsed 8.824643778800965 minutes\n",
      "Epoch 138, training loss 0.196639, accuracy 0.921875\n",
      "Validation accuracy 0.926875\n",
      "Time elapsed 8.906104485193888 minutes\n",
      "Epoch 139, training loss 0.125654, accuracy 0.968750\n",
      "Validation accuracy 0.922500\n",
      "Time elapsed 9.001228257020314 minutes\n",
      "Epoch 140, training loss 0.210401, accuracy 0.937500\n",
      "Validation accuracy 0.920625\n",
      "Time elapsed 9.073844730854034 minutes\n",
      "Epoch 141, training loss 0.168510, accuracy 0.937500\n",
      "Validation accuracy 0.926875\n",
      "Time elapsed 9.14050660530726 minutes\n",
      "Epoch 142, training loss 0.226160, accuracy 0.890625\n",
      "Validation accuracy 0.929375\n",
      "Time elapsed 9.213860762119293 minutes\n",
      "Epoch 143, training loss 0.169731, accuracy 0.953125\n",
      "Validation accuracy 0.930625\n",
      "Time elapsed 9.306748926639557 minutes\n",
      "Epoch 144, training loss 0.145729, accuracy 0.953125\n",
      "Validation accuracy 0.925000\n",
      "Time elapsed 9.393833553791046 minutes\n",
      "Epoch 145, training loss 0.173021, accuracy 0.937500\n",
      "Validation accuracy 0.923750\n",
      "Time elapsed 9.474670004844665 minutes\n",
      "Epoch 146, training loss 0.208167, accuracy 0.937500\n",
      "Validation accuracy 0.926250\n",
      "Time elapsed 9.532886862754822 minutes\n",
      "Epoch 147, training loss 0.248473, accuracy 0.937500\n",
      "Validation accuracy 0.925000\n",
      "Time elapsed 9.594130357106527 minutes\n",
      "Epoch 148, training loss 0.185001, accuracy 0.921875\n",
      "Validation accuracy 0.921875\n",
      "Time elapsed 9.644585045178731 minutes\n",
      "Epoch 149, training loss 0.189956, accuracy 0.937500\n",
      "Validation accuracy 0.920625\n",
      "Time elapsed 9.70099798043569 minutes\n",
      "Epoch 150, training loss 0.156911, accuracy 0.953125\n",
      "Validation accuracy 0.926250\n",
      "Time elapsed 9.753947408994039 minutes\n",
      "Epoch 151, training loss 0.126020, accuracy 0.953125\n",
      "Validation accuracy 0.929375\n",
      "Time elapsed 9.807068916161855 minutes\n",
      "Epoch 152, training loss 0.205423, accuracy 0.906250\n",
      "Validation accuracy 0.924375\n",
      "Time elapsed 9.86240041255951 minutes\n",
      "Epoch 153, training loss 0.277662, accuracy 0.921875\n",
      "Validation accuracy 0.923125\n",
      "Time elapsed 9.913631018002828 minutes\n",
      "Epoch 154, training loss 0.144401, accuracy 0.953125\n",
      "Validation accuracy 0.928750\n",
      "Time elapsed 9.96249114672343 minutes\n",
      "Epoch 155, training loss 0.244841, accuracy 0.921875\n",
      "Validation accuracy 0.910000\n",
      "Time elapsed 10.01219551563263 minutes\n",
      "Epoch 156, training loss 0.180166, accuracy 0.953125\n",
      "Validation accuracy 0.930625\n",
      "Time elapsed 10.062472077210744 minutes\n",
      "Epoch 157, training loss 0.153160, accuracy 0.953125\n",
      "Validation accuracy 0.924375\n",
      "Time elapsed 10.110981086889902 minutes\n",
      "Epoch 158, training loss 0.183717, accuracy 0.921875\n",
      "Validation accuracy 0.930000\n",
      "Time elapsed 10.162057340145111 minutes\n",
      "Epoch 159, training loss 0.204667, accuracy 0.921875\n",
      "Validation accuracy 0.926250\n",
      "Time elapsed 10.232270276546478 minutes\n",
      "Epoch 160, training loss 0.159132, accuracy 0.968750\n",
      "Validation accuracy 0.923750\n",
      "Time elapsed 10.302210895220439 minutes\n",
      "Epoch 161, training loss 0.126430, accuracy 0.953125\n",
      "Validation accuracy 0.927500\n",
      "Time elapsed 10.371064166227976 minutes\n",
      "Epoch 162, training loss 0.219020, accuracy 0.921875\n",
      "Validation accuracy 0.924375\n",
      "Time elapsed 10.438592199484507 minutes\n",
      "Epoch 163, training loss 0.086434, accuracy 0.968750\n",
      "Validation accuracy 0.930000\n",
      "Time elapsed 10.50839167435964 minutes\n",
      "Epoch 164, training loss 0.177692, accuracy 0.921875\n",
      "Validation accuracy 0.915625\n",
      "Time elapsed 10.620499483744304 minutes\n",
      "Epoch 165, training loss 0.152749, accuracy 0.921875\n",
      "Validation accuracy 0.928125\n",
      "Time elapsed 10.709675121307374 minutes\n",
      "Epoch 166, training loss 0.173491, accuracy 0.921875\n",
      "Validation accuracy 0.938125\n",
      "Time elapsed 10.806787784894308 minutes\n",
      "Epoch 167, training loss 0.204738, accuracy 0.921875\n",
      "Validation accuracy 0.927500\n",
      "Time elapsed 10.874233106772104 minutes\n",
      "Epoch 168, training loss 0.126452, accuracy 0.953125\n",
      "Validation accuracy 0.924375\n",
      "Time elapsed 10.944906862576802 minutes\n",
      "Epoch 169, training loss 0.171207, accuracy 0.937500\n",
      "Validation accuracy 0.921875\n",
      "Time elapsed 11.030448691050212 minutes\n",
      "Epoch 170, training loss 0.223251, accuracy 0.921875\n",
      "Validation accuracy 0.918125\n",
      "Time elapsed 11.112321650981903 minutes\n",
      "Epoch 171, training loss 0.121110, accuracy 0.968750\n",
      "Validation accuracy 0.923125\n",
      "Time elapsed 11.19939565261205 minutes\n",
      "Epoch 172, training loss 0.094772, accuracy 0.984375\n",
      "Validation accuracy 0.921875\n",
      "Time elapsed 11.289602637290955 minutes\n",
      "Epoch 173, training loss 0.144854, accuracy 0.937500\n",
      "Validation accuracy 0.926875\n",
      "Time elapsed 11.382733805974324 minutes\n",
      "Epoch 174, training loss 0.247934, accuracy 0.906250\n",
      "Validation accuracy 0.933750\n",
      "Time elapsed 11.465780794620514 minutes\n",
      "Epoch 175, training loss 0.305865, accuracy 0.875000\n",
      "Validation accuracy 0.890625\n",
      "Time elapsed 11.53692661523819 minutes\n",
      "Epoch 176, training loss 0.152215, accuracy 0.937500\n",
      "Validation accuracy 0.927500\n",
      "Time elapsed 11.607089992364248 minutes\n",
      "Epoch 177, training loss 0.163930, accuracy 0.968750\n",
      "Validation accuracy 0.925000\n",
      "Time elapsed 11.681053868929546 minutes\n",
      "Epoch 178, training loss 0.129343, accuracy 0.968750\n",
      "Validation accuracy 0.930625\n",
      "Time elapsed 11.753252267837524 minutes\n",
      "Epoch 179, training loss 0.208814, accuracy 0.921875\n",
      "Validation accuracy 0.934375\n",
      "Time elapsed 11.846823398272196 minutes\n",
      "Epoch 180, training loss 0.106767, accuracy 0.968750\n",
      "Validation accuracy 0.926875\n",
      "Time elapsed 11.933320248126984 minutes\n",
      "Epoch 181, training loss 0.244273, accuracy 0.921875\n",
      "Validation accuracy 0.926875\n",
      "Time elapsed 12.005327053864796 minutes\n",
      "Epoch 182, training loss 0.185757, accuracy 0.921875\n",
      "Validation accuracy 0.933750\n",
      "Time elapsed 12.089572203159332 minutes\n",
      "Epoch 183, training loss 0.258871, accuracy 0.906250\n",
      "Validation accuracy 0.925000\n",
      "Time elapsed 12.160527916749318 minutes\n",
      "Epoch 184, training loss 0.118962, accuracy 0.968750\n",
      "Validation accuracy 0.927500\n",
      "Time elapsed 12.233240203062694 minutes\n",
      "Epoch 185, training loss 0.088439, accuracy 0.984375\n",
      "Validation accuracy 0.928125\n",
      "Time elapsed 12.333096909523011 minutes\n",
      "Epoch 186, training loss 0.217606, accuracy 0.890625\n",
      "Validation accuracy 0.925000\n",
      "Time elapsed 12.410310804843903 minutes\n",
      "Epoch 187, training loss 0.210586, accuracy 0.906250\n",
      "Validation accuracy 0.928125\n",
      "Time elapsed 12.47268313964208 minutes\n",
      "Epoch 188, training loss 0.157730, accuracy 0.953125\n",
      "Validation accuracy 0.928750\n",
      "Time elapsed 12.530013008912404 minutes\n",
      "Epoch 189, training loss 0.141123, accuracy 0.953125\n",
      "Validation accuracy 0.934375\n",
      "Time elapsed 12.604293918609619 minutes\n",
      "Epoch 190, training loss 0.183879, accuracy 0.906250\n",
      "Validation accuracy 0.933750\n",
      "Time elapsed 12.706884463628134 minutes\n",
      "Epoch 191, training loss 0.133083, accuracy 0.953125\n",
      "Validation accuracy 0.930000\n",
      "Time elapsed 12.804950265089671 minutes\n",
      "Epoch 192, training loss 0.143411, accuracy 0.968750\n",
      "Validation accuracy 0.924375\n",
      "Time elapsed 12.89148158232371 minutes\n",
      "Epoch 193, training loss 0.127389, accuracy 0.968750\n",
      "Validation accuracy 0.928750\n",
      "Time elapsed 12.972168978055318 minutes\n",
      "Epoch 194, training loss 0.199637, accuracy 0.937500\n",
      "Validation accuracy 0.935625\n",
      "Time elapsed 13.055107112725576 minutes\n",
      "Epoch 195, training loss 0.151079, accuracy 0.937500\n",
      "Validation accuracy 0.918750\n",
      "Time elapsed 13.1650417526563 minutes\n",
      "Epoch 196, training loss 0.130499, accuracy 0.968750\n",
      "Validation accuracy 0.931875\n",
      "Time elapsed 13.245586415131887 minutes\n",
      "Epoch 197, training loss 0.207765, accuracy 0.921875\n",
      "Validation accuracy 0.928125\n",
      "Time elapsed 13.330844942728678 minutes\n",
      "Epoch 198, training loss 0.179873, accuracy 0.937500\n",
      "Validation accuracy 0.933125\n",
      "Time elapsed 13.410518213113148 minutes\n",
      "Epoch 199, training loss 0.133228, accuracy 0.968750\n",
      "Validation accuracy 0.933750\n",
      "Time elapsed 13.484877149264017 minutes\n",
      "Testing set accuracy 0.915179\n"
     ]
    }
   ],
   "source": [
    "# Train model\n",
    "merged = tf.summary.merge_all()\n",
    "train_writer = tf.summary.FileWriter(SUMMARIES_DIR+'/train',\n",
    "                                     sess.graph)\n",
    "validation_writer = tf.summary.FileWriter(SUMMARIES_DIR+'/validation')\n",
    "sess.run(tf.global_variables_initializer())\n",
    "cifar10.reset()\n",
    "print(\"Trainable variables\")\n",
    "for n in tf.trainable_variables():\n",
    "    print(n.name)\n",
    "\n",
    "epochs = 200\n",
    "mean_gradients = np.zeros([len(tf.trainable_variables()), epochs])\n",
    "std_gradients = np.zeros([len(tf.trainable_variables()), epochs])\n",
    "\n",
    "t_i = time.time()\n",
    "n_batches = cifar10.n_batches\n",
    "while cifar10.getEpoch() < epochs:\n",
    "    epoch = cifar10.getEpoch()\n",
    "    batch, batch_idx = cifar10.nextBatch()\n",
    "    batch_data = batch[0]\n",
    "    batch_labels = batch[1]\n",
    "    \n",
    "    # just a training iteration\n",
    "    _ = sess.run(train_step,\n",
    "                feed_dict={\n",
    "            model_input: batch_data,\n",
    "            target: batch_labels,\n",
    "            keep_prob: 0.5\n",
    "        })\n",
    "    \n",
    "    step = batch_idx+epoch*n_batches\n",
    "    \n",
    "    # Write training summary\n",
    "    if step%50==0:\n",
    "        summary = sess.run(merged,\n",
    "                          feed_dict={\n",
    "                model_input: batch_data,\n",
    "                target: batch_labels,\n",
    "                keep_prob: 0.5 # set to 1.0 at inference time\n",
    "            })\n",
    "        train_writer.add_summary(summary, step)\n",
    "        \n",
    "    # gradient (by layer) statistics over last training batch & validation summary\n",
    "    if batch_idx==0:\n",
    "        loss, acc, grads = sess.run((cross_entropy, accuracy, grads_vars), \n",
    "                      feed_dict={\n",
    "            model_input: batch_data,\n",
    "            target: batch_labels,\n",
    "            keep_prob: 1.0\n",
    "        })\n",
    "\n",
    "        for layer in range(len(tf.trainable_variables())):\n",
    "            mean_gradients[layer, epoch] = np.mean(np.abs(grads[layer][0]))\n",
    "            std_gradients[layer, epoch] = np.std(np.abs(grads[layer][0]))\n",
    "        print(\"Epoch %d, training loss %f, accuracy %f\" % (epoch, loss, acc))\n",
    "        \n",
    "        summary, validation_accuracy = validate()\n",
    "        validation_writer.add_summary(summary, step)\n",
    "        print(\"Validation accuracy %f\" % validation_accuracy)\n",
    "        print(\"Time elapsed\", (time.time()-t_i)/60.0, \"minutes\")\n",
    "train_writer.flush()\n",
    "validation_writer.flush()\n",
    "test_acc = test()\n",
    "print(\"Testing set accuracy %f\" % test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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L4kuNW4SWRKykkLMSgqzmeJTzFoigSK9Ric+n9eWOW8WnrR3k5DX95CwrjBP1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83xtfR7PPS21aIgpxVdIM13asaWGbjVmRuLFmk6KvOwQrHb7VXJdsx+FsKvEYqcE+n7dt3uWi1l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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f417fa98358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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MTWjyKgcDTLx0RS1QKHG7fEhWw7ForAf8J/Ej5wx+t30w+q7ieMwVc48mCt+9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\nas0woYdBdaTznr1BQHFCgAX/lz3we2Hkc5RXQD4vhyhi8ZSn9kJACd8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n6rt4tHar1DDh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TePsuw46wR/EXd5W+6VWXuVJEtVsiUbSzYWi8GAMWuDjacxCDPNkehhMfT0NNOMu8/p6dOIBuYM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\nLAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4175145d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting gradients\n",
    "n_layers = len(tf.trainable_variables()) // 2\n",
    "x = np.arange(epochs)\n",
    "i = 0\n",
    "plt.figure()\n",
    "while i < n_layers*2:\n",
    "    plt.errorbar(x,mean_gradients[i,:],std_gradients[i,:])\n",
    "    i = i + 2\n",
    "plt.yscale('log')\n",
    "plt.ylabel('Gradient')\n",
    "plt.xlabel('Epochs')\n",
    "plt.title('Weights Gradient by Layer')\n",
    "plt.legend([\"conv1\",\"conv2\",\"fc1\",\"fc2\"][-n_layers:])\n",
    "plt.xlim(-0.2, epochs)\n",
    "plt.show()\n",
    "i = 1\n",
    "plt.figure()\n",
    "while i < n_layers*2:\n",
    "    plt.errorbar(x,mean_gradients[i,:],std_gradients[i,:])\n",
    "    i = i + 2\n",
    "plt.yscale('log')\n",
    "plt.ylabel('Gradient')\n",
    "plt.xlabel('Epochs')\n",
    "plt.title('Biases Gradient by Layer')\n",
    "plt.legend([\"conv1\",\"conv2\",\"fc1\",\"fc2\"][-n_layers:])\n",
    "plt.xlim(-0.2, epochs)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'CIFAR10' object has no attribute 'training_data'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-58da638e24cd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     25\u001b[0m accs = sess.run(accuracy,\n\u001b[1;32m     26\u001b[0m              feed_dict={\n\u001b[0;32m---> 27\u001b[0;31m             \u001b[0mmodel_input\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcifar10\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_data\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m                        \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcifar10\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_labels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m             \u001b[0mkeep_prob\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'CIFAR10' object has no attribute 'training_data'"
     ]
    }
   ],
   "source": [
    "def confusion_matrix(targets, outputs):\n",
    "    '''Returns a confusion matrix. Both targets and outputs\n",
    "    should be 1-D arrays of zeros and ones.'''\n",
    "    encoded_data = 2*targets+outputs  # Map targets and outputs to {0, 1, 2, 3}\n",
    "    TN = np.sum(encoded_data == 0)  # True negatives\n",
    "    FP = np.sum(encoded_data == 1)  # False positives\n",
    "    FN = np.sum(encoded_data == 2)  # False negatives\n",
    "    TP = np.sum(encoded_data == 3)  # True positives\n",
    "    return ((TP, FP), (FN, TN))\n",
    "\n",
    "def roc_curve(targets, outputs):\n",
    "    '''Returns a ROC curve. Outputs should be in range 0-1\n",
    "    in order to move the threshold.'''\n",
    "    tpr = []\n",
    "    fpr = []\n",
    "    for threshold in np.linspace(0, 1, 1000):\n",
    "        outputs_with_threshold = (outputs > threshold).astype(np.float)\n",
    "        ((TP, FP), (FN, TN)) = confusion_matrix(\n",
    "            targets, \n",
    "            outputs_with_threshold)\n",
    "        tpr.append(TP/(TP+FN))\n",
    "        fpr.append(FP/(FP+TN))\n",
    "    return np.array(tpr), np.array(fpr)\n",
    "\n",
    "accs = sess.run(accuracy,\n",
    "             feed_dict={\n",
    "            model_input: cifar10.training_data,\n",
    "                       target: cifar10.training_labels,\n",
    "            keep_prob: 1.0\n",
    "        })\n",
    "\n",
    "accs = np.asarray(accs)\n",
    "[[VN,FP],[FN,VP]] = confusion_matrix(\n",
    "    accs,\n",
    "    (accs>0.5).astype(np.float))\n",
    "print('VP: %d, VN: %d, FP: %d, FN: %d' %(VP,VN,FP,FN))\n",
    "print('Porcentaje de clasificaciones correctas: %%%f' %(100.0*(VP+VN)/(VP+VN+FP+FN)))\n",
    "print('Precision: %%%f' %(100.0*VP/(VP+FP)))\n",
    "print('Recall: %%%f' %(100.0*VP/(VP+FN)))\n",
    "print('')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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