HAR-CNN-Inception.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HAR CNN training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import numpy as np\n",
    "import os\n",
    "from utils.utilities import *\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gabriele/Documents/TestingArea/DeepLearningHAR/utils/utilities.py:37: FutureWarning: Method .as_matrix will be removed in a future version. Use .values instead.\n",
      "  X[:,:,i_ch] = dat_.as_matrix()\n"
     ]
    }
   ],
   "source": [
    "X_train, labels_train, list_ch_train = read_data(data_path=\"./UCIHAR/\", split=\"train\") # train\n",
    "X_test, labels_test, list_ch_test = read_data(data_path=\"./UCIHAR/\", split=\"test\") # test\n",
    "\n",
    "assert list_ch_train == list_ch_test, \"Mistmatch in channels!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize\n",
    "X_train, X_test = standardize(X_train, X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Train/Validation Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_tr, X_vld, lab_tr, lab_vld = train_test_split(X_train, labels_train, \n",
    "                                                stratify = labels_train, random_state = 123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One-hot encoding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_tr = one_hot(lab_tr)\n",
    "y_vld = one_hot(lab_vld)\n",
    "y_test = one_hot(labels_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 600       # Batch size\n",
    "seq_len = 128          # Number of steps\n",
    "learning_rate = 0.0001\n",
    "epochs =1000\n",
    "\n",
    "n_classes = 6\n",
    "n_channels = 9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construct the graph\n",
    "Placeholders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = tf.Graph()\n",
    "\n",
    "# Construct placeholders\n",
    "with graph.as_default():\n",
    "    inputs_ = tf.placeholder(tf.float32, [None, seq_len, n_channels], name = 'inputs')\n",
    "    labels_ = tf.placeholder(tf.float32, [None, n_classes], name = 'labels')\n",
    "    keep_prob_ = tf.placeholder(tf.float32, name = 'keep')\n",
    "    learning_rate_ = tf.placeholder(tf.float32, name = 'learning_rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build Convolutional Layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "with graph.as_default():\n",
    "    # (batch, 128, 9) --> (batch, 64, 18)\n",
    "    conv1 = tf.layers.conv1d(inputs=inputs_, filters=18, kernel_size=2, strides=1, \n",
    "                             padding='same', activation = tf.nn.relu)\n",
    "    max_pool_1 = tf.layers.max_pooling1d(inputs=conv1, pool_size=2, strides=2, padding='same')\n",
    "    \n",
    "    # (batch, 64, 18) --> (batch, 32, 18)\n",
    "    conv2 = tf.layers.conv1d(inputs=max_pool_1, filters=18, kernel_size=2, strides=1, \n",
    "                             padding='same', activation = tf.nn.relu)\n",
    "    max_pool_2 = tf.layers.max_pooling1d(inputs=conv2, pool_size=2, strides=2, padding='same')\n",
    "    \n",
    "    # (batch, 32, 18) --> (batch, 16, 36)\n",
    "    conv3 = tf.layers.conv1d(inputs=max_pool_2, filters=36, kernel_size=2, strides=1, \n",
    "                             padding='same', activation = tf.nn.relu)\n",
    "    max_pool_3 = tf.layers.max_pooling1d(inputs=conv3, pool_size=2, strides=2, padding='same')\n",
    "    \n",
    "    # (batch, 16, 36) --> (batch, 8, 36)\n",
    "    conv4 = tf.layers.conv1d(inputs=max_pool_3, filters=36, kernel_size=2, strides=1, \n",
    "                             padding='same', activation = tf.nn.relu)\n",
    "    max_pool_4 = tf.layers.max_pooling1d(inputs=conv4, pool_size=2, strides=2, padding='same')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build the inception layer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](img/HAR_inception.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "with graph.as_default():\n",
    "    \n",
    "    # convolution: input to output of inception (size=1)\n",
    "    # (batch, 8, 36) --> (batch, 8, 36)\n",
    "    conv1_11 = tf.layers.conv1d(inputs=max_pool_4, filters=36, kernel_size=1, strides=1,\n",
    "                               padding='same', activation = tf.nn.relu)\n",
    "    \n",
    "    # convolution: input to middle layer of inception (size=1)\n",
    "    # (batch, 8, 36) --> (batch, 8, 18)\n",
    "    conv1_21 = tf.layers.conv1d(inputs=max_pool_4, filters=18, kernel_size=1, strides=1,\n",
    "                               padding='same', activation = tf.nn.relu)\n",
    "    \n",
    "    # convolution: input to middle layer of inception (size=1)\n",
    "    # (batch, 8, 36) --> (batch, 8, 18)\n",
    "    conv1_31 = tf.layers.conv1d(inputs=max_pool_4, filters=18, kernel_size=1, strides=1,\n",
    "                               padding='same', activation = tf.nn.relu)\n",
    "    \n",
    "    # average pool: input to middle layer of inception\n",
    "    # (batch, 8, 36) --> (batch, 8, 36)\n",
    "    avg_pool_41 = tf.layers.average_pooling1d(inputs=max_pool_4, pool_size=2, strides=1, padding='same')\n",
    "    \n",
    "    ## Middle layer of inception\n",
    "    \n",
    "    # convolution: middle to out layer of inception (size=2)\n",
    "    # (batch, 8, 18) --> (batch, 8, 36)\n",
    "    conv2_22 = tf.layers.conv1d(inputs=conv1_21, filters=36, kernel_size=2, strides=1,\n",
    "                               padding='same', activation=tf.nn.relu)\n",
    "    \n",
    "    # convolution: middle to out layer of inception (size=4)\n",
    "    # (batch, 8, 18) --> (batch, 8, 36)\n",
    "    conv4_32 = tf.layers.conv1d(inputs=conv1_31, filters=36, kernel_size=4, strides=1,\n",
    "                               padding='same', activation=tf.nn.relu)\n",
    "    \n",
    "    # convolution: middle to out layer of inception (size=1)\n",
    "    # (batch, 8, 36) --> (batch, 8, 36)\n",
    "    conv1_42 = tf.layers.conv1d(inputs=avg_pool_41, filters=36, kernel_size=1, strides=1,\n",
    "                               padding='same', activation=tf.nn.relu)\n",
    "    \n",
    "    ## Out layer: Concatenate filters\n",
    "    # (batch, 8, 4*36)\n",
    "    inception_out = tf.concat([conv1_11, conv2_22, conv4_32, conv1_42], axis=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, flatten and pass to the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "with graph.as_default():\n",
    "    # Flatten and add dropout\n",
    "    flat = tf.reshape(inception_out, (-1, 8*144))\n",
    "    flat = tf.nn.dropout(flat, keep_prob=keep_prob_)\n",
    "    \n",
    "    # Predictions\n",
    "    logits = tf.layers.dense(flat, n_classes)\n",
    "    \n",
    "    # Cost function and optimizer\n",
    "    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels_))\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate_).minimize(cost)\n",
    "    \n",
    "    # Accuracy\n",
    "    correct_pred = tf.equal(tf.argmax(logits, 1), tf.argmax(labels_, 1))\n",
    "    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32), name='accuracy')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "if (os.path.exists('checkpoints-cnn') == False):\n",
    "    !mkdir checkpoints-cnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0/1000 Iteration: 5 Train loss: 1.782729 Train acc: 0.128333\n",
      "Epoch: 1/1000 Iteration: 10 Train loss: 1.755476 Train acc: 0.128333\n",
      "Epoch: 1/1000 Iteration: 10 Validation loss: 1.715920 Validation acc: 0.142778\n",
      "Epoch: 1/1000 Iteration: 15 Train loss: 1.722988 Train acc: 0.150000\n",
      "Epoch: 2/1000 Iteration: 20 Train loss: 1.693135 Train acc: 0.156667\n",
      "Epoch: 2/1000 Iteration: 20 Validation loss: 1.673748 Validation acc: 0.161667\n",
      "Epoch: 2/1000 Iteration: 25 Train loss: 1.675700 Train acc: 0.161667\n",
      "Epoch: 3/1000 Iteration: 30 Train loss: 1.676574 Train acc: 0.151667\n",
      "Epoch: 3/1000 Iteration: 30 Validation loss: 1.638451 Validation acc: 0.172778\n",
      "Epoch: 3/1000 Iteration: 35 Train loss: 1.644043 Train acc: 0.180000\n",
      "Epoch: 4/1000 Iteration: 40 Train loss: 1.612645 Train acc: 0.198333\n",
      "Epoch: 4/1000 Iteration: 40 Validation loss: 1.607058 Validation acc: 0.193889\n",
      "Epoch: 4/1000 Iteration: 45 Train loss: 1.619012 Train acc: 0.208333\n",
      "Epoch: 5/1000 Iteration: 50 Train loss: 1.620923 Train acc: 0.248333\n",
      "Epoch: 5/1000 Iteration: 50 Validation loss: 1.577163 Validation acc: 0.315000\n",
      "Epoch: 6/1000 Iteration: 55 Train loss: 1.607747 Train acc: 0.241667\n",
      "Epoch: 6/1000 Iteration: 60 Train loss: 1.574476 Train acc: 0.296667\n",
      "Epoch: 6/1000 Iteration: 60 Validation loss: 1.546931 Validation acc: 0.362222\n",
      "Epoch: 7/1000 Iteration: 65 Train loss: 1.538880 Train acc: 0.323333\n",
      "Epoch: 7/1000 Iteration: 70 Train loss: 1.540610 Train acc: 0.310000\n",
      "Epoch: 7/1000 Iteration: 70 Validation loss: 1.514241 Validation acc: 0.387778\n",
      "Epoch: 8/1000 Iteration: 75 Train loss: 1.534408 Train acc: 0.315000\n",
      "Epoch: 8/1000 Iteration: 80 Train loss: 1.514040 Train acc: 0.336667\n",
      "Epoch: 8/1000 Iteration: 80 Validation loss: 1.477019 Validation acc: 0.408889\n",
      "Epoch: 9/1000 Iteration: 85 Train loss: 1.471346 Train acc: 0.370000\n",
      "Epoch: 9/1000 Iteration: 90 Train loss: 1.475702 Train acc: 0.341667\n",
      "Epoch: 9/1000 Iteration: 90 Validation loss: 1.433619 Validation acc: 0.426111\n",
      "Epoch: 10/1000 Iteration: 95 Train loss: 1.430155 Train acc: 0.408333\n",
      "Epoch: 11/1000 Iteration: 100 Train loss: 1.428187 Train acc: 0.371667\n",
      "Epoch: 11/1000 Iteration: 100 Validation loss: 1.382660 Validation acc: 0.444444\n",
      "Epoch: 11/1000 Iteration: 105 Train loss: 1.391954 Train acc: 0.413333\n",
      "Epoch: 12/1000 Iteration: 110 Train loss: 1.342060 Train acc: 0.456667\n",
      "Epoch: 12/1000 Iteration: 110 Validation loss: 1.325499 Validation acc: 0.492222\n",
      "Epoch: 12/1000 Iteration: 115 Train loss: 1.339030 Train acc: 0.406667\n",
      "Epoch: 13/1000 Iteration: 120 Train loss: 1.331775 Train acc: 0.435000\n",
      "Epoch: 13/1000 Iteration: 120 Validation loss: 1.264100 Validation acc: 0.555556\n",
      "Epoch: 13/1000 Iteration: 125 Train loss: 1.281301 Train acc: 0.466667\n",
      "Epoch: 14/1000 Iteration: 130 Train loss: 1.266640 Train acc: 0.470000\n",
      "Epoch: 14/1000 Iteration: 130 Validation loss: 1.202367 Validation acc: 0.615556\n",
      "Epoch: 14/1000 Iteration: 135 Train loss: 1.251748 Train acc: 0.478333\n",
      "Epoch: 15/1000 Iteration: 140 Train loss: 1.160153 Train acc: 0.566667\n",
      "Epoch: 15/1000 Iteration: 140 Validation loss: 1.142803 Validation acc: 0.669445\n",
      "Epoch: 16/1000 Iteration: 145 Train loss: 1.151682 Train acc: 0.585000\n",
      "Epoch: 16/1000 Iteration: 150 Train loss: 1.114771 Train acc: 0.615000\n",
      "Epoch: 16/1000 Iteration: 150 Validation loss: 1.085395 Validation acc: 0.712778\n",
      "Epoch: 17/1000 Iteration: 155 Train loss: 1.073405 Train acc: 0.631667\n",
      "Epoch: 17/1000 Iteration: 160 Train loss: 1.075017 Train acc: 0.645000\n",
      "Epoch: 17/1000 Iteration: 160 Validation loss: 1.028056 Validation acc: 0.755000\n",
      "Epoch: 18/1000 Iteration: 165 Train loss: 1.068654 Train acc: 0.645000\n",
      "Epoch: 18/1000 Iteration: 170 Train loss: 1.046791 Train acc: 0.685000\n",
      "Epoch: 18/1000 Iteration: 170 Validation loss: 0.968533 Validation acc: 0.778333\n",
      "Epoch: 19/1000 Iteration: 175 Train loss: 1.011326 Train acc: 0.673333\n",
      "Epoch: 19/1000 Iteration: 180 Train loss: 0.996144 Train acc: 0.695000\n",
      "Epoch: 19/1000 Iteration: 180 Validation loss: 0.906134 Validation acc: 0.802222\n",
      "Epoch: 20/1000 Iteration: 185 Train loss: 0.902338 Train acc: 0.760000\n",
      "Epoch: 21/1000 Iteration: 190 Train loss: 0.898708 Train acc: 0.743333\n",
      "Epoch: 21/1000 Iteration: 190 Validation loss: 0.840957 Validation acc: 0.820000\n",
      "Epoch: 21/1000 Iteration: 195 Train loss: 0.836502 Train acc: 0.776667\n",
      "Epoch: 22/1000 Iteration: 200 Train loss: 0.799009 Train acc: 0.770000\n",
      "Epoch: 22/1000 Iteration: 200 Validation loss: 0.773852 Validation acc: 0.842222\n",
      "Epoch: 22/1000 Iteration: 205 Train loss: 0.809178 Train acc: 0.768333\n",
      "Epoch: 23/1000 Iteration: 210 Train loss: 0.784779 Train acc: 0.780000\n",
      "Epoch: 23/1000 Iteration: 210 Validation loss: 0.706205 Validation acc: 0.850556\n",
      "Epoch: 23/1000 Iteration: 215 Train loss: 0.736922 Train acc: 0.793333\n",
      "Epoch: 24/1000 Iteration: 220 Train loss: 0.710785 Train acc: 0.795000\n",
      "Epoch: 24/1000 Iteration: 220 Validation loss: 0.640128 Validation acc: 0.862778\n",
      "Epoch: 24/1000 Iteration: 225 Train loss: 0.707757 Train acc: 0.808333\n",
      "Epoch: 25/1000 Iteration: 230 Train loss: 0.620367 Train acc: 0.815000\n",
      "Epoch: 25/1000 Iteration: 230 Validation loss: 0.576647 Validation acc: 0.868333\n",
      "Epoch: 26/1000 Iteration: 235 Train loss: 0.592635 Train acc: 0.820000\n",
      "Epoch: 26/1000 Iteration: 240 Train loss: 0.540137 Train acc: 0.843333\n",
      "Epoch: 26/1000 Iteration: 240 Validation loss: 0.518426 Validation acc: 0.877778\n",
      "Epoch: 27/1000 Iteration: 245 Train loss: 0.554699 Train acc: 0.825000\n",
      "Epoch: 27/1000 Iteration: 250 Train loss: 0.516999 Train acc: 0.835000\n",
      "Epoch: 27/1000 Iteration: 250 Validation loss: 0.466707 Validation acc: 0.887222\n",
      "Epoch: 28/1000 Iteration: 255 Train loss: 0.524998 Train acc: 0.813333\n",
      "Epoch: 28/1000 Iteration: 260 Train loss: 0.465712 Train acc: 0.845000\n",
      "Epoch: 28/1000 Iteration: 260 Validation loss: 0.421115 Validation acc: 0.886111\n",
      "Epoch: 29/1000 Iteration: 265 Train loss: 0.483513 Train acc: 0.823333\n",
      "Epoch: 29/1000 Iteration: 270 Train loss: 0.435477 Train acc: 0.871667\n",
      "Epoch: 29/1000 Iteration: 270 Validation loss: 0.382299 Validation acc: 0.892222\n",
      "Epoch: 30/1000 Iteration: 275 Train loss: 0.420283 Train acc: 0.855000\n",
      "Epoch: 31/1000 Iteration: 280 Train loss: 0.402739 Train acc: 0.870000\n",
      "Epoch: 31/1000 Iteration: 280 Validation loss: 0.350014 Validation acc: 0.897778\n",
      "Epoch: 31/1000 Iteration: 285 Train loss: 0.356033 Train acc: 0.888333\n",
      "Epoch: 32/1000 Iteration: 290 Train loss: 0.363952 Train acc: 0.870000\n",
      "Epoch: 32/1000 Iteration: 290 Validation loss: 0.321427 Validation acc: 0.901667\n",
      "Epoch: 32/1000 Iteration: 295 Train loss: 0.355591 Train acc: 0.891667\n",
      "Epoch: 33/1000 Iteration: 300 Train loss: 0.366129 Train acc: 0.860000\n",
      "Epoch: 33/1000 Iteration: 300 Validation loss: 0.298498 Validation acc: 0.906667\n",
      "Epoch: 33/1000 Iteration: 305 Train loss: 0.318457 Train acc: 0.900000\n",
      "Epoch: 34/1000 Iteration: 310 Train loss: 0.342330 Train acc: 0.875000\n",
      "Epoch: 34/1000 Iteration: 310 Validation loss: 0.277520 Validation acc: 0.905556\n",
      "Epoch: 34/1000 Iteration: 315 Train loss: 0.341182 Train acc: 0.883333\n",
      "Epoch: 35/1000 Iteration: 320 Train loss: 0.301020 Train acc: 0.885000\n",
      "Epoch: 35/1000 Iteration: 320 Validation loss: 0.260224 Validation acc: 0.912778\n",
      "Epoch: 36/1000 Iteration: 325 Train loss: 0.282515 Train acc: 0.883333\n",
      "Epoch: 36/1000 Iteration: 330 Train loss: 0.255528 Train acc: 0.901667\n",
      "Epoch: 36/1000 Iteration: 330 Validation loss: 0.244393 Validation acc: 0.913889\n",
      "Epoch: 37/1000 Iteration: 335 Train loss: 0.281355 Train acc: 0.905000\n",
      "Epoch: 37/1000 Iteration: 340 Train loss: 0.259822 Train acc: 0.910000\n",
      "Epoch: 37/1000 Iteration: 340 Validation loss: 0.230786 Validation acc: 0.917222\n",
      "Epoch: 38/1000 Iteration: 345 Train loss: 0.279685 Train acc: 0.890000\n",
      "Epoch: 38/1000 Iteration: 350 Train loss: 0.224791 Train acc: 0.915000\n",
      "Epoch: 38/1000 Iteration: 350 Validation loss: 0.219103 Validation acc: 0.920000\n",
      "Epoch: 39/1000 Iteration: 355 Train loss: 0.255127 Train acc: 0.908333\n",
      "Epoch: 39/1000 Iteration: 360 Train loss: 0.246999 Train acc: 0.921667\n",
      "Epoch: 39/1000 Iteration: 360 Validation loss: 0.208400 Validation acc: 0.924444\n",
      "Epoch: 40/1000 Iteration: 365 Train loss: 0.232725 Train acc: 0.911667\n",
      "Epoch: 41/1000 Iteration: 370 Train loss: 0.218705 Train acc: 0.921667\n",
      "Epoch: 41/1000 Iteration: 370 Validation loss: 0.199174 Validation acc: 0.927222\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 41/1000 Iteration: 375 Train loss: 0.195691 Train acc: 0.933333\n",
      "Epoch: 42/1000 Iteration: 380 Train loss: 0.211949 Train acc: 0.933333\n",
      "Epoch: 42/1000 Iteration: 380 Validation loss: 0.190854 Validation acc: 0.929444\n",
      "Epoch: 42/1000 Iteration: 385 Train loss: 0.204587 Train acc: 0.925000\n",
      "Epoch: 43/1000 Iteration: 390 Train loss: 0.234474 Train acc: 0.898333\n",
      "Epoch: 43/1000 Iteration: 390 Validation loss: 0.183069 Validation acc: 0.932778\n",
      "Epoch: 43/1000 Iteration: 395 Train loss: 0.179814 Train acc: 0.930000\n",
      "Epoch: 44/1000 Iteration: 400 Train loss: 0.221030 Train acc: 0.915000\n",
      "Epoch: 44/1000 Iteration: 400 Validation loss: 0.176874 Validation acc: 0.933333\n",
      "Epoch: 44/1000 Iteration: 405 Train loss: 0.218150 Train acc: 0.923333\n",
      "Epoch: 45/1000 Iteration: 410 Train loss: 0.191037 Train acc: 0.931667\n",
      "Epoch: 45/1000 Iteration: 410 Validation loss: 0.171056 Validation acc: 0.935000\n",
      "Epoch: 46/1000 Iteration: 415 Train loss: 0.176766 Train acc: 0.941667\n",
      "Epoch: 46/1000 Iteration: 420 Train loss: 0.168061 Train acc: 0.936667\n",
      "Epoch: 46/1000 Iteration: 420 Validation loss: 0.164919 Validation acc: 0.935556\n",
      "Epoch: 47/1000 Iteration: 425 Train loss: 0.164905 Train acc: 0.945000\n",
      "Epoch: 47/1000 Iteration: 430 Train loss: 0.174059 Train acc: 0.933333\n",
      "Epoch: 47/1000 Iteration: 430 Validation loss: 0.160713 Validation acc: 0.936111\n",
      "Epoch: 48/1000 Iteration: 435 Train loss: 0.202928 Train acc: 0.928333\n",
      "Epoch: 48/1000 Iteration: 440 Train loss: 0.134180 Train acc: 0.953333\n",
      "Epoch: 48/1000 Iteration: 440 Validation loss: 0.157453 Validation acc: 0.937222\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 963/1000 Iteration: 8670 Train loss: 0.010006 Train acc: 0.998333\n",
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     ]
    }
   ],
   "source": [
    "validation_acc = []\n",
    "validation_loss = []\n",
    "\n",
    "train_acc = []\n",
    "train_loss = []\n",
    "\n",
    "with graph.as_default():\n",
    "    saver = tf.train.Saver()\n",
    "\n",
    "with tf.Session(graph=graph) as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    iteration = 1\n",
    "   \n",
    "    # Loop over epochs\n",
    "    for e in range(epochs):\n",
    "        \n",
    "        # Loop over batches\n",
    "        for x,y in get_batches(X_tr, y_tr, batch_size):\n",
    "            \n",
    "            # Feed dictionary\n",
    "            feed = {inputs_ : x, labels_ : y, keep_prob_ : 0.5, learning_rate_ : learning_rate}\n",
    "            \n",
    "            # Loss\n",
    "            loss, _ , acc = sess.run([cost, optimizer, accuracy], feed_dict = feed)\n",
    "            train_acc.append(acc)\n",
    "            train_loss.append(loss)\n",
    "            \n",
    "            # Print at each 5 iters\n",
    "            if (iteration % 5 == 0):\n",
    "                print(\"Epoch: {}/{}\".format(e, epochs),\n",
    "                      \"Iteration: {:d}\".format(iteration),\n",
    "                      \"Train loss: {:6f}\".format(loss),\n",
    "                      \"Train acc: {:.6f}\".format(acc))\n",
    "            \n",
    "            # Compute validation loss at every 10 iterations\n",
    "            if (iteration%10 == 0):                \n",
    "                val_acc_ = []\n",
    "                val_loss_ = []\n",
    "                \n",
    "                for x_v, y_v in get_batches(X_vld, y_vld, batch_size):\n",
    "                    # Feed\n",
    "                    feed = {inputs_ : x_v, labels_ : y_v, keep_prob_ : 1.0}  \n",
    "                    \n",
    "                    # Loss\n",
    "                    loss_v, acc_v = sess.run([cost, accuracy], feed_dict = feed)                    \n",
    "                    val_acc_.append(acc_v)\n",
    "                    val_loss_.append(loss_v)\n",
    "                \n",
    "                # Print info\n",
    "                print(\"Epoch: {}/{}\".format(e, epochs),\n",
    "                      \"Iteration: {:d}\".format(iteration),\n",
    "                      \"Validation loss: {:6f}\".format(np.mean(val_loss_)),\n",
    "                      \"Validation acc: {:.6f}\".format(np.mean(val_acc_)))\n",
    "                \n",
    "                # Store\n",
    "                validation_acc.append(np.mean(val_acc_))\n",
    "                validation_loss.append(np.mean(val_loss_))\n",
    "            \n",
    "            # Iterate \n",
    "            iteration += 1\n",
    "    \n",
    "    saver.save(sess,\"checkpoints-cnn/har.ckpt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a77e6da90>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot training and test loss\n",
    "t = np.arange(iteration-1)\n",
    "\n",
    "plt.figure(figsize = (6,6))\n",
    "plt.plot(t, np.array(train_loss), 'r-', t[t % 10 == 0], np.array(validation_loss), 'b*')\n",
    "plt.xlabel(\"iteration\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(['train', 'validation'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1a77e6de80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Accuracies\n",
    "plt.figure(figsize = (6,6))\n",
    "\n",
    "plt.plot(t, np.array(train_acc), 'r-', t[t % 10 == 0], validation_acc, 'b*')\n",
    "plt.xlabel(\"iteration\")\n",
    "plt.ylabel(\"Accuray\")\n",
    "plt.legend(['train', 'validation'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from checkpoints-cnn/har.ckpt\n",
      "Test accuracy: 0.900833\n"
     ]
    }
   ],
   "source": [
    "test_acc = []\n",
    "\n",
    "with tf.Session(graph=graph) as sess:\n",
    "    # Restore\n",
    "    saver.restore(sess, tf.train.latest_checkpoint('checkpoints-cnn'))\n",
    "    \n",
    "    for x_t, y_t in get_batches(X_test, y_test, batch_size):\n",
    "        feed = {inputs_: x_t,\n",
    "                labels_: y_t,\n",
    "                keep_prob_: 1}\n",
    "        \n",
    "        batch_acc = sess.run(accuracy, feed_dict=feed)\n",
    "        test_acc.append(batch_acc)\n",
    "    print(\"Test accuracy: {:.6f}\".format(np.mean(test_acc)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels: ['WALKING', 'WALKING_UPSTAIRS', 'WALKING_DOWNSTAIRS', 'SITTING', 'STANDING', 'LAYING']\n",
      "\n",
      "INFO:tensorflow:Restoring parameters from checkpoints-cnn/har.ckpt\n",
      "------- Scanning Test Set (#elements: 2947) one-by-one --------\n",
      "\n",
      "------------------\n",
      "Encountered 'wrong predictions' 255 over 2947 total\n",
      "Home-made accuracy: 91.35%\n",
      "------------------\n"
     ]
    }
   ],
   "source": [
    "#print(y_test)\n",
    "#print(labels_test)\n",
    "\n",
    "activities = []\n",
    "with open(\"./UCIHAR/activity_labels.txt\") as file:\n",
    "    for line in file:\n",
    "        field = line.split(' ')\n",
    "        field[1] = field[1][:-1]\n",
    "        activities.append(field[1])\n",
    "print(\"Labels: {}\\n\".format(activities))\n",
    "with tf.Session(graph=graph) as sess:\n",
    "    # Restore\n",
    "    saver.restore(sess, tf.train.latest_checkpoint('checkpoints-cnn'))\n",
    "    i = wrong_pred = 0\n",
    "    test_set_len = len(X_test)\n",
    "    print(\"------- Scanning Test Set (#elements: {}) one-by-one --------\".format(test_set_len))\n",
    "    #print(\"- Displaying wrong predictions ONLY: \")\n",
    "    for x_t, y_t in get_batches(X_test, y_test, 1):\n",
    "        feed = {inputs_: x_t,\n",
    "                labels_: y_t,\n",
    "                keep_prob_: 1}\n",
    "        classification_tensor = logits   #teoricamente -> tf.nn.softmax(logits)\n",
    "        prediction = sess.run(classification_tensor, feed_dict=feed)\n",
    "        index_p = np.argmax(prediction)\n",
    "        #print(\"{} <- {}\\t|\\tpredicted {}\\n\\t{}\\t{}\\n\".format(activities[np.argmax(y_t)],np.argmax(y_t), index_p, y_t, np.round(prediction)))\n",
    "        #print(\"iter-{} Predicted {} -> {} | real {} -> {}\".format(i,index_p,activities[index_p], np.argmax(y_t), activities[np.argmax(y_t)]))\n",
    "        if index_p != np.argmax(y_t):\n",
    "            #print(\"iter-{} Predicted {} -> {} | real {} -> {}\".format(i,index_p,activities[index_p], np.argmax(y_t), activities[np.argmax(y_t)]))\n",
    "            wrong_pred = wrong_pred+1\n",
    "        i = i+1\n",
    "    print(\"\\n------------------\")\n",
    "    print(\"Encountered 'wrong predictions' {} over {} total\".format(wrong_pred, test_set_len))\n",
    "    print(\"Home-made accuracy: {:.2f}%\".format((test_set_len-wrong_pred)*100/test_set_len))\n",
    "    print(\"------------------\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.6.0"
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 "nbformat": 4,
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