HAR-CNN.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# HAR CNN training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Prepare data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X_train, labels_train, list_ch_train = read_data(data_path=\"./data/\", split=\"train\") # train\n",
    "X_test, labels_test, list_ch_test = read_data(data_path=\"./data/\", split=\"test\") # test\n",
    "\n",
    "assert list_ch_train == list_ch_test, \"Mistmatch in channels!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Normalize?\n",
    "X_train, X_test = standardize(X_train, X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Train/Validation Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "One-hot encoding:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "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": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Imports\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Construct the graph\n",
    "Placeholders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Build Convolutional Layers\n",
    "\n",
    "Note: Should we use a different activation? Like tf.nn.tanh?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "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, 36)\n",
    "    conv2 = tf.layers.conv1d(inputs=max_pool_1, filters=36, 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, 36) --> (batch, 16, 72)\n",
    "    conv3 = tf.layers.conv1d(inputs=max_pool_2, filters=72, 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, 72) --> (batch, 8, 144)\n",
    "    conv4 = tf.layers.conv1d(inputs=max_pool_3, filters=144, 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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Now, flatten and pass to the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "with graph.as_default():\n",
    "    # Flatten and add dropout\n",
    "    flat = tf.reshape(max_pool_4, (-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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Train the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "if (os.path.exists('checkpoints-cnn') == False):\n",
    "    !mkdir checkpoints-cnn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0/1000 Iteration: 5 Train loss: 1.805539 Train acc: 0.175000\n",
      "Epoch: 1/1000 Iteration: 10 Train loss: 1.752156 Train acc: 0.213333\n",
      "Epoch: 1/1000 Iteration: 10 Validation loss: 1.731410 Validation acc: 0.242778\n",
      "Epoch: 1/1000 Iteration: 15 Train loss: 1.717095 Train acc: 0.186667\n",
      "Epoch: 2/1000 Iteration: 20 Train loss: 1.689471 Train acc: 0.210000\n",
      "Epoch: 2/1000 Iteration: 20 Validation loss: 1.670297 Validation acc: 0.178889\n",
      "Epoch: 2/1000 Iteration: 25 Train loss: 1.670956 Train acc: 0.183333\n",
      "Epoch: 3/1000 Iteration: 30 Train loss: 1.643886 Train acc: 0.205000\n",
      "Epoch: 3/1000 Iteration: 30 Validation loss: 1.622423 Validation acc: 0.191111\n",
      "Epoch: 3/1000 Iteration: 35 Train loss: 1.608606 Train acc: 0.221667\n",
      "Epoch: 4/1000 Iteration: 40 Train loss: 1.592912 Train acc: 0.233333\n",
      "Epoch: 4/1000 Iteration: 40 Validation loss: 1.579607 Validation acc: 0.311111\n",
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      "Epoch: 5/1000 Iteration: 50 Train loss: 1.555073 Train acc: 0.311667\n",
      "Epoch: 5/1000 Iteration: 50 Validation loss: 1.536482 Validation acc: 0.364444\n",
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      "Epoch: 999/1000 Iteration: 8995 Train loss: 0.014398 Train acc: 0.995000\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": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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wcWPc4YiIxEYJowPzbi9sHlb71ltxhyMiEpu+cQeQq4qLoa4OGk9ROTdRPhWK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbc2d64f0f0>"
      ]
     },
     "metadata": {},
     "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": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WrNy///PPuy9a5P7GG+7/+7+h8H/tNfe1a9t/V7ZsCRf53d23bXMvK3N/4IHCfj/7sV1J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\nIpKhpCAiIhn/DwAGYABzepTRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbbde98f828>"
      ]
     },
     "metadata": {},
     "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": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Evaluate on test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy: 0.930417\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)))"
   ]
  }
 ],
 "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": "3.5.2"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}