PersonSpecific.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training/Valid data shape: (2115, 25, 1000)\n",
      "Test data shape: (443, 25, 1000)\n",
      "Training/Valid target shape: (2115,)\n",
      "Test target shape: (443,)\n",
      "Person train/valid shape: (2115, 1)\n",
      "Person test shape: (443, 1)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from tensorflow.python import keras as kt\n",
    "# RNN imports\n",
    "import keras\n",
    "from keras.models import Model, Sequential\n",
    "from keras.layers import Input, LSTM, Dense, RNN, SimpleRNN, GRU\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "X_test = np.load(\"X_test.npy\")\n",
    "y_test = np.load(\"y_test.npy\")\n",
    "person_train_valid = np.load(\"person_train_valid.npy\")\n",
    "X_train_valid = np.load(\"X_train_valid.npy\")\n",
    "y_train_valid = np.load(\"y_train_valid.npy\")\n",
    "person_test = np.load(\"person_test.npy\")\n",
    "\n",
    "print ('Training/Valid data shape: {}'.format(X_train_valid.shape))\n",
    "print ('Test data shape: {}'.format(X_test.shape))\n",
    "print ('Training/Valid target shape: {}'.format(y_train_valid.shape))\n",
    "print ('Test target shape: {}'.format(y_test.shape))\n",
    "print ('Person train/valid shape: {}'.format(person_train_valid.shape))\n",
    "print ('Person test shape: {}'.format(person_test.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(237, 25, 1000)\n",
      "(237,)\n",
      "(50, 25, 1000)\n",
      "(50,)\n"
     ]
    }
   ],
   "source": [
    "# Separate data by subject\n",
    "\n",
    "# samples from subject 0\n",
    "idx_p0_train = np.where(person_train_valid == 0)[0]\n",
    "idx_p0_test = np.where(person_test == 0)[0]\n",
    "\n",
    "p0_train_X = X_train_valid[idx_p0_train] \n",
    "p0_train_y = y_train_valid[idx_p0_train]\n",
    "\n",
    "p0_test_X = X_test[idx_p0_test]\n",
    "p0_test_y = y_test[idx_p0_test]\n",
    "\n",
    "print(p0_train_X.shape)\n",
    "print(p0_train_y.shape)\n",
    "print(p0_test_X.shape)\n",
    "print(p0_test_y.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original Data: (190, 25, 1000) (190,)\n",
      "Cropped Data: (133190, 25, 300) (133190,)\n",
      "Original Data: (47, 25, 1000) (47,)\n",
      "Cropped Data: (32947, 25, 300) (32947,)\n"
     ]
    }
   ],
   "source": [
    "################################\n",
    "# Sliding window subsampling\n",
    "\n",
    "# data size (1000, 25)\n",
    "# sample size (100, 25)\n",
    "# for loop over each trial\n",
    "# for loop over each valid index\n",
    "# append the cut out section to the new dataset\n",
    "# append the cut out section's label tothe new dataset labels\n",
    "\n",
    "test_array_x = np.array([[[1,2,3,4,5,6,7,8,9,10]],\n",
    "                       [[-1,-2,-3,-4,-5,-6,-7,-8,-9,-10]],\n",
    "                       [[1,2,3,4,5,6,7,8,9,10]]])\n",
    "\n",
    "\n",
    "test_array_y = np.array([700,701,702])\n",
    "\n",
    "def crop_data_aug(X, y, crop):\n",
    "    crop_size = crop # 3 # 100\n",
    "    original_train_X = X  # test_array_x # p0_train_X\n",
    "    original_train_y = y # test_array_y # p0_train_y\n",
    "\n",
    "    N, C, T = original_train_X.shape\n",
    "    print(\"Original Data:\", original_train_X.shape, original_train_y.shape)\n",
    "    #print(\"X\", original_train_X[300:305], \"Y\", original_train_y[300:305])\n",
    "    cropped_train_X = np.zeros((N*(T-crop_size+1), C, crop_size))\n",
    "    cropped_train_y = np.zeros(N*(T-crop_size+1))\n",
    "    crops_per_sample = T-crop_size+1\n",
    "\n",
    "\n",
    "    for n in np.arange(N):\n",
    "        crop_count = 0\n",
    "        for t in np.arange(T-crop_size+1):\n",
    "            idx = n*crops_per_sample + crop_count\n",
    "            cropped_train_X[idx] = original_train_X[n, :, t:t+crop_size]\n",
    "            cropped_train_y[idx] = original_train_y[n]\n",
    "            crop_count = crop_count + 1\n",
    "            \n",
    "    print(\"Cropped Data:\", cropped_train_X.shape, cropped_train_y.shape)\n",
    "    \n",
    "    return cropped_train_X, cropped_train_y\n",
    "\n",
    "# separate the train and val test sets before actually cropping \n",
    "idx_val = np.arange(p0_train_X.shape[0])\n",
    "np.random.shuffle(idx_val)\n",
    "val_fraction = int(0.2*idx_val.shape[0])\n",
    "\n",
    "# separated train and test splits\n",
    "data_val_X = p0_train_X[0:val_fraction]\n",
    "data_val_y = p0_train_y[0:val_fraction]\n",
    "data_train_X = p0_train_X[val_fraction:]\n",
    "data_train_y = p0_train_y[val_fraction:]\n",
    "\n",
    "# augment using cropping\n",
    "cropped_train_X, cropped_train_y = crop_data_aug(data_train_X, data_train_y, 300)\n",
    "cropped_val_X, cropped_val_y = crop_data_aug(data_val_X, data_val_y, 300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Swapped axes: (133190, 300, 25) (50, 1000, 25)\n",
      "Removed VOG channels: (133190, 300, 22) (50, 1000, 22)\n",
      "Raw labels: [771. 771. 771. 771. 771. 771. 771. 771. 771. 771.]\n",
      "Fixed: [2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]\n",
      "Categorical one-hot encoding:\n",
      " [[0. 0. 1. 0.]\n",
      " [0. 0. 1. 0.]\n",
      " [0. 0. 1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "################################\n",
    "# rearrange data for Keras modules\n",
    "\n",
    "##!!!!!! Now train and test data dimensions do not match\n",
    "\n",
    "# fomat data to (batch_size, timesteps, input_dim)\n",
    "k_X_train = np.swapaxes(cropped_train_X, 1, 2)\n",
    "k_X_val = np.swapaxes(cropped_val_X, 1, 2)\n",
    "k_X_test = np.swapaxes(p0_test_X, 1, 2)\n",
    "print(\"Swapped axes:\",k_X_train.shape, k_X_test.shape)\n",
    "# remove VOG channels (23-25)\n",
    "k_X_train = k_X_train[: , : , :22 ]\n",
    "k_X_val = k_X_val[:, :, :22]\n",
    "k_X_test = k_X_test[: , : , :22 ]\n",
    "print(\"Removed VOG channels:\", k_X_train.shape, k_X_test.shape)\n",
    "\n",
    "# encode output labels\n",
    "print(\"Raw labels:\", cropped_train_y[0:10])\n",
    "k_y_train = cropped_train_y - 769\n",
    "k_y_val = cropped_val_y - 769\n",
    "k_y_test = p0_test_y - 769\n",
    "print(\"Fixed:\", k_y_train[0:10])\n",
    "k_y_train_categ = to_categorical(k_y_train, 4)\n",
    "k_y_val_categ = to_categorical(k_y_val, 4)\n",
    "k_y_test_categ = to_categorical(k_y_test, 4)\n",
    "print(\"Categorical one-hot encoding:\\n\",k_y_train_categ[0:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_1 (Conv1D)            (None, 291, 22)           4862      \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 291, 22)           88        \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 282, 22)           4862      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 282, 22)           88        \n",
      "_________________________________________________________________\n",
      "cu_dnngru_1 (CuDNNGRU)       (None, 128)               58368     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4)                 516       \n",
      "=================================================================\n",
      "Total params: 69,296\n",
      "Trainable params: 68,952\n",
      "Non-trainable params: 344\n",
      "_________________________________________________________________\n",
      "None\n",
      "Train on 133190 samples, validate on 32947 samples\n",
      "Epoch 1/5\n",
      "133190/133190 [==============================] - 30s 222us/step - loss: 1.1444 - acc: 0.4524 - val_loss: 1.1188 - val_acc: 0.4326\n",
      "Epoch 2/5\n",
      "133190/133190 [==============================] - 28s 211us/step - loss: 0.5296 - acc: 0.7733 - val_loss: 3.7601 - val_acc: 0.3425\n",
      "Epoch 3/5\n",
      "133190/133190 [==============================] - 28s 213us/step - loss: 0.0458 - acc: 0.9861 - val_loss: 2.4222 - val_acc: 0.5678\n",
      "Epoch 4/5\n",
      "133190/133190 [==============================] - 28s 214us/step - loss: 0.0151 - acc: 0.9954 - val_loss: 1.8215 - val_acc: 0.6885\n",
      "Epoch 5/5\n",
      "133190/133190 [==============================] - 28s 212us/step - loss: 0.0086 - acc: 0.9975 - val_loss: 2.2624 - val_acc: 0.6567\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#################### RNN paramters\n",
    "hids = 100\n",
    "actv = 'tanh'\n",
    "dcy = 0.005\n",
    "reg = 0.1\n",
    "lrt = 0.00005\n",
    "\n",
    "#1 Plain\n",
    "\n",
    "#################### RNN training\n",
    "from keras.layers import Dropout\n",
    "input_dim = k_X_train.shape[1:]\n",
    "gru_units_sub = 128\n",
    "\n",
    "model = Sequential()\n",
    "model.add(keras.layers.Conv1D(22, 10, input_shape=input_dim))\n",
    "model.add(keras.layers.BatchNormalization())\n",
    "model.add(keras.layers.Conv1D(22, 10))\n",
    "model.add(keras.layers.BatchNormalization())\n",
    "#model.add(keras.layers.CuDNNLSTM(64, input_shape=input_dim, return_sequences=True))\n",
    "#model.add(Dropout(0.5))\n",
    "model.add(keras.layers.CuDNNGRU(gru_units_sub, input_shape=input_dim))\n",
    "model.add(keras.layers.BatchNormalization())\n",
    "model.add(Dense(4, activation=\"softmax\"))\n",
    "model.compile(loss=\"categorical_crossentropy\",\n",
    "              optimizer=\"rmsprop\",\n",
    "              metrics=[\"accuracy\"])\n",
    "\n",
    "print(model.summary())\n",
    "\n",
    "history = model.fit(k_X_train, k_y_train_categ, \n",
    "                    validation_data=(k_X_val, k_y_val_categ),\n",
    "                    epochs=5, verbose=1, batch_size=256)\n",
    "\n",
    "\n",
    "\n",
    "#################### RNN loss and accuracy plotting\n",
    "# Plot training & validation accuracy values\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()\n",
    "# Plot training & validation loss values\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_1 (Conv1D)            (None, 291, 22)           4862      \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 291, 22)           88        \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 282, 22)           4862      \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 282, 22)           88        \n",
      "_________________________________________________________________\n",
      "cu_dnngru_1 (CuDNNGRU)       (None, 128)               58368     \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 4)                 516       \n",
      "=================================================================\n",
      "Total params: 69,296\n",
      "Trainable params: 68,952\n",
      "Non-trainable params: 344\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()\n",
    "model.save(\"Conv-BN-Conv-BN-GRU1-128units-BN-subsampling300-valacc65.h5\")\n",
    "model.save_weights(\"Conv-BN-Conv-BN-GRU1-128units-BN-subsampling300-valacc65-weights.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1d_29 (Conv1D)           (None, 291, 20)           4420      \n",
      "_________________________________________________________________\n",
      "batch_normalization_45 (Batc (None, 291, 20)           80        \n",
      "_________________________________________________________________\n",
      "conv1d_30 (Conv1D)           (None, 282, 20)           4020      \n",
      "_________________________________________________________________\n",
      "batch_normalization_46 (Batc (None, 282, 20)           80        \n",
      "_________________________________________________________________\n",
      "cu_dnngru_12 (CuDNNGRU)      (None, 128)               57600     \n",
      "_________________________________________________________________\n",
      "batch_normalization_47 (Batc (None, 128)               512       \n",
      "_________________________________________________________________\n",
      "dense_26 (Dense)             (None, 4)                 516       \n",
      "=================================================================\n",
      "Total params: 67,228\n",
      "Trainable params: 66,892\n",
      "Non-trainable params: 336\n",
      "_________________________________________________________________\n",
      "None\n",
      "Train on 133190 samples, validate on 32947 samples\n",
      "Epoch 1/5\n",
      "133190/133190 [==============================] - 30s 224us/step - loss: 1.8486 - acc: 0.3808 - val_loss: 1.5614 - val_acc: 0.3771\n",
      "Epoch 2/5\n",
      "133190/133190 [==============================] - 26s 197us/step - loss: 1.0099 - acc: 0.5970 - val_loss: 2.2082 - val_acc: 0.4190\n",
      "Epoch 3/5\n",
      "133190/133190 [==============================] - 26s 197us/step - loss: 0.8448 - acc: 0.6886 - val_loss: 2.2059 - val_acc: 0.3953\n",
      "Epoch 4/5\n",
      "133190/133190 [==============================] - 26s 197us/step - loss: 0.7551 - acc: 0.7408 - val_loss: 8.1799 - val_acc: 0.2128\n",
      "Epoch 5/5\n",
      "133190/133190 [==============================] - 26s 197us/step - loss: 0.6630 - acc: 0.7923 - val_loss: 2.2050 - val_acc: 0.5312\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#2 Regularization\n",
    "\n",
    "#################### RNN training\n",
    "input_dim = k_X_train.shape[1:]\n",
    "gru_units_sub = 128\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(keras.layers.Conv1D(20, 10, input_shape=input_dim, \n",
    "                             kernel_regularizer=keras.regularizers.l2(0.05)))\n",
    "model.add(keras.layers.BatchNormalization())\n",
    "model.add(keras.layers.Conv1D(20, 10, kernel_regularizer=keras.regularizers.l2(0.05)))\n",
    "model.add(keras.layers.BatchNormalization())\n",
    "model.add(keras.layers.CuDNNGRU(gru_units_sub, input_shape=input_dim,\n",
    "                recurrent_regularizer=keras.regularizers.l2(0.01), \n",
    "                kernel_regularizer=keras.regularizers.l2(0.01)))\n",
    "model.add(keras.layers.BatchNormalization())\n",
    "model.add(Dense(4, activation=\"softmax\"))\n",
    "model.compile(loss=\"categorical_crossentropy\",\n",
    "              optimizer=\"rmsprop\",\n",
    "              metrics=[\"accuracy\"])\n",
    "\n",
    "print(model.summary())\n",
    "\n",
    "history = model.fit(k_X_train, k_y_train_categ, \n",
    "                    validation_data=(k_X_val, k_y_val_categ),\n",
    "                    epochs=5, verbose=1, batch_size=256)\n",
    "\n",
    "\n",
    "\n",
    "#################### RNN loss and accuracy plotting\n",
    "# Plot training & validation accuracy values\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()\n",
    "# Plot training & validation loss values\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"Conv-BN-Conv-BN-GRU1-128units-reg-subsampling300-valacc53.h5\")\n",
    "\n",
    "\n",
    "#3 Dense to output\n",
    "\n",
    "\n",
    "#4 Increase crop length\n",
    "\n",
    "#5 LSTM"
   ]
  },
  {
   "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}