LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train an LSTM classifier on fMRI activity of visual regions of the brain to predict type of visual stimuli"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we will train a simple LSTM to predict which type of image was seen based on the brain activity of visual regions. For the first pass, we will simply classify whether the image came from ImageNet, COCO, or SUN. These databses have distinct types of images: focused single object, naturalistic objects, environmental scenes, respectively.\n",
    "\n",
    "We will train a separate LSTM for each of the ten brain regions that were extracted from each subject viewing these images. Perhaps we will find that in isolation neither region provides enough information to successfully classify, or one or two region will have particular higher accuracies. Whatever result we find will dictate how we proceed next. Maybe we can train combining all regions into one large training set, or use stacked LSTMs or hierarchical LSTMs. Let's find out using Keras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, LSTM, GRU\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "data_dir = 'dataset/traindata/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's train an LSTM using some default hyperparameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "from sklearn.feature_selection import SelectKBest, f_classif\n",
    "\n",
    "\"\"\"\n",
    "Load class numbers associated with each image as one-hot vectors\n",
    "\"\"\"\n",
    "def load_classes(Y, encoding = 'onehot'):\n",
    "    if encoding not in ['onehot', 'interval']:\n",
    "        raise ValueError('incorrect type of encoding')\n",
    "    df = pd.read_csv(os.path.join(data_dir, 'image2labels.tsv'), sep='\\t', header = None, names = ['ImgName', 'ClassNum', 'ClassLabel'])\n",
    "    image2class = pd.Series(df.ClassNum.values,index=df.ImgName).to_dict()\n",
    "    max_class_num = np.max(list(image2class.values()))\n",
    "    if encoding == 'onehot':\n",
    "        Y_out = np.zeros((Y.shape[0], max_class_num+2))\n",
    "        for ind,img in enumerate(Y):\n",
    "            if img == 'none':\n",
    "                Y_out[ind][max_class_num+1] = 1\n",
    "            else:\n",
    "                Y_out[ind][image2class[img]] = 1\n",
    "    elif encoding == 'interval':\n",
    "        Y_out = np.zeros((Y.shape[0],))\n",
    "        for ind,img in enumerate(Y):\n",
    "            if img == 'none':\n",
    "                Y_out[ind] = max_class_num + 1\n",
    "            else:\n",
    "                Y_out[ind] = image2class[img]\n",
    "    \n",
    "    return Y_out\n",
    "        \n",
    "\n",
    "\"\"\"\n",
    "Subtract the mean signal from the resting state data\n",
    "\"\"\"\n",
    "def rest_normalize(X,Y):\n",
    "    with open('dataset/traindata/sample2session.p', 'rb') as f:\n",
    "        sample2session = pickle.load(f)\n",
    "    subjects = list(set(sample2session['Subject']))\n",
    "    baseline = {}\n",
    "    for sub in subjects:\n",
    "        rest = np.array([x for ind,x in enumerate(X) if Y[ind][16] == 1 and sample2session['Subject'].values[ind] == sub])\n",
    "        baseline[sub] = np.mean(rest, axis = (0,1))\n",
    "    X_new = np.array([x - baseline[sample2session['Subject'].values[ind]] for ind,x in enumerate(X)])\n",
    "    \n",
    "    return X_new    \n",
    "    \n",
    "\n",
    "\"\"\"\n",
    "Load in the dataset.\n",
    "\n",
    "Return train, dev, test splits, standardized\n",
    "\"\"\"\n",
    "def load_data(dim_reduce = False, num_dims = 1000):\n",
    "    # Load from disk\n",
    "    with open(data_dir + 'X_raw.p', 'rb') as f:\n",
    "        X = np.array(pickle.load(f))\n",
    "    with open(data_dir + 'Y_raw.p', 'rb') as f:\n",
    "        Yimages = np.array(pickle.load(f))\n",
    "    if dim_reduce:\n",
    "        classes = load_classes(Yimages, encoding = 'interval')\n",
    "        X_mean = np.mean(X, axis=1)\n",
    "        kbest = SelectKBest(f_classif, k = num_dims).fit(X_mean,classes)\n",
    "        best_voxels = np.argsort(kbest.scores_)[::-1][:num_dims]\n",
    "        X = X[:,:,best_voxels]\n",
    "    # Convert class labels as one hot vectors\n",
    "    classes = load_classes(Yimages)\n",
    "    # Standardize data\n",
    "    X_scaled = rest_normalize(X, classes)\n",
    "    # Split into train,dev,test\n",
    "    X_train, X_devtest, Y_train, Y_devtest = train_test_split(X_scaled, classes, test_size = 0.4)\n",
    "    X_dev, X_test, Y_dev, Y_test = train_test_split(X_devtest, Y_devtest, test_size = 0.5)\n",
    "    \n",
    "    print('%20s %20s' % ('Shape of X_train:', str(X_train.shape)))\n",
    "    print('%20s %20s' % ('Shape of Y_train:', str(Y_train.shape)))\n",
    "    print('%20s %20s' % ('Shape of X_dev:', str(X_dev.shape)))\n",
    "    print('%20s %20s' % ('Shape of Y_dev:', str(Y_dev.shape)))\n",
    "    print('%20s %20s' % ('Shape of X_test:', str(X_test.shape)))\n",
    "    print('%20s %20s' % ('Shape of Y_test:', str(Y_test.shape)))\n",
    "    \n",
    "    return X_train, X_dev, X_test, Y_train, Y_dev, Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Shape of X_train:     (11628, 5, 9598)\n",
      "   Shape of Y_train:          (11628, 17)\n",
      "     Shape of X_dev:      (3876, 5, 9598)\n",
      "     Shape of Y_dev:           (3876, 17)\n",
      "    Shape of X_test:      (3876, 5, 9598)\n",
      "    Shape of Y_test:           (3876, 17)\n"
     ]
    }
   ],
   "source": [
    "# Load with reduced dimensions\n",
    "X_train, X_dev, X_test, Y_train, Y_dev, Y_test = load_data(dim_reduce = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Create LSTM model using given hyperparameters\n",
    "\n",
    "\"\"\"\n",
    "def create_model(n_timesteps, n_features, n_hidden, n_classes, dropout = 0.5):\n",
    "    model = Sequential()\n",
    "    model.add(GRU(n_hidden, input_shape=(n_timesteps,n_features)))\n",
    "    model.add(Dropout(dropout))\n",
    "    #model.add(Dense(n_hidden, activation='relu'))\n",
    "    model.add(Dense(n_classes, activation='softmax'))\n",
    "    model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "gru_1 (GRU)                  (None, 100)               2909700   \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 17)                1717      \n",
      "=================================================================\n",
      "Total params: 2,911,417\n",
      "Trainable params: 2,911,417\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "n_timesteps, n_features, n_classes = X_train.shape[1], X_train.shape[2], Y_train.shape[1]\n",
    "n_hidden = 100\n",
    "dropout = 0.5\n",
    "\n",
    "model = create_model(n_timesteps, n_features, n_hidden, n_classes, dropout = dropout)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Train model and output train and dev accuracy history\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "def train_model(model, X_train, X_dev, Y_train, Y_dev, epochs, batch_size = 64, verbose = 1):\n",
    "    train_history = model.fit(X_train, Y_train, validation_data = (X_dev, Y_dev), epochs=epochs, batch_size=batch_size, verbose=verbose)\n",
    "    train_acc,dev_acc = train_history.history['accuracy'], train_history.history['val_accuracy']\n",
    "    \n",
    "    return model,train_acc, dev_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11628 samples, validate on 3876 samples\n",
      "Epoch 1/30\n",
      "11628/11628 [==============================] - 32s 3ms/step - loss: 3.3815 - accuracy: 0.0842 - val_loss: 2.8260 - val_accuracy: 0.1102\n",
      "Epoch 2/30\n",
      "11628/11628 [==============================] - 29s 3ms/step - loss: 3.0143 - accuracy: 0.1022 - val_loss: 2.7290 - val_accuracy: 0.1161\n",
      "Epoch 3/30\n",
      "11628/11628 [==============================] - 29s 3ms/step - loss: 2.7984 - accuracy: 0.1224 - val_loss: 2.6708 - val_accuracy: 0.1324\n",
      "Epoch 4/30\n",
      "11628/11628 [==============================] - 29s 3ms/step - loss: 2.7164 - accuracy: 0.1305 - val_loss: 2.6346 - val_accuracy: 0.1393\n",
      "Epoch 5/30\n",
      "11628/11628 [==============================] - 29s 2ms/step - loss: 2.6594 - accuracy: 0.1427 - val_loss: 2.6258 - val_accuracy: 0.1406\n",
      "Epoch 6/30\n",
      "11628/11628 [==============================] - 29s 3ms/step - loss: 2.6232 - accuracy: 0.1521 - val_loss: 2.6051 - val_accuracy: 0.1574\n",
      "Epoch 7/30\n",
      "11628/11628 [==============================] - 30s 3ms/step - loss: 2.6047 - accuracy: 0.1568 - val_loss: 2.5954 - val_accuracy: 0.1551\n",
      "Epoch 8/30\n",
      "11628/11628 [==============================] - 30s 3ms/step - loss: 2.5721 - accuracy: 0.1659 - val_loss: 2.5897 - val_accuracy: 0.1589\n",
      "Epoch 9/30\n",
      "11628/11628 [==============================] - 29s 3ms/step - loss: 2.5705 - accuracy: 0.1682 - val_loss: 2.5839 - val_accuracy: 0.1651\n",
      "Epoch 10/30\n",
      "11628/11628 [==============================] - 29s 3ms/step - loss: 2.5704 - accuracy: 0.1666 - val_loss: 2.5836 - val_accuracy: 0.1633\n",
      "Epoch 11/30\n",
      "11628/11628 [==============================] - 29s 3ms/step - loss: 2.5676 - accuracy: 0.1659 - val_loss: 2.5707 - val_accuracy: 0.1695\n",
      "Epoch 12/30\n",
      "11628/11628 [==============================] - 29s 3ms/step - loss: 2.5525 - accuracy: 0.1737 - val_loss: 2.5689 - val_accuracy: 0.1610\n",
      "Epoch 13/30\n",
      "11628/11628 [==============================] - 31s 3ms/step - loss: 2.5482 - accuracy: 0.1733 - val_loss: 2.5592 - val_accuracy: 0.1713\n",
      "Epoch 14/30\n",
      "11628/11628 [==============================] - 31s 3ms/step - loss: 2.5441 - accuracy: 0.1717 - val_loss: 2.5689 - val_accuracy: 0.1646\n",
      "Epoch 15/30\n",
      "11628/11628 [==============================] - 31s 3ms/step - loss: 2.5378 - accuracy: 0.1777 - val_loss: 2.5549 - val_accuracy: 0.1726\n",
      "Epoch 16/30\n",
      "11628/11628 [==============================] - 32s 3ms/step - loss: 2.5426 - accuracy: 0.1768 - val_loss: 2.5715 - val_accuracy: 0.1687\n",
      "Epoch 17/30\n",
      "11628/11628 [==============================] - 33s 3ms/step - loss: 2.5311 - accuracy: 0.1787 - val_loss: 2.5537 - val_accuracy: 0.1646\n",
      "Epoch 18/30\n",
      "11628/11628 [==============================] - 35s 3ms/step - loss: 2.5415 - accuracy: 0.1736 - val_loss: 2.5590 - val_accuracy: 0.1662\n",
      "Epoch 19/30\n",
      "11628/11628 [==============================] - 38s 3ms/step - loss: 2.5206 - accuracy: 0.1851 - val_loss: 2.5461 - val_accuracy: 0.1723\n",
      "Epoch 20/30\n",
      "11628/11628 [==============================] - 32s 3ms/step - loss: 2.5262 - accuracy: 0.1806 - val_loss: 2.5526 - val_accuracy: 0.1749\n",
      "Epoch 21/30\n",
      "11628/11628 [==============================] - 31s 3ms/step - loss: 2.5288 - accuracy: 0.1827 - val_loss: 2.5526 - val_accuracy: 0.1698\n",
      "Epoch 22/30\n",
      "11628/11628 [==============================] - 31s 3ms/step - loss: 2.5287 - accuracy: 0.1797 - val_loss: 2.5408 - val_accuracy: 0.1819\n",
      "Epoch 23/30\n",
      "11628/11628 [==============================] - 31s 3ms/step - loss: 2.5158 - accuracy: 0.1834 - val_loss: 2.5470 - val_accuracy: 0.1760\n",
      "Epoch 24/30\n",
      "11628/11628 [==============================] - 31s 3ms/step - loss: 2.5286 - accuracy: 0.1772 - val_loss: 2.5398 - val_accuracy: 0.1767\n",
      "Epoch 25/30\n",
      "11628/11628 [==============================] - 31s 3ms/step - loss: 2.5204 - accuracy: 0.1788 - val_loss: 2.5331 - val_accuracy: 0.1762\n",
      "Epoch 26/30\n",
      "11628/11628 [==============================] - 31s 3ms/step - loss: 2.5199 - accuracy: 0.1826 - val_loss: 2.5414 - val_accuracy: 0.1687\n",
      "Epoch 27/30\n",
      "11628/11628 [==============================] - 31s 3ms/step - loss: 2.5188 - accuracy: 0.1797 - val_loss: 2.5352 - val_accuracy: 0.1695\n",
      "Epoch 28/30\n",
      "11628/11628 [==============================] - 32s 3ms/step - loss: 2.5238 - accuracy: 0.1801 - val_loss: 2.5355 - val_accuracy: 0.1760\n",
      "Epoch 29/30\n",
      "11628/11628 [==============================] - 31s 3ms/step - loss: 2.5127 - accuracy: 0.1825 - val_loss: 2.5412 - val_accuracy: 0.1741\n",
      "Epoch 30/30\n",
      "11628/11628 [==============================] - 34s 3ms/step - loss: 2.5156 - accuracy: 0.1824 - val_loss: 2.5307 - val_accuracy: 0.1713\n"
     ]
    }
   ],
   "source": [
    "model, train_acc, dev_acc = train_model(model, X_train, X_dev, Y_train, Y_dev, 30, batch_size = 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Plot the training history\n",
    "\"\"\"\n",
    "def plot_acc(train_acc, dev_acc, title = 'LSTM model accuracy'):\n",
    "    plt.close('all')\n",
    "    fig, ax = plt.subplots(1,1, figsize = (6,4))\n",
    "    ax.plot(train_acc, color = 'blue', label = 'train')\n",
    "    ax.plot(dev_acc, color = 'green', label = 'dev')\n",
    "    ax.set_xlabel('Epoch')\n",
    "    ax.set_ylabel('Accuracy')\n",
    "    ax.set_title(title)\n",
    "    ax.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc(train_acc, dev_acc, title = 'GRU model accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11628 samples, validate on 3876 samples\n",
      "Epoch 1/20\n",
      " 4480/11628 [==========>...................] - ETA: 1:46 - loss: 3.5481 - accuracy: 0.0879"
     ]
    }
   ],
   "source": [
    "dropout = 0.5\n",
    "n_hidden = 500\n",
    "model = create_model(n_timesteps, n_features, n_hidden, n_classes, dropout = dropout)\n",
    "model, train_acc, dev_acc = train_model(model, X_train, X_dev, Y_train, Y_dev, epochs = 20, batch_size = 64, verbose = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clearly overfitting, maybe we can try to reduce the variance. How about increasing dropout?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "W1120 18:38:13.326150 140042351974144 nn_ops.py:4283] Large dropout rate: 0.8 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11628 samples, validate on 3876 samples\n",
      "Epoch 1/20\n",
      "11628/11628 [==============================] - 4s 342us/step - loss: 3.4526 - accuracy: 0.1011 - val_loss: 2.5688 - val_accuracy: 0.1705\n",
      "Epoch 2/20\n",
      "11628/11628 [==============================] - 3s 293us/step - loss: 2.9534 - accuracy: 0.1298 - val_loss: 2.5194 - val_accuracy: 0.1852\n",
      "Epoch 3/20\n",
      "11628/11628 [==============================] - 3s 296us/step - loss: 2.7090 - accuracy: 0.1540 - val_loss: 2.4760 - val_accuracy: 0.1950\n",
      "Epoch 4/20\n",
      "11628/11628 [==============================] - 3s 298us/step - loss: 2.5839 - accuracy: 0.1722 - val_loss: 2.4624 - val_accuracy: 0.1974\n",
      "Epoch 5/20\n",
      "11628/11628 [==============================] - 3s 298us/step - loss: 2.5166 - accuracy: 0.1858 - val_loss: 2.4422 - val_accuracy: 0.2121\n",
      "Epoch 6/20\n",
      "11628/11628 [==============================] - 3s 297us/step - loss: 2.4657 - accuracy: 0.2021 - val_loss: 2.4275 - val_accuracy: 0.2183\n",
      "Epoch 7/20\n",
      "11628/11628 [==============================] - 3s 295us/step - loss: 2.4506 - accuracy: 0.2058 - val_loss: 2.4183 - val_accuracy: 0.2121\n",
      "Epoch 8/20\n",
      "11628/11628 [==============================] - 3s 293us/step - loss: 2.4271 - accuracy: 0.2140 - val_loss: 2.4025 - val_accuracy: 0.2154\n",
      "Epoch 9/20\n",
      "11628/11628 [==============================] - 3s 292us/step - loss: 2.4075 - accuracy: 0.2198 - val_loss: 2.3972 - val_accuracy: 0.2105\n",
      "Epoch 10/20\n",
      "11628/11628 [==============================] - 3s 290us/step - loss: 2.3974 - accuracy: 0.2200 - val_loss: 2.3963 - val_accuracy: 0.2149\n",
      "Epoch 11/20\n",
      "11628/11628 [==============================] - 3s 292us/step - loss: 2.3751 - accuracy: 0.2314 - val_loss: 2.3916 - val_accuracy: 0.2193\n",
      "Epoch 12/20\n",
      "11628/11628 [==============================] - 3s 290us/step - loss: 2.3599 - accuracy: 0.2300 - val_loss: 2.3860 - val_accuracy: 0.2206\n",
      "Epoch 13/20\n",
      "11628/11628 [==============================] - 3s 290us/step - loss: 2.3449 - accuracy: 0.2356 - val_loss: 2.3798 - val_accuracy: 0.2227\n",
      "Epoch 14/20\n",
      "11628/11628 [==============================] - 3s 298us/step - loss: 2.3256 - accuracy: 0.2399 - val_loss: 2.3817 - val_accuracy: 0.2175\n",
      "Epoch 15/20\n",
      "11628/11628 [==============================] - 3s 294us/step - loss: 2.3105 - accuracy: 0.2421 - val_loss: 2.3790 - val_accuracy: 0.2167\n",
      "Epoch 16/20\n",
      "11628/11628 [==============================] - 3s 291us/step - loss: 2.2957 - accuracy: 0.2517 - val_loss: 2.3899 - val_accuracy: 0.2201\n",
      "Epoch 17/20\n",
      "11628/11628 [==============================] - 3s 291us/step - loss: 2.2891 - accuracy: 0.2558 - val_loss: 2.3869 - val_accuracy: 0.2178\n",
      "Epoch 18/20\n",
      "11628/11628 [==============================] - 3s 290us/step - loss: 2.2645 - accuracy: 0.2582 - val_loss: 2.3926 - val_accuracy: 0.2291\n",
      "Epoch 19/20\n",
      "11628/11628 [==============================] - 3s 295us/step - loss: 2.2358 - accuracy: 0.2611 - val_loss: 2.3863 - val_accuracy: 0.2232\n",
      "Epoch 20/20\n",
      "11628/11628 [==============================] - 3s 291us/step - loss: 2.2230 - accuracy: 0.2658 - val_loss: 2.3962 - val_accuracy: 0.2270\n"
     ]
    }
   ],
   "source": [
    "dropout = 0.8\n",
    "n_hidden = 100\n",
    "model = create_model(n_timesteps, n_features, n_hidden, n_classes, dropout = dropout)\n",
    "model, train_acc, dev_acc = train_model(model, X_train, X_dev, Y_train, Y_dev, epochs = 20, batch_size = 64, verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc(train_acc, dev_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That did not improve our validation error. How about keeping dropout but changing the number of hidden nodes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 11628 samples, validate on 3876 samples\n",
      "Epoch 1/30\n",
      "11628/11628 [==============================] - 3s 231us/step - loss: 2.8701 - accuracy: 0.1247 - val_loss: 2.5710 - val_accuracy: 0.1770\n",
      "Epoch 2/30\n",
      "11628/11628 [==============================] - 2s 185us/step - loss: 2.6294 - accuracy: 0.1707 - val_loss: 2.5132 - val_accuracy: 0.1907\n",
      "Epoch 3/30\n",
      "11628/11628 [==============================] - 2s 178us/step - loss: 2.5108 - accuracy: 0.2016 - val_loss: 2.4997 - val_accuracy: 0.1922\n",
      "Epoch 4/30\n",
      "11628/11628 [==============================] - 2s 181us/step - loss: 2.4408 - accuracy: 0.2096 - val_loss: 2.4648 - val_accuracy: 0.2025\n",
      "Epoch 5/30\n",
      "11628/11628 [==============================] - 2s 180us/step - loss: 2.3863 - accuracy: 0.2303 - val_loss: 2.4442 - val_accuracy: 0.2147\n",
      "Epoch 6/30\n",
      "11628/11628 [==============================] - 2s 185us/step - loss: 2.3470 - accuracy: 0.2449 - val_loss: 2.4474 - val_accuracy: 0.2116\n",
      "Epoch 7/30\n",
      "11628/11628 [==============================] - 2s 186us/step - loss: 2.3069 - accuracy: 0.2587 - val_loss: 2.4315 - val_accuracy: 0.2074\n",
      "Epoch 8/30\n",
      "11628/11628 [==============================] - 2s 181us/step - loss: 2.2758 - accuracy: 0.2646 - val_loss: 2.4271 - val_accuracy: 0.2098\n",
      "Epoch 9/30\n",
      "11628/11628 [==============================] - 2s 181us/step - loss: 2.2560 - accuracy: 0.2723 - val_loss: 2.4172 - val_accuracy: 0.2152\n",
      "Epoch 10/30\n",
      "11628/11628 [==============================] - 2s 181us/step - loss: 2.2266 - accuracy: 0.2730 - val_loss: 2.4217 - val_accuracy: 0.2193\n",
      "Epoch 11/30\n",
      "11628/11628 [==============================] - 2s 191us/step - loss: 2.2064 - accuracy: 0.2813 - val_loss: 2.4198 - val_accuracy: 0.2134\n",
      "Epoch 12/30\n",
      "11628/11628 [==============================] - 2s 185us/step - loss: 2.1881 - accuracy: 0.2928 - val_loss: 2.4197 - val_accuracy: 0.2152\n",
      "Epoch 13/30\n",
      "11628/11628 [==============================] - 2s 183us/step - loss: 2.1518 - accuracy: 0.2974 - val_loss: 2.4294 - val_accuracy: 0.2139\n",
      "Epoch 14/30\n",
      "11628/11628 [==============================] - 2s 187us/step - loss: 2.1414 - accuracy: 0.2987 - val_loss: 2.4238 - val_accuracy: 0.2208\n",
      "Epoch 15/30\n",
      "11628/11628 [==============================] - 2s 186us/step - loss: 2.1061 - accuracy: 0.3123 - val_loss: 2.4476 - val_accuracy: 0.2108\n",
      "Epoch 16/30\n",
      "11628/11628 [==============================] - 2s 196us/step - loss: 2.0919 - accuracy: 0.3178 - val_loss: 2.4346 - val_accuracy: 0.2201\n",
      "Epoch 17/30\n",
      "11628/11628 [==============================] - 2s 201us/step - loss: 2.0753 - accuracy: 0.3219 - val_loss: 2.4477 - val_accuracy: 0.2092\n",
      "Epoch 18/30\n",
      "11628/11628 [==============================] - 2s 195us/step - loss: 2.0379 - accuracy: 0.3364 - val_loss: 2.4582 - val_accuracy: 0.2092\n",
      "Epoch 19/30\n",
      "11628/11628 [==============================] - 2s 213us/step - loss: 2.0239 - accuracy: 0.3415 - val_loss: 2.4555 - val_accuracy: 0.2105\n",
      "Epoch 20/30\n",
      " 9600/11628 [=======================>......] - ETA: 0s - loss: 1.9897 - accuracy: 0.3521"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-33-34f572b8b107>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mn_hidden\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_timesteps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_hidden\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_classes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdropout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdropout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdev_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-20-54b21d84d058>\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(model, X_train, X_dev, Y_train, Y_dev, epochs, batch_size, verbose)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtrain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mtrain_history\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_dev\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0mtrain_acc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdev_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m   1237\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1238\u001b[0m                                         \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m                                         validation_freq=validation_freq)\n\u001b[0m\u001b[1;32m   1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1241\u001b[0m     def evaluate(self,\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, fit_function, fit_inputs, out_labels, batch_size, epochs, verbose, callbacks, val_function, val_inputs, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq)\u001b[0m\n\u001b[1;32m    194\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    197\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3738\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3739\u001b[0m       \u001b[0mconverted_inputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3740\u001b[0;31m     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mconverted_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3741\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3742\u001b[0m     \u001b[0;31m# EagerTensor.numpy() will often make a copy to ensure memory safety.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1079\u001b[0m       \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mFor\u001b[0m \u001b[0minvalid\u001b[0m \u001b[0mpositional\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mkeyword\u001b[0m \u001b[0margument\u001b[0m \u001b[0mcombinations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1080\u001b[0m     \"\"\"\n\u001b[0;32m-> 1081\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1082\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1083\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1119\u001b[0m       raise TypeError(\"Keyword arguments {} unknown. Expected {}.\".format(\n\u001b[1;32m   1120\u001b[0m           list(kwargs.keys()), list(self._arg_keywords)))\n\u001b[0;32m-> 1121\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1222\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1223\u001b[0m       flat_outputs = forward_function.call(\n\u001b[0;32m-> 1224\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager)\n\u001b[0m\u001b[1;32m   1225\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1226\u001b[0m       \u001b[0mgradient_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_delayed_rewrite_functions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    509\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"executor_type\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"config_proto\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 511\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    512\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,\n\u001b[1;32m     60\u001b[0m                                                \u001b[0mop_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m                                                num_outputs)\n\u001b[0m\u001b[1;32m     62\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "dropout = 0.5\n",
    "n_hidden = 50\n",
    "model = create_model(n_timesteps, n_features, n_hidden, n_classes, dropout = dropout)\n",
    "model, train_acc, dev_acc = train_model(model, X_train, X_dev, Y_train, Y_dev, epochs = 30, batch_size = 64, verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc(train_acc, dev_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decreasing number of hidden units may not help either... the other alternative is to get more data. We may have to use multiple regions to train one model. We can first train an individual model for each region to see if any region can get good accuracy. Then we can try combining the regions. If none of those improve performance, then we'll have to look into combining multiple LSTMs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One model per region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Shape of X_train:      (11628, 5, 100)\n",
      "   Shape of Y_train:          (11628, 17)\n",
      "     Shape of X_dev:       (3876, 5, 100)\n",
      "     Shape of Y_dev:           (3876, 17)\n",
      "    Shape of X_test:       (3876, 5, 100)\n",
      "    Shape of Y_test:           (3876, 17)\n"
     ]
    },
    {
     "data": {
      "image/png": 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o6Jz775HeyMwGmlmSmSVt27at8CMVKUW++MLPKmrUyCcEDShLXoENNJtZGeBF4L6jneucG+acS3TOJdaqVSvywYlEqQ8/hEsvhfbt4X//g5NOCjoiKW4imRQ2AA3zPG4QOparMtAWmGpma4CuwFgNNosUvo0b/X4H118PZ50FX38NCQlBRyXFUSRXNM8FWphZU3wyGABck/ukcy4FCFdeN7OpwP3OuaQIxiRSKuTk+M1uxo3zt++/98f79YMRI/z+ByL5iVhScM5lmdldwEQgBnjbObfYzJ4EkpxzYyP12SKl1bZtvk7RZ5/5iqZlyvgpp8884zfDOfVUTTmVI4to7SPn3Hhg/CHHHj/MuT0iGYtItJs0CW68EXbu9Dui9enjB5S1E5ocCxXEEynhMjLgkUd8ies2bWDiRD+QLHI8lBRESrCffoJrrvHbYd55p98Ip3z5oKOSkky1j0RKIOf8bminnw7JyX7twcsvKyHIiVNSEClhtm6FSy6B22/300sXLvSDyCKFQUlBpAQZN86XpJg0CQYPhvHjoW7doKOSaKKkIFIC7N/vWwYXX+yTQFIS3HOPn3IqUpg00CxSDGzfDitWQI0a/la9OpQN/d+ZlATXXuufv/9+v+dBXFyw8Ur0UlIQCdi33/qVxjt3Hny8ShWfIJKTfevg66+hZ89gYpTSQ0lBJEDvvw+33ALNmsFbb0Fqqk8OubcdO/zis8cf960HkUhTUhAJQE4O/PWvvivonHP8Fpj60pfiQElBpIilpcHNN8NHH/lWwquv+t3PRIoDJQWRIrRlix8/mD0bnnvODxyrQJ0UJ0oKIkVkwQKfELZuhU8+8ZvdiBQ3muUsUgQ+/hi6dYOsLL/jmRKCFFdKCiIRlJMDjz0GV14JHTr4NQeJ2ltQijF1H4lEyJ49cN11vljdLbfA0KFadCbFn5KCSASsWOGL1q1Y4auX3nGHBpSlZFBSEClk48bB9df7MhWTJ0OPHkFHJFJwRx1TMLO7zUzLakSOIjvbrzy++GJo2hTmzlVCkJKnIAPNdYC5ZjbKzHqbqREscqgdO/yeyH//u1+YNmMGNGkSdFQix+6oScE59xjQAngLuAlYYWb/MLPmEY5NpERISvI7oE2ZAsOG+RpG2gFNSqoCTUl1zjlgc+iWBVQHRpvZcxGMTaRYc84ngO7d/f3p0+HWWzWgLCXbUQeazWwQcAOwHXgTeMA5l2lmZYAVwIORDVGk+MjJgTlzYMwY+OwzWL4cevWC4cN9NVORkq4gs49qAJc559bmPeicyzEz7QwrUS8ry3cNjRkDn38OGzf6mUU9e/raRb//PcTEBB2lSOEoSFKYAIS3/zCzKkBr59xs59xPEYtMpBg4cAD69oWJE6FCBejd25eo6NNHpa4lOhUkKbwKdMrzeF8+x0SiTnY23HCDTwiDB8Mf/6gBZIl+BUkKFhpoBsLdRlr0JlHNOfjTn/yeB88+C/fcE3REIkWjILOPVpvZn8wsNnQbBKyOdGAiQXriCXjlFT9m8KCmUkgpUpCkcBvwG2ADkAycAQyMZFAiQXr5ZZ8UbrrJb4QjUpoctRvIObcVGFAEsYgEbsQI323Uty+88YbWHEjpU5B1CvHALcCpQHzucefc7yMYl0iR+/xzP7D829/CyJF+2qlIaVOQ7qP3gbrA+cA0oAGwN5JBiRSVnBy/30H37n6rzLZtYexYzTKS0qsgSeFk59xfgP3OuXeBPvhxBZES68ABePddaNfOdxUlJ8OQIb6QXdWqQUcnEpyCNJAzQz93m1lbfP2j2pELSSRyMjPh1Vfh+ed9ImjfHj74wG+XGRsbdHQiwStIUhgW2k/hMWAsUAn4S0SjEomABQt8SYr58+Hss/1A8vnnazBZJK8jJoVQ0bs9zrldwP+AZkUSlUghSk+Hp57yi9ASEuCTT+Cyy4KOSqR4OuKYgnMuB1VBlRJs1izo2BGefhquuw6WLFFCEDmSggw0f2Vm95tZQzOrkXuLeGQiJyAtzZem6NYNUlPhyy/hnXeghn5zRY6oIGMKV4V+3pnnmKMAXUlm1hv4NxADvOmc++chz98Wet9sfKG9gc65JQWISeSwli+HK66AH3+EO++EZ56BypWDjkqkZCjIiuamx/PGZhYDDAV64ctjzDWzsYd86Q93zr0WOr8v8CLQ+3g+TwT8orNbb4W4OJgwwZe6FpGCK8iK5hvyO+6ce+8oL+0CrHTOrQ69z0jgEiCcFJxze/KcXxHfAhE5Zunp8Oc/w2uv+S6jkSOhQYOgoxIpeQrSfdQ5z/144Fzge+BoSaE+sD7P49xiegcxszuBe4FywDn5vZGZDSRUhK9Ro0YFCFlKk5Ur/TqD+fN9RdOnntKaA5HjVZDuo7vzPjazasDIwgrAOTcUGGpm1+DXQtyYzznDgGEAiYmJak1I2Mcfwy23+DpFX3wBF2mDWJETUpDZR4faDxRknGED0DDP4wahY4czEuh3HPFIKZSe7geRr7wS2rTxrQQlBJETV5AxhS/4pa+/DNAGGFWA954LtDCzpvhkMAC45pD3buGcWxF62AdYgchRrFjhk8GCBXDfffCPf0C5ckFHJRIdCjKm8EKe+1nAWudc8tFe5JzLMrO7gIn4KalvO+cWm9mTQJJzbixwl5n9Dl9faRf5dB2J5PXRR352UdmyvprpxRcHHZFIdClIUlgHbHLOpQOYWXkza+KcW3O0FzrnxgPjDzn2eJ77g44tXCmt0tLg3nv97KIzz/SzizTnQKTwFWRM4WMgJ8/j7NAxkYg6cADGj4ebb4b69X1CePBBmDZNCUEkUgrSUijrnDuQ+8A5d8DM1IMrEZGeDpMmwejRvnsoJQWqVIFLLvGzjM4+O+gIRaJbQZLCNjPrGxoDwMwuAbZHNiwpbdatg1de8eWsd+6E6tV94br+/eHcc/0KZRGJvIIkhduAD83s5dDjZCDfVc4ix8I5mD7d73g2Zox/fOmlfiD5nHO0AE0kCAVZvLYK6GpmlUKP90U8Kol6o0bBP//p1xdUr+6nlt5xBzRuHHRkIqXbUQeazewfZlbNObfPObfPzKqb2VNFEZxEp2++gauugowMeP11vy3ms88qIYgUBwWZfXSBc2537oPQLmwXRi4kiWbZ2b5V0LgxzJsHAwdChQpBRyUiuQoyphBjZnHOuQzw6xQADfvJcXn/fb8SefhwiI8POhoROVRBksKHwNdm9g5gwE3Au5EMSqLT/v3w6KPQpQsMGBB0NCKSn4IMND9rZj8Av8PXQJoIqPdXjtmLL8LGjX6Q2SzoaEQkPwWtkroFnxCuwO958FPEIpKotGmTH0y+/HK/CY6IFE+HbSmY2SnA1aHbduAjwJxzPYsoNokijz/uy1Y8+2zQkYjIkRyp+2gp8C1wkXNuJYCZ/blIopKosnAhvP02DBoEzZsHHY2IHMmRuo8uAzYBU8zsDTM7Fz/QLHJM7r8fqlaFxx4LOhIROZrDJgXn3GfOuQFAK2AKcA9Q28xeNbPziipAKdm+/NIXuPvLX6BGjaCjEZGjMecKvuWxmVXHDzZf5Zw7N2JRHUFiYqJLSkoK4qPlGGVlQYcOvvLpkiXaHU0kSGY2zzmXeLTzjmmPZufcLufcsKASgpQcS5b4MteLF8NzzykhiJQUx5QURI4mIwP+9jffQli2DN57z5fAFpGSoSArmkUKZMYMX/b6p5/g2mth8GCoVSvoqETkWKilICdsyxZf9rp7d0hNhQkT4IMPlBBESiK1FOS4bN0Kn37qS1ZMm+aP/fnP8OSTUKlSsLGJyPFTUpAC27kTPv7YJ4KpUyEnB1q29EXurrkGWrUKOkIROVFKCnJUmZl+/+S//Q1274ZTToFHHoErr4S2bVXcTiSaKCnIEU2aBPfc4wePe/XyW2h27KhEIBKtNNAs+Vq5Ei65BM4/3xey+/xzmDgROnVSQhCJZkoKcpCtW+GBB6BNG7+X8rPP+gVoffsqGYiUBuo+EsDvd/DCC/Dqq74sxQ03wDPPQL16QUcmIkVJSaGUS072ZSiGDfO1iq65xg8iayaRSOmkpFBKpaX5ktZvvumnlt54Izz8sPY7ECntlBRKodRUP4j89dcwcCA89BA0aRJ0VCJSHCgplDKpqX7Q+Jtv4J13fAtBRCSXkkIpsn8/XHyxX438n//4wWQRkbyUFEqJ/fvhoovgf//z5ayvuy7oiESkOFJSKAX274c+feDbb31CuPbaoCMSkeJKi9ei3O7dcOGFPiF88IESgogcmZJClHIOhg/36w1mzIAPP4Srrw46KhEp7pQUotCKFXDeeb5V0KgRzJ0LAwYEHZWIlAQRTQpm1tvMlpnZSjN7KJ/n7zWzJWb2o5l9bWaNIxlPtMvIgCeegHbtYM4cGDoUZs3yVU1FRAoiYknBzGKAocAFQBvgajNrc8hp84FE51x7YDTwXKTiiXbffQft2/s9Dy69FJYu9VtkxsQEHZmIlCSRbCl0AVY651Y75w4AI4FL8p7gnJvinEsNPfwOaBDBeKLW559Dz56+xPWXX8KIESpkJyLHJ5JJoT6wPs/j5NCxw7kFmJDfE2Y20MySzCxp27ZthRhiyff663DZZb90GZ1/ftARiUhJViwGms3sOiAReD6/551zw5xzic65xFq1ahVtcMWUc/DXv8Jtt0Hv3jBlCuifRkROVCQXr20AGuZ53CB07CBm9jvgUeBs51xGBOOJGllZcPvtvsLpzTf71kJsbNBRiUg0iGRLYS7Qwsyamlk5YAAwNu8JZtYReB3o65zbGsFYokZqqh9IfvNNePRReOstJQQRKTwRayk457LM7C5gIhADvO2cW2xmTwJJzrmx+O6iSsDH5vd6XOec6xupmEqypUv9ArQPPoC1a+GVV3xrQUSkMEW09pFzbjww/pBjj+e5/7tIfn5Jt3EjjBzpk8H330OZMnDuufDaaxpQFpHIUEG8YmjrVrjlFvjvf/2AcufOMHgwXHWVppqKSGQpKRQz8+f7XdG2b4fHHvMlrk85JeioRKS0UFIoRkaNgptugoQEmD4dOnUKOiIRKW2KxTqF0i4nB/7yF9891LEjJCUpIYhIMNRSCNjevXD99b5Uxe9/72cVxcUFHZWIlFZqKQQkKws+/RS6doVx4+Df//ZrD5QQRCRIaikUse3b/Zf/q6/CunXQuDFMmAC9egUdmYiIkkKRmT8f/u///G5oGRlwzjm+dXDxxSpvLSLFh5JChDnny1E88wxUqOBrFd11F5x6atCRiYj8mpJCBDkHDz4IL7zgF6O98AJUqxZ0VCIih6ekECHOwX33+ZXId97pu458eScRkeJLs48iwDm45x6fEAYNUkIQkZJDSaGQ5eT4MYMhQ+Dee31iUEIQkZJCSaEQ5eTAHXf4BWi5YwlKCCJSkmhMoZDs2QO33urrFz38MDz9tBKCiJQ8SgqFICnJ1y1auxaefRYeeEAJQURKJnUfnYCcHHjxRfjNb3zZimnTfLeREoKIlFRqKRynbdt8mevx43/ZM7lGjaCjEhE5MUoKx2HqVLjmGti5E4YO9Xslq3UgItFA3UfH6K23fPG6KlXgu+/8bCMlBBGJFkoKBZSTA488An/4A5x7LsyZAx06BB2ViEjhUvdRAaSn+/GDjz7y006HDoXY2KCjEhEpfEoKR7F9O/TrBzNmaLqpiEQ/JYUjWLECLrwQ1q/3rYQrrww6IhGRyFJSOIyvvvIL0sqUgW++8WsRikJGVgbT1k7jq9VfUb5seVrWbEmrmq04JeEUKpWrVDRBiEippaRwCOf8grQHH4TWreHzz6F58+N/v5T0FEYvGc2s5FnUr1yf5jWa07x6c5rXaE6dinUwM9alrGPCigmMXzmer1Z/RWpmKuViypGVk0WOywm/V4MqDWiZ0JLT6pxG90bd6daoG7Ur1i6EqxYR8cw5F3QMxyQxMdElJSVF5L1TU/3sohEj4PLL4Z13oHLlY3+frJwsJq+azHs/vsdnSz8jPSudGuVrsCttF45f/r0rxFagVoVarE1ZC0Djqo3p06IPF7a4kJ5NexJjMazcuZKl25eybMcylm5fytLtS/lxy49kZGcAcHKNk32CaNiNrg260jKhJbExxz8K7pxj9a7VZLts6lWqR6VylbB8BlHTsZ3dAAARnElEQVQyszNZvWt1OLb9B/ZzdpOz6dawG3Fl447780UkMsxsnnMu8ajnKSl4P//sVyb/+KMvZvfQQ8c2oJyVk8XcDXMZvWQ0Hy78kC37t1CjfA2ubns1N5x2A51P6syB7AOs2b2G1btWs2rXKlbtXMWmfZvofFJnLmxxIa1qtsr3C/hQGVkZfL/pe6avm86M9TOYsX4G21O3AxBbJpZWNVvRrk472tZqS7s67WhdszV1KtWhYmzFX72/c46VO1cydc1UpqyZwtQ1U9m0b1P4+QqxFahXqR71KtejbqW6HMg+wLLty1i1axVZOVnh88pYGXJcDuXLluesxmfRq1kvzmt+Hm1rty3QNYlIZCkpHIPJk2HAAL8WYcQI6N376K9xzrFq1yomrZrE5NWTmfLzFFIyUogtE0ufU/pwQ/sb6HNKH8rFlCvUWA8Xy/Idy0namMTCrQtZtHURC7cuZF3KuoPOi4uJo2aFmuFbxXIVmbdxHhv2bgCgbqW69GzSk7Mbn03FchXZtHcTm/b52+Z9m9m0dxMxZWJoVbMVLRP8WEfueEeMxTBt7TQmr5rMpNWTWLp9afg9+7XsxxWnXsFZjc+ibJmi6bFcuXMlg2cNpmp81XB3XbPqzahfuT4xZWIOOjcjK4OUjBT2ZOwhKyeL2DKxxMbEHvSzYrmKEY19Xco6ylgZGlRpELHPkNJNSaGA5syBM8+ENm1gzBg4+eT8z8vKyWLR1kXMTp7N7A2zmbJmCmt2rwGgUdVGnNfsPHo178Xvmv2OGuWLRxGklPQUFm9bzLLty9ieuv2XW5r/mZKeQtvabenZpCc9mvTglIRTCu2v+vUp6/lq9VeMXzme8SvGk5qZSs0KNbm01aX0b9Ofnk16nlA31+E453j3h3e5e8LdZOVkhW+5ysWUo3HVxjgcKek+EeR2xR1JXEwcHet1pMtJXTijwRl0qd+F5tWbn/C/196MvTz1v6cY/N1g4svG896l79GvVb8Tek+R/CgpFNCVV8LEmRuZ9L/dxMSnkpqZSlpmGqmZqezJ2MOPW35kzsY5zNs4j7SsNAASyifQrVG3cCJoUaOFukiOIDUzlS9XfsnoJaP5YvkX7Duwj+rx1TmjwRnhLq52tdvRulZr4svGA/7LfWfaTtbvWc+6lHWsT1lPpXKV6NeqH1Xjq+b7ObvSdnHbf29j1OJRnN34bN6/9H3qVa7H+pT1B3XZ/bz7Z2LKxFA1ripV46pSJa4KVeP9/bJlypKZk0lWThaZ2Zlk5mSSmZ3Jhr0bmLNhDvM2zSM1MxWAGuVr0LFuR+pWqktC+YSDWmG1K9amU71OVI7Lf1Aqx+Xw3g/v8fDXD7N532ZuOO0Gftr2E3M3zuXR3z7KEz2e+FWLRuREKCkcRY7L4e3p47j17RehybTDnhcXE0enep3oUr8LZ9T3fyE2q95MSeA4pWelM2nVJD5b+hnzN89nybYlHMg+APhxidwEuy5lXfjLN6/4svH0a9WPG9rfQK/mvcJdOt+u/ZZrP72WTfs28WSPJ3mw24MR+VLNysli8dbFzNkwh9kbZrNw68JwC2xPxp6Dzo2xGE4/6XR6NO5Bz6Y96dawG5XjKvNd8nf8acKfmLtxLmfUP4MhFwyhS/0upGelc9f4u3hr/lv0Prk3wy8bTvXy1Y8pvq37tzLl5yk0rd6UTvU6HbXLa2/GXqavm/6rrsZcNcrX4OKWF4eTtZRcSgqHsf/Aft794V0GfzeYlTtXwu5GPHjubZzetDnly5anQmyFg26NqzUuknGB0iorJ4uVO1eycMvC8HhITJkYGlVpRMOqDWlUtRGNqjaiYZWGrNm9hvd/fJ8Ri0awM20ndSrW4dp21xIbE8vzM5+nabWmDL98OF3qdwnkWg5kH2BH6g62p25n496NTF83nalrpzI7eTaZOZnEWAyta7Vm0dZFnFT5JJ793bNc0+4aytgvJciccwybN4y7J9xNw6oNGXPVGNrXaX/Ez928bzNjfhrDx0s+ZtraaeFpzJXLVea3jX8bTkod6nYgPSvdxxWaWDBv4zyyXfYR379mhZrc2ulWbk+8nYZVG574P1QRysrJImljEjXK1+CUhFOCDidQSgqH2LBnAy/PeZnX573OrvRdJNbtwpK37uOCJpcxepSWa5QkB7IPMH7FeN774T3GLR9HZk4mN3W4iSG9hxy2uyZIqZmpzFw/k6lrpjIreRZnNjiTh7o/dMTFiLPWz6L/x/3Znb6bJ3o8Qd1KdX91zrb92/hs2Wd8u/ZbHI5WNVvRv3V/+pzSh3Up65jy8xSmrp0aHvSvXK4yqZmpZLtsYsvEckaDM+jRuAc9mvSgda3WGL9u/S7ZtoSX577M2GVjMYx+rfpxd5e7OavxWcWytZw7m27y6slMWjWJKWumhFtwF5x8AfeeeS/nNj23WMYeaUoKh3j6f0/z+NTHubTVpdx75r0s+OJM7rzTmDGj6FYrS+HbkbqDjXs30q5Ou6BDKXSb923mio+vYPq66Yc9p23ttvRv3Z/+bfpzau1TD/s+U9dM5du131Itvho9m/bkzAZnUrFcxQLHsmb3Gl6d+ypvzn+TnWk7aVWzFc2rN/fjMXFVqRrvx2aqxFUhOyeb1MzUg29ZqZxU6SS6N+rObxr+5rDdYjkuhxU7VjBnwxw27dvEuU3PpVO9Tkf8Es/MzmTqmql88tMnTFw1MTwBpEm1JvRq5id/LNu+jJfnvszW/VtpV7sdf+76Z65pd82v1tTkuBx2pO4gNTOVRlUbRVXyUFI4xK60XexK30Wz6s3IyfGzjapUgdmzVeBOiq8cl8Oa3WsOWtmeq3zZ8tSvUr9I40nNTGXEwhGMWjIqPI6Skp5CSkZKeGwol2Hhbtj4svFs2rcpPBOsTa02dG/oV+VXi6/GnA1zmLNhDnM3zmV3+u6D3qdhlYb0bdmXS1pewtlNzqZcTDkOZB/g69VfM3rJaD5b9hk703ZSMbYivZr3Cq+ROXR2WHpWOiMWjmDwd4NZuHUhdSrW4YIWF7AjdYefch2aep0bY+6Ekm4N/S3xpMRCW5iZnpVORlYGVeKqHDbxpGels3DLQr7f9D3zN8/n+03f88hvHznu2WlKCkcwYYIvdPfhh34HNRE5celZ6ezJ2ENsmVgqxFagXEy5g77wUjNTmbNhDjPWzWD6+unMWj+LlIwUwA/Kt6vTji4ndfGTOhqcQa0KtZiwcgKfL/uciSsnkpaVRtW4qnRt0JXvkr8jJSOFKnFV6NuyL/1b9+e85udRPrb8UeN0zvH1z1/z4qwX+X7T99SpVCe8QLNeJb9Is1xMOR/r+hks37Ec8JNOEk9KJPGkRDrV60THuh1pXav1rwbzD2QfYPmO5Szc4sfI1qasPWhK+I60Hew7sC983QkVDp65FhcTx6Kti1iybUl4vKdafDU61u3IfWfeR59T+hzXfx8lhSM4/3xYtMivYi6nMWSRQOS4HBZvXcyejD10qNvhiN1ZqZmpfLX6Kz5f+jkz1s+ga4Ou9G/Tn17NekW8rMrW/VuZuX4m09dNZ+b6mSzYvCA8PT2+bDztarejQ90O7D2wl4VbFrJsx7Jwa6NsmbI0rNKQWhVrUbNCzYOmLsfFxLEzbWc4UeQmjf2Z+2lTqw0d63YMJ58m1ZqccFdWsUgKZtYb+DcQA7zpnPvnIc+fBbwEtAcGOOdGH+09TzQpLF4Mbdv6UhaPPHLcbyMipVR2TjbLdixj/qb54a6dH7b8QOVylcNrbtrWbku72u1oWbNlsZm9WNCkELFpN2YWAwwFegHJwFwzG+ucW5LntHXATcD9kYrjUEOGQHw8DBxYVJ8oItEkpkwMbWq1oU2tNlzb/tqgwyl0kZyL2QVY6ZxbDWBmI4FLgHBScM6tCT3361G0CNixA957D66/HmrWLIpPFBEpWcoc/ZTjVh9Yn+dxcujYMTOzgWaWZGZJ27ZtO+6Ahg3z+y0PGnTcbyEiEtUimRQKjXNumHMu0TmXWKtWreN6j8xMePll6NULTs1/OreISKkXye6jDUDeNfENQscCMXo0bNwIb7wRVAQiIsVfJFsKc4EWZtbUzMoBA4CxEfy8I6pcGfr1K9heCSIipVXEkoJzLgu4C5gI/ASMcs4tNrMnzawvgJl1NrNk4ArgdTNbHKl4LrrI75dQpkR0mImIBCOileCcc+OB8YccezzP/bn4biURESkG9HeziIiEKSmIiEiYkoKIiIQpKYiISJiSgoiIhCkpiIhImJKCiIiElbhNdsxsG7D2OF9eE9heiOGUFKX1uqH0Xruuu3QpyHU3ds4dtXhciUsKJ8LMkgqyyUS0Ka3XDaX32nXdpUthXre6j0REJExJQUREwkpbUhgWdAABKa3XDaX32nXdpUuhXXepGlMQEZEjK20tBREROQIlBRERCSs1ScHMepvZMjNbaWYPBR1PpJjZ22a21cwW5TlWw8wmm9mK0M/qQcYYCWbW0MymmNkSM1tsZoNCx6P62s0s3szmmNkPoet+InS8qZnNDv2+fxTa/TDqmFmMmc03s3Ghx1F/3Wa2xswWmtkCM0sKHSu03/NSkRTMLAYYClwAtAGuNrM2wUYVMf8BDt109CHga+dcC+Dr0ONokwXc55xrA3QF7gz9N472a88AznHOnQZ0AHqbWVfgWWCwc+5kYBdwS4AxRtIg/M6OuUrLdfd0znXIszah0H7PS0VSALoAK51zq51zB4CRwCUBxxQRzrn/ATsPOXwJ8G7o/rtAvyINqgg45zY5574P3d+L/6KoT5Rfu/P2hR7Ghm4OOAcYHToeddcNYGYNgD7Am6HHRim47sMotN/z0pIU6gPr8zxODh0rLeo45zaF7m8G6gQZTKSZWROgIzCbUnDtoS6UBcBWYDKwCtgd2icdovf3/SXgQSAn9DiB0nHdDphkZvPMbGDoWKH9nkd0j2Ypfpxzzsyidh6ymVUCPgHucc7t8X88etF67c65bKCDmVUDxgCtAg4p4szsImCrc26emfUIOp4i1t05t8HMagOTzWxp3idP9Pe8tLQUNgAN8zxuEDpWWmwxs3oAoZ9bA44nIswsFp8QPnTOfRo6XCquHcA5txuYApwJVDOz3D/6ovH3vRvQ18zW4LuDzwH+TfRfN865DaGfW/F/BHShEH/PS0tSmAu0CM1MKAcMAMYGHFNRGgvcGLp/I/B5gLFERKg/+S3gJ+fci3meiuprN7NaoRYCZlYe6IUfT5kC9A+dFnXX7Zx72DnXwDnXBP//8zfOuWuJ8us2s4pmVjn3PnAesIhC/D0vNSuazexCfB9kDPC2c+7pgEOKCDMbAfTAl9LdAvwV+AwYBTTClx2/0jl36GB0iWZm3YFvgYX80sf8CH5cIWqv3cza4wcWY/B/5I1yzj1pZs3wf0HXAOYD1znnMoKLNHJC3Uf3O+cuivbrDl3fmNDDssBw59zTZpZAIf2el5qkICIiR1dauo9ERKQAlBRERCRMSUFERMKUFEREJExJQUREwpQURA5hZtmhCpS5t0IromdmTfJWsBUpblTmQuTX0pxzHYIOQiQIaimIFFCojv1zoVr2c8zs5NDxJmb2jZn9aGZfm1mj0PE6ZjYmtNfBD2b2m9BbxZjZG6H9DyaFViKLFAtKCiK/Vv6Q7qOr8jyX4pxrB7yMXyEP8H/Au8659sCHwJDQ8SHAtNBeB52AxaHjLYChzrlTgd3A5RG+HpEC04pmkUOY2T7nXKV8jq/Bb2izOlR8b7NzLsHMtgP1nHOZoeObnHM1zWwb0CBvmYVQWe/Joc1QMLP/B8Q6556K/JWJHJ1aCiLHxh3m/rHIW4snG43tSTGipCBybK7K83NW6P5MfKVOgGvxhfnAb4t4O4Q3wqlaVEGKHC/9hSLya+VDO5nl+tI5lzsttbqZ/Yj/a//q0LG7gXfM7AFgG3Bz6PggYJiZ3YJvEdwObEKkGNOYgkgBhcYUEp1z24OORSRS1H0kIiJhaimIiEiYWgoiIhKmpCAiImFKCiIiEqakICIiYUoKIiIS9v8B8kEZH3T31jIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Shape of X_train:      (11628, 5, 190)\n",
      "   Shape of Y_train:          (11628, 17)\n",
      "     Shape of X_dev:       (3876, 5, 190)\n",
      "     Shape of Y_dev:           (3876, 17)\n",
      "    Shape of X_test:       (3876, 5, 190)\n",
      "    Shape of Y_test:           (3876, 17)\n"
     ]
    },
    {
     "data": {
      "image/png": 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kqYhMFpGWxV1IREaKSJKIJIX6/EbGeCkz001BsWqVG2Q2ZAjcf7+bxTQ5Ge64A5Ytc2scjxnjpqcwocXrwWvTgQRV7Qx8BbxZ3EmqOk5VE1U1sVGjRhUaoDHhIi8PLrsMvvvOTVV9zjm/Pyfixho8+aRra9i4Ef70J89CNUEUzKTwG1D4m3+875ifqu5U1Wzf7mtA9yDGY4w5AlX3IT99Ovz3v3DppUc+t1o1aFlsmd6EgmAmhUVAexFpIyI1gBHAtMIniEizQrtDgJVBjMcYcwT33gtvvOG6l95yi9fRGC8FbZyCquaJyCjgCyACGK+qK0TkISBJVacBt4nIECAP2AVcG6x4jDGHy811K5o99RTcdJNLCia8SVVb4jExMVGTkpK8DsOYKu+nn9waBj/+6Aaf/e9/EBHhdVQmWERksaomlnSe1w3NxpgKlpPjSgSJiZCaCpMnu6ojSwgGbJoLY8LKokWudLB8OVx1lZuvqEEDr6MylYmVFIwJA9nZboGbnj3daORPPoG33rKEYA5nJQVjQtyyZfB//+d+3nADPPOMm7HUmOJYScGYEFVQ4Ba2SUyEtDRXOnj1VUsI5uispGBMCNq40fUomjPHrYU8bhzYZAAmEFZSMCaE5Oa6nkQnnwxJSTB+vJu0zhKCCZSVFIyp4goKYP58eO89+OADN7tpr17w9tvQpo3X0ZmqxpKCMVXUsmXw7rsuGWzYALVquVlNL78cBg+2cQembCwpGFPFLFjgBp999ZX74D/vPLdO8tChEBPjdXSmqrOkYEwVsWiRSwaffebaCJ56Cq691toLTPmypGBMJbdkiUsG06dD/frwxBNw660QHe11ZCYUWVIwppLKyYG//AVeegliY+Hhh+G226BuXa8jM6HMkoIxldCWLXDJJa5X0ejR8OCDLjEYE2yWFIypZBYsgGHDYO9emDjRLZFpTEWxwWvGVCLjxsFZZ7nupQsWWEIwFc+SgjGVQHY2jBwJN94IZ5/tehp17ux1VCYcWVIwxmMzZ8Kpp7rJ6u67Dz791PUyMsYLlhSM8ciKFTBwIJx7LqSnw9Sp8NhjNhLZeMuSgjEVbOtWV03UubNrN3jmGVi1yk1RYYzXrPeRMRUkL8+NQn7sMTcG4bbb4IEHbPUzU7lYUjCmAqSmwhVXwLx5cNFFLjm0a+d1VMYczpKCMUH2ySdujqLsbJgwwS2NaUxlZW0KxgRJTg7ccQdccAG0bAmLF1tCMJWflRSMCYK1a2HECLf62ahR8PTTEBXldVTGlMySgjHlaMcOeP55t0VEwJQpcPHFXkdlTOAsKRhTDrZsgWefhZdfhsxMlwieeQYSEryOzJjSsaRgzDHYsMH1JPrf/1yX08svd6OSO3b0OjJjysaSgjGllJbmRh9/9JFbElPE9S665x447jivozPm2AS195GIDBCR1SKyRkTuPcp5w0RERSQxmPEYU1YbN7p2grPOgmbN3OR1q1e73kXr1rnZTS0hmFAQtJKCiEQAY4BzgVRgkYhMU9WUIufFAKOBhcGKxZiyWrQI/v53+OILt3/yyW7/4ovdYxFv4zOmvAWz+uhUYI2qrgMQkYnAUCClyHkPA08CdwUxFmNKZfly9+H/8cduGoqHH3ZrG7Rv73VkxgRXMKuPWgCbCu2n+o75icgpQEtV/fRoFxKRkSKSJCJJ27dvL/9IjfH55Rc3HUXnzjB7Njz0EPz6q5ujyBKCCQclJgUR+bOIxJX3G4tINeDfwF9LOldVx6lqoqomNmrUqLxDMQZwH/wdOrhG5Hvuccng73+HmBivIzOm4gRSUmiCaw/4wNdwHGgt6m9Ay0L78b5jB8UAnYA5IrIe6AlMs8Zm44V334VHH3VdSteuhccft4VuTHgqMSmo6gNAe+B/wLXALyLymIiU1NdiEdBeRNqISA1gBDCt0HX3qmpDVU1Q1QTge2CIqiaV7VaMKZs1a+Cmm6BXL3j9dWja1OuIjPFOQG0KqqrAVt+WB8QBk0XkqaO8Jg8YBXwBrAQ+UNUVIvKQiNhyIqZSyMlxpYOICHjnHahuI3dMmCvxT0BERgNXAzuA14C7VDXX1ybwC3D3kV6rqjOAGUWO/eMI5/YNPGxjysf997tJ66ZMgdatvY7GGO8F8r2oPnCxqm4ofFBVC0RkcHDCMib4Zsxw8xXdcotNWmfMQYFUH30G7Dq4IyJ1ReQ0AFVdGazAjAmmzZvhmmtc19Nnn/U6GmMqj0CSwstARqH9DN8xY6qk/Hy48ko4cAAmTrR1DowpLJDqI/E1NAP+aiNrjjNVUn4+/Otf8PXXbmbTDh28jsiYyiWQD/d1InIbv5cObgHWBS8kY8pXVhbMmuVmNZ02DbZvdz2OrrvO68iMqXwCSQo3AS8ADwAKzAJGBjMoY45VQQFMnuy2zz6DjAw3MnnQILjwQtewbJPZGXO4EpOCqqbhBp4ZUyVkZsJVV7lupk2auLmMLroI+vWDmjW9js6Yyi2QcQpRwB+BkwB/k5yqXh/EuIwpk7Q0GDoUFi50y2H+5S9QLairhhgTWgL5c3kbaAr0B+bi5jDaF8ygjCmLVaugZ09ITnbVRn/9qyUEY0orkD+Zdqr6d2C/qr4JDAJOC25YxpTO3Llwxhmu7WDOHBuMZkxZBZIUcn0/94hIJ6Ae0Dh4IRlTOhMmwLnnuvaD77+H0+wrizFlFkjvo3G+9RQewM1yGg38PahRGXMEqrBhAyxY4BLAggVuycy+feHDDyGu3Ff+MCa8HDUp+Ca9S1fV3cA3QNsKicqYIr78El56ySWCbdvcsdq1oUcPtzraPfdAjRrexmhMKDhqUvCNXr4b+KCC4jHmMG+/7QaaNWsG553nGpNPPx1OPtmmujamvAXyJzVTRO4E3gf2HzyoqruO/BJjyseYMTBqFJx9tlsmMzra64iMCW2BJIXLfD9vLXRMsaokE0SqbknMv/3NjTuwieuMqRiBjGhuUxGBGHOQqmsjePppN5vp+PEQGel1VMaEh0BGNF9d3HFVfav8wzHhLj/fLXozbpz7+eKLNgDNmIoUSPVRj0KPo4A/AD8ClhRMucrOdgvfvP++WybzkUds0jpjKlog1Ud/LrwvIrHAxKBFZMJSerqbtG72bHjqKbjrLq8jMiY8laVD337A2hlMudmyBQYOhOXL4a233AynxhhvBNKmMB3X2wjctBgdsXELppysXg0DBriFbz75BPr39zoiY8JbICWFZwo9zgM2qGpqkOIxYWThQrfoTbVqbhK7xESvIzLGBJIUNgJbVDULQERqiUiCqq4PamQmpH36KQwfDs2bw+efQ7t2XkdkjIHAZkmdBBQU2s/3HTOm1HbuhBtvhAsugI4d4bvvLCEYU5kEkhSqq2rOwR3fY5t6zJRKfj688gocfzz8739w++2uyqhJE68jM8YUFkhS2C4iQw7uiMhQYEfwQjKhZuFCt8bBTTe5SeySk+Hf/7Z5jIypjAJpU7gJeEdE/uvbTwWKHeVsTGEHDsDo0fDaa67t4L334LLLbECaMZVZIIPX1gI9RSTat58R9KhMSLjrLldVdOed8I9/QEyM1xEZY0pSYvWRiDwmIrGqmqGqGSISJyKPBHJxERkgIqtFZI2I3FvM8zeJyDIRSRaRb0WkY1luwlQ+s2e7RXFuv91NbGcJwZiqQVT16CeILFHVbkWO/aiqp5TwugjgZ+BcXJXTIuByVU0pdE5dVU33PR4C3KKqA4523cTERE1KSjpqzMZb+/ZB585uZtPkZLdCmjHGWyKyWFVLHA0USJtChIjUVNVs34VrATUDeN2pwBpVXed73URgKOBPCgcTgk8dfh85baqwu+926yjPm2cJwZiqJpCk8A4wS0ReBwS4FngzgNe1ADYV2k8FTit6kojcCtyB6+Z6dnEXEpGRwEiAVq1aBfDWxiszZ8LYsXDHHdCrl9fRGGNKq8Q2BVV9EngE6ACcAHwBtC6vAFR1jKoeB9wDPHCEc8apaqKqJjZq1Ki83tqUs/R0+OMf3ViERwJqdTLGVDaBzpK6DVe1Mxz4FZgSwGt+A1oW2o/3HTuSicDLAcZjKqG77oLUVPj2W6hVy+tojDFlccSkICLHA5f7th3A+7iG6X4BXnsR0F5E2uCSwQjgiiLv0V5Vf/HtDgJ+wVRJX37pVku78044/XSvozHGlNXRSgqrgHnAYFVdAyAifwn0wqqaJyKjcNVNEcB4VV0hIg8BSao6DRglIucAucBu4Joy3ofx0J49cMMNcMIJ8NBDXkdjjDkWR0sKF+O+3X8tIp/jqndKNRZVVWcAM4oc+0ehx6NLcz1TeezfD599BlOmuHUQDhxwk9tZtZExVdsRk4Kqfgx8LCJ1cF1Jbwcai8jLwEeq+mUFxWgqifR0N+X15MkuIWRmQsOGMGIEXHst9OzpdYTGmGMVyDQX+4F3gXdFJA7X2HwPYEkhDOzb50oCH3zgEkF2NjRrBtdfD8OGQZ8+UL0si7oaYyqlUv05q+puYJxvMyFq/36XCN5/3yWCrCxo0QJuvhkuucQ1JFcLZH5dY0yVY9/xzCF27oTu3d2I5GbNYORIuPRSSwTGhAtLCsZP1fUi2rzZtR0MGGCJwJhwY0nB+L36Knz8MTzzDAwc6HU0xhgv2PdAA8CqVW6a63PPhb8EPBrFGBNqLCkYsrPhiivcjKZvvGFVRsaEM6s+MjzwACxZAlOnumUzjTHhy74ThrmZM10bwk03wZAhXkdjjPGaJYUwtmMHXH01nHgiPPus19EYYyoDqz4KU9nZbu2DHTtc91NbIc0YA1ZSCDtZWTBmDLRrB9OmwZNPQrduJb/OGBMeLCmEicLJYNQoaN3arYFg3U+NMYVZ9VGIy82FV16Bxx93I5V794Y334SzzwYp1UToxphwYEkhhKWnw/DhrkTQpw+8/Tb062fJwBhzZJYUQtRvv8GgQbB8Obz2mpvq2pKBMaYklhRC0LJlbu6iPXtcz6L+/b2OyBhTVVhDc4iZNcu1GxQUwLx5lhCMMaVjSSGEvPWWm+66VSv4/nvo2tXriIwxVY0lhRCQnQ333APXXANnnulKCC1beh2VMaYqsjaFKu6nn+Cqq1w7wo03wgsvQI0aXkdljKmqrKRQReXnwxNPQI8ekJbm1lQeO9YSgjHm2FhJoQpas8ZVFc2fD5dcAi+/DA0beh2VMSYUWEmhinnzTejSBVJS4J134IMPLCEYY8qPlRSqkLfegmuvdVNUvPUWtGjhdUTGmFBjSaGK+PBDuO46+MMfXPtBVJTXERljQpFVH1UBX3wBI0bAaafBxx9bQjDGBI8lhUru22/hoovgpJNgxgyIjvY6ImNMKAtqUhCRASKyWkTWiMi9xTx/h4ikiMhSEZklIq2DGU9V8+OPblK7Vq1caSE21uuIjDGhLmhJQUQigDHA+UBH4HIR6VjktCVAoqp2BiYDTwUrnqomJQXOOw/i4uCrr6BxY68jMsaEg2CWFE4F1qjqOlXNASYCQwufoKpfq+oB3+73QHwQ46kyZs92PYwiI2HmTJuywhhTcYKZFFoAmwrtp/qOHckfgc+Ke0JERopIkogkbd++vRxDrFzy8uCBB+Ccc1xV0ezZbvlMY4ypKJWioVlErgQSgaeLe15Vx6lqoqomNmrUqGKDqyAbN0LfvvDoo67r6eLF0KGD11EZY8JNMMcp/AYUrviI9x07hIicA/wNOEtVs4MYT6X10UduZbT8fDdK+YorvI7IGBOugllSWAS0F5E2IlIDGAFMK3yCiHQDXgGGqGpaEGOplNLT4dZb4eKL4bjjXG8jSwjGGC8FLSmoah4wCvgCWAl8oKorROQhERniO+1pIBqYJCLJIjLtCJcLKXl5bkbT9u3hpZfgL39xk9tZ+4ExxmtBneZCVWcAM4oc+0ehx+cE8/0rG1W3ZvJdd8GqVdCnj5uyokcPryMzxhinUjQ0h4MlS1yvogsucG0HH30Ec+daQjDGVC6WFCrA2LHQvbtbJe3FF2HFCrjwQhDxOjJjjDmUzZIaZJ995hqTBw50PYvq1fM6ImOMOTJLCkG0dClceqlbFGfiRJvMzhhT+Vn1UZBs2QKDB7uSwfTplhCMMVWDlRSCYP9+16C8a5eb+tpWSDPGVBWWFMpZfj5ceaWKG8faAAAS6UlEQVTrbTR1KnTt6nVExhgTOEsK5ezee93qaM8/76qPjDGmKrE2hXL04ovwzDMwahTcdpvX0RhjTOlZUignTzzhEsHQofCf/3gdjTHGlI0lhWOk6qqM7rvPTWY3aRJUt0o5Y0wVZR9fx6CgwA1MGzsWbroJxoyBapZmjTFVmH2ElVFuLlx1lUsI99zjZju1hGCMqeqspFAGWVlupPL06fD44676yBhjQoElhVLKyYEhQ2DmTFc6uPlmryMyxpjyY0mhFFRd28FXX8H48W4tZWOMCSVWC14Kjz8Or78O//ynJQRjTGiypBCg99+Hv/3NTWHxz396HY0xxgSHJYUAzJ8P11zjls987TVbHMcYE7osKZRg7Vo3SrlVK7eEZs2aXkdkjDHBY0nhKHbtgkGD3CC1Tz+FBg28jsgYY4LLksIRrF3rup7++qub9bR9+5Jfs2rHKqavnk5qeiqqGvwgjTGmnFmX1CLS0uCRR9xI5erV4a23XFvCkagq8zbO4+n5T/PJz5/4jzep04TE5on0aN6DxOaJnNjwRA7kHmBP1p5Dtsy8TAa2H0inxp0q4O6MMebopKp9o01MTNSkpKRyv25GBvz73/D005CZCTfc4HoZNWtW/Pn5Bfl8tOojnp7/ND/89gMNazfkz6f+mX4J/fhp208s2ryIpM1JrNy+EqXkf+PerXpzc+LNDOswjJrVA2+4yM3PZWfmTnYc2EGBFtA2ri3RNWztT2PMoURksaomlnheuCcFVXjlFXjwQdi2DYYNg0cfhRNOKP78zNxMXk9+nWcXPMu63etoV78dfz39r1zT5RpqRdY67Px92ftYsnUJa3etJaZmDLFRsYds+QX5vL30bcYmjWXt7rU0rN2Q67tez42JN9KodiPW71l/6LZ3PZv3bWbHgR3sOLCDPVl7DnvPptFNaVe/He3rt6dd/XYc3+B4To8/nRZ1g7cuaIEWkLY/jdT0VFLTU9m0dxOp6amkZ6cz5IQhnHfceURUiwja+xtjjs6SQoDefhuuvtpVET31FPTsWfx5e7P28tKil3hu4XOk7U+jZ3xP7jrjLoaeMLRcPuwKtICZ62byctLLTFs9jQItOOycWtVrkRCbQPOY5jSq04hGtRvRsHZD/wawdtda1uxaw5rda/hl5y9sydjif31CbAJ9WvWhd6ve9G7Vmw4NOyC+/rV5BXlk5GSQkZPB/pz9tKjbosQSR2ZuJlNXT+X15Nf5+tevyS3IPeT5GhE1qBFRg4ycDFrWbcn13a7n+m7X06peq2P95yqWqpKdn+2/j5z8HBJiE6gRUSMo72dMVWJJIQB5edChYwHEL+CDd+rQsl48DWo18H9QAmzN2Mpz3z/Hy0kvk56dzoB2A7iv9330adXnkPPKU2p6Ku8uexdwH+QHt0a1G5X6Pffn7CdlewrfbfqObzd+y7yN80jbnwZAvZr1iIyIZF/2PrLzsw95XfVq1enRvAd9E/rSL6EfZ7Q8gzo16qCqLNq8iNeXvM57y99jb/ZeWtVrxSUdLuG4+scRXzfevzWs3ZC8gjymrZ7Gqz++yldrvwKgf7v+/LHbHzku7rhS3cu+nH2HlEJS97lSydaMrezL3kdGTgb5mn/IayKrRdKxUUe6Nu1Kt6bd6Nq0K12adiE2KrZU7x0ucvNz2Z+73//vGVMzhvi68V6HZcqBJYUAvPD6ZkbPuRrazvIfi6oe5f9Qq1ezHp+v+Zyc/ByGnzSce3vdS7dm3crlvb2iqqzZtYZvN37LD7/9QDWpRnSN6EO2WpG1SNmewpz1c1i0eRF5BXlUr1adU1ucyp6sPaRsTyGqehTDOgzjuq7X0a9NP6pJyR3Z1u9Zz/gl4xm/ZDy/7fvtmO6jXs16xNeNp2W9ljSNbkrdGnUPu4+IahGs3L6SJVuXsGTrEn8yBDiz9Znc0O0GLul4SbHVfmWVmZvJut3r2LB3A1HVow6pKqxXsx4R1SLYk7WHFWkrWJ62nGVpy1ietpzlacupU6MOg9oP4oLjL6Bfm35EVY8qt7gK25u1l5+2/cSSLUtI3pZM8tZkNu3dREZOxmFfDqpJNW7qfhOPnP0IcbXighJPIFSVVTtW8fmaz/l207f+38nCW2S1SHo078FFHS6icZ3GQYsjWF8Gg82SQgmmrvyEi9+6Fq2eyXODnqBF3eb++vDUfe7b6NaMrfRL6Mfdve6mfYMA+qSGoIycDL7b+B1z1s9hzoY5VK9Wnas6X8VlJ11Gvah6ZbpmXkEe3278lr1Ze0v1utqRtWlZryUtYloQUzOm1O+7NWMryVuTWZi6kAnLJrBm1xpio2K58uQr+VP3P9G5SecjvlZV2Zezz9+Ws+PADrbv387WjK2s3b2WX3b9wppda0hNTz1qDNE1osnIyfDvx9SIoVPjTpzU6CR2ZO7gy7VfciD3ALUja3Nu23MZfPxg+h/Xn/i68WX6MMrMzeTHLT/yfer3fP/b9yzevJhf9/zqf75xncZ0a9qN4+KOI6ZmzGGJ9buN3/FS0kvUr1WfJ/7wBNd1u+6oXwDyC/LLre0oIyeDWetm8fmaz/lszWds2LsBgOPijiO6RjR5BXmHbAdyD7D9wHaqSTX6JvTlkg6XcHGHi2kS3cR/zW0Z20janOTvCLIrcxfHNzieExueSIeGHTix4Ym0jWtLZEQkOw/sPCx5rt6xms5NOjOg3QDOb3c+p8WfRvVqVaMTZ6VICiIyAHgeiABeU9Unijx/JvAc0BkYoaqTS7rmsSaFrLws7v7qbl784UXY0pXnz3yP2644sczXM1VTgRYwd/1cXlvyGlNSppCdn02P5j04pdkph3Ub3pO1h91Zu8nJzyn2Wo1qN6J9A9eo3y6uHe3qtyMhNoGc/Jxir9U0uimdGneiU+NOtKrX6pAP+6y8LOasn8P01dOZ/vN0NqVvAiAuKo5OjTtxcuOT/a9t36A9Ofk5ZORk+Kt7MnIy2J21258Ikrcm+9t62sS2IbF5or8arWvTrjSLOUL3ukJ+2voToz4bxbcbv+XUFqcyZuAYEpu7z5YDuQeYv2k+c9bP4ev1X/PDbz/QoFYDujXrRtcm7j26NetGu/rtAipNZuVlMX31dCYsm8Bnv3xGbkEu0TWi+UObP3B+u/Pp364/CbEJxb5WVVm6bSmTUyYzKWUSq3euRhDObH0m9WvVZ9HmRf6kLQgdG3WkcZ3G/Lzz50NKrtWrVadBrQZs27/Nf6xFTAt3H3HtWLR5EQtSF1CgBcRGxXJu23M5v935dG/enYTYBOrWrFvifRZVoAUs3baUr3/9mpTtKXRp2oXerXpzcuOTyy3Jep4URCQC+Bk4F0gFFgGXq2pKoXMSgLrAncC0YCeFlO0pXD7lcpZuW0rcqttpvvIJlv5Y01ZMC3M7D+xkwtIJjE8ez9aMrYf1EIut6X4W17jfuE7jMpVaAqGqLEtbxtz1c1mx/ffqpvTs9BJfWzuyNj2a9+D0+NPpGd+TnvE9D/nGXJZYJiydwF1f3UXa/jSGnzSczfs2szB1IbkFuURIBInNE+nVshe7snaxZMsSVmxfQV5BHgB1IuvQuUnnQxJSp8adqBVZiwIt4JsN3zBh6QQmpUwiPTud5jHNGXHSCAYfP5herXqVurOAqrJi+womrZjEh6s+JCsvix7Ne/jHDXVr1u2QjhTp2ems3rGaVTtWsXLHSrZmbKVDww50a9aNLk260KhOo0OuvztzNzPXzeTzNZ/z+drP2bxvs/+5uKi4Q9oCm8c0P+T3KS4qjtioWDJyMvwl8Lnr57I7azcAsVGx/l6FdWvW5YyWZ9C7ZW/6tO5Dj+Y9ylzdWRmSwunAg6ra37d/H4CqPl7MuW8AnwQzKby77F1umHYD0TWiuTb2DZ4eOZDJk10XVGOqClUlNT2V5WnLWbd7HbUiax1W5RNTI4Y2cW2CUq2xN2svD855kFd/fJWOjTr6OyL0atXrsG/I2XnZrNyxkuStyYdUwRxMahESwYkNTyQ9O51N6ZuIrhHNsA7DuKrzVfRN6FtlujAfTEArt6/0dx3/dc+v/seZeZlHfX3buLb0bd2Xfm36cVbrs2hZryUb925k3oZ5/s4hK7avAODZ857ljtPvKFOclSEpXAIMUNUbfPtXAaep6qhizn2DoyQFERkJjARo1apV9w0bNpQ6nq9//Zqn5j/Fq4PGc07PZkRGwk8/2brKxlQkVeXXPb+SvNUliCVbl1BNqjHipBEMPXEotSNrex1iuVJVMnIyiq1GrCbV6NO6T0BdtHdl7uK7jd/RuUlnWse2LlMsgSaFKtFCoqrjgHHgSgpluUa/Nv3om9CXiROF1ath0iRLCMZUNBGhbVxb2sa15eIOF3sdTtCJCDE1Y4ipGUPLei3LfJ36tepzwQkXlGNkRxbMj8XfgML/CvG+Y54pKBAeegg6dYKLQ//30RhjSi2YJYVFQHsRaYNLBiOAK4L4fiWaNAlWrYIPPrBSgjHGFCdoH42qmgeMAr4AVgIfqOoKEXlIRIYAiEgPEUkFhgOviMiKYMWTnw8PPwwnnWSNy8YYcyRBbVNQ1RnAjCLH/lHo8SJctVLQTZ4MKSlurWUrJRhjTPHC5uMxOhouvBAuucTrSIwxpvKqEr2PysOgQW4zxhhzZGFTUjDGGFMySwrGGGP8LCkYY4zxs6RgjDHGz5KCMcYYP0sKxhhj/CwpGGOM8bOkYIwxxq/KrdEsItuB0i+o4DQEdpRjOFVFuN43hO+9232Hl0Duu7WqNirhnKqXFI6FiCQFsshEqAnX+4bwvXe77/BSnvdt1UfGGGP8LCkYY4zxC7ekMM7rADwSrvcN4Xvvdt/hpdzuO6zaFIwxxhxduJUUjDHGHIUlBWOMMX5hkxREZICIrBaRNSJyr9fxBIuIjBeRNBFZXuhYfRH5SkR+8f2M8zLGYBCRliLytYikiMgKERntOx7S9y4iUSLyg4j85Lvvf/mOtxGRhb7f9/dFpIbXsQaDiESIyBIR+cS3H/L3LSLrRWSZiCSLSJLvWLn9nodFUhCRCGAMcD7QEbhcRDp6G1XQvAEMKHLsXmCWqrYHZvn2Q00e8FdV7Qj0BG71/R+H+r1nA2erahegKzBARHoCTwL/UdV2wG7gjx7GGEyjgZWF9sPlvvupatdCYxPK7fc8LJICcCqwRlXXqWoOMBEY6nFMQaGq3wC7ihweCrzpe/wmcGGFBlUBVHWLqv7oe7wP90HRghC/d3UyfLuRvk2Bs4HJvuMhd98AIhIPDAJe8+0LYXDfR1Buv+fhkhRaAJsK7af6joWLJqq6xfd4K9DEy2CCTUQSgG7AQsLg3n1VKMlAGvAVsBbYo6p5vlNC9ff9OeBuoMC334DwuG8FvhSRxSIy0nes3H7Pqx9rdKZqUVUVkZDthywi0cAU4HZVTXdfHp1QvXdVzQe6ikgs8BFwoschBZ2IDAbSVHWxiPT1Op4K1ltVfxORxsBXIrKq8JPH+nseLiWF34CWhfbjfcfCxTYRaQbg+5nmcTxBISKRuITwjqp+6DscFvcOoKp7gK+B04FYETn4pS8Uf997AUNEZD2uOvhs4HlC/75R1d98P9NwXwJOpRx/z8MlKSwC2vt6JtQARgDTPI6pIk0DrvE9vgaY6mEsQeGrT/4fsFJV/13oqZC+dxFp5CshICK1gHNx7SlfA5f4Tgu5+1bV+1Q1XlUTcH/Ps1X1/wjx+xaROiISc/AxcB6wnHL8PQ+bEc0iMhBXBxkBjFfVRz0OKShE5D2gL24q3W3AP4GPgQ+AVrhpxy9V1aKN0VWaiPQG5gHL+L2O+X5cu0LI3ruIdMY1LEbgvuR9oKoPiUhb3Dfo+sAS4EpVzfYu0uDxVR/dqaqDQ/2+fff3kW+3OvCuqj4qIg0op9/zsEkKxhhjShYu1UfGGGMCYEnBGGOMnyUFY4wxfpYUjDHG+FlSMMYY42dJwZgiRCTfNwPlwa3cJtETkYTCM9gaU9nYNBfGHC5TVbt6HYQxXrCSgjEB8s1j/5RvLvsfRKSd73iCiMwWkaUiMktEWvmONxGRj3xrHfwkImf4LhUhIq/61j/40jcS2ZhKwZKCMYerVaT66LJCz+1V1ZOB/+JGyAO8CLypqp2Bd4AXfMdfAOb61jo4BVjhO94eGKOqJwF7gGFBvh9jAmYjmo0pQkQyVDW6mOPrcQvarPNNvrdVVRuIyA6gmarm+o5vUdWGIrIdiC88zYJvWu+vfIuhICL3AJGq+kjw78yYkllJwZjS0SM8Lo3Cc/HkY217phKxpGBM6VxW6OcC3+P5uJk6Af4PNzEfuGURbwb/Qjj1KipIY8rKvqEYc7havpXMDvpcVQ92S40TkaW4b/uX+479GXhdRO4CtgPX+Y6PBsaJyB9xJYKbgS0YU4lZm4IxAfK1KSSq6g6vYzEmWKz6yBhjjJ+VFIwxxvhZScEYY4yfJQVjjDF+lhSMMcb4WVIwxhjjZ0nBGGOM3/8DWNi1RXzB0msAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Shape of X_train:       (11628, 5, 70)\n",
      "   Shape of Y_train:          (11628, 17)\n",
      "     Shape of X_dev:        (3876, 5, 70)\n",
      "     Shape of Y_dev:           (3876, 17)\n",
      "    Shape of X_test:        (3876, 5, 70)\n",
      "    Shape of Y_test:           (3876, 17)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Shape of X_train:       (11628, 5, 80)\n",
      "   Shape of Y_train:          (11628, 17)\n",
      "     Shape of X_dev:        (3876, 5, 80)\n",
      "     Shape of Y_dev:           (3876, 17)\n",
      "    Shape of X_test:        (3876, 5, 80)\n",
      "    Shape of Y_test:           (3876, 17)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Shape of X_train:      (11628, 5, 130)\n",
      "   Shape of Y_train:          (11628, 17)\n",
      "     Shape of X_dev:       (3876, 5, 130)\n",
      "     Shape of Y_dev:           (3876, 17)\n",
      "    Shape of X_test:       (3876, 5, 130)\n",
      "    Shape of Y_test:           (3876, 17)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Shape of X_train:      (11628, 5, 170)\n",
      "   Shape of Y_train:          (11628, 17)\n",
      "     Shape of X_dev:       (3876, 5, 170)\n",
      "     Shape of Y_dev:           (3876, 17)\n",
      "    Shape of X_test:       (3876, 5, 170)\n",
      "    Shape of Y_test:           (3876, 17)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Shape of X_train:       (11628, 5, 30)\n",
      "   Shape of Y_train:          (11628, 17)\n",
      "     Shape of X_dev:        (3876, 5, 30)\n",
      "     Shape of Y_dev:           (3876, 17)\n",
      "    Shape of X_test:        (3876, 5, 30)\n",
      "    Shape of Y_test:           (3876, 17)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-25-9771e527a825>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcreate_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_timesteps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_features\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_hidden\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_classes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdropout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdropout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_acc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdev_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mregion_acc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain_acc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdev_acc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-6-54b21d84d058>\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(model, X_train, X_dev, Y_train, Y_dev, epochs, batch_size, verbose)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtrain_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m     \u001b[0mtrain_history\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mX_dev\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_dev\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mepochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m     \u001b[0mtrain_acc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdev_acc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_history\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'val_accuracy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)\u001b[0m\n\u001b[1;32m   1237\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1238\u001b[0m                                         \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_steps\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m                                         validation_freq=validation_freq)\n\u001b[0m\u001b[1;32m   1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1241\u001b[0m     def evaluate(self,\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, fit_function, fit_inputs, out_labels, batch_size, epochs, verbose, callbacks, val_function, val_inputs, shuffle, initial_epoch, steps_per_epoch, validation_steps, validation_freq)\u001b[0m\n\u001b[1;32m    194\u001b[0m                     \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfit_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    197\u001b[0m                 \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/keras/backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   3738\u001b[0m         \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath_ops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3739\u001b[0m       \u001b[0mconverted_inputs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3740\u001b[0;31m     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_graph_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mconverted_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3741\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3742\u001b[0m     \u001b[0;31m# EagerTensor.numpy() will often make a copy to ensure memory safety.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1079\u001b[0m       \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mFor\u001b[0m \u001b[0minvalid\u001b[0m \u001b[0mpositional\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mkeyword\u001b[0m \u001b[0margument\u001b[0m \u001b[0mcombinations\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1080\u001b[0m     \"\"\"\n\u001b[0;32m-> 1081\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1082\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1083\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, args, kwargs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1119\u001b[0m       raise TypeError(\"Keyword arguments {} unknown. Expected {}.\".format(\n\u001b[1;32m   1120\u001b[0m           list(kwargs.keys()), list(self._arg_keywords)))\n\u001b[0;32m-> 1121\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_flat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcaptured_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcancellation_manager\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1122\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_filtered_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1222\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1223\u001b[0m       flat_outputs = forward_function.call(\n\u001b[0;32m-> 1224\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager)\n\u001b[0m\u001b[1;32m   1225\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1226\u001b[0m       \u001b[0mgradient_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_delayed_rewrite_functions\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    509\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    510\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"executor_type\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexecutor_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"config_proto\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 511\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    512\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    513\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[0;32m~/anaconda3/envs/tensorflow_p36/lib/python3.6/site-packages/tensorflow_core/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tensorflow.TFE_Py_Execute(ctx._handle, device_name,\n\u001b[1;32m     60\u001b[0m                                                \u001b[0mop_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m                                                num_outputs)\n\u001b[0m\u001b[1;32m     62\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "n_hidden = 100\n",
    "dropout = 0.5\n",
    "epochs = 50\n",
    "\n",
    "masks = set([f.split('_')[1][:-4] for f in os.listdir(data_dir) if os.path.isfile(os.path.join(data_dir,f)) and '.npy' in f and 'all' not in f])\n",
    "\n",
    "region_acc = {}\n",
    "\n",
    "for mask in masks:   \n",
    "    X_train, X_dev, X_test, Y_train, Y_dev, Y_test = load_region_data(mask)\n",
    "    n_timesteps, n_features, n_classes = X_train.shape[1], X_train.shape[2], Y_train.shape[1]\n",
    "\n",
    "    model = create_model(n_timesteps, n_features, n_hidden, n_classes, dropout = dropout)\n",
    "    model, train_acc, dev_acc = train_model(model, X_train, X_dev, Y_train, Y_dev, epochs = epochs, batch_size = 64, verbose = 0)\n",
    "    \n",
    "    region_acc[mask] = (train_acc[-1], dev_acc[-1])\n",
    "    \n",
    "    plot_acc(train_acc, dev_acc, title = mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No region looks promising... maybe we could try combining the regional data. Just worried about curse of dimensionality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One model for all regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Load in all the regions\n",
    "\"\"\"\n",
    "def load_all_data():\n",
    "    \n",
    "    masks = set([f.split('_')[1][:-4] for f in os.listdir(data_dir) if os.path.isfile(os.path.join(data_dir,f)) and '.npy' in f and 'all' not in f])\n",
    "\n",
    "    Y_all = np.load(os.path.join(data_dir,'Ylabels_LHLOC.npy'))\n",
    "    n_samples = Y_all.shape[0]\n",
    "\n",
    "    X_all = [np.load(os.path.join(data_dir,'X_' + region + '.npy'))[:n_samples] for region in masks]\n",
    "    X_all = np.concatenate(X_all, axis = 2)\n",
    "    \n",
    "    classes = load_classes(Y_all)\n",
    "    \n",
    "    # Standardize data\n",
    "    X_scaled = np.array([(x - np.mean(x,axis=1)[:,np.newaxis]) / np.std(x,axis=1)[:,np.newaxis] for x in X_all])\n",
    "    # Split into train,dev,test\n",
    "    X_train, X_devtest, Y_train, Y_devtest = train_test_split(X_scaled, classes, test_size = 0.4)\n",
    "    X_dev, X_test, Y_dev, Y_test = train_test_split(X_devtest, Y_devtest, test_size = 0.5)\n",
    "    \n",
    "    print('%20s %20s' % ('Shape of X_train:', str(X_train.shape)))\n",
    "    print('%20s %20s' % ('Shape of Y_train:', str(Y_train.shape)))\n",
    "    print('%20s %20s' % ('Shape of X_dev:', str(X_dev.shape)))\n",
    "    print('%20s %20s' % ('Shape of Y_dev:', str(Y_dev.shape)))\n",
    "    print('%20s %20s' % ('Shape of X_test:', str(X_test.shape)))\n",
    "    print('%20s %20s' % ('Shape of Y_test:', str(Y_test.shape)))\n",
    "    \n",
    "    return X_train, X_dev, X_test, Y_train, Y_dev, Y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Shape of X_train:     (11628, 5, 1230)\n",
      "   Shape of Y_train:          (11628, 17)\n",
      "     Shape of X_dev:      (3876, 5, 1230)\n",
      "     Shape of Y_dev:           (3876, 17)\n",
      "    Shape of X_test:      (3876, 5, 1230)\n",
      "    Shape of Y_test:           (3876, 17)\n",
      "Train on 11628 samples, validate on 3876 samples\n",
      "Epoch 1/50\n",
      "11628/11628 [==============================] - 7s 597us/step - loss: 2.7513 - accuracy: 0.1090 - val_loss: 2.6466 - val_accuracy: 0.1489\n",
      "Epoch 2/50\n",
      "11628/11628 [==============================] - 5s 451us/step - loss: 2.5912 - accuracy: 0.1595 - val_loss: 2.5899 - val_accuracy: 0.1610\n",
      "Epoch 3/50\n",
      "11628/11628 [==============================] - 5s 435us/step - loss: 2.4967 - accuracy: 0.1866 - val_loss: 2.5693 - val_accuracy: 0.1700\n",
      "Epoch 4/50\n",
      "11628/11628 [==============================] - 5s 435us/step - loss: 2.4128 - accuracy: 0.2164 - val_loss: 2.5448 - val_accuracy: 0.1659\n",
      "Epoch 5/50\n",
      "11628/11628 [==============================] - 5s 439us/step - loss: 2.3383 - accuracy: 0.2468 - val_loss: 2.5511 - val_accuracy: 0.1662\n",
      "Epoch 6/50\n",
      "11628/11628 [==============================] - 5s 435us/step - loss: 2.2673 - accuracy: 0.2601 - val_loss: 2.5621 - val_accuracy: 0.1664\n",
      "Epoch 7/50\n",
      "11628/11628 [==============================] - 6s 482us/step - loss: 2.1906 - accuracy: 0.2831 - val_loss: 2.5533 - val_accuracy: 0.1803\n",
      "Epoch 8/50\n",
      "11628/11628 [==============================] - 7s 589us/step - loss: 2.1067 - accuracy: 0.3129 - val_loss: 2.5801 - val_accuracy: 0.1741\n",
      "Epoch 9/50\n",
      "11628/11628 [==============================] - 7s 575us/step - loss: 2.0435 - accuracy: 0.3337 - val_loss: 2.6062 - val_accuracy: 0.1726\n",
      "Epoch 10/50\n",
      "11628/11628 [==============================] - 7s 588us/step - loss: 1.9769 - accuracy: 0.3524 - val_loss: 2.6245 - val_accuracy: 0.1716\n",
      "Epoch 11/50\n",
      "11628/11628 [==============================] - 7s 606us/step - loss: 1.9137 - accuracy: 0.3794 - val_loss: 2.6619 - val_accuracy: 0.1641\n",
      "Epoch 12/50\n",
      "11628/11628 [==============================] - 8s 656us/step - loss: 1.8327 - accuracy: 0.4042 - val_loss: 2.6856 - val_accuracy: 0.1754\n",
      "Epoch 13/50\n",
      "11628/11628 [==============================] - 8s 655us/step - loss: 1.7665 - accuracy: 0.4211 - val_loss: 2.7313 - val_accuracy: 0.1692\n",
      "Epoch 14/50\n",
      "11628/11628 [==============================] - 8s 654us/step - loss: 1.6952 - accuracy: 0.4479 - val_loss: 2.7764 - val_accuracy: 0.1708\n",
      "Epoch 15/50\n",
      "11628/11628 [==============================] - 8s 684us/step - loss: 1.6427 - accuracy: 0.4663 - val_loss: 2.8131 - val_accuracy: 0.1744\n",
      "Epoch 16/50\n",
      "11628/11628 [==============================] - 6s 536us/step - loss: 1.5800 - accuracy: 0.4827 - val_loss: 2.8913 - val_accuracy: 0.1651\n",
      "Epoch 17/50\n",
      "11628/11628 [==============================] - 7s 583us/step - loss: 1.5132 - accuracy: 0.5049 - val_loss: 2.9020 - val_accuracy: 0.1631\n",
      "Epoch 18/50\n",
      "11628/11628 [==============================] - 6s 530us/step - loss: 1.4795 - accuracy: 0.5115 - val_loss: 2.9540 - val_accuracy: 0.1615\n",
      "Epoch 19/50\n",
      "11628/11628 [==============================] - 7s 616us/step - loss: 1.4091 - accuracy: 0.5351 - val_loss: 2.9914 - val_accuracy: 0.1646\n",
      "Epoch 20/50\n",
      "11628/11628 [==============================] - 6s 517us/step - loss: 1.3534 - accuracy: 0.5498 - val_loss: 3.0342 - val_accuracy: 0.1659\n",
      "Epoch 21/50\n",
      "11628/11628 [==============================] - 5s 434us/step - loss: 1.3346 - accuracy: 0.5585 - val_loss: 3.0717 - val_accuracy: 0.1631\n",
      "Epoch 22/50\n",
      "11628/11628 [==============================] - 5s 462us/step - loss: 1.2636 - accuracy: 0.5820 - val_loss: 3.1154 - val_accuracy: 0.1659\n",
      "Epoch 23/50\n",
      "11628/11628 [==============================] - 5s 464us/step - loss: 1.2193 - accuracy: 0.6010 - val_loss: 3.1444 - val_accuracy: 0.1659\n",
      "Epoch 24/50\n",
      "11628/11628 [==============================] - 5s 441us/step - loss: 1.2010 - accuracy: 0.6035 - val_loss: 3.1911 - val_accuracy: 0.1597\n",
      "Epoch 25/50\n",
      "11628/11628 [==============================] - 6s 540us/step - loss: 1.1514 - accuracy: 0.6166 - val_loss: 3.2252 - val_accuracy: 0.1656\n",
      "Epoch 26/50\n",
      "11628/11628 [==============================] - 7s 570us/step - loss: 1.1227 - accuracy: 0.6255 - val_loss: 3.2752 - val_accuracy: 0.1592\n",
      "Epoch 27/50\n",
      "11628/11628 [==============================] - 6s 487us/step - loss: 1.0699 - accuracy: 0.6487 - val_loss: 3.3110 - val_accuracy: 0.1649\n",
      "Epoch 28/50\n",
      "11628/11628 [==============================] - 5s 433us/step - loss: 1.0467 - accuracy: 0.6533 - val_loss: 3.3676 - val_accuracy: 0.1695\n",
      "Epoch 29/50\n",
      "11628/11628 [==============================] - 5s 432us/step - loss: 1.0199 - accuracy: 0.6570 - val_loss: 3.3786 - val_accuracy: 0.1571\n",
      "Epoch 30/50\n",
      "11628/11628 [==============================] - 5s 466us/step - loss: 0.9934 - accuracy: 0.6724 - val_loss: 3.4381 - val_accuracy: 0.1620\n",
      "Epoch 31/50\n",
      "11628/11628 [==============================] - 6s 504us/step - loss: 0.9752 - accuracy: 0.6737 - val_loss: 3.4694 - val_accuracy: 0.1643\n",
      "Epoch 32/50\n",
      "11628/11628 [==============================] - 6s 490us/step - loss: 0.9581 - accuracy: 0.6793 - val_loss: 3.4472 - val_accuracy: 0.1612\n",
      "Epoch 33/50\n",
      "11628/11628 [==============================] - 7s 567us/step - loss: 0.8999 - accuracy: 0.7000 - val_loss: 3.5283 - val_accuracy: 0.1680\n",
      "Epoch 34/50\n",
      "11628/11628 [==============================] - 6s 496us/step - loss: 0.8665 - accuracy: 0.7078 - val_loss: 3.5816 - val_accuracy: 0.1620\n",
      "Epoch 35/50\n",
      "11628/11628 [==============================] - 5s 435us/step - loss: 0.8513 - accuracy: 0.7153 - val_loss: 3.5838 - val_accuracy: 0.1610\n",
      "Epoch 36/50\n",
      "11628/11628 [==============================] - 6s 510us/step - loss: 0.8444 - accuracy: 0.7166 - val_loss: 3.6484 - val_accuracy: 0.1582\n",
      "Epoch 37/50\n",
      "11628/11628 [==============================] - 5s 431us/step - loss: 0.8406 - accuracy: 0.7194 - val_loss: 3.6499 - val_accuracy: 0.1703\n",
      "Epoch 38/50\n",
      "11628/11628 [==============================] - 6s 485us/step - loss: 0.8057 - accuracy: 0.7306 - val_loss: 3.6765 - val_accuracy: 0.1623\n",
      "Epoch 39/50\n",
      "11628/11628 [==============================] - 5s 432us/step - loss: 0.7748 - accuracy: 0.7416 - val_loss: 3.7069 - val_accuracy: 0.1574\n",
      "Epoch 40/50\n",
      "11628/11628 [==============================] - 5s 438us/step - loss: 0.7772 - accuracy: 0.7350 - val_loss: 3.7250 - val_accuracy: 0.1664\n",
      "Epoch 41/50\n",
      "11628/11628 [==============================] - 5s 435us/step - loss: 0.7703 - accuracy: 0.7392 - val_loss: 3.7639 - val_accuracy: 0.1612\n",
      "Epoch 42/50\n",
      "11628/11628 [==============================] - 7s 626us/step - loss: 0.7369 - accuracy: 0.7540 - val_loss: 3.8044 - val_accuracy: 0.1538\n",
      "Epoch 43/50\n",
      "11628/11628 [==============================] - 7s 610us/step - loss: 0.7262 - accuracy: 0.7544 - val_loss: 3.8131 - val_accuracy: 0.1561\n",
      "Epoch 44/50\n",
      "11628/11628 [==============================] - 7s 593us/step - loss: 0.7132 - accuracy: 0.7601 - val_loss: 3.8253 - val_accuracy: 0.1587\n",
      "Epoch 45/50\n",
      "11628/11628 [==============================] - 7s 613us/step - loss: 0.7100 - accuracy: 0.7631 - val_loss: 3.8292 - val_accuracy: 0.1502\n",
      "Epoch 46/50\n",
      "11628/11628 [==============================] - 7s 591us/step - loss: 0.6827 - accuracy: 0.7686 - val_loss: 3.8918 - val_accuracy: 0.1587\n",
      "Epoch 47/50\n",
      "11628/11628 [==============================] - 8s 710us/step - loss: 0.6592 - accuracy: 0.7804 - val_loss: 3.9356 - val_accuracy: 0.1566\n",
      "Epoch 48/50\n",
      "11628/11628 [==============================] - 8s 708us/step - loss: 0.6440 - accuracy: 0.7857 - val_loss: 3.9767 - val_accuracy: 0.1625\n",
      "Epoch 49/50\n",
      "11628/11628 [==============================] - 7s 615us/step - loss: 0.6585 - accuracy: 0.7740 - val_loss: 3.9841 - val_accuracy: 0.1584\n",
      "Epoch 50/50\n",
      "11628/11628 [==============================] - 7s 600us/step - loss: 0.6444 - accuracy: 0.7822 - val_loss: 4.0249 - val_accuracy: 0.1587\n"
     ]
    }
   ],
   "source": [
    "n_hidden = 100\n",
    "dropout = 0.5\n",
    "epochs = 50\n",
    "\n",
    "X_train, X_dev, X_test, Y_train, Y_dev, Y_test = load_all_data()\n",
    "\n",
    "n_timesteps, n_features, n_classes = X_train.shape[1], X_train.shape[2], Y_train.shape[1]\n",
    "\n",
    "model = create_model(n_timesteps, n_features, n_hidden, n_classes, dropout = dropout)\n",
    "model, train_acc, dev_acc = train_model(model, X_train, X_dev, Y_train, Y_dev, epochs = epochs, batch_size = 64, verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_acc(train_acc, dev_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yikes, no improvement... perhaps we should reconsider our architecture or try to find more data. What classes are we getting wrong?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "Y_pred = model.predict(X_test)\n",
    "matrix = confusion_matrix(Y_test.argmax(axis=1), Y_pred.argmax(axis=1))\n",
    "print(matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perhaps the class labels are not as orthogonalized as we originally expected. This may be worth revisiting."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Environment (conda_tensorflow_p36)",
   "language": "python",
   "name": "conda_tensorflow_p36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}