Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Steps\n",
    "1. Load data from json File\n",
    "2. Split data into train test split\n",
    "3. create neural net using using tensorflow and keras\n",
    "4. Train the network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import tensorflow as tf\n",
    "import tensorflow.keras as keras\n",
    "import numpy as np\n",
    "from tqdm.auto import tqdm\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Memory Limitation for KTByte VM\n",
    "# gpus = tf.config.experimental.list_physical_devices('GPU')\n",
    "# tf.config.experimental.set_memory_growth(gpus[0], True)\n",
    "# tf.config.experimental.set_virtual_device_configuration(gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=8000)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.6.4'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[PhysicalDevice(name='/physical_device:CPU:0', device_type='CPU'),\n",
       " PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(tf.__version__)\n",
    "tf.config.list_physical_devices()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def load_data(dataset_path):\n",
    "    with open(dataset_path, \"r\") as fp:\n",
    "        data = json.load(fp)\n",
    "    \n",
    "    x = np.array(data['mfcc'])\n",
    "    y = np.array(data['labels'])\n",
    "    num_segments = data['num_segments']\n",
    "    mapping = data['mapping']\n",
    "    file_origin = data['file_origin']\n",
    "    \n",
    "    \n",
    "    return x, y, num_segments, mapping, file_origin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Be patient with this step, can take up to 10 mins\n",
    "x, y, num_segments, mapping, file_origin = load_data(\"Data/data_32.json\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(79940, 129, 32)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helping Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_history(history):\n",
    "    fig, axs = plt.subplots(2, figsize = (15,15))\n",
    "    \n",
    "    \n",
    "    \n",
    "    #Create accuracy subplot\n",
    "    \n",
    "    axs[0].plot(history.history[\"acc\"], label = \"train accuracy\")\n",
    "    axs[0].plot(history.history[\"val_acc\"], label = \"test accuracy\")\n",
    "    axs[0].set_ylabel(\"accuracy\")\n",
    "    axs[0].legend(loc = \"lower right\")\n",
    "    axs[0].set_title(\"accuracy eval\")\n",
    "    \n",
    "    #Create Error subplot\n",
    "        \n",
    "    axs[1].plot(history.history[\"loss\"], label = \"train error\")\n",
    "    axs[1].plot(history.history[\"val_loss\"], label = \"test error\")\n",
    "    axs[1].set_ylabel(\"error\")\n",
    "    axs[1].set_xlabel(\"epochs\")\n",
    "    axs[1].legend(loc = \"upper right\")\n",
    "    axs[1].set_title(\"error eval\")\n",
    "    \n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict(model, X, y):\n",
    "    X = X[np.newaxis, :]\n",
    "    \n",
    "    # prediction = [[0.2, 0,2, ...]] probabilities\n",
    "    prediction = model.predict(X)\n",
    "    \n",
    "    #extrack index with max value (softmax gives you probabilities)\n",
    "    predicted_index = np.argmax(prediction, axis = 1)\n",
    "    \n",
    "    print(\"The prediction is: {}, The expected output is: {}\".format(mapping[int(predicted_index)], mapping[y]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#create label encoder for inverse fit\n",
    "encoder = LabelEncoder()\n",
    "encoder.classes_ = mapping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Train Test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Model Structure (Simple Dense Model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#this is going to be a basic neural network consisting of only dense layers\n",
    "model_1 = keras.Sequential([\n",
    "    #input layer\n",
    "    keras.layers.Flatten(input_shape = (x.shape[1], x.shape[2])),\n",
    "    \n",
    "    #1st hidden layer\n",
    "    keras.layers.Dense(512, activation = \"relu\"),\n",
    "    \n",
    "    #2nd hidden layers\n",
    "    keras.layers.Dense(256, activation = \"relu\"),\n",
    "    \n",
    "    #3rd hidden layers\n",
    "    keras.layers.Dense(64, activation = \"relu\"),\n",
    "    \n",
    "    keras.layers.Dense(len(mapping), activation = \"softmax\")\n",
    "    \n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "flatten (Flatten)            (None, 4128)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 512)               2114048   \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 256)               131328    \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 64)                16448     \n",
      "_________________________________________________________________\n",
      "dense_3 (Dense)              (None, 8)                 520       \n",
      "=================================================================\n",
      "Total params: 2,262,344\n",
      "Trainable params: 2,262,344\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "optimizer = keras.optimizers.Adam(learning_rate = 0.0001)\n",
    "model_1.compile(optimizer = optimizer, loss = \"sparse_categorical_crossentropy\", metrics = [\"acc\"])\n",
    "model_1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training The Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "469/469 [==============================] - 9s 18ms/step - loss: 4.0695 - acc: 0.2869 - val_loss: 2.3102 - val_acc: 0.3218\n",
      "Epoch 2/50\n",
      "469/469 [==============================] - 6s 14ms/step - loss: 2.0212 - acc: 0.3613 - val_loss: 1.9898 - val_acc: 0.3558\n",
      "Epoch 3/50\n",
      "469/469 [==============================] - 7s 16ms/step - loss: 1.7004 - acc: 0.4205 - val_loss: 1.8844 - val_acc: 0.3809\n",
      "Epoch 4/50\n",
      "469/469 [==============================] - 8s 17ms/step - loss: 1.5231 - acc: 0.4673 - val_loss: 1.8304 - val_acc: 0.3840\n",
      "Epoch 5/50\n",
      "469/469 [==============================] - 7s 14ms/step - loss: 1.4040 - acc: 0.5067 - val_loss: 1.8050 - val_acc: 0.3985\n",
      "Epoch 6/50\n",
      "469/469 [==============================] - 7s 15ms/step - loss: 1.2779 - acc: 0.5510 - val_loss: 1.8608 - val_acc: 0.3936\n",
      "Epoch 7/50\n",
      "469/469 [==============================] - 6s 13ms/step - loss: 1.1715 - acc: 0.5859 - val_loss: 1.8360 - val_acc: 0.4017\n",
      "Epoch 8/50\n",
      "469/469 [==============================] - 7s 14ms/step - loss: 1.0760 - acc: 0.6207 - val_loss: 1.7954 - val_acc: 0.4226\n",
      "Epoch 9/50\n",
      "469/469 [==============================] - 9s 18ms/step - loss: 0.9978 - acc: 0.6525 - val_loss: 1.8836 - val_acc: 0.4206\n",
      "Epoch 10/50\n",
      "469/469 [==============================] - 8s 17ms/step - loss: 0.9116 - acc: 0.6829 - val_loss: 1.8689 - val_acc: 0.4234\n",
      "Epoch 11/50\n",
      "469/469 [==============================] - 8s 17ms/step - loss: 0.8249 - acc: 0.7158 - val_loss: 1.9236 - val_acc: 0.4295\n",
      "Epoch 12/50\n",
      "302/469 [==================>...........] - ETA: 2s - loss: 0.7417 - acc: 0.7485"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_17164\\3618670494.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#this model overfits greatly\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mhistory_1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel_1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalidation_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m50\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m128\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\Users\\Albert\\miniconda3\\envs\\ml_env\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mfit\u001b[1;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_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1191\u001b[0m                 _r=1):\n\u001b[0;32m   1192\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1193\u001b[1;33m               \u001b[0mtmp_logs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1194\u001b[0m               \u001b[1;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1195\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Albert\\miniconda3\\envs\\ml_env\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    883\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    884\u001b[0m       \u001b[1;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 885\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    886\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    887\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Albert\\miniconda3\\envs\\ml_env\\lib\\site-packages\\tensorflow\\python\\eager\\def_function.py\u001b[0m in \u001b[0;36m_call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    915\u001b[0m       \u001b[1;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    916\u001b[0m       \u001b[1;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 917\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m  \u001b[1;31m# pylint: disable=not-callable\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    918\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    919\u001b[0m       \u001b[1;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Albert\\miniconda3\\envs\\ml_env\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   3038\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m   3039\u001b[0m     return graph_function._call_flat(\n\u001b[1;32m-> 3040\u001b[1;33m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[0;32m   3041\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3042\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Albert\\miniconda3\\envs\\ml_env\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[1;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[0;32m   1962\u001b[0m       \u001b[1;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1963\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[1;32m-> 1964\u001b[1;33m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[0;32m   1965\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[0;32m   1966\u001b[0m         \u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Albert\\miniconda3\\envs\\ml_env\\lib\\site-packages\\tensorflow\\python\\eager\\function.py\u001b[0m in \u001b[0;36mcall\u001b[1;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[0;32m    594\u001b[0m               \u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    595\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 596\u001b[1;33m               ctx=ctx)\n\u001b[0m\u001b[0;32m    597\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    598\u001b[0m           outputs = execute.execute_with_cancellation(\n",
      "\u001b[1;32mc:\\Users\\Albert\\miniconda3\\envs\\ml_env\\lib\\site-packages\\tensorflow\\python\\eager\\execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[1;32m---> 60\u001b[1;33m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[0;32m     61\u001b[0m   \u001b[1;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#this model overfits greatly\n",
    "history_1 = model_1.fit(x_train, y_train, validation_data = (x_test, y_test), epochs = 50, batch_size = 128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history_1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Model Structure (Convnet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_cnn = np.expand_dims(x_train, axis = -1)\n",
    "x_test_cnn = np.expand_dims(x_test, axis = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(59955, 129, 32, 1)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train_cnn.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_shape = x_train_cnn.shape[1:]\n",
    "model_cnn = keras.Sequential([\n",
    "    #1st convlayer (input)\n",
    "    keras.layers.Conv2D(32, (3,3), strides=1,padding = 'same',input_shape = input_shape, activation = 'relu'),\n",
    "    #Downsizing MaxPool layer\n",
    "    keras.layers.Conv2D(32, (3,3), strides=1,padding = 'same',activation = 'relu'),\n",
    "    keras.layers.MaxPool2D((3,3), strides = (2,2), padding = 'same'),\n",
    "    #normalizes activation\n",
    "    keras.layers.BatchNormalization(),\n",
    "                        \n",
    "    #2nd conv layer\n",
    "    keras.layers.Conv2D(64, (3,3),strides=1,padding = 'same', activation = 'relu'),\n",
    "    keras.layers.Conv2D(64, (3,3), strides=1,padding = 'same',activation = 'relu'),\n",
    "    keras.layers.MaxPool2D((3,3), strides = (2,2), padding = 'same'),\n",
    "    keras.layers.BatchNormalization(),\n",
    "\n",
    "    keras.layers.Conv2D(128, (3,3),strides=1,padding = 'same', activation = 'relu'),\n",
    "    keras.layers.Conv2D(128, (3,3), strides=1,padding = 'same',activation = 'relu'),\n",
    "    keras.layers.MaxPool2D((3,3), strides = (2,2), padding = 'same'),\n",
    "    keras.layers.BatchNormalization(),\n",
    "\n",
    "\n",
    "    keras.layers.Dropout(0.3),                    \n",
    "    #flatten the output to feed to dense output layer\n",
    "    keras.layers.Flatten(),\n",
    "    keras.layers.Dense(64, activation = 'relu'),\n",
    "    keras.layers.Dropout(0.3),\n",
    "                    \n",
    "    #Dense output layer\n",
    "    keras.layers.Dense(len(mapping), activation = 'softmax')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 129, 32, 32)       320       \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 129, 32, 32)       9248      \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 65, 16, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization (BatchNo (None, 65, 16, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 65, 16, 64)        18496     \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 65, 16, 64)        36928     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 33, 8, 64)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 33, 8, 64)         256       \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 33, 8, 128)        73856     \n",
      "_________________________________________________________________\n",
      "conv2d_5 (Conv2D)            (None, 33, 8, 128)        147584    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 17, 4, 128)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 17, 4, 128)        512       \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 17, 4, 128)        0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 8704)              0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 64)                557120    \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_5 (Dense)              (None, 8)                 520       \n",
      "=================================================================\n",
      "Total params: 844,968\n",
      "Trainable params: 844,520\n",
      "Non-trainable params: 448\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "optimizer = keras.optimizers.Adam(learning_rate = 0.0001)\n",
    "model_cnn.compile(optimizer = optimizer, loss = \"sparse_categorical_crossentropy\", metrics = [\"acc\"])\n",
    "model_cnn.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.9766 - acc: 0.6482 - val_loss: 1.2839 - val_acc: 0.5723\n",
      "Epoch 2/30\n",
      "469/469 [==============================] - 15s 32ms/step - loss: 0.9243 - acc: 0.6662 - val_loss: 1.2095 - val_acc: 0.5903\n",
      "Epoch 3/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.8757 - acc: 0.6822 - val_loss: 1.1830 - val_acc: 0.6035\n",
      "Epoch 4/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.8273 - acc: 0.7002 - val_loss: 1.1849 - val_acc: 0.5961\n",
      "Epoch 5/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.7906 - acc: 0.7138 - val_loss: 1.1517 - val_acc: 0.6109\n",
      "Epoch 6/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.7427 - acc: 0.7318 - val_loss: 1.1497 - val_acc: 0.6081\n",
      "Epoch 7/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.7083 - acc: 0.7413 - val_loss: 1.1764 - val_acc: 0.6090\n",
      "Epoch 8/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.6730 - acc: 0.7566 - val_loss: 1.2620 - val_acc: 0.6047\n",
      "Epoch 9/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.6369 - acc: 0.7685 - val_loss: 1.2110 - val_acc: 0.6102\n",
      "Epoch 10/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.6080 - acc: 0.7785 - val_loss: 1.2117 - val_acc: 0.6158\n",
      "Epoch 11/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.5775 - acc: 0.7914 - val_loss: 1.2117 - val_acc: 0.6151\n",
      "Epoch 12/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.5457 - acc: 0.8010 - val_loss: 1.2478 - val_acc: 0.6193\n",
      "Epoch 13/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.5212 - acc: 0.8097 - val_loss: 1.2244 - val_acc: 0.6219\n",
      "Epoch 14/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.4981 - acc: 0.8189 - val_loss: 1.3141 - val_acc: 0.6185\n",
      "Epoch 15/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.4759 - acc: 0.8275 - val_loss: 1.2648 - val_acc: 0.6295\n",
      "Epoch 16/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.4529 - acc: 0.8354 - val_loss: 1.3143 - val_acc: 0.6162\n",
      "Epoch 17/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.4374 - acc: 0.8397 - val_loss: 1.3224 - val_acc: 0.6234\n",
      "Epoch 18/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.4152 - acc: 0.8484 - val_loss: 1.3935 - val_acc: 0.6207\n",
      "Epoch 19/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.4048 - acc: 0.8542 - val_loss: 1.4208 - val_acc: 0.6168\n",
      "Epoch 20/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.3877 - acc: 0.8604 - val_loss: 1.3798 - val_acc: 0.6229\n",
      "Epoch 21/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.3737 - acc: 0.8635 - val_loss: 1.4001 - val_acc: 0.6190\n",
      "Epoch 22/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.3528 - acc: 0.8734 - val_loss: 1.4236 - val_acc: 0.6270\n",
      "Epoch 23/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.3436 - acc: 0.8749 - val_loss: 1.4161 - val_acc: 0.6255\n",
      "Epoch 24/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.3336 - acc: 0.8787 - val_loss: 1.4631 - val_acc: 0.6306\n",
      "Epoch 25/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.3236 - acc: 0.8828 - val_loss: 1.4608 - val_acc: 0.6239\n",
      "Epoch 26/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.3166 - acc: 0.8855 - val_loss: 1.4268 - val_acc: 0.6312\n",
      "Epoch 27/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.3041 - acc: 0.8907 - val_loss: 1.4591 - val_acc: 0.6204\n",
      "Epoch 28/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.3004 - acc: 0.8918 - val_loss: 1.4581 - val_acc: 0.6360\n",
      "Epoch 29/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.2882 - acc: 0.8963 - val_loss: 1.5047 - val_acc: 0.6316\n",
      "Epoch 30/30\n",
      "469/469 [==============================] - 15s 31ms/step - loss: 0.2828 - acc: 0.8986 - val_loss: 1.5371 - val_acc: 0.6277\n"
     ]
    }
   ],
   "source": [
    "history_cnn = model_cnn.fit(x_train_cnn, y_train, validation_data = (x_test_cnn, y_test), batch_size = 128, epochs = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_history(history_cnn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: models/cnn_model_32\\assets\n"
     ]
    }
   ],
   "source": [
    "#export the model\n",
    "model_cnn.save(\"models/cnn_model_32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Load the Model to save time\n",
    "model_cnn = tf.keras.models.load_model('models/cnn_model_32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ONNX Conversion & Model Validation\n",
    "For some reason, it trying to convert the saved file give me and error: AttributeError: '_UserObject' object has no attribute 'add_slot'\n",
    "\n",
    "Trying now to the conversion while the model is stored in memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import onnx\n",
    "import tf2onnx\n",
    "\n",
    "onnx_model, _ = tf2onnx.convert.from_keras(model = model_cnn, opset=13)\n",
    "onnx.save(onnx_model, \"models/cnn_model_32/model.onnx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "conv2d_input dense_5\n",
      "Onnx Prediction: [[7]], Tensorflow Prediction: [7], Actual: [7]\n",
      "(array([], dtype=int64),)\n",
      "(array([], dtype=int64), array([], dtype=int64))\n",
      "(array([], dtype=int64), array([], dtype=int64))\n",
      "0\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "# ONNX Predictions\n",
    "import onnxruntime\n",
    "session = onnxruntime.InferenceSession('models/cnn_model_32/model.onnx')\n",
    "input_name = session.get_inputs()[0].name\n",
    "output_name = session.get_outputs()[0].name\n",
    "print(input_name, output_name)\n",
    "\n",
    "start = 0\n",
    "end = 1\n",
    "\n",
    "result = session.run([output_name],\n",
    "                     {input_name: x_test_cnn[start:end].astype(np.float32)})\n",
    "onnx_prediction = np.argmax(np.array(result), axis=2)\n",
    "\n",
    "# Tensorflow Predictions\n",
    "model_cnn = tf.keras.models.load_model('models/cnn_model_32')\n",
    "tf_prediction = np.argmax(model_cnn.predict(x_test_cnn[start:end]), axis=1)\n",
    "print(\"Onnx Prediction: {}, Tensorflow Prediction: {}, Actual: {}\".format(onnx_prediction, tf_prediction, y_test[start:end]))\n",
    "\n",
    "print(np.where(tf_prediction != y_test[start:end]))\n",
    "print(np.where(onnx_prediction != y_test[start:end]))\n",
    "print(np.where(onnx_prediction != tf_prediction))\n",
    "print(np.sum(tf_prediction != y_test[start:end]))\n",
    "print(np.sum(onnx_prediction != y_test[start:end]))\n",
    "print(np.sum(onnx_prediction != tf_prediction))\n",
    "\n",
    "# Man, I spend like a lot of brainpower trying to figure out why the results weren't the same and then I realized stochastic nature of machine learning\n",
    "# This is a well deserved facepalm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving test input for Mr. Cao\n",
    "import pickle\n",
    "file_handler = open('test_case.obj', 'wb')\n",
    "pickle.dump(x_test_cnn[0], file_handler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Homework\n",
    "#Presentation\n",
    "#Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "test_case = 106\n",
    "x_case = x_test_cnn[test_case]\n",
    "y_case = y_test[test_case]\n",
    "predict(model_cnn, x_case, y_case)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(79940, 129, 13)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction_y = model_cnn.predict(np.expand_dims(x, axis = 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = np.argmax(prediction_y, axis = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = confusion_matrix(y, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x21e21455cc8>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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<use x=\"83.398438\" xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_32\">\r\n    <!-- 1.25% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(558.699896 166.249531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-49\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-53\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_33\">\r\n    <!-- 1.72% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(642.399896 166.249531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-49\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-55\"/>\r\n     <use x=\"139.013672\" 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</g>\r\n   <g id=\"text_36\">\r\n    <!-- 0.70% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(223.899896 234.199531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-55\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_37\">\r\n    <!-- 91.65% -->\r\n    <g style=\"fill:#ffffff;\" transform=\"translate(302.595052 234.199531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-57\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-49\"/>\r\n     <use x=\"111.230469\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-54\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-53\"/>\r\n     <use x=\"250.244141\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_38\">\r\n    <!-- 0.62% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(391.299896 234.199531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-54\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_39\">\r\n    <!-- 2.67% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(474.999896 234.199531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-54\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-55\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_40\">\r\n    <!-- 0.82% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(558.699896 234.199531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-56\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_41\">\r\n    <!-- 2.30% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(642.399896 234.199531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-51\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_42\">\r\n    <!-- 1.23% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(726.099896 234.199531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-49\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-51\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_43\">\r\n    <!-- 1.82% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(140.199896 302.149531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-49\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-56\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_44\">\r\n    <!-- 1.37% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(223.899896 302.149531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-49\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-51\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-55\"/>\r\n    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xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_47\">\r\n    <!-- 0.69% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(474.999896 302.149531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-54\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-57\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_48\">\r\n    <!-- 1.25% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(558.699896 302.149531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-49\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-53\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_49\">\r\n    <!-- 0.97% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(642.399896 302.149531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-57\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-55\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_50\">\r\n    <!-- 0.52% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(726.099896 302.149531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-53\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_51\">\r\n    <!-- 1.24% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(140.199896 370.099531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-49\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-52\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_52\">\r\n    <!-- 3.76% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(223.899896 370.099531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-51\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-55\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-54\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_53\">\r\n    <!-- 4.87% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(307.599896 370.099531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-52\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-56\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-55\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_54\">\r\n    <!-- 0.64% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(391.299896 370.099531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-54\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-52\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_55\">\r\n    <!-- 85.22% -->\r\n    <g style=\"fill:#ffffff;\" transform=\"translate(469.995052 370.099531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-56\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-53\"/>\r\n     <use x=\"111.230469\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-50\"/>\r\n  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xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_58\">\r\n    <!-- 1.76% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(726.099896 370.099531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-49\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-55\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-54\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_59\">\r\n    <!-- 1.16% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(140.199896 438.049531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-49\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-49\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-54\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_60\">\r\n    <!-- 0.88% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(223.899896 438.049531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-56\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-56\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_61\">\r\n    <!-- 2.24% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(307.599896 438.049531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-52\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_62\">\r\n    <!-- 2.32% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(391.299896 438.049531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-51\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_63\">\r\n    <!-- 1.04% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(474.999896 438.049531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-49\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-52\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_64\">\r\n    <!-- 90.42% -->\r\n    <g style=\"fill:#ffffff;\" transform=\"translate(553.695052 438.049531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-57\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"111.230469\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-52\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"250.244141\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_65\">\r\n    <!-- 1.28% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(642.399896 438.049531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-49\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-56\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_66\">\r\n    <!-- 0.68% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(726.099896 438.049531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-54\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-56\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_67\">\r\n    <!-- 3.57% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(140.199896 505.999531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-51\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-53\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-55\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_68\">\r\n    <!-- 3.69% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(223.899896 505.999531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-51\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-54\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-57\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_69\">\r\n    <!-- 5.73% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(307.599896 505.999531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-53\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-55\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-51\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_70\">\r\n    <!-- 3.00% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(391.299896 505.999531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-51\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_71\">\r\n    <!-- 3.04% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(474.999896 505.999531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-51\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-52\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_72\">\r\n    <!-- 2.84% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(558.699896 505.999531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-56\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-52\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_73\">\r\n    <!-- 73.40% -->\r\n    <g style=\"fill:#ffffff;\" transform=\"translate(637.395052 505.999531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-55\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-51\"/>\r\n     <use x=\"111.230469\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-52\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"250.244141\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_74\">\r\n    <!-- 4.73% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(726.099896 505.999531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-52\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-55\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-51\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_75\">\r\n    <!-- 0.81% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(140.199896 573.949531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-56\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-49\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_76\">\r\n    <!-- 2.67% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(223.899896 573.949531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-54\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-55\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_77\">\r\n    <!-- 2.79% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(307.599896 573.949531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-55\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-57\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_78\">\r\n    <!-- 0.28% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(391.299896 573.949531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-56\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_79\">\r\n    <!-- 1.94% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(474.999896 573.949531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-49\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-57\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-52\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_80\">\r\n    <!-- 1.09% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(558.699896 573.949531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-49\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-48\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-57\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_81\">\r\n    <!-- 2.75% -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(642.399896 573.949531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-50\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"83.398438\" xlink:href=\"#ArialMT-55\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-53\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_82\">\r\n    <!-- 87.68% -->\r\n    <g style=\"fill:#ffffff;\" transform=\"translate(721.095052 573.949531)scale(0.18 -0.18)\">\r\n     <use xlink:href=\"#ArialMT-56\"/>\r\n     <use x=\"55.615234\" xlink:href=\"#ArialMT-55\"/>\r\n     <use x=\"111.230469\" xlink:href=\"#ArialMT-46\"/>\r\n     <use x=\"139.013672\" xlink:href=\"#ArialMT-54\"/>\r\n     <use x=\"194.628906\" xlink:href=\"#ArialMT-56\"/>\r\n     <use x=\"250.244141\" xlink:href=\"#ArialMT-37\"/>\r\n    </g>\r\n   </g>\r\n   <g id=\"text_83\">\r\n    <!-- Confusion Matrix (CNN - Normalized by Row) -->\r\n    <g style=\"fill:#262626;\" transform=\"translate(216.622084 24.67125)scale(0.24 -0.24)\">\r\n     <defs>\r\n      <path d=\"M 58.796875 25.09375 \r\nL 68.265625 22.703125 \r\nQ 65.28125 11.03125 57.546875 4.90625 \r\nQ 49.8125 -1.21875 38.625 -1.21875 \r\nQ 27.046875 -1.21875 19.796875 3.484375 \r\nQ 12.546875 8.203125 8.765625 17.140625 \r\nQ 4.984375 26.078125 4.984375 36.328125 \r\nQ 4.984375 47.515625 9.25 55.828125 \r\nQ 13.53125 64.15625 21.40625 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      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    " \n",
    "def plot_confusion_matrix(data, labels, output_filename):\n",
    "    \"\"\"Plot confusion matrix using heatmap.\n",
    " \n",
    "    Args:\n",
    "        data (list of list): List of lists with confusion matrix data.\n",
    "        labels (list): Labels which will be plotted across x and y axis.\n",
    "        output_filename (str): Path to output file.\n",
    " \n",
    "    \"\"\"\n",
    "    seaborn.set(color_codes=True)\n",
    "    plt.figure(1, figsize=(15, 10))\n",
    " \n",
    "    plt.title(\"Confusion Matrix (CNN - Normalized by Row)\", fontsize=24, pad=35)\n",
    "    \n",
    "    seaborn.set(font_scale=1.5)\n",
    "    ax = seaborn.heatmap(data, annot=True, cmap=\"YlGnBu\", fmt=\".2%\", linewidths=.4, linecolor='black',cbar_kws={'label': 'Scale'})\n",
    "    \n",
    "    ax.set_xticklabels(labels, rotation=35, fontsize=16)\n",
    "    ax.set_yticklabels(labels, rotation=35, fontsize=16)\n",
    " \n",
    "    \n",
    "    plt.ylabel('True Genre', fontsize=20)\n",
    "    plt.xlabel('Predicted Genre', fontsize=20)\n",
    "\n",
    "    plt.savefig(output_filename, bbox_inches='tight', dip=1200)\n",
    "    plt.show()\n",
    "    plt.close()\n",
    " \n",
    "#getting the confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "prediction_y = model_cnn.predict(x_test_cnn)\n",
    "predictions = np.argmax(prediction_y, axis = -1)\n",
    "cm = confusion_matrix(y_test, predictions, normalize='true')\n",
    "\n",
    "# create confusion matrix\n",
    "plot_confusion_matrix(cm, mapping, \"graphs/confusion_matrix.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNN-LSTM Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_shape = x.shape[1:]\n",
    "model_lstm = keras.Sequential()\n",
    "\n",
    "#2 lstm layers\n",
    "model_lstm.add(keras.layers.LSTM(64, input_shape = input_shape, return_sequences = True))\n",
    "model_lstm.add(keras.layers.LSTM(64))\n",
    "\n",
    "#dense layer\n",
    "model_lstm.add(keras.layers.Dense(64, activation = \"relu\"))\n",
    "\n",
    "#dropout layer\n",
    "model_lstm.add(keras.layers.Dropout(0.3))\n",
    "\n",
    "#output layer\n",
    "model_lstm.add(keras.layers.Dense(len(mapping), activation = 'softmax'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "lstm (LSTM)                  (None, 129, 64)           19968     \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 64)                33024     \n",
      "_________________________________________________________________\n",
      "dense_6 (Dense)              (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 8)                 520       \n",
      "=================================================================\n",
      "Total params: 57,672\n",
      "Trainable params: 57,672\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "optimizer = keras.optimizers.Adam(learning_rate = 0.0001)\n",
    "model_lstm.compile(optimizer = optimizer, loss = \"sparse_categorical_crossentropy\", metrics = [\"accuracy\"])\n",
    "model_lstm.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "W0721 10:53:05.313315 139902445979456 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 59955 samples, validate on 19985 samples\n",
      "Epoch 1/30\n",
      "59955/59955 [==============================] - 130s 2ms/sample - loss: 1.9550 - acc: 0.2452 - val_loss: 1.8089 - val_acc: 0.3297\n",
      "Epoch 2/30\n",
      "59955/59955 [==============================] - 128s 2ms/sample - loss: 1.7964 - acc: 0.3269 - val_loss: 1.7372 - val_acc: 0.3569\n",
      "Epoch 3/30\n",
      "59955/59955 [==============================] - 128s 2ms/sample - loss: 1.7436 - acc: 0.3527 - val_loss: 1.6987 - val_acc: 0.3732\n",
      "Epoch 4/30\n",
      "59955/59955 [==============================] - 128s 2ms/sample - loss: 1.7057 - acc: 0.3725 - val_loss: 1.6730 - val_acc: 0.3860\n",
      "Epoch 5/30\n",
      "59955/59955 [==============================] - 128s 2ms/sample - loss: 1.6771 - acc: 0.3870 - val_loss: 1.6514 - val_acc: 0.3933\n",
      "Epoch 6/30\n",
      "59955/59955 [==============================] - 128s 2ms/sample - loss: 1.6560 - acc: 0.3960 - val_loss: 1.6339 - val_acc: 0.4010\n",
      "Epoch 7/30\n",
      "59955/59955 [==============================] - 130s 2ms/sample - loss: 1.6346 - acc: 0.4073 - val_loss: 1.6216 - val_acc: 0.4097\n",
      "Epoch 8/30\n",
      "59955/59955 [==============================] - 130s 2ms/sample - loss: 1.6189 - acc: 0.4138 - val_loss: 1.6131 - val_acc: 0.4130\n",
      "Epoch 9/30\n",
      "59955/59955 [==============================] - 130s 2ms/sample - loss: 1.6013 - acc: 0.4222 - val_loss: 1.6006 - val_acc: 0.4190\n",
      "Epoch 10/30\n",
      "59955/59955 [==============================] - 130s 2ms/sample - loss: 1.5899 - acc: 0.4276 - val_loss: 1.5877 - val_acc: 0.4247\n",
      "Epoch 11/30\n",
      "59955/59955 [==============================] - 130s 2ms/sample - loss: 1.5761 - acc: 0.4351 - val_loss: 1.5817 - val_acc: 0.4233\n",
      "Epoch 12/30\n",
      "59955/59955 [==============================] - 130s 2ms/sample - loss: 1.5613 - acc: 0.4400 - val_loss: 1.5747 - val_acc: 0.4288\n",
      "Epoch 13/30\n",
      "59955/59955 [==============================] - 130s 2ms/sample - loss: 1.5521 - acc: 0.4458 - val_loss: 1.5641 - val_acc: 0.4343\n",
      "Epoch 14/30\n",
      "59955/59955 [==============================] - 130s 2ms/sample - loss: 1.5420 - acc: 0.4496 - val_loss: 1.5562 - val_acc: 0.4377\n",
      "Epoch 15/30\n",
      "59955/59955 [==============================] - 130s 2ms/sample - loss: 1.5307 - acc: 0.4557 - val_loss: 1.5468 - val_acc: 0.4431\n",
      "Epoch 16/30\n",
      "59955/59955 [==============================] - 130s 2ms/sample - loss: 1.5232 - acc: 0.4589 - val_loss: 1.5439 - val_acc: 0.4434\n",
      "Epoch 17/30\n",
      "59955/59955 [==============================] - 131s 2ms/sample - loss: 1.5145 - acc: 0.4623 - val_loss: 1.5391 - val_acc: 0.4431\n",
      "Epoch 18/30\n",
      "59955/59955 [==============================] - 128s 2ms/sample - loss: 1.5039 - acc: 0.4644 - val_loss: 1.5349 - val_acc: 0.4482\n",
      "Epoch 19/30\n",
      "59955/59955 [==============================] - 128s 2ms/sample - loss: 1.4953 - acc: 0.4685 - val_loss: 1.5297 - val_acc: 0.4490\n",
      "Epoch 20/30\n",
      "59955/59955 [==============================] - 128s 2ms/sample - loss: 1.4898 - acc: 0.4709 - val_loss: 1.5273 - val_acc: 0.4499\n",
      "Epoch 21/30\n",
      "59955/59955 [==============================] - 128s 2ms/sample - loss: 1.4792 - acc: 0.4748 - val_loss: 1.5223 - val_acc: 0.4524\n",
      "Epoch 22/30\n",
      "59955/59955 [==============================] - 128s 2ms/sample - loss: 1.4750 - acc: 0.4777 - val_loss: 1.5203 - val_acc: 0.4524\n",
      "Epoch 23/30\n",
      "59955/59955 [==============================] - 128s 2ms/sample - loss: 1.4665 - acc: 0.4808 - val_loss: 1.5213 - val_acc: 0.4517\n",
      "Epoch 24/30\n",
      "59955/59955 [==============================] - 133s 2ms/sample - loss: 1.4593 - acc: 0.4817 - val_loss: 1.5086 - val_acc: 0.4551\n",
      "Epoch 25/30\n",
      "59955/59955 [==============================] - 130s 2ms/sample - loss: 1.4524 - acc: 0.4874 - val_loss: 1.5059 - val_acc: 0.4576\n",
      "Epoch 26/30\n",
      "59955/59955 [==============================] - 128s 2ms/sample - loss: 1.4482 - acc: 0.4884 - val_loss: 1.5027 - val_acc: 0.4608\n",
      "Epoch 27/30\n",
      "59955/59955 [==============================] - 128s 2ms/sample - loss: 1.4426 - acc: 0.4897 - val_loss: 1.5004 - val_acc: 0.4591\n",
      "Epoch 28/30\n",
      "59955/59955 [==============================] - 129s 2ms/sample - loss: 1.4353 - acc: 0.4910 - val_loss: 1.4972 - val_acc: 0.4634\n",
      "Epoch 29/30\n",
      "59955/59955 [==============================] - 129s 2ms/sample - loss: 1.4308 - acc: 0.4924 - val_loss: 1.4966 - val_acc: 0.4608\n",
      "Epoch 30/30\n",
      "59955/59955 [==============================] - 130s 2ms/sample - loss: 1.4219 - acc: 0.4965 - val_loss: 1.4925 - val_acc: 0.4646\n"
     ]
    }
   ],
   "source": [
    "history_lstm = model_lstm.fit(x_train, y_train, validation_data = (x_test, y_test), batch_size = 128, epochs = 30)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plot_history' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-cbd7044962ff>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplot_history\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhistory_lstm\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'plot_history' is not defined"
     ]
    }
   ],
   "source": [
    "plot_history(history_lstm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN-LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import TimeDistributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_shape = x_train_cnn.shape[1:]\n",
    "model_cnn_lstm = keras.Sequential()\n",
    "\n",
    "#CNN Model\n",
    "model_cnn_lstm.add(TimeDistributed(keras.layers.Conv2D(32, (3,3), strides=1,padding = 'same', input_shape = input_shape, activation = 'relu')))\n",
    "model_cnn_lstm.add(TimeDistributed(keras.layers.Conv2D(32, (3,3), strides=1,padding = 'same',activation = 'relu')))\n",
    "model_cnn_lstm.add(TimeDistributed(keras.layers.MaxPool2D((3,3), strides = (2,2), padding = 'same')))\n",
    "# model_cnn_lstm.add(TimeDistributed(keras.layers.Flatten()))\n",
    "                   \n",
    "#LSTM Model\n",
    "model_cnn_lstm.add(keras.layers.LSTM(64, activation = 'relu'))\n",
    "model_cnn_lstm.add(keras.layers.LSTM(64, activation = 'relu'))  \n",
    "#dense layer\n",
    "model_cnn_lstm.add(keras.layers.Dense(64, activation = \"relu\"))\n",
    "\n",
    "#dropout layer\n",
    "model_cnn_lstm.add(keras.layers.Dropout(0.3))\n",
    "\n",
    "#output layer\n",
    "model_cnn_lstm.add(keras.layers.Dense(len(mapping), activation = 'softmax'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "This model has not yet been built. Build the model first by calling `build()` or calling `fit()` with some data, or specify an `input_shape` argument in the first layer(s) for automatic build.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-d31429a340ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mAdam\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearning_rate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0.0001\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mmodel_cnn_lstm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"sparse_categorical_crossentropy\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmetrics\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"accuracy\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel_cnn_lstm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/network.py\u001b[0m in \u001b[0;36msummary\u001b[0;34m(self, line_length, positions, print_fn)\u001b[0m\n\u001b[1;32m   1451\u001b[0m     \"\"\"\n\u001b[1;32m   1452\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1453\u001b[0;31m       raise ValueError('This model has not yet been built. '\n\u001b[0m\u001b[1;32m   1454\u001b[0m                        \u001b[0;34m'Build the model first by calling `build()` or calling '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1455\u001b[0m                        \u001b[0;34m'`fit()` with some data, or specify '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: This model has not yet been built. Build the model first by calling `build()` or calling `fit()` with some data, or specify an `input_shape` argument in the first layer(s) for automatic build."
     ]
    }
   ],
   "source": [
    "optimizer = keras.optimizers.Adam(learning_rate = 0.0001)\n",
    "model_cnn_lstm.compile(optimizer = optimizer, loss = \"sparse_categorical_crossentropy\", metrics = [\"accuracy\"])\n",
    "model_cnn_lstm.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "input tensor must have rank 4",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/tensor_shape.py\u001b[0m in \u001b[0;36mmerge_with\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    927\u001b[0m       \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 928\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0massert_same_rank\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    929\u001b[0m         \u001b[0mnew_dims\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/tensor_shape.py\u001b[0m in \u001b[0;36massert_same_rank\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    982\u001b[0m         raise ValueError(\"Shapes %s and %s must have the same rank\" %\n\u001b[0;32m--> 983\u001b[0;31m                          (self, other))\n\u001b[0m\u001b[1;32m    984\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Shapes (?, 13, 1) and (?, ?, ?, ?) must have the same rank",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/tensor_shape.py\u001b[0m in \u001b[0;36mwith_rank\u001b[0;34m(self, rank)\u001b[0m\n\u001b[1;32m   1012\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1013\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmerge_with\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munknown_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrank\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrank\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1014\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/tensor_shape.py\u001b[0m in \u001b[0;36mmerge_with\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    933\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 934\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Shapes %s and %s are not compatible\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    935\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Shapes (?, 13, 1) and (?, ?, ?, ?) are not compatible",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_ops.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, input_shape, filter_shape, padding, strides, dilation_rate, name, data_format)\u001b[0m\n\u001b[1;32m   1063\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1064\u001b[0;31m       \u001b[0minput_shape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_rank\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_spatial_dims\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1065\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/framework/tensor_shape.py\u001b[0m in \u001b[0;36mwith_rank\u001b[0;34m(self, rank)\u001b[0m\n\u001b[1;32m   1014\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1015\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Shape %s must have rank %d\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrank\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1016\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Shape (?, 13, 1) must have rank 4",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-b48aa41d3ca8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhistory_cnn_lstm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_cnn_lstm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train_cnn\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_test_cnn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m128\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/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    725\u001b[0m         \u001b[0mmax_queue_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_queue_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0mworkers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mworkers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m         use_multiprocessing=use_multiprocessing)\n\u001b[0m\u001b[1;32m    728\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    729\u001b[0m   def evaluate(self,\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model, 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, **kwargs)\u001b[0m\n\u001b[1;32m    641\u001b[0m         \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    642\u001b[0m         \u001b[0mvalidation_split\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_split\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 643\u001b[0;31m         shuffle=shuffle)\n\u001b[0m\u001b[1;32m    644\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    645\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mvalidation_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[0;34m(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset)\u001b[0m\n\u001b[1;32m   2416\u001b[0m     \u001b[0;31m# First, we build the model on the fly if necessary.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2417\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2418\u001b[0;31m       \u001b[0mall_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_input\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_model_with_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2419\u001b[0m       \u001b[0mis_build_called\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2420\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_build_model_with_inputs\u001b[0;34m(self, inputs, targets)\u001b[0m\n\u001b[1;32m   2619\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2620\u001b[0m       \u001b[0mcast_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2621\u001b[0;31m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2622\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mprocessed_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtargets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_dict_inputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2623\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/training.py\u001b[0m in \u001b[0;36m_set_inputs\u001b[0;34m(self, inputs, outputs, training)\u001b[0m\n\u001b[1;32m   2706\u001b[0m           \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'training'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2707\u001b[0m       \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2708\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\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[0m\u001b[1;32m   2709\u001b[0m       \u001b[0;32mexcept\u001b[0m \u001b[0mNotImplementedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2710\u001b[0m         \u001b[0;31m# This Model or a submodel is dynamic and hasn't overridden\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    852\u001b[0m                     \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbase_layer_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmark_as_return\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0macd\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    853\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 854\u001b[0;31m                   \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcall_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcast_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\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[0m\u001b[1;32m    855\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    856\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOperatorNotAllowedInGraphError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/sequential.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, inputs, training, mask)\u001b[0m\n\u001b[1;32m    267\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'training'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtraining\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    268\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 269\u001b[0;31m       \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlayer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\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[0m\u001b[1;32m    270\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    271\u001b[0m       \u001b[0;31m# `outputs` will be the inputs to the next layer.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    822\u001b[0m           \u001b[0;31m# Build layer if applicable (if the `build` method has been\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    823\u001b[0m           \u001b[0;31m# overridden).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 824\u001b[0;31m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_build\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    825\u001b[0m           \u001b[0mcast_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    826\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/engine/base_layer.py\u001b[0m in \u001b[0;36m_maybe_build\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2144\u001b[0m         \u001b[0;31m# operations.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2145\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mtf_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_init_scope\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2146\u001b[0;31m           \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shapes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2147\u001b[0m       \u001b[0;31m# We must set self.built since user defined build functions are not\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2148\u001b[0m       \u001b[0;31m# constrained to set self.built.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/layers/wrappers.py\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(self, input_shape)\u001b[0m\n\u001b[1;32m    201\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_spec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mInputSpec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    202\u001b[0m     \u001b[0mchild_input_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minput_shape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 203\u001b[0;31m     \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mTimeDistributed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mchild_input_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    204\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    205\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/layers/wrappers.py\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(self, input_shape)\u001b[0m\n\u001b[1;32m     59\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_shape\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/keras/layers/convolutional.py\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(self, input_shape)\u001b[0m\n\u001b[1;32m    191\u001b[0m         \u001b[0mpadding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mop_padding\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m         data_format=conv_utils.convert_data_format(self.data_format,\n\u001b[0;32m--> 193\u001b[0;31m                                                    self.rank + 2))\n\u001b[0m\u001b[1;32m    194\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\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/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/nn_ops.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, input_shape, filter_shape, padding, strides, dilation_rate, name, data_format)\u001b[0m\n\u001b[1;32m   1065\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1066\u001b[0m       raise ValueError(\n\u001b[0;32m-> 1067\u001b[0;31m           \"input tensor must have rank %d\" % (num_spatial_dims + 2))\n\u001b[0m\u001b[1;32m   1068\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1069\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: input tensor must have rank 4"
     ]
    }
   ],
   "source": [
    "history_cnn_lstm = model_cnn_lstm.fit(x_train_cnn, y_train, validation_data = (x_test_cnn, y_test), batch_size = 128, epochs = 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resnet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_res = np.repeat(x_train[..., np.newaxis], 3, -1)\n",
    "x_test_res = np.repeat(x_test[..., np.newaxis], 3, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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