pain_2D_3layer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n",
      "/opt/miniconda3/envs/trial_env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/opt/miniconda3/envs/trial_env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/opt/miniconda3/envs/trial_env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/opt/miniconda3/envs/trial_env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/opt/miniconda3/envs/trial_env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/opt/miniconda3/envs/trial_env/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import st_remux as st\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.utils import to_categorical\n",
    "\n",
    "\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Config\n",
    "nopain = 'Pain_data/final_data/no_pain'\n",
    "highpain = 'Pain_data/final_data/high_pain'\n",
    "\n",
    "fs = 4000\n",
    "win_len = 40\n",
    "\n",
    "use_filter = 0   # weird results if we use python filtering\n",
    "\n",
    "use_matlab_backend = 0\n",
    "\n",
    "#high = np.genfromtxt(highpain, delimiter=',')\n",
    "#no = np.genfromtxt(nopain, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using native backend\n",
      "['.DS_Store', 'pain_large.csv']\n",
      "ydim=16 xdim=40\n",
      "['.DS_Store', 'nopain.csv']\n",
      "ydim=16 xdim=40\n"
     ]
    }
   ],
   "source": [
    "if use_matlab_backend:\n",
    "    print('Using MATLAB signal processing backend')\n",
    "    # high_dir = 'datasets/high/'\n",
    "    # low_dir = 'datasets/low/'\n",
    "\n",
    "    high_dir = 'EMG_data/power_spectrum_for_cnn_input/high/'\n",
    "    low_dir = 'EMG_data/power_spectrum_for_cnn_input/low/'\n",
    "\n",
    "    def generate_3darray(directory):\n",
    "        files = os.listdir(directory)\n",
    "        dims = np.genfromtxt(directory + files[0], delimiter=',').shape\n",
    "        length = len(files)\n",
    "        arr = np.zeros((length, dims[0], dims[1]))\n",
    "\n",
    "        for idx, val in enumerate(files):\n",
    "            arr[idx] = np.genfromtxt(directory + val, delimiter=',')\n",
    "\n",
    "        return arr\n",
    "\n",
    "    hi = generate_3darray(high_dir)\n",
    "    lo = generate_3darray(low_dir)\n",
    "    \n",
    "else: \n",
    "    print('Using native backend')\n",
    "    hi = st.raw_to_arr(highpain, fs, win_len, 0)\n",
    "    no = st.raw_to_arr(nopain, fs, win_len, 0)\n",
    "\n",
    "X = np.concatenate((hi, no))\n",
    "\n",
    "hi_Y = np.ones(len(hi), dtype=int)\n",
    "no_Y = np.zeros(len(no), dtype=int)\n",
    "\n",
    "Y = np.concatenate((hi_Y, no_Y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X, test_X, train_Y, test_Y = train_test_split(X, Y, test_size=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (2572, 16, 40) (2572,)\n",
      "Testing data shape:  (286, 16, 40) (286,)\n"
     ]
    }
   ],
   "source": [
    "print('Training data shape: ', train_X.shape, train_Y.shape)\n",
    "print('Testing data shape: ', test_X.shape, test_Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of outputs:  2\n",
      "Output classes:  [0 1]\n"
     ]
    }
   ],
   "source": [
    "classes = np.unique(train_Y)\n",
    "nClasses = len(classes)\n",
    "print('Total number of outputs: ', nClasses)\n",
    "print('Output classes: ', classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Ground Truth: 1')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x144 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=[10,2])\n",
    "\n",
    "# Display first image in training set\n",
    "plt.subplot(121)\n",
    "plt.pcolormesh(train_X[0,:,:], cmap='jet')\n",
    "plt.title(\"Ground Truth: {}\".format(train_Y[0]))\n",
    "\n",
    "# Display second image in testing set\n",
    "plt.subplot(122)\n",
    "plt.pcolormesh(test_X[1], cmap='jet')\n",
    "plt.title(\"Ground Truth: {}\".format(test_Y[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2572, 16, 40, 1), (286, 16, 40, 1))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ydim = train_X[0].shape[0]\n",
    "xdim = train_X[0].shape[1]\n",
    "train_X = train_X.reshape(-1, ydim, xdim, 1)\n",
    "test_X = test_X.reshape(-1, ydim, xdim, 1)\n",
    "train_X.shape, test_X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = train_X.astype('float32')\n",
    "test_X = test_X.astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original label:  0\n",
      "After conversion to one-hot:  [1. 0.]\n"
     ]
    }
   ],
   "source": [
    "# Change labels from categorical to one-hot encoding\n",
    "train_Y_one_hot = to_categorical(train_Y)\n",
    "test_Y_one_hot = to_categorical(test_Y)\n",
    "\n",
    "# See the difference?\n",
    "print('Original label: ', train_Y[0])\n",
    "print('After conversion to one-hot: ', train_Y_one_hot[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(286, 2)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_Y_one_hot.shape\n",
    "test_Y_one_hot.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((2057, 16, 40, 1), (515, 16, 40, 1), (2057, 2), (515, 2))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Partition data for 80% training and 20% validation\n",
    "# should reduce overfitting and boost test performance\n",
    "\n",
    "train_X, valid_X, train_label, valid_label = train_test_split(train_X, train_Y_one_hot, test_size=0.2)\n",
    "\n",
    "train_X.shape, valid_X.shape, train_label.shape, valid_label.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's get down to business\n",
    "import keras\n",
    "from keras.models import Sequential, Input, Model\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from keras.layers.normalization import BatchNormalization\n",
    "from keras.layers.advanced_activations import LeakyReLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Parameters\n",
    "batch_size = 32\n",
    "epochs = 50\n",
    "num_classes = nClasses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/miniconda3/envs/trial_env/lib/python3.7/site-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    }
   ],
   "source": [
    "pain_model = Sequential()\n",
    "pain_model.add(\n",
    "    Conv2D(\n",
    "        32, \n",
    "        kernel_size=(3, 3), \n",
    "        activation='linear', \n",
    "        input_shape=(ydim, xdim, 1), \n",
    "        padding='same'))\n",
    "pain_model.add(LeakyReLU(alpha=0.1))\n",
    "pain_model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))\n",
    "pain_model.add(Dropout(0.25))\n",
    "pain_model.add(\n",
    "    Conv2D(\n",
    "        64,\n",
    "        (3, 3),\n",
    "        activation='linear',\n",
    "        padding='same'))\n",
    "pain_model.add(LeakyReLU(alpha=0.1))\n",
    "pain_model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))\n",
    "pain_model.add(Dropout(0.25))\n",
    "pain_model.add(\n",
    "    Conv2D(\n",
    "        128,\n",
    "        (3, 3),\n",
    "        activation='linear',\n",
    "        padding='same'))\n",
    "pain_model.add(LeakyReLU(alpha=0.1))\n",
    "pain_model.add(MaxPooling2D(pool_size=(2, 2), padding='same'))\n",
    "pain_model.add(Dropout(0.4))\n",
    "pain_model.add(Flatten())\n",
    "pain_model.add(Dense(128, activation='linear'))\n",
    "pain_model.add(LeakyReLU(alpha=0.1))\n",
    "pain_model.add(Dropout(0.3))\n",
    "pain_model.add(Dense(num_classes, activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "pain_model.compile(\n",
    "    loss=keras.losses.categorical_crossentropy,\n",
    "    optimizer=keras.optimizers.Adam(),\n",
    "    metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 16, 40, 32)        320       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_1 (LeakyReLU)    (None, 16, 40, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 8, 20, 32)         0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 8, 20, 32)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 8, 20, 64)         18496     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_2 (LeakyReLU)    (None, 8, 20, 64)         0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 4, 10, 64)         0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 4, 10, 64)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 4, 10, 128)        73856     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_3 (LeakyReLU)    (None, 4, 10, 128)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 2, 5, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 2, 5, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 1280)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 128)               163968    \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_4 (LeakyReLU)    (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 128)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 2)                 258       \n",
      "=================================================================\n",
      "Total params: 256,898\n",
      "Trainable params: 256,898\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "pain_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /opt/miniconda3/envs/trial_env/lib/python3.7/site-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 2057 samples, validate on 515 samples\n",
      "Epoch 1/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.6435 - accuracy: 0.6675 - val_loss: 0.6222 - val_accuracy: 0.6757\n",
      "Epoch 2/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.6227 - accuracy: 0.6850 - val_loss: 0.5989 - val_accuracy: 0.6971\n",
      "Epoch 3/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.6056 - accuracy: 0.6971 - val_loss: 0.5928 - val_accuracy: 0.7010\n",
      "Epoch 4/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.6038 - accuracy: 0.7020 - val_loss: 0.5880 - val_accuracy: 0.7184\n",
      "Epoch 5/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.6076 - accuracy: 0.7059 - val_loss: 0.5838 - val_accuracy: 0.7165\n",
      "Epoch 6/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5970 - accuracy: 0.7088 - val_loss: 0.5810 - val_accuracy: 0.7243\n",
      "Epoch 7/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5965 - accuracy: 0.7093 - val_loss: 0.5862 - val_accuracy: 0.7204\n",
      "Epoch 8/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5959 - accuracy: 0.7069 - val_loss: 0.5828 - val_accuracy: 0.7223\n",
      "Epoch 9/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.6017 - accuracy: 0.6966 - val_loss: 0.5795 - val_accuracy: 0.7204\n",
      "Epoch 10/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5926 - accuracy: 0.7137 - val_loss: 0.5783 - val_accuracy: 0.7262\n",
      "Epoch 11/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5931 - accuracy: 0.7137 - val_loss: 0.5835 - val_accuracy: 0.7204\n",
      "Epoch 12/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5872 - accuracy: 0.7103 - val_loss: 0.5699 - val_accuracy: 0.7340\n",
      "Epoch 13/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5881 - accuracy: 0.7185 - val_loss: 0.5887 - val_accuracy: 0.7223\n",
      "Epoch 14/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5883 - accuracy: 0.7141 - val_loss: 0.5784 - val_accuracy: 0.7184\n",
      "Epoch 15/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5833 - accuracy: 0.7146 - val_loss: 0.5790 - val_accuracy: 0.7320\n",
      "Epoch 16/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5844 - accuracy: 0.7205 - val_loss: 0.5748 - val_accuracy: 0.7223\n",
      "Epoch 17/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5784 - accuracy: 0.7195 - val_loss: 0.6009 - val_accuracy: 0.7243\n",
      "Epoch 18/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5832 - accuracy: 0.7127 - val_loss: 0.5706 - val_accuracy: 0.7204\n",
      "Epoch 19/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5843 - accuracy: 0.7098 - val_loss: 0.5719 - val_accuracy: 0.7243\n",
      "Epoch 20/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5818 - accuracy: 0.7132 - val_loss: 0.5702 - val_accuracy: 0.7282\n",
      "Epoch 21/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5745 - accuracy: 0.7200 - val_loss: 0.5760 - val_accuracy: 0.7262\n",
      "Epoch 22/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5738 - accuracy: 0.7185 - val_loss: 0.5713 - val_accuracy: 0.7301\n",
      "Epoch 23/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5688 - accuracy: 0.7253 - val_loss: 0.5796 - val_accuracy: 0.7243\n",
      "Epoch 24/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5710 - accuracy: 0.7219 - val_loss: 0.5732 - val_accuracy: 0.7320\n",
      "Epoch 25/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5769 - accuracy: 0.7195 - val_loss: 0.5831 - val_accuracy: 0.7301\n",
      "Epoch 26/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5660 - accuracy: 0.7282 - val_loss: 0.5824 - val_accuracy: 0.7320\n",
      "Epoch 27/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5656 - accuracy: 0.7253 - val_loss: 0.5759 - val_accuracy: 0.7301\n",
      "Epoch 28/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5622 - accuracy: 0.7263 - val_loss: 0.5791 - val_accuracy: 0.7223\n",
      "Epoch 29/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5582 - accuracy: 0.7307 - val_loss: 0.5884 - val_accuracy: 0.7340\n",
      "Epoch 30/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5615 - accuracy: 0.7239 - val_loss: 0.5757 - val_accuracy: 0.7282\n",
      "Epoch 31/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5492 - accuracy: 0.7375 - val_loss: 0.5938 - val_accuracy: 0.7340\n",
      "Epoch 32/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5572 - accuracy: 0.7331 - val_loss: 0.5848 - val_accuracy: 0.7223\n",
      "Epoch 33/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5577 - accuracy: 0.7268 - val_loss: 0.5946 - val_accuracy: 0.7204\n",
      "Epoch 34/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5442 - accuracy: 0.7312 - val_loss: 0.5891 - val_accuracy: 0.7223\n",
      "Epoch 35/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5479 - accuracy: 0.7312 - val_loss: 0.5946 - val_accuracy: 0.7204\n",
      "Epoch 36/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5334 - accuracy: 0.7389 - val_loss: 0.5949 - val_accuracy: 0.7204\n",
      "Epoch 37/50\n",
      "2057/2057 [==============================] - 4s 2ms/step - loss: 0.5400 - accuracy: 0.7423 - val_loss: 0.5943 - val_accuracy: 0.7243\n",
      "Epoch 38/50\n",
      "2057/2057 [==============================] - 3s 2ms/step - loss: 0.5380 - accuracy: 0.7375 - val_loss: 0.5936 - val_accuracy: 0.7262\n",
      "Epoch 39/50\n",
      "2057/2057 [==============================] - 3s 2ms/step - loss: 0.5378 - accuracy: 0.7438 - val_loss: 0.6126 - val_accuracy: 0.7282\n",
      "Epoch 40/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5438 - accuracy: 0.7355 - val_loss: 0.6048 - val_accuracy: 0.7126\n",
      "Epoch 41/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5285 - accuracy: 0.7448 - val_loss: 0.6095 - val_accuracy: 0.7126\n",
      "Epoch 42/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5213 - accuracy: 0.7462 - val_loss: 0.6199 - val_accuracy: 0.7165\n",
      "Epoch 43/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5308 - accuracy: 0.7394 - val_loss: 0.6074 - val_accuracy: 0.7184\n",
      "Epoch 44/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5305 - accuracy: 0.7540 - val_loss: 0.6242 - val_accuracy: 0.6951\n",
      "Epoch 45/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5161 - accuracy: 0.7535 - val_loss: 0.6243 - val_accuracy: 0.7087\n",
      "Epoch 46/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.5021 - accuracy: 0.7705 - val_loss: 0.6646 - val_accuracy: 0.7126\n",
      "Epoch 47/50\n",
      "2057/2057 [==============================] - 3s 2ms/step - loss: 0.5074 - accuracy: 0.7715 - val_loss: 0.6250 - val_accuracy: 0.7087\n",
      "Epoch 48/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.4953 - accuracy: 0.7691 - val_loss: 0.6235 - val_accuracy: 0.6932\n",
      "Epoch 49/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.4996 - accuracy: 0.7647 - val_loss: 0.6374 - val_accuracy: 0.7146\n",
      "Epoch 50/50\n",
      "2057/2057 [==============================] - 3s 1ms/step - loss: 0.4938 - accuracy: 0.7735 - val_loss: 0.6450 - val_accuracy: 0.7107\n"
     ]
    }
   ],
   "source": [
    "pain_train = pain_model.fit(train_X,train_label,\n",
    "    batch_size=batch_size,\n",
    "    epochs=epochs,\n",
    "    verbose=1,\n",
    "    validation_data=(valid_X, valid_label))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_eval = pain_model.evaluate(test_X, test_Y_one_hot, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss:  0.7255903436587408\n",
      "Test accuracy:  0.6748251914978027\n"
     ]
    }
   ],
   "source": [
    "print('Test loss: ', test_eval[0])\n",
    "print('Test accuracy: ', test_eval[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CFVe4KSm6d4dataB3b7cK2r//Dbt2lY6RyYGimTkjWA1X4LRN5YEWQDegAfC5iLTxVQ2JSD3gJeA6Vc31trkb+AEXJMYDdwGjCnyR6njvczIyMmKYLiq0zEwYNswNRQdYv969B1tdzZhU9vjjrudPly5uXiH/R/36LlBEa/p0+OknmDMH/vtfmD0b3n7bfdawIfToUTzHUBTRBIRNQEO/9w2ALUHyzFPVo8BaEVmBCxDzReQk4B3gXlWd59tAVbd6Lw+LyGTgz4U8hogyM12f39//3r0fOTIvGPgcOODSLSAYk5qWL4fvv3dX8bfcEp99nnSSW+Ogr1fJvno1fPghtG4NaWnx+Y54iqbKaD7QQkSaikhFYAAwKyDPG0B3ABGpjatCWuPlnwm8qKr/57+Bd9eAiAjQD1hSlAMJ54034LHH8t5v2BA8X6h0Y0zye/1199yvX/F9R/PmcNNNcO65xfcdRRExIKhqDnArMBtYDkxX1aUiMkpE+njZZgM7RWQZMAfXe2gncCVwPjAkSPfSTBFZDCwGagMPxfXI/HTp4qqFNm927xs1Cp4vVLoxJvnNnAlnn+2qhlJVVOMQVPVdVT1dVZur6mgv7W+qOst7rap6p6q2UtW2qjrNS5+qqhVUtYPfY5H32QVe3jaqOlhV9xfXQXbp4p6//NI9jx5dcKh41aou3RiTHI4eheuug7lzI+ddvx4WLIDf/Kb4y1WapcRI5Q4d3An/iy/c+0GDYPz4vFkJGzd27639wJjkMWkSvPiiaxuMZOZM95zqAUE0lnXeEiwjI0OzsrIKtW337rB/vxsdaIxJbocOufUFdu50r7/91l0YhtK1K+zeDdnZJVfGkiQiC1Q14iQZKXGHAPCrX7k/ip9/TnRJjDHF7bnnXJvhK6+42oGxY0Pn3bYNPv8cLr+85MpXWqVMQOjSBY4dg2++SXRJjDHFaf9++Mc/XD//fv1cO8LLL0Ooca2zZrm5ilK9ughSKCCcc4579rUjGGOS09ix7uT/kNdv8Q9/cNNFjB8fPP/rr7sRyO3alVwZS6uUCQg1a7rBIBYQjElee/a40ca9e8Mvf+nSWraEiy92U0YcPZo//9698NFHrrqoNM06migpExDAVRt99RXk5kbOa4wpe/75TxcU/v73/Om33w5btsBrr+VPf+cdFySs/cBJuYCwdy8sXZrokhhjQvnf/2DevMj5Au3YAWPGwJVXFpxyumdPNw/R00/nT3/9dTd5ne9uItWlXEAAqzYypjS76SbXTXzFiti2e+QRNyfZgw8W/KxcOdeWMG9eXseSgwfhvfdcw3O5lDoThpZSP4ZmzeCUUywgGFNa/fSTG1l86JDrHZSTE912mze7NoJrr4UzzgieZ8gQqFYtrwvqf//rAohVF+VJqYAg4u4SfFNYGGNKl48/dkHg5pvh66/zT0oZzujRrlv5/feHzlOtmluDYPp0twLa66+7ziZdu8an7MkgpQICuICwZg388EP89mmL7RgTH++/707cvraABx6A774Lv838+TBhAgwd6v7/wrn1Vhdw/vUvN/6gd2+oUCFepS/7UjIgQPyqjXyL7axf7wa3+BbbsaBgTGxUXUC48EK33vC//+1WGrv2WjeOIJi334Zu3dwMpX/7W+TvOO00t/rZ44+73khWXZRfygWEM8+EypVDB4RYr/bDLbZjjIneihXugqqntwBverq78s/ODt5Q/J//uIVnWrZ03cnr1o3ue26/3d0lVK3qxieYPCkXECpWhLPOCh4QCnO1b4vtGBMf77/vnn/967y0yy5z9f6PPupO+uDGEd1zDwwf7oLHJ59EHwzA3YFkZLg1j6tUiVvxk0LKBQRw1UYLFxa8si/M1b4ttmNMfLz/vushFNgO8NRT0KCB63W0e7erQnr4YXex9uabcOKJsX2PiLsgnDAhbkVPGikbEHJyCk6FHelqP1h1ki22Y0zRHTwIn36aV13k76ST4IUXYOVKN7jM93/33HNQPppV4YOoWLHw2yazlAwIv/qVew6sNgp3tR+qOglssR1jiurTT93Yg2ABAdxAtTvucOMUXnrJVRnZ3EPxl5IBoVYt1xAVGBDCXe2Hq04aNAjWrXN1m+vWFU8wsK6tJpm9/77r7HH++aHz/POfbu2CwYNLrlypJiUDAuQNUPOf6C7c0pqJbDy2rq0m2b3/vus+Gq6RV8QNJDPFJ6qAICI9RWSFiKwSkREh8lwpIstEZKmIvOyXfp2IrPQe1/mldxKRxd4+x4qU7A1gly6uH/Ly5fnTQ13tJ7Lx2Lq2mmS2dq3rchqqusiUnIgBQUTSgHFAL6AVMFBEWgXkaQHcDXRR1dbAH730WsD9wC+BzsD9IuKL8c8Cw4AW3qNE/xx8A9SincYikY3HydS1dfVq15Vw795El8SUFrNnu2cLCIkXzR1CZ2CVqq5R1SPANKBvQJ7fAeNUdTeAqm730n8NfKCqu7zPPgB6ikg94CRV/UpVFXgR6BeH44naaadBnTrRj1gOV51U3JKpa+trr7k56G2CQePz/vuuXez00xNdEhNNQKgPbPR7v8lL83c6cLqIfCEi80SkZ4Rt63uvw+0TABEZJiJZIpK1I9SiqIXgm+hu7lxXLx+NWBuP49UQnExdW31dfRcvTmw5TOlw5IhbsaxnT+s1VBpEExCC/ZoCT6HlcdU+3YCBwAQRqRFm22j26RJVx6tqhqpm1KlTJ4riRu+yy1wVxqhRcd0tEL4hONZAkci7k3jLynLP2dmJLYcpHb78Evbvt+qi0iKagLAJaOj3vgGwJUieN1X1qKquBVbgAkSobTd5r8Pts9jdcIObI/2BB2Dy5PjuO1RD8O23h+8xFCpYlETX1uK2Y4crO9gdgnFmz3YDxC64INElMQCoatgH7up/DdAUqAh8B7QOyNMTmOK9ro2rJkoHagFrgZreYy1Qy8s3Hzgbd7fwHnBJpLJ06tRJ4+3IEdWLLlItX1519uz47VdE1Z3yo3s0bqw6dapq1ar506tWdenJ4L333DGdfbb7eR8+nOgSmUTr0EG1W7dElyL5AVka4fyqqpHvEFQ1B7gVmA0sB6ar6lIRGSUifbxss4GdIrIMmAP8RVV3quou4O/eyX8+MMpLA7gZmACsAlZ7QaHEVagAM2ZAq1bQv3/kudejFWuD74YNyd+91Nd+MGSImzok1iUSTXLZuhUWLbLqotJENNoW1VIgIyNDs3yV0HG2eTOcfbarkpk3Dxo2jLxNOL42BP8TfNWqbuDNzp0F8zdu7IJCsF+HSP4BdNF+/8iRbp+NGrkG6ERXM/Xp4xZQf+01aNPGlfHqqxNbplT3wgvud5GREV3+b791007/9BPs25f32L/frVlQr577e2vUyP0P+V43bVpwUNmUKe7iYNEiaN8+3kdm/InIAlWN/FuO5jaitDyKo8rIX3a26kknqbZurbp7d9H3N3WqqwoSyasSClct1Lhx6OqkeH1HItWrpzp4sKumq1BB9a67ElueZLN5s+q996oeOhRd/rlz3d/GiSeqfvll5PwLFqhWr+7yn3aa6plnqp5/vuoll6hedZXqNdeoXnihaosWqpUrF/w7rllTtVMn1SuuUB0xQvWcc1Tr1lXNzS3acZvIiLLKKOEn+VgexR0QVFU//NCdrLp3Vz14sHi+I9hJ3Jcey4k8VP709NgDS3HbtMmV4emn3ft27dyJxMTPffe5n/Ho0ZHz5uaq/upX7oR82mnuRL9gQej8ixe7v6vGjVXXr49u/9u3q2Zlqb72muoTT6jefLPqr3/tvq98eVfW4cOjPjxTBBYQiuDFF91P5vzzVXftKpGvPC5UsAgm1B1FqIdICR1EEG+84crwxRfu/eDBqg0aJK48yejMM93PuEqVyCftmTNd3v/8x+Vt3Fi1Vi13lxzo++9VTzlF9dRTVVetik9Zc3JUN2ywjgUlJdqAkLKT24VzzTXwyiuuLeG882DjxsjbxEss3UtjnboikSOb58+HtDTo0MG9b9sWNm1yC56Yotu82dXv//737v2f/hQ6b04OjBjhFqO54Qb3d/HRR2620R494Pvv8/KuWeNWGFN1eZo3j09509JcG0PFivHZn4kPCwghDBjghtRv3AjnnANLliS6RAWFOsGnp8d3ZHM8RlzPn+8aL33latfOPdt4hPh49133fPPNrjPBjBnw4YfB806c6Hp4PfJI3iIxzZu7Ez64ALB6tfvbv/BCt3jNhx+6AGKSXDS3EaXlUVJVRv6++87dKlevrvrJJyX+9WGFa3OIperJf3/F0UCdm+uqI268MS/N16bwzDOxHbMJrk8f9zvLzXVtX82bq55xRsEqmX37XLtBly7BG3Ozs93vqnFj1zh80kmuHcCUbVgbQvysX6/asqVqxYqq06cnpAghxXriD9egXaVKwRN/PBqoV6922zz3XF5abq7rdXLTTTEdbkpZtMjV30dy4ID7Xd1yS17a22+7n/kTT+TP++CDLj1cryJfb6ITTshr8zFlmwWEONu5011Viaj+4x+qx44lrCiFVpgur/FooJ42zW0T2Iula1fX9dAUtHOnu1L/xS8id8t89133833vvfzpl13muohu2eLe//CDe3/55ZG/f9Uq1eXLC1d2U/pEGxCsDSFKtWrBBx/AVVe59Vx/8xu3wE5ZEm4kdHE2UGdlucbDNm3yp7dt69pmYh10lwpGjYJdu1xdv69uP5S333ZtM9265U8fM8bNJvrXv+bt8+BBePjhyN/fvLm1GaQiCwgxqFIFXn4Znn7aNeJlZBR+1s4XXoBf/SryP3s8hVtoJ9TI7MI0UAc2Qr/9tutdFNijpG1bN8p1/fro95UKy4Z+/z2MG+dG8dapA888Ezqvqvv5XnSR6yXkr3lzFwymToVJk9wMucOG2boDJoxobiNKyyORVUaB5s51jc1VqrhxC7EYN87d4vtGcw4Zovrjj8VTTn/hRkL//e8F0ytVCt9AHW0jNKj26FGwPF995T57882yNeK6uPXq5Rpzt29Xvece1XLlVNetC5538WL3c3n++eCf//yzaqNGLs8JJ7hqI5N6sDaE4vfDD26mRt+Iy2imDHjySZe/Tx/VPXtU777bjdqsU0f15ZeLdxh/uBPsqFHufYMG7jktLXz9dayjpNPTC+7jp5/cZ/37l50R18XN1x7gawxev94FhBEjgud/+GGXf/Pm0PucMcPleeCB+JfXlA0WEErI0aOqf/2r+0m2aOEaUEM1OPv+efv3z98d8LvvVM86y33Wq1foq8FYvfNOwW6doa72zzlHNSMjL98zz7jyhJoSPNZGaAi+n2bNgt9RRGrQLky32njav1912TLXjTNejhxxXUVbtMj/9/Gb37jgGGwqlS5dVDt2jLzvRYvKZkcIEx8WEErYe++ptmnjfqLt26u+9Vbe1XVurrs6A9Wrr3ZBJFBOjuqYMe62/oQTVB9/3J0gCuuFF9yVJbhun+Hs3Ony3ndfXtqhQ+5Em5ER/C4h1vUeGjUK/t19+8YeWNLTE1+VdOml+cvTqZPrvXPHHa4chbnTe/ppt79Zs/Knf/SRS3/hhfzpO3a439vf/lb44zCpwQJCAuTkqGZmukFB4K66P/7YVQv52gpycsLvY906110QXICZOzf2cvjaKM47z50w7r47fP5XXnH5A/umT5zo0mfOLLhNqDuEYCfrcuVCn6zvvTf2E3+iq5J8J+jf/c51Qb7pJjdp2xln5I3lePTR2Pb5449uXEaPHgWDSW6uGwfTqVP+z156yX3XN98U/ZhMcrOAkEBHjrhJw+rXzztZ3XRT9LfsubnuJNywodv2+uvd1WA0Hn/cbdO7t6ti6N3bjUwNd7dx7bWuz3tgsDp61FVftGlT8LNoRkn7qnd69Qr93dOnu3yB0yWHG3Ed6u6kJCbvO3bMnZgbNgxehXPsmOqVV7qyvPVW9Pu99VYXOBcvDv65L8jPm5eXNmCAm3TOqoJMJBYQSoEDB1Sfekr1kUcKV4Wwf79rnyhf3p2wn38+eHWTqtv//fe73+hVV+UFgFmzXNrrrwff7tgx1ZNPVh04MPjnL7/stn/55YKfRarH//bb/NsGy798ucszbFjRZ3ktyh3C5s3RnVh9d1OB1Tf+fv7Z1etXq6ZD8HEIAAAdf0lEQVS6ZEnkfS5Z4hrxf//70Hl++snt75pr3PsjR1Rr1FC94YbI+zfGAkISWbLETcUNrn2he3fVkSPd9AQ//uiCwZ//rEGrpY4edXcqoa7Ss7LcdqG6zh47ptq2rbtTOHrU7XvjRleVNXWqm3v/7beDbzt+vNv3ypWh7yimTHF3B3/6U/Q/j3h3R50/3wXdAQPCB+7Dh10jeNu2kav+NmxwV+/NmoXvUrx3r+upVqNG5LvAW29106ds2+bm1QoX6I3xZwEhyeTmujUFbr3VXX2mpeWdDH1VU7fcEvwq97773JV3sN5LvvEH27aF/m7fWgannuoWDwp2dX7HHQXvXoYNcye63NzwV/UdO6pefHFsP4949TI6csQt1uOrsho1KnReX6PvX/4S3Xd/9ZU7gV9wQcEqu9xc1wZQt67b5/jxkcvqu5saPdpdAFSs6O4cjInEAkKS27/fXSU+/LBqv37hq6XWrXMnr2C9UQK7mwaTm6v6hz+4K+i77lJ99lnXq2r5cjeW4rbb3F9St275A8uZZ+YNSAtX73/dde7EmAgPPeTK8cYbri0FXL/9QHv3qtaurdqqVfBJAEMFhSlT8oK1z8KFrrsouO7GX38dfXl79HBjRU4/PfYgalJXXAMC0BNYAawCRgT5fAiwA1jkPYZ66d390hYBh4B+3mcvAGv9PusQqRwWEAqvZ093J+F/FR+su2lhvfiiu8pu0MD1ejl40FXD+Ho4hbtD+Oc/3evt24tejlgsX+6usq+80r0/eNAFyCpVCk7EN3KkK6Pvij6W9gtfdd5jj7kBjCJuIOLEibE3CPtWOgPVsWNj29akrrgFBCANWA00AyoC3wGtAvIMAZ6JsJ9awC6gquYFhP7RFNL3sIBQeK+/7n7b/n3cfQ2kX30Vn+9YsMCdGCtVyrtr8NVxh6v3/+9/3fuPPip6GaZOdVM3+07eoabZePFFt6ZwrVr5p3P44Qc3ZqJ+/bxZQjdvdkFiwIDC9XDKyXFtOOCq+m6/XXX37sId39GjeVNRRBpfYoxPPAPCOcBsv/d3A3cH5IkmIAwDMv3eW0AoQUeOuEbO3r3z0q691vXpj9RAGosdO1y1hu9EuWFD3meh6v1/+MHlHTOmaN89dWrBNo6KFd3i7oHByJdvypSC+1m0yDXed+7seor97ncu/+rVhe/htGePuxML1a00FpmZriuyMdGKZ0DoD0zwe39N4MnfCwhbgWxgBtAwyH4+Bi7ze/+CVw2VDTwFVArx/cOALCCrUajhriYqd9/tqog2bozc3bQojh51VSz9+0ff3bZOnaJ3oQw1YC3UVX3lyqHL56uauegi9zO77TaXnqoT7pmyLdqAEM301xIkTQPevwU0UdV2wIfAlHw7EKkHtAVm+yXfDZwBnIWrTror2Jer6nhVzVDVjDp16kRRXBPK0KFu7YHJk92C7Nu3Q69e8f+e8uXhoYfg//4PJNhfTxBt2xZtfeVZs2DnzuCfaeBfq+fQodDl69cPrrzSrYGRmwuvv+6m3h40yE0j3bix27ZxY/d+0KDCl92Y0qJ8FHk2Af6z5TcAtvhnUFX/f8XngUcD9nElMFNVj/pts9V7eVhEJgN/jrbQpnCaNYMePWDChLyT5K9/ndgy+bRr506sx45BWlpe+rFjbn2AOnXg5JODb/vJJ+7kXbGiWxAmkEjwoNC4cejyZGbCW2/lvd+0ya0lAO7kbwHAJKNo7hDmAy1EpKmIVAQGALP8M3h3AD59gOUB+xgIvBJsGxERoB+wJLaim8IYNswtiPPPf7oFfkKdZEta27Zu9bbsbPjwQ3jwQbj4YqhZ0620VrcunHcePPkkrF2bt93ChdCnjwt2Tz8dfDGf4cMLLs5TpUr4RX5GjnSri/nzrS5nTNKKpl4JuAT4H6630UgvbRTQx3v9MLAU1wNpDnCG37ZNgM1AuYB9fgwsxgWCqcCJkcphjcpFd/iwq6+H+HQ3jZdvvilY79+unWsQnjLFTcvRrl3e5+3bu3aKOnVcr5uNG91+wi3mc/LJbttTT81f51/a5ksyJt6Isg1BNFQFaymUkZGhWVlZiS5GmffXv8Ljj8OXX8I55yS6NE5ODtx5p7sj6NIFfvlLqF69YL7Vq+GNN2DmTFf+2rVh7tzCLwuZmenumvzXmq5a1d1BBGuTaNwY1q0r3HcZkygiskBVMyLms4CQevbscSfUIUOib/QtjbZtc+UvSrVXkybB13ROT3dVRoGBwhqQTVkUbUCIpg3BJJkaNeD668t2MAA45ZSit4Fs2BA8fdeu2HsTZWa6AFOunHvOzCxa2YwpaRYQTEpr1Ch0+qBBrnooN9c9RwoGw4a5uw1V9zxsWF5QsGBhyoJoup0ak7RGjw7ehhCuB1IwI0fm3wfk75Xk/x2+YAFW/WRKF7tDMCktXgPNQlU9bdgQOVgYU1rYHYJJefEYaNaoUfDG6UaNwgcLY0oTu0MwJg5Gjw4+KG706PDtFLG2LVhbhClOdodgTBz47jBGjnRX/o0auWDgSw/WTnHJJbG1LQSOmbC2CBNvNg7BmBKQmVkwWIwcGbyaKdTgt1BjJmywnInEBqYZU8qVKxd80j0R19W1qPmN8bGBacaUcrG2LYTLb0w8WEAwJkFCNUT72hYCB7ldcknohutwQjVEWwO1KSCaGfBKy8NmOzXJJthMq+GW6Qw1m2u4/Qdb4S3YsqK28lvywmY7NaZsimdbQaiG6LQ0t/hQIGugTk7WhmBMGRXPtoJQg9+CBYNw+U1qsIBgTCkTbpBbrEIFEf9lSqPJb1KDBQRjSpl4za8EoYPLsGHxCzomeVhAMKYUimXq7Uj7CRZc/v3v+AUdkzysUdkYY5KcNSobY4yJSVQBQUR6isgKEVklIiOCfD5ERHaIyCLvMdTvs2N+6bP80puKyNcislJEXhWRivE5JGOMMYURMSCISBowDugFtAIGikirIFlfVdUO3mOCX/pBv/Q+fumPAk+pagtgN3Bj4Q/DmNRmo45NPERzh9AZWKWqa1T1CDAN6FuULxURAS4AZnhJU4B+RdmnMakq0nrO8fweCzrJLZqAUB/Y6Pd+k5cW6Lciki0iM0SkoV96ZRHJEpF5IuI76acDe1Q1J8I+EZFh3vZZO3bsiKK4xqSWkliis6SCjkmsaAKCBEkL7Jr0FtBEVdsBH+Ku+H0aea3bVwNjRKR5lPt0iarjVTVDVTPq1KkTRXGNSS0lsUSnrQudGqIJCJsA/yv+BsAW/wyqulNVD3tvnwc6+X22xXteA3wCnAn8CNQQEd+KbQX2aYyJTklMi23rQqeGaALCfKCF1yuoIjAAmOWfQUTq+b3tAyz30muKSCXvdW2gC7DMm31vDtDf2+Y64M2iHIgxqSqeU12EYmsxpIaIAcGr578VmI070U9X1aUiMkpEfL2GbhORpSLyHXAbMMRLbwlkeelzgEdUdZn32V3AnSKyCtemMDFeB2VMKonnVBcQvPG4JIJOrGUy8WcjlY0xx/kaj/3bC6pWdQEGCq4LXRJTXYQrk021ER1bU9kYE7NQ6yckcp2E0limssamrjDGxKw0Nh6XxjIlKwsIxpjj4t14HI+6f2vQLjkWEIwxx8Wz8Theg9kS3aCdSiwgGGOOi2ePpXgNZot3LyoTmgUEY0w+8VqcJ1zdf6iqpFDp8SqTCa985CzGGBO7Ro2C9w6qVSt/N1JfVdIXX8CUKQXTwQJASbE7BGNMsQhV9w/Bq5LGj7f5khLNAoIxpliEqvvftSt4/mPHgqdb99KSYwHBGFNsgtX9h+oumpYWPN26l5YcCwjGmCKJdaxBqKqkYcOse2miWUAwxhRaYcYahKpK+ve/rXtpotlcRsaYQrN5hsoGm8vIGFPsyuI8Q7GOgUglNg7BGFNoocYalNaG4MCptG0MRH52h2CMKbSyNs9QqOk0bAyEYwHBGFNopWGeoViqekJVZdkYCMeqjIwxRTJoUOKqVUJVAfnKFShUFVdaWvCgUFqrvoqL3SEYY8qsWGdUtTEQ4VlAMMaUWbH2crIxEOFFNQ5BRHoCTwNpwARVfSTg8yHA48BmL+kZVZ0gIh2AZ4GTgGPAaFV91dvmBaArsNfbZoiqLgpXDhuHYIzxZ+MgohO3cQgikgaMA3oBrYCBItIqSNZXVbWD95jgpR0ArlXV1kBPYIyI1PDb5i9+24QNBsYYE6is9XIq7aKpMuoMrFLVNap6BJgG9I1m56r6P1Vd6b3eAmwH6hS2sMYY46809HJKJtEEhPrARr/3m7y0QL8VkWwRmSEiDQM/FJHOQEVgtV/yaG+bp0SkUrAvF5FhIpIlIlk7duyIorjGmFRiq6nFTzQBQYKkBTY8vAU0UdV2wIfAlHw7EKkHvARcr6q5XvLdwBnAWUAt4K5gX66q41U1Q1Uz6tSxmwtjTHRsKorYRRMQNgH+V/wNgC3+GVR1p6oe9t4+D3TyfSYiJwHvAPeq6jy/bbaqcxiYjKuaMsaYIivMLKwmuoAwH2ghIk1FpCIwAJjln8G7A/DpAyz30isCM4EXVfX/gm0jIgL0A5YU9iCMMcZfrOMTjBNxpLKq5ojIrcBsXLfTSaq6VERGAVmqOgu4TUT6ADnALmCIt/mVwPlAutc1FfK6l2aKSB1cldQiYHj8DssYk8rK4iyspYGth2CMSTo2PiE/Ww/BGJOybHxC4VhAMMYknXiOT0il3kplfrbTo0ePsmnTJg4dOpToopgoVK5cmQYNGlChQoVEF8UkuXjMwhrrbKplXZlvQ1i7di3VqlUjPT0d12HJlFaqys6dO9m3bx9NmzZNdHGMiShZ2iJSpg3h0KFDFgzKCBEhPT3d7uZMmZFqvZXKfEAALBiUIfa7MmVJqAVyknXhnKQICMYYUxxSrbdSygWEePcY2LlzJx06dKBDhw7UrVuX+vXrH39/5MiRqPZx/fXXs2LFirB5xo0bR2acujece+65LFpks40bE0mqzaZa5nsZxaI4egykp6cfP7k+8MADnHjiifz5z3/Ol0dVUVXKlQsefydPnhzxe2655ZbCFdAYUySJXDO6pKXUHUJJzm+yatUq2rRpw/Dhw+nYsSNbt25l2LBhZGRk0Lp1a0aNGnU8r++KPScnhxo1ajBixAjat2/POeecw/bt2wG49957GTNmzPH8I0aMoHPnzvziF7/gyy+/BODnn3/mt7/9Le3bt2fgwIFkZGREvBOYOnUqbdu2pU2bNtxzzz0A5OTkcM011xxPHzt2LABPPfUUrVq1on379gwePDjuPzNjTGKl1B1CSfcYWLZsGZMnT+a5554D4JFHHqFWrVrk5OTQvXt3+vfvT6tW+Ref27t3L127duWRRx7hzjvvZNKkSYwYMaLAvlWVb775hlmzZjFq1Cjef/99/vWvf1G3bl1ee+01vvvuOzp27Bi2fJs2beLee+8lKyuL6tWr06NHD95++23q1KnDjz/+yOLFiwHYs2cPAI899hjr16+nYsWKx9OMMckjpe4QSrrHQPPmzTnrrLOOv3/llVfo2LEjHTt2ZPny5SxbtqzANlWqVKFXr14AdOrUiXUhOjtffvnlBfLMnTuXAQMGANC+fXtat24dtnxff/01F1xwAbVr16ZChQpcffXVfPbZZ5x22mmsWLGC22+/ndmzZ1O9enUAWrduzeDBg8nMzLSBZcaEUVZHN6dUQCjpHgMnnHDC8dcrV67k6aef5uOPPyY7O5uePXsG7Y9fsWLF46/T0tLIyckJuu9KlSoVyBPrIMNQ+dPT08nOzubcc89l7Nix3HTTTQDMnj2b4cOH880335CRkcGxY8di+j5jUkFZXoshpQJCInsM/PTTT1SrVo2TTjqJrVu3Mnv27Lh/x7nnnsv06dMBWLx4cdA7EH9nn302c+bMYefOneTk5DBt2jS6du3Kjh07UFWuuOIKHnzwQRYuXMixY8fYtGkTF1xwAY8//jg7duzgQGCDjDGmTK/FkFJtCJC4HgMdO3akVatWtGnThmbNmtGlS5e4f8cf/vAHrr32Wtq1a0fHjh1p06bN8eqeYBo0aMCoUaPo1q0bqkrv3r259NJLWbhwITfeeCOqiojw6KOPkpOTw9VXX82+ffvIzc3lrrvuolq1anE/BmPKurI8urnMz2W0fPlyWrZsmaASlS45OTnk5ORQuXJlVq5cycUXX8zKlSspX750xX37nZlkVhrnP4p2LqPSdaYwRbJ//34uvPBCcnJyUFX+85//lLpgYEyyGz06/3gnKDujm+1skURq1KjBggULEl0MY1JGZqZrG9iwwfVWHD06r0o6VHppZgHBGGMKIdLMB2UhAARKqV5GxhgTL2W5N1EoUQUEEekpIitEZJWIFBg2KyJDRGSHiCzyHkP9PrtORFZ6j+v80juJyGJvn2PF5kU2xpQh8exNVFoGskUMCCKSBowDegGtgIEi0ipI1ldVtYP3mOBtWwu4H/gl0Bm4X0RqevmfBYYBLbxHz6IejDHGlJR4zXxQmgayRXOH0BlYpaprVPUIMA3oG+X+fw18oKq7VHU38AHQU0TqASep6lfq+r2+CPQrRPkTrlu3bgUGmY0ZM4bf//73Ybc78cQTAdiyZQv9+/cPue/AbraBxowZk2+A2CWXXBKXeYYeeOABnnjiiSLvx5hkFa+ZD0pT1VM0AaE+sNHv/SYvLdBvRSRbRGaISMMI29b3XkfaJyIyTESyRCRrx44dURS3ZA0cOJBp06blS5s2bRoDBw6MavtTTz2VGTNmFPr7AwPCu+++S40aNQq9P2NMdOI180FpGsgWTS+jYHX7gaPZ3gJeUdXDIjIcmAJcEGbbaPbpElXHA+PBDUwLV9A//hHive5Lhw7gzTodVP/+/bn33ns5fPgwlSpVYt26dWzZsoVzzz2X/fv307dvX3bv3s3Ro0d56KGH6Ns3/83VunXruOyyy1iyZAkHDx7k+uuvZ9myZbRs2ZKDBw8ez3fzzTczf/58Dh48SP/+/XnwwQcZO3YsW7ZsoXv37tSuXZs5c+bQpEkTsrKyqF27Nk8++SSTJk0CYOjQofzxj39k3bp19OrVi3PPPZcvv/yS+vXr8+abb1KlSpWQx7ho0SKGDx/OgQMHaN68OZMmTaJmzZqMHTuW5557jvLly9OqVSumTZvGp59+yu233w645TI/++wzG9FsklY8ehM1ahR8IFsilumM5g5hE9DQ730DYIt/BlXdqaqHvbfPA50ibLvJex1yn2VFeno6nTt35v333wfc3cFVV12FiFC5cmVmzpzJwoULmTNnDn/605/CTkD37LPPUrVqVbKzsxk5cmS+MQWjR48mKyuL7OxsPv30U7Kzs7nttts49dRTmTNnDnPmzMm3rwULFjB58mS+/vpr5s2bx/PPP8+3334LuIn2brnlFpYuXUqNGjV47bXXwh7jtddey6OPPkp2djZt27blwQcfBNx03t9++y3Z2dnHp/h+4oknGDduHIsWLeLzzz8PG2iMSTXBGo9L0zKd0dwhzAdaiEhTYDMwALjaP4OI1FPVrd7bPsBy7/Vs4B9+DckXA3er6i4R2SciZwNfA9cC/yraoYS/ki9Ovmqjvn37Mm3atONX5arKPffcw2effUa5cuXYvHkz27Zto27dukH389lnn3HbbbcB0K5dO9q1a3f8s+nTpzN+/HhycnLYunUry5Yty/d5oLlz5/Kb3/zm+Iyrl19+OZ9//jl9+vShadOmdOjQAQg/xTa49Rn27NlD165dAbjuuuu44oorjpdx0KBB9OvXj379XBNQly5duPPOOxk0aBCXX345DRo0CLlvY1JJqHEL48e7R2kYyBbxDkFVc4BbcSf35cB0VV0qIqNEpI+X7TYRWSoi3wG3AUO8bXcBf8cFlfnAKC8N4GZgArAKWA28F7ejKmH9+vXjo48+YuHChRw8ePD4wjSZmZns2LGDBQsWsGjRIk455ZSgU177C9b7du3atTzxxBN89NFHZGdnc+mll0bcT7g7Ed/U2RB+iu1I3nnnHW655RYWLFhAp06dyMnJYcSIEUyYMIGDBw9y9tln8/333xdq38Ykm3CNx4MGuXmOcnPdsy8YlHR31KjGIajqu6p6uqo2V9XRXtrfVHWW9/puVW2tqu1Vtbuqfu+37SRVPc17TPZLz1LVNt4+b9WyNMtegBNPPJFu3bpxww035GtM3rt3LyeffDIVKlRgzpw5rA9WUejn/PPPJ9P7jS9ZsoTs7GzATZ19wgknUL16dbZt28Z77+XFzmrVqrFv376g+3rjjTc4cOAAP//8MzNnzuS8886L+diqV69OzZo1+fzzzwF46aWX6Nq1K7m5uWzcuJHu3bvz2GOPsWfPHvbv38/q1atp27Ytd911FxkZGRYQjPHE2niciO6oNnVFnAwcOJDLL788X4+jQYMG0bt3bzIyMujQoQNnnHFG2H3cfPPNXH/99bRr144OHTrQuXNnwK1+duaZZ9K6desCU2cPGzaMXr16Ua9evXztCB07dmTIkCHH9zF06FDOPPPMsNVDoUyZMuV4o3KzZs2YPHkyx44dY/DgwezduxdV5Y477qBGjRrcd999zJkzh7S0NFq1anV89TdjUl2sjceR7iiKg01/bUqc/c5MKgpsQwDXeByqq2q5cu7OIJCIq1qKRbTTX9tcRsYYUwJiHbdQ0mvAgwUEY4wpMaEaj4NJRHfUpAgIZanaK9XZ78qY6CRiDfgy36hcuXJldu7cSXp6etAum6b0UFV27txJ5cqVE10UY8qEkl5XocwHhAYNGrBp0yZK4zxHpqDKlSvbYDVjSqkyHxAqVKhA06ZNE10MY4wp85KiDcEYY0zRWUAwxhgDWEAwxhjjKVMjlUVkBxB+QqDQagM/xrE4ZYUdd2pJ1eOG1D32aI67sarWibSjMhUQikJEsqIZup1s7LhTS6oeN6TuscfzuK3KyBhjDGABwRhjjCeVAsL4RBcgQey4U0uqHjek7rHH7bhTpg3BGGNMeKl0h2CMMSYMCwjGGGOAFAkIItJTRFaIyCoRGZHo8hQXEZkkIttFZIlfWi0R+UBEVnrPNRNZxuIgIg1FZI6ILBeRpSJyu5ee1McuIpVF5BsR+c477ge99KYi8rV33K+KSMVEl7U4iEiaiHwrIm9775P+uEVknYgsFpFFIpLlpcXt7zzpA4KIpAHjgF5AK2CgiLRKbKmKzQtAz4C0EcBHqtoC+Mh7n2xygD+pakvgbOAW73ec7Md+GLhAVdsDHYCeInI28CjwlHfcu4EbE1jG4nQ7sNzvfaocd3dV7eA39iBuf+dJHxCAzsAqVV2jqkeAaUDfBJepWKjqZ8CugOS+wBTv9RSgX4kWqgSo6lZVXei93oc7SdQnyY9dnf3e2wreQ4ELgBleetIdN4CINAAuBSZ474UUOO4Q4vZ3ngoBoT6w0e/9Ji8tVZyiqlvBnTiBkxNcnmIlIk2AM4GvSYFj96pNFgHbgQ+A1cAeVc3xsiTr3/sY4K+Ab7n5dFLjuBX4r4gsEJFhXlrc/s7L/HoIUQi2jJr1tU1CInIi8BrwR1X9KRVW0FPVY0AHEakBzARaBstWsqUqXiJyGbBdVReISDdfcpCsSXXcni6qukVETgY+EJHv47nzVLhD2AQ09HvfANiSoLIkwjYRqQfgPW9PcHmKhYhUwAWDTFV93UtOiWMHUNU9wCe4NpQaIuK72EvGv/cuQB8RWYerAr4Ad8eQ7MeNqm7xnrfjLgA6E8e/81QICPOBFl4PhIrAAGBWgstUkmYB13mvrwPeTGBZioVXfzwRWK6qT/p9lNTHLiJ1vDsDRKQK0APXfjIH6O9lS7rjVtW7VbWBqjbB/T9/rKqDSPLjFpETRKSa7zVwMbCEOP6dp8RIZRG5BHcFkQZMUtXRCS5SsRCRV4BuuOlwtwH3A28A04FGwAbgClUNbHgu00TkXOBzYDF5dcr34NoRkvbYRaQdrhExDXdxN11VR4lIM9yVcy3gW2Cwqh5OXEmLj1dl9GdVvSzZj9s7vpne2/LAy6o6WkTSidPfeUoEBGOMMZGlQpWRMcaYKFhAMMYYA1hAMMYY47GAYIwxBrCAYIwxxmMBwRhjDGABwRhjjOf/AV/DVpdrm7AXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accuracy = pain_train.history['accuracy']\n",
    "val_accuracy = pain_train.history['val_accuracy']\n",
    "loss = pain_train.history['loss']\n",
    "val_loss = pain_train.history['val_loss']\n",
    "epoch = range(len(accuracy))\n",
    "plt.plot(epoch, accuracy, 'bo', label='Training accuracy')\n",
    "plt.plot(epoch, val_accuracy, 'b', label='Validation accuracy')\n",
    "plt.title('Training and validation accuracy')\n",
    "plt.legend()\n",
    "plt.figure()\n",
    "plt.plot(epoch, loss, 'bo', label='Training loss')\n",
    "plt.plot(epoch, val_loss, 'b', label='Validation loss')\n",
    "plt.title('Training and validation loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_classes = pain_model.predict(test_X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicted_classes = np.argmax(np.round(predicted_classes), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((286,), (286,))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_classes.shape, test_Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 193 correct labels\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "correct = np.where(predicted_classes==test_Y)[0]\n",
    "print(\"Found %d correct labels\" % len(correct))\n",
    "for i, correct, in enumerate(correct[:9]):\n",
    "    plt.subplot(3, 3, i+1)\n",
    "    plt.pcolormesh(test_X[correct].reshape(ydim, xdim), cmap='jet')\n",
    "    plt.title(\"Predicted {}, Class {}\".format(predicted_classes[correct], test_Y[correct]))\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 93 incorrect labels\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "incorrect = np.where(predicted_classes!=test_Y)[0]\n",
    "print(\"Found %d incorrect labels\" % len(incorrect))\n",
    "for i, incorrect, in enumerate(incorrect[:9]):\n",
    "    plt.subplot(3, 3, i+1)\n",
    "    plt.pcolormesh(test_X[incorrect].reshape(ydim, xdim), cmap='jet')\n",
    "    plt.title(\"Predicted {}, Class {}\".format(predicted_classes[incorrect], test_Y[incorrect]))\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "    Class 0       0.67      0.94      0.78       176\n",
      "    Class 1       0.72      0.25      0.38       110\n",
      "\n",
      "avg / total       0.69      0.67      0.62       286\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "target_names = [\"Class {}\".format(i) for i in range(num_classes)]\n",
    "print(classification_report(test_Y, predicted_classes, target_names=target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}