IMG_net_2-autoKeras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try knocking out some frequency bands\n",
    "## maybe Merged456 will work better?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make sure you don't hog all the video memory\n",
    "import tensorflow as tf\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "sess = tf.Session(config=config)\n",
    "from keras import backend as K\n",
    "K.set_session(sess)\n",
    "###################################\n",
    "\n",
    "import scipy.io as sio\n",
    "# from URL_download_2 import gdown\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import preprocessing\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import autokeras as ak\n",
    "from keras.datasets import mnist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "144\n",
      "Initializing search.\n",
      "Initialization finished.\n",
      "Training model  0\n"
     ]
    }
   ],
   "source": [
    "(X_train, Y_train), (X_test, Y_test) = mnist.load_data()\n",
    "X_train = X_train.reshape(X_train.shape + (1,))\n",
    "X_test = X_test.reshape(X_test.shape + (1,))\n",
    "\n",
    "from sys import getsizeof\n",
    "print(getsizeof(X_train))\n",
    "clf = ak.ImageClassifier(verbose=True, augment=False)\n",
    "clf.fit(X_train, Y_train)\n",
    "results = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def download_data_from_GDRIVE():\n",
    "    # imports the custom GDrive downloader library:\n",
    "    # provide file ID:\n",
    "    file_id = '1YPr_K8xSuKghONozyjnVThe47EsiDEZj'\n",
    "\n",
    "    # instantiate class (create a class instance):\n",
    "    G = gdown()\n",
    "\n",
    "    # Download data from Google Drive as necessary:\n",
    "    G.download_file(file_id)\n",
    "\n",
    "def group_frequency_bands(X):\n",
    "    X_ = np.zeros([X.shape[0],X.shape[1],X.shape[2], 2])\n",
    "#     X_[:,:,:,0] = np.mean(X[:,:,:,3:6],axis=3)\n",
    "#     X_[:,:,:,1] = np.mean(X[:,:,:,7:11],axis=3)\n",
    "#     X_[:,:,:,2] = np.mean(X[:,:,:,12:29],axis=3)\n",
    "    \n",
    "#     processing frequencies: 0 16\n",
    "#     processing frequencies: 16 32\n",
    "#     processing frequencies: 32 49\n",
    "#     processing frequencies: 49 65\n",
    "#     X_[:,:,:,0] = np.mean(X[:,:,:,0:16],axis=3)\n",
    "#     X_[:,:,:,1] = np.mean(X[:,:,:,16:32],axis=3)\n",
    "    X_[:,:,:,0] = np.mean(X[:,:,:,32:49],axis=3)\n",
    "    X_[:,:,:,1] = np.mean(X[:,:,:,49:65],axis=3)\n",
    "    \n",
    "    X_.shape\n",
    "    # subplot(1,3,1)\n",
    "    # imagesc(squeeze(mean(X(1,:,:,4:7), 4)))\n",
    "    # title('Theta')\n",
    "\n",
    "    # subplot(1,3,2)\n",
    "    # imagesc(squeeze(mean(X(1,:,:,8:12), 4)))\n",
    "    # title('Alpha')\n",
    "\n",
    "    # subplot(1,3,3)\n",
    "    # imagesc(squeeze(mean(X(1,:,:,13:30), 4)))\n",
    "    # title('Beta')\n",
    "    return X_\n",
    "\n",
    "def im_resize(X_, height, width):\n",
    "    \n",
    "    if width>0 and height>0:\n",
    "        X__ = np.zeros([X_.shape[0], height, width, X_.shape[3]])\n",
    "        for i in range(X_.shape[0]):\n",
    "                for j in range(X_.shape[3]):\n",
    "                    im = Image.fromarray(X_[i,:,:,j])\n",
    "                    X__[i,:,:,j] = im.resize((height, width), Image.ANTIALIAS)\n",
    "        print (X__.shape)\n",
    "\n",
    "        plt.subplot(121)\n",
    "        plt.imshow(np.array(X_[200,:,:,1]))\n",
    "        plt.subplot(122)\n",
    "        plt.imshow(np.array(X__[200,:,:,1]))\n",
    "    else:\n",
    "        print('IMAGES NOT RESIZED')\n",
    "    return X__\n",
    "\n",
    "def downsample_frequency_domain(n_bins):\n",
    "    print('Downsampling the frequency domain from', X.shape[3],'to', n_bins)\n",
    "    xx = np.zeros([X.shape[0],X.shape[1],X.shape[2],n_bins])\n",
    "    print(xx.shape)\n",
    "    a = np.round(np.linspace(0,X.shape[3],n_bins+1))\n",
    "    a = a.astype('uint8')\n",
    "    print(a)\n",
    "    for i in range(n_bins):\n",
    "        xx[:,:,:,i] = np.mean(X[:,:,:,a[i]:a[i+1]], axis=3)   \n",
    "        print('processing frequencies:', a[i], a[i+1])\n",
    "    print(xx.shape)\n",
    "    return xx\n",
    "\n",
    "def parse_array(in_array):\n",
    "    new_array = []\n",
    "    for i in range(len(in_array)):\n",
    "        if str(in_array[i]).find(\"left\")>0:\n",
    "            new_array.append(\"left\")\n",
    "        if str(in_array[i]).find(\"right\")>0:\n",
    "            new_array.append(\"right\")\n",
    "        if (str(in_array[i]).find(\"left\")<0 and \n",
    "            str(in_array[i]).find(\"right\")<0):\n",
    "            print(i)\n",
    "            new_array.append(\"unknown\")\n",
    "    return new_array\n",
    "\n",
    "def find_right(Q, label):\n",
    "    idx = []\n",
    "    for i in range(len(Q)):\n",
    "        if Q[i] == label:\n",
    "            idx.append(i)\n",
    "    return(idx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = 'Merged456-197-289_ICA(-eyes)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat' #0.77\n",
    "# file = 'Merged456-197-289_ICA(-eyes)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=1, win=2, TOPO, .mat' #2# #0.77\n",
    "# file = 'Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat' #1# #0.76 #0.79\n",
    "# file = 'EEG_ICA(-123_ICs)+proc_AUD_101_192_Merged456.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat'\n",
    "\n",
    "\n",
    "\n",
    "# file = 'Merged123_1_64_ICA(-eyes)AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=1, win=1, TOPO, .mat'\n",
    "# file = 'Merged123_75_134_ICA(-eyes)AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=1, win=2, TOPO, .mat'\n",
    "# file = 'Merged123-143-202_ICA(-Eyes)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=1, win=2, TOPO, .mat' #0.56\n",
    "\n",
    "SHUFFLE = False\n",
    "BATCH_SIZE = 20\n",
    "TEST_TRAIN = 0.2\n",
    "DS_FREQ = 1 #(0 1 2)\n",
    "n_bins = 8\n",
    "RESIZE = 1\n",
    "width = 15\n",
    "height = 15\n",
    "SENSITIVITY = 0\n",
    "knockouts = [0, 1, 2]\n",
    "\n",
    "path = '/home/amplifier/home/DATASETS/' + file\n",
    "mat_contents = sio.loadmat(path)\n",
    "X = mat_contents['X']\n",
    "Y = mat_contents['Z']\n",
    "# Z = mat_contents['Z']\n",
    "Q = parse_array(mat_contents['Q'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/amplifier/home/DATASETS/Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat\n",
      "Concatenating datasets...\n"
     ]
    }
   ],
   "source": [
    "# IN THIS IS CELL WE'RE CONCATENATING THE DATASETS:\n",
    "SHUFFLE = False\n",
    "BATCH_SIZE = 20\n",
    "TEST_TRAIN = 0.2\n",
    "DS_FREQ = 1 #(0 1 2)\n",
    "n_bins = 8\n",
    "RESIZE = 1\n",
    "width = 15\n",
    "height = 15\n",
    "SENSITIVITY = 0\n",
    "knockouts = [0, 1, 2]\n",
    "\n",
    "X = list()\n",
    "Y = list()\n",
    "Q = list()\n",
    "# 'EEG_ICA(-123_ICs)+proc_AUD_101_192_Merged456.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat'\n",
    "# 'Merged456-197-289_ICA(-eyes)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat',\n",
    "# 'EEG_ICA(-123_ICs)+proc_AUD_101_192_Merged456.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat'\n",
    "files = list(['Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS2=64Hz, FIR=1-30Hz, centnorm=1, step=2, win=2, TOPO, .mat'])\n",
    "\n",
    "for file_name in files:\n",
    "    path = '/home/amplifier/home/DATASETS/' + file_name\n",
    "    print (path)\n",
    "    mat_contents = sio.loadmat(path)\n",
    "    X.append(mat_contents['X'])\n",
    "    Y.append(mat_contents['Z'])\n",
    "    Q.append(parse_array(mat_contents['Q']))\n",
    "    \n",
    "print('Concatenating datasets...')\n",
    "X = np.concatenate(X, axis=0)\n",
    "Y = np.concatenate(Y, axis=0)\n",
    "Q = np.concatenate(Q,axis=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downsampling the frequency domain from 65 to 8\n",
      "(450, 67, 67, 8)\n",
      "[ 0  8 16 24 32 41 49 57 65]\n",
      "processing frequencies: 0 8\n",
      "processing frequencies: 8 16\n",
      "processing frequencies: 16 24\n",
      "processing frequencies: 24 32\n",
      "processing frequencies: 32 41\n",
      "processing frequencies: 41 49\n",
      "processing frequencies: 49 57\n",
      "processing frequencies: 57 65\n",
      "(450, 67, 67, 8)\n",
      "Downsampled Frequency Domain shape (450, 67, 67, 8)\n",
      "(450, 15, 15, 8)\n",
      "Resized shape (450, 15, 15, 8)\n",
      "+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n",
      "Original data type: float64\n",
      "y_test.shape before (90, 1)\n",
      "y_test.shape after (90, 2)\n",
      "Normalized data type: float16\n",
      "Prepped test input shape (90, 15, 15, 8) Noralized MEAN: 0.09375 min -0.02908 max 1.109\n",
      "Prepped train input shape (360, 15, 15, 8) Normalized MEAN: 0.0941 min -0.0571 max 2.4\n",
      "Prepped test labels shape (90, 2) Normalized MEAN: 0.5 min 0.0 max 1.0\n",
      "Prepped train labels shape (360, 2) Normalized MEAN: 0.5 min 0.0 max 1.0\n",
      "EEG in dataset: (450, 15, 15, 8)\n",
      "Labels in dataset: (450, 1)\n",
      "Length of textual labels: 450\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "if DS_FREQ==1:\n",
    "    X = downsample_frequency_domain(n_bins)\n",
    "    print('Downsampled Frequency Domain shape', X.shape)\n",
    "if DS_FREQ==2:\n",
    "    X = group_frequency_bands(X)\n",
    "    print('Grouped Frequency Bands shape', X.shape)\n",
    "if RESIZE==1:\n",
    "    X = im_resize(X, height, width)\n",
    "    print('Resized shape', X.shape)\n",
    "\n",
    "if SENSITIVITY==1:\n",
    "    X = np.delete(X, knockouts, axis=3)\n",
    "    print('sens', X.shape)\n",
    "if X.shape[1]<X.shape[2]:\n",
    "    X = np.transpose(X,[0,2,1])\n",
    "\n",
    "if Y.shape[1] > Y.shape[0]:\n",
    "    Y = Y.T\n",
    "    \n",
    "# verify that the model REALLY finds a mapping between the input and the labels. If we get\n",
    "# our accuracy by chance, then we should get the same accuracy on a permuted dataset:\n",
    "# Y = np.random.permutation(Y)\n",
    "\n",
    "print('+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++')\n",
    "x_train, x_test, y_train, y_test, q_train, q_test = train_test_split(X, Y, Q, test_size=TEST_TRAIN, shuffle=SHUFFLE)\n",
    "print('Original data type:', x_train.dtype)\n",
    "\n",
    "\n",
    "# convert to float64 for numerical stability:\n",
    "x_train = x_train.astype('float64')\n",
    "y_train = y_train.astype('float64')\n",
    "x_test = x_test.astype('float64')\n",
    "y_test = y_test.astype('float64')\n",
    "print('y_test.shape before', y_test.shape)\n",
    "\n",
    "\n",
    "# # normalize to unit variance and zero mean:\n",
    "# for i in range(x_train.shape[0]):\n",
    "#     for j in range(x_train.shape[3]):\n",
    "#         x_train[i,:,:,j] = preprocessing.scale(x_train[i,:,:,j], axis=1)\n",
    "#         x_train[i,:,:,j] = preprocessing.minmax_scale(x_train[i,:,:,j], axis=1)\n",
    "# #     y_train[i,:] = preprocessing.scale(y_train[i,:], axis=0)\n",
    "# #     y_train[i,:] = preprocessing.minmax_scale(y_train[i,:], axis=0)\n",
    "\n",
    "# for i in range(x_test.shape[0]):\n",
    "#     for j in range(x_train.shape[3]):\n",
    "#         x_test[i,:,:,j] = preprocessing.scale(x_test[i,:,:,j], axis=1)\n",
    "#         x_test[i,:,:,j] = preprocessing.minmax_scale(x_test[i,:,:,j], axis=1)\n",
    "# #     y_test[i,:] = preprocessing.scale(y_test[i,:], axis=0)\n",
    "# #     y_test[i,:] = preprocessing.minmax_scale(y_test[i,:], axis=0)\n",
    "    \n",
    "\n",
    "# one hot encode the labels:\n",
    "onehot_encoder = preprocessing.OneHotEncoder(sparse=False)\n",
    "y_train = onehot_encoder.fit_transform(y_train)\n",
    "y_test = onehot_encoder.fit_transform(y_test)\n",
    "print('y_test.shape after', y_test.shape)\n",
    "\n",
    "# convert to float16 to save space:\n",
    "x_train = x_train.astype('float16')\n",
    "y_train = y_train.astype('float16')\n",
    "x_test = x_test.astype('float16')\n",
    "y_test = y_test.astype('float16')\n",
    "print('Normalized data type:', x_train.dtype)\n",
    "\n",
    "leng = X.shape[1] # if you work in the FD, this is the height of the sample time-frequency image, othewise EEG channels\n",
    "chan = X.shape[2] # if you work in the FD, this is the width of the sample time-frequency image, othewise time samples of EEG signal\n",
    "\n",
    "if len(X.shape)==3:\n",
    "    streams = 1 # this is EEG channels if you work with frequency domain, in the TD streams = 1\n",
    "if len(X.shape)==4:\n",
    "    streams = X.shape[3] # this is EEG channels if you work with frequency domain, in the TD streams = 1\n",
    "\n",
    "\n",
    "print('Prepped test input shape', x_test.shape, \"Noralized MEAN:\", np.mean(x_test), \"min\", np.min(x_test),\"max\", np.max(x_test))\n",
    "print('Prepped train input shape', x_train.shape, \"Normalized MEAN:\", np.mean(x_train), \"min\", np.min(x_train),\"max\", np.max(x_train))\n",
    "\n",
    "print('Prepped test labels shape', y_test.shape, \"Normalized MEAN:\", np.mean(y_test), \"min\", np.min(y_test),\"max\", np.max(y_test))\n",
    "print('Prepped train labels shape', y_train.shape, \"Normalized MEAN:\", np.mean(y_train), \"min\", np.min(y_train),\"max\", np.max(y_train))\n",
    "\n",
    "# print('Window length', winsize)\n",
    "# print('Step size:', stepsize)\n",
    "# print('Trial length:', trial_len)\n",
    "\n",
    "print('EEG in dataset:', X.shape)\n",
    "print('Labels in dataset:', Y.shape)\n",
    "print('Length of textual labels:', len(Q))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(450, 15, 15, 8)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'25-30 Hz')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(X.shape)\n",
    "\n",
    "f, axs = plt.subplots(1,4,figsize=(18,18))\n",
    "\n",
    "axs[0].imshow(X[100,:,:,2])\n",
    "axs[0].set_title('1-5 Hz')\n",
    "axs[1].imshow(X[300,:,:,4])\n",
    "axs[1].set_title('5-10 Hz')\n",
    "axs[2].imshow(X[300,:,:,6])\n",
    "axs[2].set_title('15-20 Hz')\n",
    "axs[3].imshow(X[300,:,:,7])\n",
    "axs[3].set_title('25-30 Hz')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 15, 15, 8)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 15, 15, 30)        2190      \n",
      "_________________________________________________________________\n",
      "batch_normalization_15 (Batc (None, 15, 15, 30)        120       \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 15, 15, 30)        8130      \n",
      "_________________________________________________________________\n",
      "batch_normalization_16 (Batc (None, 15, 15, 30)        120       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 8, 8, 30)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_13 (Conv2D)           (None, 8, 8, 60)          16260     \n",
      "_________________________________________________________________\n",
      "batch_normalization_17 (Batc (None, 8, 8, 60)          240       \n",
      "_________________________________________________________________\n",
      "conv2d_14 (Conv2D)           (None, 8, 8, 60)          32460     \n",
      "_________________________________________________________________\n",
      "batch_normalization_18 (Batc (None, 8, 8, 60)          240       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 4, 4, 60)          0         \n",
      "_________________________________________________________________\n",
      "conv2d_15 (Conv2D)           (None, 4, 4, 120)         28920     \n",
      "_________________________________________________________________\n",
      "batch_normalization_19 (Batc (None, 4, 4, 120)         480       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 2, 2, 120)         0         \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (None, 480)               0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 480)               0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 100)               48100     \n",
      "_________________________________________________________________\n",
      "batch_normalization_20 (Batc (None, 100)               400       \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 100)               0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 10)                1010      \n",
      "_________________________________________________________________\n",
      "batch_normalization_21 (Batc (None, 10)                40        \n",
      "_________________________________________________________________\n",
      "dense_9 (Dense)              (None, 2)                 22        \n",
      "=================================================================\n",
      "Total params: 138,732\n",
      "Trainable params: 137,912\n",
      "Non-trainable params: 820\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "dilation_rate = 1\n",
    "\n",
    "def activation():\n",
    "    return ELU(alpha=1.0) # Activation('relu') #\n",
    "\n",
    "from keras.layers import Input, Dense, Conv2D, MaxPooling2D, Flatten, BatchNormalization, Dropout, ELU\n",
    "from keras.models import Model\n",
    "from keras.callbacks import TensorBoard, EarlyStopping\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "sh = x_train.shape\n",
    "\n",
    "\n",
    "input_img = Input(shape=(sh[1], sh[2], sh[3]))  # adapt this if using `channels_first` image data format\n",
    "\n",
    "x = Conv2D(30, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(input_img)\n",
    "x = BatchNormalization()(x)\n",
    "x = Conv2D(30, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "\n",
    "x = Conv2D(60, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Conv2D(60, (3, 3), activation='relu', padding='same', dilation_rate=dilation_rate)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "\n",
    "x = Conv2D(120, (2, 2), activation='relu', padding='same', dilation_rate=dilation_rate)(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = MaxPooling2D((2, 2), padding='same')(x)\n",
    "\n",
    "x = Flatten()(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(100, activation='relu')(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dropout(0.5)(x)\n",
    "x = Dense(10, activation='relu')(x)\n",
    "x = BatchNormalization()(x)\n",
    "x = Dense(2, activation='softmax')(x)\n",
    "\n",
    "model = Model(input_img, x)\n",
    "optimizer=Adam(0.00006)\n",
    "model.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 360 samples, validate on 90 samples\n",
      "Epoch 1/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  5e-05\n",
      "360/360 [==============================] - 3s 9ms/step - loss: 0.7882 - acc: 0.5389 - val_loss: 0.7030 - val_acc: 0.5111\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.51111, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  5e-05 \n",
      "\n",
      "\n",
      "Epoch 2/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  5e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7222 - acc: 0.5639 - val_loss: 0.7028 - val_acc: 0.5222\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.51111 to 0.52222, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  5e-05 \n",
      "\n",
      "\n",
      "Epoch 3/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  5e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7659 - acc: 0.5306 - val_loss: 0.6969 - val_acc: 0.5111\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.52222\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  5e-05 \n",
      "\n",
      "\n",
      "Epoch 4/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  5e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7768 - acc: 0.5194 - val_loss: 0.6900 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00004: val_acc improved from 0.52222 to 0.54444, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  5e-05 \n",
      "\n",
      "\n",
      "Epoch 5/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  5e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7554 - acc: 0.5361 - val_loss: 0.6875 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00005: val_acc improved from 0.54444 to 0.55556, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  5e-05 \n",
      "\n",
      "\n",
      "Epoch 6/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  5e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7456 - acc: 0.5500 - val_loss: 0.6781 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.55556\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  5e-05 \n",
      "\n",
      "\n",
      "Epoch 7/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  5e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7413 - acc: 0.5250 - val_loss: 0.6797 - val_acc: 0.5667\n",
      "\n",
      "Epoch 00007: val_acc improved from 0.55556 to 0.56667, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  5e-05 \n",
      "\n",
      "\n",
      "Epoch 8/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  5e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7671 - acc: 0.5250 - val_loss: 0.6786 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.56667\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  5e-05 \n",
      "\n",
      "\n",
      "Epoch 9/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  5e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7181 - acc: 0.5750 - val_loss: 0.6793 - val_acc: 0.5556\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.56667\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH END =  5e-05 \n",
      "\n",
      "\n",
      "Epoch 10/300\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH START =  5e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7371 - acc: 0.5667 - val_loss: 0.6964 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.56667\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH END =  5e-05 \n",
      "\n",
      "\n",
      "Epoch 11/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.975e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7194 - acc: 0.5667 - val_loss: 0.6894 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.56667\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.975e-05 \n",
      "\n",
      "\n",
      "Epoch 12/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.975e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7626 - acc: 0.5028 - val_loss: 0.6867 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.56667\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.975e-05 \n",
      "\n",
      "\n",
      "Epoch 13/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.975e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7248 - acc: 0.5722 - val_loss: 0.6868 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.56667\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.975e-05 \n",
      "\n",
      "\n",
      "Epoch 14/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.975e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7128 - acc: 0.5556 - val_loss: 0.6745 - val_acc: 0.5889\n",
      "\n",
      "Epoch 00014: val_acc improved from 0.56667 to 0.58889, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.975e-05 \n",
      "\n",
      "\n",
      "Epoch 15/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.975e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7421 - acc: 0.5250 - val_loss: 0.6721 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.58889\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.975e-05 \n",
      "\n",
      "\n",
      "Epoch 16/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.975e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7420 - acc: 0.5806 - val_loss: 0.6833 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00016: val_acc did not improve from 0.58889\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.975e-05 \n",
      "\n",
      "\n",
      "Epoch 17/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.975e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7273 - acc: 0.5861 - val_loss: 0.6888 - val_acc: 0.5667\n",
      "\n",
      "Epoch 00017: val_acc did not improve from 0.58889\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.975e-05 \n",
      "\n",
      "\n",
      "Epoch 18/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.975e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7214 - acc: 0.5611 - val_loss: 0.6805 - val_acc: 0.5333\n",
      "\n",
      "Epoch 00018: val_acc did not improve from 0.58889\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.975e-05 \n",
      "\n",
      "\n",
      "Epoch 19/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.975e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7109 - acc: 0.5500 - val_loss: 0.6730 - val_acc: 0.5444\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.58889\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.975e-05 \n",
      "\n",
      "\n",
      "Epoch 20/300\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.975e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6714 - acc: 0.6000 - val_loss: 0.6676 - val_acc: 0.5667\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.58889\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.975e-05 \n",
      "\n",
      "\n",
      "Epoch 21/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.9253744e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6852 - acc: 0.5889 - val_loss: 0.6642 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.58889\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.9253744e-05 \n",
      "\n",
      "\n",
      "Epoch 22/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.9253744e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7209 - acc: 0.5528 - val_loss: 0.6699 - val_acc: 0.5889\n",
      "\n",
      "Epoch 00022: val_acc improved from 0.58889 to 0.58889, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.9253744e-05 \n",
      "\n",
      "\n",
      "Epoch 23/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.9253744e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6859 - acc: 0.5583 - val_loss: 0.6722 - val_acc: 0.5889\n",
      "\n",
      "Epoch 00023: val_acc did not improve from 0.58889\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.9253744e-05 \n",
      "\n",
      "\n",
      "Epoch 24/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.9253744e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6830 - acc: 0.5917 - val_loss: 0.6694 - val_acc: 0.5778\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.58889\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.9253744e-05 \n",
      "\n",
      "\n",
      "Epoch 25/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.9253744e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7065 - acc: 0.5806 - val_loss: 0.6620 - val_acc: 0.6000\n",
      "\n",
      "Epoch 00025: val_acc improved from 0.58889 to 0.60000, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.9253744e-05 \n",
      "\n",
      "\n",
      "Epoch 26/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.9253744e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6719 - acc: 0.6139 - val_loss: 0.6643 - val_acc: 0.5889\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.60000\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.9253744e-05 \n",
      "\n",
      "\n",
      "Epoch 27/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.9253744e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6647 - acc: 0.6194 - val_loss: 0.6630 - val_acc: 0.5889\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.60000\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.9253744e-05 \n",
      "\n",
      "\n",
      "Epoch 28/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.9253744e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6667 - acc: 0.6250 - val_loss: 0.6543 - val_acc: 0.5889\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.60000\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.9253744e-05 \n",
      "\n",
      "\n",
      "Epoch 29/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.9253744e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.7079 - acc: 0.5639 - val_loss: 0.6519 - val_acc: 0.5889\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.60000\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.9253744e-05 \n",
      "\n",
      "\n",
      "Epoch 30/300\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.9253744e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6670 - acc: 0.6194 - val_loss: 0.6503 - val_acc: 0.6000\n",
      "\n",
      "Epoch 00030: val_acc improved from 0.60000 to 0.60000, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.9253744e-05 \n",
      "\n",
      "\n",
      "Epoch 31/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.8518625e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6688 - acc: 0.5722 - val_loss: 0.6490 - val_acc: 0.5889\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.60000\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.8518625e-05 \n",
      "\n",
      "\n",
      "Epoch 32/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.8518625e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6556 - acc: 0.6222 - val_loss: 0.6469 - val_acc: 0.5889\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.60000\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.8518625e-05 \n",
      "\n",
      "\n",
      "Epoch 33/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.8518625e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6813 - acc: 0.6056 - val_loss: 0.6480 - val_acc: 0.5889\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.60000\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.8518625e-05 \n",
      "\n",
      "\n",
      "Epoch 34/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.8518625e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6796 - acc: 0.5639 - val_loss: 0.6515 - val_acc: 0.6111\n",
      "\n",
      "Epoch 00034: val_acc improved from 0.60000 to 0.61111, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.8518625e-05 \n",
      "\n",
      "\n",
      "Epoch 35/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.8518625e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6617 - acc: 0.5833 - val_loss: 0.6517 - val_acc: 0.5889\n",
      "\n",
      "Epoch 00035: val_acc did not improve from 0.61111\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.8518625e-05 \n",
      "\n",
      "\n",
      "Epoch 36/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.8518625e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6612 - acc: 0.6056 - val_loss: 0.6456 - val_acc: 0.5889\n",
      "\n",
      "Epoch 00036: val_acc did not improve from 0.61111\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.8518625e-05 \n",
      "\n",
      "\n",
      "Epoch 37/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.8518625e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6226 - acc: 0.6444 - val_loss: 0.6462 - val_acc: 0.6222\n",
      "\n",
      "Epoch 00037: val_acc improved from 0.61111 to 0.62222, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.8518625e-05 \n",
      "\n",
      "\n",
      "Epoch 38/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.8518625e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6511 - acc: 0.6194 - val_loss: 0.6420 - val_acc: 0.6333\n",
      "\n",
      "Epoch 00038: val_acc improved from 0.62222 to 0.63333, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.8518625e-05 \n",
      "\n",
      "\n",
      "Epoch 39/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.8518625e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6608 - acc: 0.5972 - val_loss: 0.6431 - val_acc: 0.6333\n",
      "\n",
      "Epoch 00039: val_acc did not improve from 0.63333\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.8518625e-05 \n",
      "\n",
      "\n",
      "Epoch 40/300\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.8518625e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6335 - acc: 0.6583 - val_loss: 0.6424 - val_acc: 0.6444\n",
      "\n",
      "Epoch 00040: val_acc improved from 0.63333 to 0.64444, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.8518625e-05 \n",
      "\n",
      "\n",
      "Epoch 41/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.7555506e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6334 - acc: 0.6417 - val_loss: 0.6444 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00041: val_acc improved from 0.64444 to 0.66667, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.7555506e-05 \n",
      "\n",
      "\n",
      "Epoch 42/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.7555506e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6407 - acc: 0.6417 - val_loss: 0.6433 - val_acc: 0.6556\n",
      "\n",
      "Epoch 00042: val_acc did not improve from 0.66667\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.7555506e-05 \n",
      "\n",
      "\n",
      "Epoch 43/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.7555506e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5781 - acc: 0.7000 - val_loss: 0.6404 - val_acc: 0.6556\n",
      "\n",
      "Epoch 00043: val_acc did not improve from 0.66667\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.7555506e-05 \n",
      "\n",
      "\n",
      "Epoch 44/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.7555506e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6498 - acc: 0.6194 - val_loss: 0.6450 - val_acc: 0.6556\n",
      "\n",
      "Epoch 00044: val_acc did not improve from 0.66667\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.7555506e-05 \n",
      "\n",
      "\n",
      "Epoch 45/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.7555506e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6426 - acc: 0.6250 - val_loss: 0.6462 - val_acc: 0.6333\n",
      "\n",
      "Epoch 00045: val_acc did not improve from 0.66667\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.7555506e-05 \n",
      "\n",
      "\n",
      "Epoch 46/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.7555506e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6243 - acc: 0.6556 - val_loss: 0.6516 - val_acc: 0.6111\n",
      "\n",
      "Epoch 00046: val_acc did not improve from 0.66667\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.7555506e-05 \n",
      "\n",
      "\n",
      "Epoch 47/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.7555506e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5911 - acc: 0.6639 - val_loss: 0.6478 - val_acc: 0.6222\n",
      "\n",
      "Epoch 00047: val_acc did not improve from 0.66667\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.7555506e-05 \n",
      "\n",
      "\n",
      "Epoch 48/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.7555506e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6088 - acc: 0.6722 - val_loss: 0.6384 - val_acc: 0.6556\n",
      "\n",
      "Epoch 00048: val_acc did not improve from 0.66667\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.7555506e-05 \n",
      "\n",
      "\n",
      "Epoch 49/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.7555506e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5774 - acc: 0.6806 - val_loss: 0.6371 - val_acc: 0.6556\n",
      "\n",
      "Epoch 00049: val_acc did not improve from 0.66667\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.7555506e-05 \n",
      "\n",
      "\n",
      "Epoch 50/300\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.7555506e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6039 - acc: 0.6528 - val_loss: 0.6254 - val_acc: 0.6778\n",
      "\n",
      "Epoch 00050: val_acc improved from 0.66667 to 0.67778, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.7555506e-05 \n",
      "\n",
      "\n",
      "Epoch 51/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.637845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6259 - acc: 0.6722 - val_loss: 0.6176 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00051: val_acc improved from 0.67778 to 0.70000, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.637845e-05 \n",
      "\n",
      "\n",
      "Epoch 52/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.637845e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6220 - acc: 0.6472 - val_loss: 0.6222 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00052: val_acc did not improve from 0.70000\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.637845e-05 \n",
      "\n",
      "\n",
      "Epoch 53/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.637845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5860 - acc: 0.7028 - val_loss: 0.6282 - val_acc: 0.6444\n",
      "\n",
      "Epoch 00053: val_acc did not improve from 0.70000\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.637845e-05 \n",
      "\n",
      "\n",
      "Epoch 54/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.637845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5871 - acc: 0.6861 - val_loss: 0.6217 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00054: val_acc did not improve from 0.70000\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.637845e-05 \n",
      "\n",
      "\n",
      "Epoch 55/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.637845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6031 - acc: 0.6917 - val_loss: 0.6239 - val_acc: 0.6556\n",
      "\n",
      "Epoch 00055: val_acc did not improve from 0.70000\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.637845e-05 \n",
      "\n",
      "\n",
      "Epoch 56/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.637845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5718 - acc: 0.7139 - val_loss: 0.6165 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00056: val_acc did not improve from 0.70000\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.637845e-05 \n",
      "\n",
      "\n",
      "Epoch 57/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.637845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6220 - acc: 0.6639 - val_loss: 0.6163 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00057: val_acc did not improve from 0.70000\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.637845e-05 \n",
      "\n",
      "\n",
      "Epoch 58/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.637845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6014 - acc: 0.6861 - val_loss: 0.6112 - val_acc: 0.6778\n",
      "\n",
      "Epoch 00058: val_acc did not improve from 0.70000\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.637845e-05 \n",
      "\n",
      "\n",
      "Epoch 59/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.637845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.6023 - acc: 0.7139 - val_loss: 0.5981 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00059: val_acc did not improve from 0.70000\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.637845e-05 \n",
      "\n",
      "\n",
      "Epoch 60/300\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.637845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5830 - acc: 0.7000 - val_loss: 0.5989 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00060: val_acc did not improve from 0.70000\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.637845e-05 \n",
      "\n",
      "\n",
      "Epoch 61/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.5004374e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5553 - acc: 0.7139 - val_loss: 0.5958 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00061: val_acc did not improve from 0.70000\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.5004374e-05 \n",
      "\n",
      "\n",
      "Epoch 62/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.5004374e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5632 - acc: 0.7111 - val_loss: 0.5891 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00062: val_acc improved from 0.70000 to 0.71111, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.5004374e-05 \n",
      "\n",
      "\n",
      "Epoch 63/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.5004374e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5910 - acc: 0.6972 - val_loss: 0.6010 - val_acc: 0.6778\n",
      "\n",
      "Epoch 00063: val_acc did not improve from 0.71111\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.5004374e-05 \n",
      "\n",
      "\n",
      "Epoch 64/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.5004374e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5403 - acc: 0.7361 - val_loss: 0.6041 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00064: val_acc did not improve from 0.71111\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.5004374e-05 \n",
      "\n",
      "\n",
      "Epoch 65/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.5004374e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5649 - acc: 0.7083 - val_loss: 0.6117 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00065: val_acc did not improve from 0.71111\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.5004374e-05 \n",
      "\n",
      "\n",
      "Epoch 66/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.5004374e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5448 - acc: 0.7111 - val_loss: 0.6095 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00066: val_acc did not improve from 0.71111\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.5004374e-05 \n",
      "\n",
      "\n",
      "Epoch 67/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.5004374e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5446 - acc: 0.7500 - val_loss: 0.5999 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00067: val_acc did not improve from 0.71111\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.5004374e-05 \n",
      "\n",
      "\n",
      "Epoch 68/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.5004374e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5561 - acc: 0.7250 - val_loss: 0.6011 - val_acc: 0.6556\n",
      "\n",
      "Epoch 00068: val_acc did not improve from 0.71111\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.5004374e-05 \n",
      "\n",
      "\n",
      "Epoch 69/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.5004374e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5433 - acc: 0.7361 - val_loss: 0.6047 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00069: val_acc did not improve from 0.71111\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.5004374e-05 \n",
      "\n",
      "\n",
      "Epoch 70/300\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.5004374e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5553 - acc: 0.7083 - val_loss: 0.6080 - val_acc: 0.6778\n",
      "\n",
      "Epoch 00070: val_acc did not improve from 0.71111\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.5004374e-05 \n",
      "\n",
      "\n",
      "Epoch 71/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.3452652e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5657 - acc: 0.7111 - val_loss: 0.5876 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00071: val_acc did not improve from 0.71111\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.3452652e-05 \n",
      "\n",
      "\n",
      "Epoch 72/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.3452652e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5266 - acc: 0.7528 - val_loss: 0.5853 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00072: val_acc did not improve from 0.71111\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.3452652e-05 \n",
      "\n",
      "\n",
      "Epoch 73/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.3452652e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5576 - acc: 0.7250 - val_loss: 0.5930 - val_acc: 0.6556\n",
      "\n",
      "Epoch 00073: val_acc did not improve from 0.71111\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.3452652e-05 \n",
      "\n",
      "\n",
      "Epoch 74/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.3452652e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5246 - acc: 0.7361 - val_loss: 0.5960 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00074: val_acc did not improve from 0.71111\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.3452652e-05 \n",
      "\n",
      "\n",
      "Epoch 75/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.3452652e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5134 - acc: 0.7611 - val_loss: 0.5954 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00075: val_acc did not improve from 0.71111\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.3452652e-05 \n",
      "\n",
      "\n",
      "Epoch 76/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.3452652e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5461 - acc: 0.7417 - val_loss: 0.5981 - val_acc: 0.6778\n",
      "\n",
      "Epoch 00076: val_acc did not improve from 0.71111\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.3452652e-05 \n",
      "\n",
      "\n",
      "Epoch 77/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.3452652e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5188 - acc: 0.7778 - val_loss: 0.5922 - val_acc: 0.6778\n",
      "\n",
      "Epoch 00077: val_acc did not improve from 0.71111\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.3452652e-05 \n",
      "\n",
      "\n",
      "Epoch 78/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.3452652e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5539 - acc: 0.7194 - val_loss: 0.5808 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00078: val_acc did not improve from 0.71111\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.3452652e-05 \n",
      "\n",
      "\n",
      "Epoch 79/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.3452652e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5005 - acc: 0.7389 - val_loss: 0.5785 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00079: val_acc did not improve from 0.71111\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.3452652e-05 \n",
      "\n",
      "\n",
      "Epoch 80/300\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.3452652e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5249 - acc: 0.7611 - val_loss: 0.5708 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00080: val_acc did not improve from 0.71111\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.3452652e-05 \n",
      "\n",
      "\n",
      "Epoch 81/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.174466e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5217 - acc: 0.7472 - val_loss: 0.5686 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00081: val_acc did not improve from 0.71111\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.174466e-05 \n",
      "\n",
      "\n",
      "Epoch 82/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.174466e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5028 - acc: 0.7778 - val_loss: 0.5812 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00082: val_acc did not improve from 0.71111\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.174466e-05 \n",
      "\n",
      "\n",
      "Epoch 83/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.174466e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5024 - acc: 0.7917 - val_loss: 0.5837 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00083: val_acc did not improve from 0.71111\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.174466e-05 \n",
      "\n",
      "\n",
      "Epoch 84/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.174466e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5256 - acc: 0.7278 - val_loss: 0.5952 - val_acc: 0.6556\n",
      "\n",
      "Epoch 00084: val_acc did not improve from 0.71111\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.174466e-05 \n",
      "\n",
      "\n",
      "Epoch 85/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.174466e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.5116 - acc: 0.7806 - val_loss: 0.5862 - val_acc: 0.6667\n",
      "\n",
      "Epoch 00085: val_acc did not improve from 0.71111\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.174466e-05 \n",
      "\n",
      "\n",
      "Epoch 86/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.174466e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4670 - acc: 0.8222 - val_loss: 0.5791 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00086: val_acc did not improve from 0.71111\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.174466e-05 \n",
      "\n",
      "\n",
      "Epoch 87/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.174466e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4928 - acc: 0.7750 - val_loss: 0.5758 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00087: val_acc did not improve from 0.71111\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.174466e-05 \n",
      "\n",
      "\n",
      "Epoch 88/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.174466e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4883 - acc: 0.7667 - val_loss: 0.5741 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00088: val_acc did not improve from 0.71111\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.174466e-05 \n",
      "\n",
      "\n",
      "Epoch 89/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.174466e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4951 - acc: 0.7833 - val_loss: 0.5657 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00089: val_acc did not improve from 0.71111\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.174466e-05 \n",
      "\n",
      "\n",
      "Epoch 90/300\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH START =  4.174466e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4870 - acc: 0.7861 - val_loss: 0.5776 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00090: val_acc did not improve from 0.71111\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH END =  4.174466e-05 \n",
      "\n",
      "\n",
      "Epoch 91/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.9903287e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4641 - acc: 0.7972 - val_loss: 0.5766 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00091: val_acc did not improve from 0.71111\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.9903287e-05 \n",
      "\n",
      "\n",
      "Epoch 92/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.9903287e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4967 - acc: 0.7778 - val_loss: 0.5709 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00092: val_acc did not improve from 0.71111\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.9903287e-05 \n",
      "\n",
      "\n",
      "Epoch 93/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.9903287e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4538 - acc: 0.8167 - val_loss: 0.5756 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00093: val_acc did not improve from 0.71111\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.9903287e-05 \n",
      "\n",
      "\n",
      "Epoch 94/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.9903287e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4474 - acc: 0.8222 - val_loss: 0.5792 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00094: val_acc did not improve from 0.71111\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.9903287e-05 \n",
      "\n",
      "\n",
      "Epoch 95/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.9903287e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4703 - acc: 0.7833 - val_loss: 0.5740 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00095: val_acc did not improve from 0.71111\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.9903287e-05 \n",
      "\n",
      "\n",
      "Epoch 96/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.9903287e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4677 - acc: 0.7889 - val_loss: 0.5694 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00096: val_acc did not improve from 0.71111\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.9903287e-05 \n",
      "\n",
      "\n",
      "Epoch 97/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.9903287e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4845 - acc: 0.7722 - val_loss: 0.5701 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00097: val_acc did not improve from 0.71111\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.9903287e-05 \n",
      "\n",
      "\n",
      "Epoch 98/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.9903287e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4444 - acc: 0.8167 - val_loss: 0.6040 - val_acc: 0.6444\n",
      "\n",
      "Epoch 00098: val_acc did not improve from 0.71111\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.9903287e-05 \n",
      "\n",
      "\n",
      "Epoch 99/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.9903287e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4816 - acc: 0.7694 - val_loss: 0.5900 - val_acc: 0.6778\n",
      "\n",
      "Epoch 00099: val_acc did not improve from 0.71111\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.9903287e-05 \n",
      "\n",
      "\n",
      "Epoch 100/300\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.9903287e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4409 - acc: 0.8222 - val_loss: 0.5711 - val_acc: 0.6889\n",
      "\n",
      "Epoch 00100: val_acc did not improve from 0.71111\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.9903287e-05 \n",
      "\n",
      "\n",
      "Epoch 101/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.795242e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4451 - acc: 0.8056 - val_loss: 0.5670 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00101: val_acc did not improve from 0.71111\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.795242e-05 \n",
      "\n",
      "\n",
      "Epoch 102/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.795242e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4479 - acc: 0.7972 - val_loss: 0.5682 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00102: val_acc did not improve from 0.71111\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.795242e-05 \n",
      "\n",
      "\n",
      "Epoch 103/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.795242e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4466 - acc: 0.8194 - val_loss: 0.5580 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00103: val_acc improved from 0.71111 to 0.73333, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.795242e-05 \n",
      "\n",
      "\n",
      "Epoch 104/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.795242e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4212 - acc: 0.8361 - val_loss: 0.5615 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00104: val_acc did not improve from 0.73333\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.795242e-05 \n",
      "\n",
      "\n",
      "Epoch 105/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.795242e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4107 - acc: 0.8361 - val_loss: 0.5598 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00105: val_acc did not improve from 0.73333\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.795242e-05 \n",
      "\n",
      "\n",
      "Epoch 106/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.795242e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4377 - acc: 0.8306 - val_loss: 0.5513 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00106: val_acc did not improve from 0.73333\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.795242e-05 \n",
      "\n",
      "\n",
      "Epoch 107/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.795242e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4350 - acc: 0.8222 - val_loss: 0.5440 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00107: val_acc did not improve from 0.73333\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.795242e-05 \n",
      "\n",
      "\n",
      "Epoch 108/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.795242e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4322 - acc: 0.8028 - val_loss: 0.5449 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00108: val_acc did not improve from 0.73333\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.795242e-05 \n",
      "\n",
      "\n",
      "Epoch 109/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.795242e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4496 - acc: 0.7917 - val_loss: 0.5369 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00109: val_acc improved from 0.73333 to 0.74444, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.795242e-05 \n",
      "\n",
      "\n",
      "Epoch 110/300\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.795242e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4000 - acc: 0.8528 - val_loss: 0.5451 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00110: val_acc did not improve from 0.74444\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.795242e-05 \n",
      "\n",
      "\n",
      "Epoch 111/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.5916448e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4239 - acc: 0.8250 - val_loss: 0.5501 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00111: val_acc did not improve from 0.74444\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.5916448e-05 \n",
      "\n",
      "\n",
      "Epoch 112/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.5916448e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4212 - acc: 0.8111 - val_loss: 0.5435 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00112: val_acc did not improve from 0.74444\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.5916448e-05 \n",
      "\n",
      "\n",
      "Epoch 113/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.5916448e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4040 - acc: 0.8306 - val_loss: 0.5500 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00113: val_acc did not improve from 0.74444\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.5916448e-05 \n",
      "\n",
      "\n",
      "Epoch 114/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.5916448e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3977 - acc: 0.8222 - val_loss: 0.5474 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00114: val_acc did not improve from 0.74444\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.5916448e-05 \n",
      "\n",
      "\n",
      "Epoch 115/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.5916448e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4114 - acc: 0.8417 - val_loss: 0.5272 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00115: val_acc did not improve from 0.74444\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.5916448e-05 \n",
      "\n",
      "\n",
      "Epoch 116/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.5916448e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4006 - acc: 0.8500 - val_loss: 0.5387 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00116: val_acc did not improve from 0.74444\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.5916448e-05 \n",
      "\n",
      "\n",
      "Epoch 117/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.5916448e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.4029 - acc: 0.8417 - val_loss: 0.5360 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00117: val_acc did not improve from 0.74444\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.5916448e-05 \n",
      "\n",
      "\n",
      "Epoch 118/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.5916448e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3699 - acc: 0.8750 - val_loss: 0.5415 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00118: val_acc did not improve from 0.74444\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.5916448e-05 \n",
      "\n",
      "\n",
      "Epoch 119/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.5916448e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3901 - acc: 0.8611 - val_loss: 0.5278 - val_acc: 0.7556\n",
      "\n",
      "Epoch 00119: val_acc improved from 0.74444 to 0.75556, saving model to /home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.5916448e-05 \n",
      "\n",
      "\n",
      "Epoch 120/300\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.5916448e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3756 - acc: 0.8528 - val_loss: 0.5429 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00120: val_acc did not improve from 0.75556\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.5916448e-05 \n",
      "\n",
      "\n",
      "Epoch 121/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.3819746e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3949 - acc: 0.8500 - val_loss: 0.5427 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00121: val_acc did not improve from 0.75556\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.3819746e-05 \n",
      "\n",
      "\n",
      "Epoch 122/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.3819746e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3640 - acc: 0.8639 - val_loss: 0.5438 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00122: val_acc did not improve from 0.75556\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.3819746e-05 \n",
      "\n",
      "\n",
      "Epoch 123/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.3819746e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3699 - acc: 0.8611 - val_loss: 0.5567 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00123: val_acc did not improve from 0.75556\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.3819746e-05 \n",
      "\n",
      "\n",
      "Epoch 124/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.3819746e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3924 - acc: 0.8306 - val_loss: 0.5655 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00124: val_acc did not improve from 0.75556\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.3819746e-05 \n",
      "\n",
      "\n",
      "Epoch 125/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.3819746e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3967 - acc: 0.8361 - val_loss: 0.5640 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00125: val_acc did not improve from 0.75556\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.3819746e-05 \n",
      "\n",
      "\n",
      "Epoch 126/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.3819746e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3858 - acc: 0.8444 - val_loss: 0.5579 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00126: val_acc did not improve from 0.75556\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.3819746e-05 \n",
      "\n",
      "\n",
      "Epoch 127/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.3819746e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3654 - acc: 0.8333 - val_loss: 0.5509 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00127: val_acc did not improve from 0.75556\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.3819746e-05 \n",
      "\n",
      "\n",
      "Epoch 128/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.3819746e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3548 - acc: 0.8778 - val_loss: 0.5311 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00128: val_acc did not improve from 0.75556\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.3819746e-05 \n",
      "\n",
      "\n",
      "Epoch 129/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.3819746e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3861 - acc: 0.8361 - val_loss: 0.5381 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00129: val_acc did not improve from 0.75556\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.3819746e-05 \n",
      "\n",
      "\n",
      "Epoch 130/300\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.3819746e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3550 - acc: 0.8750 - val_loss: 0.5377 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00130: val_acc did not improve from 0.75556\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.3819746e-05 \n",
      "\n",
      "\n",
      "Epoch 131/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.1686217e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3755 - acc: 0.8222 - val_loss: 0.5392 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00131: val_acc did not improve from 0.75556\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.1686217e-05 \n",
      "\n",
      "\n",
      "Epoch 132/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.1686217e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3517 - acc: 0.8694 - val_loss: 0.5415 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00132: val_acc did not improve from 0.75556\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.1686217e-05 \n",
      "\n",
      "\n",
      "Epoch 133/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.1686217e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3375 - acc: 0.8750 - val_loss: 0.5360 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00133: val_acc did not improve from 0.75556\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.1686217e-05 \n",
      "\n",
      "\n",
      "Epoch 134/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.1686217e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3277 - acc: 0.8833 - val_loss: 0.5393 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00134: val_acc did not improve from 0.75556\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.1686217e-05 \n",
      "\n",
      "\n",
      "Epoch 135/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.1686217e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3327 - acc: 0.8833 - val_loss: 0.5262 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00135: val_acc did not improve from 0.75556\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.1686217e-05 \n",
      "\n",
      "\n",
      "Epoch 136/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.1686217e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3590 - acc: 0.8750 - val_loss: 0.5311 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00136: val_acc did not improve from 0.75556\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.1686217e-05 \n",
      "\n",
      "\n",
      "Epoch 137/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.1686217e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3394 - acc: 0.8722 - val_loss: 0.5278 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00137: val_acc did not improve from 0.75556\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.1686217e-05 \n",
      "\n",
      "\n",
      "Epoch 138/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.1686217e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3258 - acc: 0.8861 - val_loss: 0.5253 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00138: val_acc did not improve from 0.75556\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.1686217e-05 \n",
      "\n",
      "\n",
      "Epoch 139/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.1686217e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3470 - acc: 0.8667 - val_loss: 0.5271 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00139: val_acc did not improve from 0.75556\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.1686217e-05 \n",
      "\n",
      "\n",
      "Epoch 140/300\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH START =  3.1686217e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3277 - acc: 0.8917 - val_loss: 0.5293 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00140: val_acc did not improve from 0.75556\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH END =  3.1686217e-05 \n",
      "\n",
      "\n",
      "Epoch 141/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.9538845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3331 - acc: 0.8833 - val_loss: 0.5290 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00141: val_acc did not improve from 0.75556\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.9538845e-05 \n",
      "\n",
      "\n",
      "Epoch 142/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.9538845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3241 - acc: 0.8750 - val_loss: 0.5422 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00142: val_acc did not improve from 0.75556\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.9538845e-05 \n",
      "\n",
      "\n",
      "Epoch 143/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.9538845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3313 - acc: 0.8639 - val_loss: 0.5701 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00143: val_acc did not improve from 0.75556\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.9538845e-05 \n",
      "\n",
      "\n",
      "Epoch 144/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.9538845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3520 - acc: 0.8722 - val_loss: 0.5791 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00144: val_acc did not improve from 0.75556\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.9538845e-05 \n",
      "\n",
      "\n",
      "Epoch 145/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.9538845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3330 - acc: 0.8722 - val_loss: 0.5586 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00145: val_acc did not improve from 0.75556\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.9538845e-05 \n",
      "\n",
      "\n",
      "Epoch 146/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.9538845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3498 - acc: 0.8611 - val_loss: 0.5432 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00146: val_acc did not improve from 0.75556\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.9538845e-05 \n",
      "\n",
      "\n",
      "Epoch 147/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.9538845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3212 - acc: 0.8806 - val_loss: 0.5276 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00147: val_acc did not improve from 0.75556\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.9538845e-05 \n",
      "\n",
      "\n",
      "Epoch 148/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.9538845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2992 - acc: 0.9056 - val_loss: 0.5275 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00148: val_acc did not improve from 0.75556\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.9538845e-05 \n",
      "\n",
      "\n",
      "Epoch 149/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.9538845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2963 - acc: 0.8944 - val_loss: 0.5427 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00149: val_acc did not improve from 0.75556\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.9538845e-05 \n",
      "\n",
      "\n",
      "Epoch 150/300\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.9538845e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3184 - acc: 0.8944 - val_loss: 0.5366 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00150: val_acc did not improve from 0.75556\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.9538845e-05 \n",
      "\n",
      "\n",
      "Epoch 151/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.7399316e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3019 - acc: 0.8972 - val_loss: 0.5343 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00151: val_acc did not improve from 0.75556\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.7399316e-05 \n",
      "\n",
      "\n",
      "Epoch 152/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.7399316e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3367 - acc: 0.8722 - val_loss: 0.5480 - val_acc: 0.7444\n",
      "\n",
      "Epoch 00152: val_acc did not improve from 0.75556\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.7399316e-05 \n",
      "\n",
      "\n",
      "Epoch 153/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.7399316e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2948 - acc: 0.8889 - val_loss: 0.5479 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00153: val_acc did not improve from 0.75556\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.7399316e-05 \n",
      "\n",
      "\n",
      "Epoch 154/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.7399316e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2898 - acc: 0.9167 - val_loss: 0.5370 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00154: val_acc did not improve from 0.75556\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.7399316e-05 \n",
      "\n",
      "\n",
      "Epoch 155/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.7399316e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3052 - acc: 0.8833 - val_loss: 0.5283 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00155: val_acc did not improve from 0.75556\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.7399316e-05 \n",
      "\n",
      "\n",
      "Epoch 156/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.7399316e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2987 - acc: 0.9111 - val_loss: 0.5469 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00156: val_acc did not improve from 0.75556\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.7399316e-05 \n",
      "\n",
      "\n",
      "Epoch 157/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.7399316e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2829 - acc: 0.9167 - val_loss: 0.5430 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00157: val_acc did not improve from 0.75556\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.7399316e-05 \n",
      "\n",
      "\n",
      "Epoch 158/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.7399316e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2668 - acc: 0.9333 - val_loss: 0.5390 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00158: val_acc did not improve from 0.75556\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.7399316e-05 \n",
      "\n",
      "\n",
      "Epoch 159/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.7399316e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2738 - acc: 0.9167 - val_loss: 0.5653 - val_acc: 0.7000\n",
      "\n",
      "Epoch 00159: val_acc did not improve from 0.75556\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.7399316e-05 \n",
      "\n",
      "\n",
      "Epoch 160/300\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.7399316e-05\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2770 - acc: 0.9111 - val_loss: 0.5428 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00160: val_acc did not improve from 0.75556\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.7399316e-05 \n",
      "\n",
      "\n",
      "Epoch 161/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.5287682e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2775 - acc: 0.9028 - val_loss: 0.5200 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00161: val_acc did not improve from 0.75556\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.5287682e-05 \n",
      "\n",
      "\n",
      "Epoch 162/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.5287682e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2612 - acc: 0.9389 - val_loss: 0.5274 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00162: val_acc did not improve from 0.75556\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.5287682e-05 \n",
      "\n",
      "\n",
      "Epoch 163/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.5287682e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2779 - acc: 0.9000 - val_loss: 0.5457 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00163: val_acc did not improve from 0.75556\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.5287682e-05 \n",
      "\n",
      "\n",
      "Epoch 164/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.5287682e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2763 - acc: 0.9111 - val_loss: 0.5563 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00164: val_acc did not improve from 0.75556\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.5287682e-05 \n",
      "\n",
      "\n",
      "Epoch 165/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.5287682e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2808 - acc: 0.9139 - val_loss: 0.5578 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00165: val_acc did not improve from 0.75556\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.5287682e-05 \n",
      "\n",
      "\n",
      "Epoch 166/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.5287682e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2682 - acc: 0.9250 - val_loss: 0.5544 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00166: val_acc did not improve from 0.75556\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.5287682e-05 \n",
      "\n",
      "\n",
      "Epoch 167/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.5287682e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2330 - acc: 0.9250 - val_loss: 0.5441 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00167: val_acc did not improve from 0.75556\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.5287682e-05 \n",
      "\n",
      "\n",
      "Epoch 168/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.5287682e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2504 - acc: 0.9111 - val_loss: 0.5398 - val_acc: 0.7222\n",
      "\n",
      "Epoch 00168: val_acc did not improve from 0.75556\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.5287682e-05 \n",
      "\n",
      "\n",
      "Epoch 169/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.5287682e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2845 - acc: 0.9056 - val_loss: 0.5368 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00169: val_acc did not improve from 0.75556\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.5287682e-05 \n",
      "\n",
      "\n",
      "Epoch 170/300\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.5287682e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2555 - acc: 0.9083 - val_loss: 0.5337 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00170: val_acc did not improve from 0.75556\n",
      "CHECK 9\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.5287682e-05 \n",
      "\n",
      "\n",
      "Epoch 171/300\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.3222094e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2675 - acc: 0.8972 - val_loss: 0.5352 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00171: val_acc did not improve from 0.75556\n",
      "CHECK 0\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.3222094e-05 \n",
      "\n",
      "\n",
      "Epoch 172/300\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.3222094e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2692 - acc: 0.9333 - val_loss: 0.5226 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00172: val_acc did not improve from 0.75556\n",
      "CHECK 1\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.3222094e-05 \n",
      "\n",
      "\n",
      "Epoch 173/300\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.3222094e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2804 - acc: 0.9111 - val_loss: 0.5384 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00173: val_acc did not improve from 0.75556\n",
      "CHECK 2\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.3222094e-05 \n",
      "\n",
      "\n",
      "Epoch 174/300\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.3222094e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2743 - acc: 0.8917 - val_loss: 0.5280 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00174: val_acc did not improve from 0.75556\n",
      "CHECK 3\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.3222094e-05 \n",
      "\n",
      "\n",
      "Epoch 175/300\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.3222094e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.3078 - acc: 0.8806 - val_loss: 0.5437 - val_acc: 0.7111\n",
      "\n",
      "Epoch 00175: val_acc did not improve from 0.75556\n",
      "CHECK 4\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.3222094e-05 \n",
      "\n",
      "\n",
      "Epoch 176/300\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.3222094e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2848 - acc: 0.9111 - val_loss: 0.5435 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00176: val_acc did not improve from 0.75556\n",
      "CHECK 5\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.3222094e-05 \n",
      "\n",
      "\n",
      "Epoch 177/300\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.3222094e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2661 - acc: 0.9194 - val_loss: 0.5200 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00177: val_acc did not improve from 0.75556\n",
      "CHECK 6\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.3222094e-05 \n",
      "\n",
      "\n",
      "Epoch 178/300\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.3222094e-05\n",
      "360/360 [==============================] - 2s 6ms/step - loss: 0.2498 - acc: 0.9278 - val_loss: 0.5392 - val_acc: 0.7333\n",
      "\n",
      "Epoch 00178: val_acc did not improve from 0.75556\n",
      "CHECK 7\n",
      "OPTIMIZERS lrate AT EPOCH END =  2.3222094e-05 \n",
      "\n",
      "\n",
      "Epoch 179/300\n",
      "CHECK 8\n",
      "OPTIMIZERS lrate AT EPOCH START =  2.3222094e-05\n",
      "350/360 [============================>.] - ETA: 0s - loss: 0.2341 - acc: 0.9086"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-f7160b3c9917>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     55\u001b[0m                 \u001b[0mshuffle\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     56\u001b[0m                 \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 57\u001b[0;31m                 callbacks=[checkpointer, early_stopping, lr_decay, loss_history])\n\u001b[0m",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m   1035\u001b[0m                                         \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1036\u001b[0m                                         \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1037\u001b[0;31m                                         validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m   1038\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1039\u001b[0m     def evaluate(self, x=None, y=None,\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m    210\u001b[0m                         val_outs = test_loop(model, val_f, val_ins,\n\u001b[1;32m    211\u001b[0m                                              \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbatch_size\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m                                              verbose=0)\n\u001b[0m\u001b[1;32m    213\u001b[0m                         \u001b[0mval_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval_outs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m                         \u001b[0;31m# Same labels assumed.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mtest_loop\u001b[0;34m(model, f, ins, batch_size, verbose, steps)\u001b[0m\n\u001b[1;32m    390\u001b[0m                 \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 392\u001b[0;31m             \u001b[0mbatch_outs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    393\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch_outs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    394\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mbatch_index\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2670\u001b[0m                     \u001b[0;34m'In order to feed symbolic tensors to a Keras model '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2671\u001b[0m                     'in TensorFlow, you need tensorflow 1.8 or higher.')\n\u001b[0;32m-> 2672\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\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[0m\u001b[1;32m   2673\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_legacy_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m   2652\u001b[0m         \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2653\u001b[0m         updated = session.run(fetches=fetches, feed_dict=feed_dict,\n\u001b[0;32m-> 2654\u001b[0;31m                               **self.session_kwargs)\n\u001b[0m\u001b[1;32m   2655\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    903\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    904\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 905\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    906\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    907\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1135\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1136\u001b[0m       results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m-> 1137\u001b[0;31m                              feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[1;32m   1138\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1139\u001b[0m       \u001b[0mresults\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/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m   1353\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1354\u001b[0m       return self._do_call(_run_fn, self._session, feeds, fetches, targets,\n\u001b[0;32m-> 1355\u001b[0;31m                            options, run_metadata)\n\u001b[0m\u001b[1;32m   1356\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1357\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m   1359\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\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[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1360\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1361\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\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[0m\n\u001b[0m\u001b[1;32m   1362\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1363\u001b[0m       \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m   1338\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1339\u001b[0m           return tf_session.TF_Run(session, options, feed_dict, fetch_list,\n\u001b[0;32m-> 1340\u001b[0;31m                                    target_list, status, run_metadata)\n\u001b[0m\u001b[1;32m   1341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1342\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "init_lr = 0.00005\n",
    "lr_drop_by = 0.995\n",
    "drop_every = 10\n",
    "\n",
    "# Training time!\n",
    "# Try 20 epochs using batch size = 3, then switch to 4\n",
    "from keras.callbacks import TensorBoard, EarlyStopping, LearningRateScheduler, Callback, ModelCheckpoint\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=100, mode='min')\n",
    "\n",
    "K.set_value(model.optimizer.lr, init_lr)\n",
    "\n",
    "def step_decay(epoch, init_lr=init_lr, lr_drop=lr_drop_by, drop_every=drop_every):\n",
    "    if epoch%drop_every==0:\n",
    "        lrate = init_lr * (lr_drop_by ** np.floor((1+epoch)/drop_every))\n",
    "    else:\n",
    "        lrate = K.get_value(model.optimizer.lr)\n",
    "    print('CHECK', epoch%drop_every)\n",
    "    return lrate\n",
    "\n",
    "class LossHistory(Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        self.losses = []\n",
    "        self.lr = []\n",
    "        self.batch_loss = []\n",
    "        self.val_acc = []\n",
    " \n",
    "    def on_epoch_end(self, epoch, batch, logs={}):\n",
    "        lrate = step_decay(epoch)\n",
    "        txt = K.get_value(model.optimizer.lr)\n",
    "        self.lr.append(txt)\n",
    "#         self.lr.append(lrate)\n",
    "#         txt = K.eval(self.model.optimizer.lr)\n",
    "        print('OPTIMIZERS lrate AT EPOCH END = ', txt, '\\n\\n')\n",
    "    \n",
    "    def on_epoch_begin(self, epoch, batch, logs={}):\n",
    "        txt = K.get_value(model.optimizer.lr)\n",
    "        print('OPTIMIZERS lrate AT EPOCH START = ', txt)\n",
    "\n",
    "    def on_batch_end(self, batch, logs={}):\n",
    "        self.batch_loss.append(logs.get('loss'))\n",
    "\n",
    "lr_decay = LearningRateScheduler(schedule=step_decay)\n",
    "loss_history = LossHistory()\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=100, mode='min')\n",
    "checkpointer = ModelCheckpoint(filepath='/home/amplifier/home/NEW_DL/weights/IMG_net_2_wts_autoKeras.h5',\n",
    "                               verbose=1,\n",
    "                               monitor='val_acc',\n",
    "                               save_best_only=True)\n",
    "\n",
    "train_history = model.fit(x_train, y_train,\n",
    "                epochs=300,\n",
    "                batch_size=10,\n",
    "                shuffle=True,\n",
    "                validation_data=(x_test, y_test),\n",
    "                callbacks=[checkpointer, early_stopping, lr_decay, loss_history])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axs = plt.subplots(1,3,figsize=(15,5))\n",
    "plt.subplot(131)\n",
    "plt.plot(loss_history.batch_loss)\n",
    "tic = np.linspace(0, len(loss_history.batch_loss), len(train_history.history['val_loss']))\n",
    "plt.plot(tic, train_history.history['loss'])\n",
    "plt.plot(tic, train_history.history['val_loss'])\n",
    "plt.title('Loss dynamics')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('batch')\n",
    "plt.legend(['loss (by batch)', 'loss', 'val_loss'])\n",
    "\n",
    "plt.subplot(132)\n",
    "plt.plot(loss_history.lr,'bo')\n",
    "plt.title('Learning rate dynamics over epochs')\n",
    "plt.ylabel('lrate')\n",
    "plt.xlabel('epoch')\n",
    "\n",
    "asa = []\n",
    "optimizer_name = model.optimizer.__class__.__name__\n",
    "plt.subplot(133)\n",
    "plt.plot(train_history.history['val_acc'])\n",
    "plt.plot(train_history.history['acc'])\n",
    "asa.append('val_acc ' + optimizer_name)\n",
    "asa.append('acc ' + optimizer_name)\n",
    "plt.title('Accuracy dynamics over epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(asa)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('/home/amplifier/home/NEW_DL/weights/IMG_net_2_wts.h5')\n",
    "# let's predict a TRAINING sammple:\n",
    "decoded_train = model.predict(x_train)\n",
    "decoded_test = model.predict(x_test)\n",
    "print(decoded_train.shape)\n",
    "print(decoded_test.shape)\n",
    "\n",
    "# now test sample by sample\n",
    "cor_test = []\n",
    "L = x_test.shape\n",
    "for sample_no in range(x_test.shape[0]):\n",
    "    pred = model.predict(np.expand_dims(x_test[sample_no],0))\n",
    "    if ((pred[0][0]<pred[0][1]) and (y_test[sample_no,0] < y_test[sample_no,1]) or\n",
    "       ((pred[0][0]>pred[0][1]) and (y_test[sample_no,0] > y_test[sample_no,1]))):\n",
    "        cor_test.append(1)\n",
    "    else:\n",
    "        cor_test.append(0)\n",
    "\n",
    "cor_train = []\n",
    "L = x_train.shape\n",
    "for sample_no in range(x_train.shape[0]):\n",
    "    pred = model.predict(np.expand_dims(x_train[sample_no],0))\n",
    "    if ((pred[0][0]<pred[0][1]) and (y_train[sample_no,0] < y_train[sample_no,1]) or\n",
    "       ((pred[0][0]>pred[0][1]) and (y_train[sample_no,0] > y_train[sample_no,1]))):\n",
    "        cor_train.append(1)\n",
    "    else:\n",
    "        cor_train.append(0)\n",
    "\n",
    "# Report accuracies\n",
    "print('Accuracy on the test data:', np.mean(cor_test))\n",
    "print('Accuracy on the training data:', np.mean(cor_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save cor_test as csv for future analysis:\n",
    "import csv\n",
    "with open('img_test.csv', 'w', newline='') as myfile:\n",
    "    wr = csv.writer(myfile, quoting=csv.QUOTE_ALL)\n",
    "    wr.writerow(cor_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = '1+2, see desc.'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>SHUFFLE</th>\n",
       "      <th>BATCH_SIZE</th>\n",
       "      <th>TEST_TRAIN</th>\n",
       "      <th>DS_FREQ</th>\n",
       "      <th>n_bins</th>\n",
       "      <th>RESIZE</th>\n",
       "      <th>width</th>\n",
       "      <th>height</th>\n",
       "      <th>SENSITIVITY</th>\n",
       "      <th>dataset</th>\n",
       "      <th>val_acc</th>\n",
       "      <th>acc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.961111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.788889</td>\n",
       "      <td>0.805556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>EEG_ICA(-123_ICs)+proc_AUD_101_192_Merged456.m...</td>\n",
       "      <td>0.580645</td>\n",
       "      <td>0.973118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged456-197-289_ICA(-eyes)+AUDpreproc.mat, D...</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.963889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged123_1_64_ICA(-eyes)AUDpreproc.mat, DS2=6...</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.881944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged123_75_134_ICA(-eyes)AUDpreproc.mat, DS2...</td>\n",
       "      <td>0.591716</td>\n",
       "      <td>0.599702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.591667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.655556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.708333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.980556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.755556</td>\n",
       "      <td>0.725000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.969444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>0.738889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>[1, 2, 3]</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.788889</td>\n",
       "      <td>0.616667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>[1, 2, 3]</td>\n",
       "      <td>Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...</td>\n",
       "      <td>0.788889</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>[0, 1, 2]</td>\n",
       "      <td>1+2, see desc.</td>\n",
       "      <td>0.787879</td>\n",
       "      <td>0.741477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>TOPO</td>\n",
       "      <td>False</td>\n",
       "      <td>20</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>[0, 1, 2]</td>\n",
       "      <td>1+2, see desc.</td>\n",
       "      <td>0.688889</td>\n",
       "      <td>0.773611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   algorithm  SHUFFLE  BATCH_SIZE  TEST_TRAIN  DS_FREQ  n_bins  RESIZE  width  \\\n",
       "0       TOPO    False          20         0.2        1       8       1     15   \n",
       "1       TOPO    False          20         0.2        1       8       1     15   \n",
       "2       TOPO    False          20         0.2        1       8       1     15   \n",
       "3       TOPO    False          20         0.2        1       8       1     15   \n",
       "4       TOPO    False          20         0.2        1       8       1     15   \n",
       "5       TOPO    False          20         0.2        1       8       1     15   \n",
       "6       TOPO    False          20         0.2        1       8       1     15   \n",
       "7       TOPO    False          20         0.2        1       8       1     15   \n",
       "8       TOPO    False          20         0.2        1       8       1     15   \n",
       "9       TOPO    False          20         0.2        1       8       1     15   \n",
       "10      TOPO    False          20         0.2        1       8       1     15   \n",
       "11      TOPO    False          20         0.2        1       8       1     15   \n",
       "12      TOPO    False          20         0.2        1       8       1     15   \n",
       "13      TOPO    False          20         0.2        1       8       1     15   \n",
       "14      TOPO    False          20         0.2        1       8       1     15   \n",
       "15      TOPO    False          20         0.2        1       8       1     15   \n",
       "16      TOPO    False          20         0.2        1       8       1     15   \n",
       "17      TOPO    False          20         0.2        1       8       1     15   \n",
       "18      TOPO    False          20         0.2        1       8       1     15   \n",
       "\n",
       "    height SENSITIVITY                                            dataset  \\\n",
       "0       15           1  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "1       15           1  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "2       15           1  EEG_ICA(-123_ICs)+proc_AUD_101_192_Merged456.m...   \n",
       "3       15           1  Merged456-197-289_ICA(-eyes)+AUDpreproc.mat, D...   \n",
       "4       15           1  Merged123_1_64_ICA(-eyes)AUDpreproc.mat, DS2=6...   \n",
       "5       15           1  Merged123_75_134_ICA(-eyes)AUDpreproc.mat, DS2...   \n",
       "6       15           1  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "7       15           1  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "8       15           1  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "9       15           2  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "10      15           3  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "11      15           3  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "12      15           3  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "13      15           1  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "14      15           1  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "15      15   [1, 2, 3]  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "16      15   [1, 2, 3]  Merged456-1-94_ICA(-2,3ICs)+AUDpreproc.mat, DS...   \n",
       "17      15   [0, 1, 2]                                     1+2, see desc.   \n",
       "18      15   [0, 1, 2]                                     1+2, see desc.   \n",
       "\n",
       "     val_acc       acc  \n",
       "0   0.766667  0.961111  \n",
       "1   0.788889  0.805556  \n",
       "2   0.580645  0.973118  \n",
       "3   0.800000  0.963889  \n",
       "4   0.666667  0.881944  \n",
       "5   0.591716  0.599702  \n",
       "6   0.722222  0.591667  \n",
       "7   0.755556  0.655556  \n",
       "8   0.755556  0.666667  \n",
       "9   0.777778  0.708333  \n",
       "10  0.777778  0.980556  \n",
       "11  0.755556  0.725000  \n",
       "12  0.722222  0.969444  \n",
       "13  0.800000  0.733333  \n",
       "14  0.766667  0.738889  \n",
       "15  0.788889  0.616667  \n",
       "16  0.788889  0.666667  \n",
       "17  0.787879  0.741477  \n",
       "18  0.688889  0.773611  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df_ = pd.DataFrame(columns=['algorithm',\n",
    "                           'SHUFFLE',\n",
    "                           'BATCH_SIZE',\n",
    "                           'TEST_TRAIN',\n",
    "                           'DS_FREQ',\n",
    "                           'n_bins',\n",
    "                           'RESIZE',\n",
    "                           'width',\n",
    "                           'height',\n",
    "                           'SENSITIVITY',\n",
    "                           'dataset',\n",
    "                           'val_acc',\n",
    "                           'acc'])\n",
    "\n",
    "df_.loc[0] = ['TOPO',\n",
    "              SHUFFLE,\n",
    "              BATCH_SIZE,\n",
    "              TEST_TRAIN,\n",
    "              DS_FREQ,\n",
    "              n_bins,\n",
    "              RESIZE,\n",
    "              width,\n",
    "              height,\n",
    "              str(knockouts),\n",
    "              file,\n",
    "              np.mean(cor_test),\n",
    "              np.mean(cor_train)]\n",
    "# df.to_csv('/home/amplifier/home/NEW_DL/weights/blank_document.csv', index=False)\n",
    "df = pd.read_csv('/home/amplifier/home/NEW_DL/weights/performances.csv')\n",
    "df = df.append(df_, ignore_index=True)\n",
    "df.drop_duplicates(subset=None, keep='first', inplace=True)\n",
    "df.to_csv('/home/amplifier/home/NEW_DL/weights/performances.csv', index=False)\n",
    "df = pd.read_csv('/home/amplifier/home/NEW_DL/weights/performances.csv')\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # to CLEAR all the data in the dataframe and save\n",
    "# df = df[:0]\n",
    "# df.to_csv('/home/amplifier/home/NEW_DL/weights/performances.csv', index=False)\n",
    "# df.head()\n",
    "\n",
    "# df.drop_duplicates(subset=None, keep='first', inplace=True)\n",
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample: 0 Prediction: [[ 0.96013433  0.03986574]] Ground Truth [ 0.  1.] right\n",
      "Sample: 1 Prediction: [[ 0.25404075  0.74595928]] Ground Truth [ 0.  1.] right\n",
      "Sample: 2 Prediction: [[ 0.32668161  0.67331839]] Ground Truth [ 0.  1.] right\n",
      "Sample: 3 Prediction: [[ 0.74391818  0.25608185]] Ground Truth [ 0.  1.] right\n",
      "Sample: 4 Prediction: [[ 0.60861325  0.39138672]] Ground Truth [ 0.  1.] right\n",
      "Sample: 5 Prediction: [[ 0.3831735   0.61682653]] Ground Truth [ 0.  1.] right\n",
      "Sample: 6 Prediction: [[ 0.49828056  0.50171942]] Ground Truth [ 0.  1.] right\n",
      "Sample: 7 Prediction: [[ 0.41862848  0.58137155]] Ground Truth [ 0.  1.] right\n",
      "Sample: 8 Prediction: [[ 0.35809729  0.64190269]] Ground Truth [ 0.  1.] right\n",
      "Sample: 9 Prediction: [[ 0.47699139  0.52300864]] Ground Truth [ 0.  1.] right\n",
      "Sample: 10 Prediction: [[ 0.37415457  0.62584543]] Ground Truth [ 0.  1.] right\n",
      "Sample: 11 Prediction: [[ 0.39874229  0.60125768]] Ground Truth [ 0.  1.] right\n",
      "Sample: 12 Prediction: [[ 0.44023103  0.55976897]] Ground Truth [ 0.  1.] right\n",
      "Sample: 13 Prediction: [[ 0.44007784  0.55992216]] Ground Truth [ 0.  1.] right\n",
      "Sample: 14 Prediction: [[ 0.58019108  0.41980895]] Ground Truth [ 0.  1.] right\n",
      "Sample: 15 Prediction: [[ 0.5972892   0.40271077]] Ground Truth [ 1.  0.] left\n",
      "Sample: 16 Prediction: [[ 0.38457316  0.6154269 ]] Ground Truth [ 1.  0.] left\n",
      "Sample: 17 Prediction: [[ 0.635369    0.36463097]] Ground Truth [ 1.  0.] left\n",
      "Sample: 18 Prediction: [[ 0.48101315  0.51898682]] Ground Truth [ 1.  0.] left\n",
      "Sample: 19 Prediction: [[ 0.7774868   0.22251318]] Ground Truth [ 1.  0.] left\n",
      "Sample: 20 Prediction: [[ 0.66724747  0.33275253]] Ground Truth [ 1.  0.] left\n",
      "Sample: 21 Prediction: [[ 0.57681233  0.42318767]] Ground Truth [ 1.  0.] left\n",
      "Sample: 22 Prediction: [[ 0.65336633  0.34663364]] Ground Truth [ 1.  0.] left\n",
      "Sample: 23 Prediction: [[ 0.55167353  0.44832647]] Ground Truth [ 1.  0.] left\n",
      "Sample: 24 Prediction: [[ 0.61871529  0.38128471]] Ground Truth [ 1.  0.] left\n",
      "Sample: 25 Prediction: [[ 0.70293671  0.29706326]] Ground Truth [ 1.  0.] left\n",
      "Sample: 26 Prediction: [[ 0.8275004  0.1724996]] Ground Truth [ 1.  0.] left\n",
      "Sample: 27 Prediction: [[ 0.91670561  0.08329436]] Ground Truth [ 1.  0.] left\n",
      "Sample: 28 Prediction: [[ 0.58379686  0.41620314]] Ground Truth [ 1.  0.] left\n",
      "Sample: 29 Prediction: [[ 0.54316795  0.45683205]] Ground Truth [ 1.  0.] left\n",
      "Sample: 30 Prediction: [[ 0.46432355  0.53567642]] Ground Truth [ 0.  1.] right\n",
      "Sample: 31 Prediction: [[ 0.48211145  0.51788861]] Ground Truth [ 0.  1.] right\n",
      "Sample: 32 Prediction: [[ 0.21449119  0.78550881]] Ground Truth [ 0.  1.] right\n",
      "Sample: 33 Prediction: [[ 0.21206328  0.78793675]] Ground Truth [ 0.  1.] right\n",
      "Sample: 34 Prediction: [[ 0.22660996  0.77339   ]] Ground Truth [ 0.  1.] right\n",
      "Sample: 35 Prediction: [[ 0.46444872  0.53555125]] Ground Truth [ 0.  1.] right\n",
      "Sample: 36 Prediction: [[ 0.69434208  0.30565789]] Ground Truth [ 0.  1.] right\n",
      "Sample: 37 Prediction: [[ 0.24021384  0.75978613]] Ground Truth [ 0.  1.] right\n",
      "Sample: 38 Prediction: [[ 0.53315288  0.46684715]] Ground Truth [ 0.  1.] right\n",
      "Sample: 39 Prediction: [[ 0.51542366  0.48457634]] Ground Truth [ 0.  1.] right\n",
      "Sample: 40 Prediction: [[ 0.40901107  0.59098893]] Ground Truth [ 0.  1.] right\n",
      "Sample: 41 Prediction: [[ 0.86995429  0.13004577]] Ground Truth [ 0.  1.] right\n",
      "Sample: 42 Prediction: [[ 0.40727648  0.59272355]] Ground Truth [ 0.  1.] right\n",
      "Sample: 43 Prediction: [[ 0.64499575  0.35500422]] Ground Truth [ 0.  1.] right\n",
      "Sample: 44 Prediction: [[ 0.24289264  0.75710738]] Ground Truth [ 0.  1.] right\n",
      "Sample: 45 Prediction: [[ 0.5658223   0.43417764]] Ground Truth [ 1.  0.] left\n",
      "Sample: 46 Prediction: [[ 0.39136311  0.60863686]] Ground Truth [ 1.  0.] left\n",
      "Sample: 47 Prediction: [[ 0.40332261  0.59667742]] Ground Truth [ 1.  0.] left\n",
      "Sample: 48 Prediction: [[ 0.50617278  0.49382722]] Ground Truth [ 1.  0.] left\n",
      "Sample: 49 Prediction: [[ 0.57524401  0.42475599]] Ground Truth [ 1.  0.] left\n",
      "Sample: 50 Prediction: [[ 0.5554865   0.44451353]] Ground Truth [ 1.  0.] left\n",
      "Sample: 51 Prediction: [[ 0.7292304  0.2707696]] Ground Truth [ 1.  0.] left\n",
      "Sample: 52 Prediction: [[ 0.5645346   0.43546537]] Ground Truth [ 1.  0.] left\n",
      "Sample: 53 Prediction: [[ 0.52866584  0.47133416]] Ground Truth [ 1.  0.] left\n",
      "Sample: 54 Prediction: [[ 0.8364619   0.16353813]] Ground Truth [ 1.  0.] left\n",
      "Sample: 55 Prediction: [[ 0.63456202  0.36543792]] Ground Truth [ 1.  0.] left\n",
      "Sample: 56 Prediction: [[ 0.69037157  0.30962849]] Ground Truth [ 1.  0.] left\n",
      "Sample: 57 Prediction: [[ 0.58467048  0.41532946]] Ground Truth [ 1.  0.] left\n",
      "Sample: 58 Prediction: [[ 0.64185321  0.35814676]] Ground Truth [ 1.  0.] left\n",
      "Sample: 59 Prediction: [[ 0.60528386  0.39471614]] Ground Truth [ 1.  0.] left\n",
      "Sample: 60 Prediction: [[ 0.43205687  0.56794316]] Ground Truth [ 0.  1.] right\n",
      "Sample: 61 Prediction: [[ 0.43844175  0.56155825]] Ground Truth [ 0.  1.] right\n",
      "Sample: 62 Prediction: [[ 0.54402059  0.45597941]] Ground Truth [ 0.  1.] right\n",
      "Sample: 63 Prediction: [[ 0.28261474  0.71738523]] Ground Truth [ 0.  1.] right\n",
      "Sample: 64 Prediction: [[ 0.58668691  0.41331312]] Ground Truth [ 0.  1.] right\n",
      "Sample: 65 Prediction: [[ 0.57500339  0.42499664]] Ground Truth [ 0.  1.] right\n",
      "Sample: 66 Prediction: [[ 0.45714927  0.54285073]] Ground Truth [ 0.  1.] right\n",
      "Sample: 67 Prediction: [[ 0.70555103  0.29444894]] Ground Truth [ 0.  1.] right\n",
      "Sample: 68 Prediction: [[ 0.38692731  0.61307275]] Ground Truth [ 0.  1.] right\n",
      "Sample: 69 Prediction: [[ 0.43439525  0.56560475]] Ground Truth [ 0.  1.] right\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0688fa4898>"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f06970382b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for sample_no in range(70):\n",
    "    pred = model.predict(np.expand_dims(x_test[sample_no],0))\n",
    "    print('Sample:', sample_no, 'Prediction:', pred, 'Ground Truth', y_test[sample_no], q_test[sample_no])\n",
    "sample_no = 2\n",
    "\n",
    "plt.imshow(x_test[sample_no,:,:,2].astype('float64'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'Topographies ')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0688f4c358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_nos = [2, 30]\n",
    "I = 2\n",
    "J = x_test.shape[3]\n",
    "c = 0\n",
    "fig, axs = plt.subplots(I,J)\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "axs = axs.ravel()\n",
    "for i in range(I):\n",
    "    for j in range(J):\n",
    "        axs[c].imshow(x_test[sample_nos[i],:,:,j].astype('float64'))\n",
    "        axs[c].get_xaxis().set_visible(False)\n",
    "        axs[c].get_yaxis().set_visible(False)        \n",
    "        axs[c].set_title('Sample number ' + str(sample_nos[i]) + ' (' + q_test[sample_nos[i]] + '), Freq. band ' + str(j+1))\n",
    "        c = c + 1\n",
    "plt.suptitle('Topographies ', fontsize=26)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'Topographies Averaged within Classes ')"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f068e21bc18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "avg_right = np.zeros([height, width, x_test.shape[3]])\n",
    "avg_left = np.zeros([height, width, x_test.shape[3]])\n",
    "\n",
    "for b in range(x_test.shape[3]):\n",
    "    avg_right[:,:,b] = np.mean(x_test[np.array(find_right(q_test, label='right')),:,:,b],axis=0)\n",
    "    avg_left[:,:,b] = np.mean(x_test[np.array(find_right(q_test, label='left')),:,:,b],axis=0)\n",
    "\n",
    "I = x_test.shape[3]\n",
    "J = 2\n",
    "\n",
    "fig, axs = plt.subplots(I,J)\n",
    "fig.set_size_inches(18.5, 10.5)\n",
    "axs = axs.ravel()\n",
    "\n",
    "c = 0\n",
    "for b in range(x_test.shape[3]):\n",
    "    axs[c].imshow(avg_left[:,:,b].astype('float64'))\n",
    "    axs[c].set_title('Average Left trials')\n",
    "    c = c + 1\n",
    "    axs[c].imshow(avg_right[:,:,b].astype('float64'))\n",
    "    axs[c].set_title('Average Right trials')\n",
    "    c = c + 1\n",
    "#         axs[c].get_xaxis().set_visible(False)\n",
    "#         axs[c].get_yaxis().set_visible(False)        \n",
    "#         axs[c].set_title('Sample number ' + str(sample_nos[i]) + ' (' + q_test[sample_nos[i]] + '), Freq. band ' + str(j+1))\n",
    "plt.tight_layout()\n",
    "plt.suptitle('Topographies Averaged within Classes ', fontsize=26)"
   ]
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   "execution_count": null,
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