Aditya_CNN_5.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Aditya_CNN_5.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"bMF6LQrnTgkq","colab_type":"code","colab":{}},"source":["import pandas as pd\n","import numpy as np\n","import pickle\n","import matplotlib.pyplot as plt\n","import cv2 as cv\n","from PIL import Image\n","from sklearn.model_selection import train_test_split\n","from sklearn.utils import shuffle\n","from keras.utils import np_utils"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"HOgfptbUT03E","colab_type":"code","outputId":"6d83a6b6-9c44-4815-f25a-9f88526a0f70","executionInfo":{"status":"ok","timestamp":1571659068471,"user_tz":-330,"elapsed":18258,"user":{"displayName":"Aditya Jyoti Paul","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAMad35J2c-RgfgGVzZ2LlMwP8c4gDDYwQwYLAV1w=s64","userId":"18365019252305581582"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["from google.colab import drive\n","drive.mount('/content/gdrive',force_remount=True)"],"execution_count":37,"outputs":[{"output_type":"stream","text":["Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"E1XUxdQLTlTt","colab_type":"code","colab":{}},"source":["root = r'/content/gdrive/My Drive/'"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"1Am6YxnkT0DB","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":162},"outputId":"3daca2a8-1bac-4dcb-b45f-5441c25d5894","executionInfo":{"status":"error","timestamp":1571658715356,"user_tz":-330,"elapsed":14337,"user":{"displayName":"Aditya Jyoti Paul","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAMad35J2c-RgfgGVzZ2LlMwP8c4gDDYwQwYLAV1w=s64","userId":"18365019252305581582"}}},"source":["df = pd.concat([pickle.load(open(root+\"df1.pickle\", \"rb\")),pickle.load(open(root+\"df2.pickle\", \"rb\"))])"],"execution_count":15,"outputs":[{"output_type":"error","ename":"OSError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)","\u001b[0;32m<ipython-input-15-b9207036144f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"df1.pickle\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mpickle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m\"df2.pickle\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mOSError\u001b[0m: [Errno 107] Transport endpoint is not connected"]}]},{"cell_type":"code","metadata":{"id":"YZg7psEbNyPY","colab_type":"code","outputId":"603c112d-169d-4b96-d4b2-14b7174724e1","executionInfo":{"status":"ok","timestamp":1571658734891,"user_tz":-330,"elapsed":1432,"user":{"displayName":"Aditya Jyoti Paul","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAMad35J2c-RgfgGVzZ2LlMwP8c4gDDYwQwYLAV1w=s64","userId":"18365019252305581582"}},"colab":{"base_uri":"https://localhost:8080/","height":402}},"source":["print(df.shape)\n","\n","print(df.level.value_counts())\n","df.describe()"],"execution_count":16,"outputs":[{"output_type":"stream","text":["(88702, 3)\n","0    65343\n","2    13153\n","1     6205\n","3     2087\n","4     1914\n","Name: level, dtype: int64\n"],"name":"stdout"},{"output_type":"execute_result","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>level</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>88702.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>0.523415</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.972693</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>4.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["              level\n","count  88702.000000\n","mean       0.523415\n","std        0.972693\n","min        0.000000\n","25%        0.000000\n","50%        0.000000\n","75%        1.000000\n","max        4.000000"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"id":"h4OesvHoUN3I","colab_type":"code","outputId":"92e00edc-944d-47a5-9dda-841783004085","executionInfo":{"status":"ok","timestamp":1571658741831,"user_tz":-330,"elapsed":2233,"user":{"displayName":"Aditya Jyoti Paul","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAMad35J2c-RgfgGVzZ2LlMwP8c4gDDYwQwYLAV1w=s64","userId":"18365019252305581582"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["plt.imshow(df.eq_img[141], cmap='gray')\n","df.level[141]"],"execution_count":17,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0"]},"metadata":{"tags":[]},"execution_count":17},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQYAAAD8CAYAAACVSwr3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLBUTF6KBZ4rxOJcvjwzDf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uHREBLXABbYCpvleO9VCThe708aI/LIMOC9N03TNGE4Hu7D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\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i0Lq4uGggIAYjn89rc3MzxGZk0nTW6sNqjMVipnngKdbNZlMXL160hjDLy8taX19XNptV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\nFjuG2GKLbZP9C3afZnaQ0TJ/AAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"sorjaBIsf3_c","colab_type":"code","outputId":"d1575882-d861-4a83-df0a-726a9709913e","executionInfo":{"status":"ok","timestamp":1571658741833,"user_tz":-330,"elapsed":1835,"user":{"displayName":"Aditya Jyoti Paul","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAMad35J2c-RgfgGVzZ2LlMwP8c4gDDYwQwYLAV1w=s64","userId":"18365019252305581582"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["zerodf=df[df['level']==0][:1910]\n","onedf = df[df['level']==1][:1910]\n","twodf = df[df['level']==2][:1910]\n","threedf = df[df['level']==3][:1910]\n","fourdf = df[df['level']==4][:1910]\n","neww=pd.DataFrame()\n","neww=neww.append(zerodf).append(onedf).append(twodf).append(threedf).append(fourdf)\n","neww.shape"],"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(9550, 3)"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"code","metadata":{"id":"RR4hf1b4l0m_","colab_type":"code","outputId":"b9e612e6-cfe3-478f-9092-4759301a9b6a","executionInfo":{"status":"ok","timestamp":1571658744110,"user_tz":-330,"elapsed":789,"user":{"displayName":"Aditya Jyoti Paul","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAMad35J2c-RgfgGVzZ2LlMwP8c4gDDYwQwYLAV1w=s64","userId":"18365019252305581582"}},"colab":{"base_uri":"https://localhost:8080/","height":385}},"source":["print (neww.level.value_counts())\n","neww.describe()"],"execution_count":19,"outputs":[{"output_type":"stream","text":["4    1910\n","3    1910\n","2    1910\n","1    1910\n","0    1910\n","Name: level, dtype: int64\n"],"name":"stdout"},{"output_type":"execute_result","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>level</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>9550.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>2.000000</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>1.414288</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>2.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>3.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>4.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["             level\n","count  9550.000000\n","mean      2.000000\n","std       1.414288\n","min       0.000000\n","25%       1.000000\n","50%       2.000000\n","75%       3.000000\n","max       4.000000"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"id":"-eWALOZjlrxc","colab_type":"code","colab":{}},"source":["df = 0;\n","zerodf = 0\n","onedf = 0\n","twodf = 0\n","threedf = 0\n","fourdf = 0"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"4v_ySXp1VFt1","colab_type":"code","colab":{}},"source":["images = neww.eq_img\n","labels = neww.level\n","img = []\n","\n","for row in images:\n","    img.append(np.array(row).flatten())"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"0lx9cyfPprkA","colab_type":"code","colab":{}},"source":["images = 0\n","neww = 0"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"xD5NrP4HVIcN","colab_type":"code","colab":{}},"source":["img = np.array(img)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"I2Am7lRnVKcv","colab_type":"code","colab":{}},"source":["img.shape\n","\n","img_rows = 256\n","img_cols = 256"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Dko5rPvcVNKJ","colab_type":"code","outputId":"d22ae9c9-e992-4237-8401-ed5a9d529fed","executionInfo":{"status":"ok","timestamp":1571658755946,"user_tz":-330,"elapsed":5870,"user":{"displayName":"Aditya Jyoti Paul","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAMad35J2c-RgfgGVzZ2LlMwP8c4gDDYwQwYLAV1w=s64","userId":"18365019252305581582"}},"colab":{"base_uri":"https://localhost:8080/","height":101}},"source":["(X, y) = (img, labels)\n","\n","#Splitting the data into Train and test sets\n","\n","X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=24)\n","# X_train = img[:14000]\n","# X_test = img[14000:]\n","# y_train = labels[:14000]\n","# y_test = labels[14000:]\n","\n","print(X_train.shape)\n","print(X_test.shape)\n","\n","img = 0\n","labels = 0\n","\n","#Reshaping the flattened numpy array into a image matrix \n","X_train = X_train.reshape(X_train.shape[0], img_cols, img_rows, 1)\n","X_test = X_test.reshape(X_test.shape[0], img_cols, img_rows, 1)\n","\n","\n","X_train = X_train.astype('float32')\n","X_test = X_test.astype('float32')\n","\n","#Normalizing\n","X_train /= 255\n","X_test /= 255\n","\n","print('X_train shape:', X_train.shape)\n","print(X_train.shape[0], 'train samples')\n","print(X_test.shape[0], 'test samples')"],"execution_count":25,"outputs":[{"output_type":"stream","text":["(8595, 65536)\n","(955, 65536)\n","X_train shape: (8595, 256, 256, 1)\n","8595 train samples\n","955 test samples\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"c6ZA9hnvVPvD","colab_type":"code","outputId":"93899fcc-a5d4-431c-c15e-36332dbbabc5","executionInfo":{"status":"ok","timestamp":1571658755947,"user_tz":-330,"elapsed":1520,"user":{"displayName":"Aditya Jyoti Paul","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAMad35J2c-RgfgGVzZ2LlMwP8c4gDDYwQwYLAV1w=s64","userId":"18365019252305581582"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["# convert class vectors to binary class matrices\n","Y_train = np_utils.to_categorical(y_train, 5)\n","Y_test = np_utils.to_categorical(y_test, 5)\n","\n","Y_train[0]"],"execution_count":26,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0., 0., 1., 0., 0.], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":26}]},{"cell_type":"code","metadata":{"id":"qEe30WbCVSdP","colab_type":"code","colab":{}},"source":["from keras.models import Sequential\n","import keras\n","from keras.layers.core import Dense, Dropout, Activation, Flatten\n","from keras.layers import LeakyReLU, BatchNormalization\n","from keras.layers.convolutional import Conv2D, MaxPooling2D\n","from keras.optimizers import SGD,RMSprop,adam\n","from keras.preprocessing.image import ImageDataGenerator\n","\n","# import optimizer\n","\n","\n","#batch_size to train\n","batch_size = 64\n","# number of output classes\n","nb_classes = 5\n","# number of epochs to train\n","nb_epoch = 5\n","# number of convolutional filters to use\n","nb_filters = 32\n","# size of pooling area for max pooling\n","nb_pool = 2\n","# convolution kernel size\n","nb_conv = 3"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"6Kp4ezVBVXeT","colab_type":"code","colab":{}},"source":["# create generators  - training data will be augmented images\n","validationdatagenerator = ImageDataGenerator()\n","traindatagenerator = ImageDataGenerator(width_shift_range=0.1,height_shift_range=0.1,rotation_range=15,zoom_range=0.1 )\n","\n","batchsize=8\n","train_generator=traindatagenerator.flow(X_train, Y_train, batch_size=batchsize) \n","validation_generator=validationdatagenerator.flow(X_test, Y_test,batch_size=batchsize)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"hy4R8DBtYeqd","colab_type":"code","outputId":"62ca9057-f353-41d4-bff3-be6ede8311f5","executionInfo":{"status":"ok","timestamp":1571658772041,"user_tz":-330,"elapsed":7392,"user":{"displayName":"Aditya Jyoti Paul","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAMad35J2c-RgfgGVzZ2LlMwP8c4gDDYwQwYLAV1w=s64","userId":"18365019252305581582"}},"colab":{"base_uri":"https://localhost:8080/","height":524}},"source":["model = Sequential()\n","\n","model.add(Conv2D(32,kernel_size=(3,3),padding='valid', input_shape=(img_cols, img_rows,1), activity_regularizer=keras.regularizers.l2(0.001), strides=(2,2)))\n","model.add(BatchNormalization())\n","model.add(LeakyReLU(alpha=0.01))\n","\n","model.add(Conv2D(64,kernel_size=(3,3),padding='valid', strides=(2,2)))\n","model.add(BatchNormalization())\n","model.add(LeakyReLU(alpha=0.01))\n","\n","\n","\n","\n","model.add(Conv2D(128,kernel_size=(3,3),padding='valid', strides=(2,2)))\n","model.add(BatchNormalization())\n","model.add(LeakyReLU(alpha=0.01))\n","# model.add(MaxPooling2D(pool_size=(2,2)))\n","\n","# model.add(Dropout(rate=0.1))         #add dropout to prettify this big-ass cnn\n","\n","\n","\n","model.add(Conv2D(256,kernel_size=(3,3),padding='valid', strides=(2,2)))\n","model.add(BatchNormalization())\n","model.add(LeakyReLU(alpha=0.01))\n","# model.add(MaxPooling2D(pool_size=(2,2)))\n","# model.add(Dropout(rate=0.1))         #add dropout to prettify this big-ass cnn\n","model.add(Conv2D(256,kernel_size=(3,3),padding='valid', strides=(2,2)))\n","model.add(BatchNormalization())\n","model.add(LeakyReLU(alpha=0.01))\n","# model.add(MaxPooling2D(pool_size=(2,2)))\n","# model.add(Dropout(rate=0.1))\n","\n","\n","model.add(Conv2D(512,kernel_size=(3,3),padding='valid', strides=(2,2)))\n","model.add(BatchNormalization())\n","model.add(LeakyReLU(alpha=0.01))\n","model.add(Dropout(rate=0.1))         #add dropout to prettify this big-ass cnn\n","model.add(Conv2D(512,kernel_size=(3,3),padding='valid', strides=(2,2)))\n","model.add(BatchNormalization())\n","model.add(LeakyReLU(alpha=0.01))\n","# model.add(MaxPooling2D(pool_size=(2,2)))\n","\n","model.add(Dropout(rate=0.4))         #add dropout to prettify this big-ass cnn\n","\n","model.add(Flatten())\n","model.add(Dense(1024))\n","model.add(BatchNormalization())\n","model.add(LeakyReLU(0.01))\n","model.add(Dropout(rate=0.4))\n","\n","\n","model.add(Dense(1024))\n","model.add(LeakyReLU(0.01))\n","\n","model.add(Dense(nb_classes))\n","model.add(Activation('softmax'))\n","\n","manual_optimizer=keras.optimizers.Adadelta(lr=2.0, rho=0.95, epsilon=None, decay=0)\n","model.compile(loss='categorical_crossentropy', optimizer=manual_optimizer, metrics=['accuracy'])"],"execution_count":29,"outputs":[{"output_type":"stream","text":["WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:66: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:541: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:4432: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:190: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:197: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:203: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:207: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:216: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:223: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:2041: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:148: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3733: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n","Instructions for updating:\n","Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n","\n","WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3576: The name tf.log is deprecated. Please use tf.math.log instead.\n","\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"3e7_QccQPLzg","colab_type":"code","colab":{}},"source":["from keras.callbacks import History\n","history = History()\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"sE9xmiyhVaKs","colab_type":"code","outputId":"5e1259c2-be50-4adb-8592-ba03e777f80f","executionInfo":{"status":"ok","timestamp":1561111395806,"user_tz":-330,"elapsed":821071,"user":{"displayName":"Aditya Jyoti Paul","photoUrl":"https://lh5.googleusercontent.com/-pUpj01En2hU/AAAAAAAAAAI/AAAAAAAABz0/srA9xDrjvII/s64/photo.jpg","userId":"18365019252305581582"}},"colab":{"base_uri":"https://localhost:8080/","height":357}},"source":["# history = model.fit_generator(train_generator, steps_per_epoch=int(len(X_train)/8), epochs=10, validation_data=validation_generator,verbose=True, validation_steps=int(len(X_test)/8))\n","history = model.fit(X_, steps_per_epoch=int(len(X_train)/8), epochs=10, validation_data=validation_generator,verbose=True, validation_steps=int(len(X_test)/8))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Epoch 1/10\n","1074/1074 [==============================] - 83s 77ms/step - loss: 1.9534 - acc: 0.2132 - val_loss: 1.8391 - val_acc: 0.2492\n","Epoch 2/10\n","1074/1074 [==============================] - 81s 75ms/step - loss: 1.8216 - acc: 0.2243 - val_loss: 2.1217 - val_acc: 0.2186\n","Epoch 3/10\n","1074/1074 [==============================] - 81s 75ms/step - loss: 1.7947 - acc: 0.2540 - val_loss: 1.8405 - val_acc: 0.2429\n","Epoch 4/10\n","1074/1074 [==============================] - 83s 77ms/step - loss: 1.7637 - acc: 0.2546 - val_loss: 1.6774 - val_acc: 0.2851\n","Epoch 5/10\n","1074/1074 [==============================] - 84s 78ms/step - loss: 1.7585 - acc: 0.2715 - val_loss: 1.6639 - val_acc: 0.2756\n","Epoch 6/10\n","1074/1074 [==============================] - 85s 80ms/step - loss: 1.7200 - acc: 0.2872 - val_loss: 1.7168 - val_acc: 0.2397\n","Epoch 7/10\n","1074/1074 [==============================] - 85s 79ms/step - loss: 1.6975 - acc: 0.2942 - val_loss: 1.7803 - val_acc: 0.2270\n","Epoch 8/10\n","1074/1074 [==============================] - 84s 79ms/step - loss: 1.6993 - acc: 0.3037 - val_loss: 2.9227 - val_acc: 0.2291\n","Epoch 9/10\n","1074/1074 [==============================] - 84s 78ms/step - loss: 1.6842 - acc: 0.3054 - val_loss: 2.1301 - val_acc: 0.2503\n","Epoch 10/10\n","1074/1074 [==============================] - 71s 66ms/step - loss: 1.6865 - acc: 0.3134 - val_loss: 4.2402 - val_acc: 0.2133\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"TFTeiPbyO9Iu","colab_type":"code","colab":{}},"source":["model.save('cnn_model.hdf5')"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"FpmuEjZvKr-s","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":438},"outputId":"7ef5207d-ff49-414d-ad7a-d3ea429fdeab","executionInfo":{"status":"error","timestamp":1571659022988,"user_tz":-330,"elapsed":1264,"user":{"displayName":"Aditya Jyoti Paul","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mAMad35J2c-RgfgGVzZ2LlMwP8c4gDDYwQwYLAV1w=s64","userId":"18365019252305581582"}}},"source":["from keras.models import load_model\n","\n","godmodel = load_model(root+'')"],"execution_count":36,"outputs":[{"output_type":"error","ename":"OSError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)","\u001b[0;32m<ipython-input-36-fae039d2e70e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mgodmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroot\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py\u001b[0m in \u001b[0;36mload_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    456\u001b[0m                 \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmp_filepath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    457\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 458\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mload_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    459\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    460\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mload_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile)\u001b[0m\n\u001b[1;32m    547\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mH5Dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_supported_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\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--> 549\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mH5Dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mh5dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    550\u001b[0m             \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deserialize_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh5dict\u001b[0m\u001b[0;34m,\u001b[0m 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'No such file or directory', flags = 0, o_flags = 0)"]}]},{"cell_type":"code","metadata":{"id":"J1Phxz4KVU6d","colab_type":"code","colab":{}},"source":["# #Keras CNN Architecture   old\n","\n","# model = Sequential()\n","\n","# model.add(Convolution2D(nb_filters, nb_conv, nb_conv,\n","#                         padding='valid',\n","#                         input_shape=(img_cols, img_rows, 1)))\n","\n","# convout1 = Activation('relu')\n","# model.add(convout1)\n","\n","# model.add(Convolution2D(nb_filters+32, nb_conv, nb_conv))\n","# convout2 = Activation('relu')\n","# model.add(convout2)\n","# model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))\n","\n","# model.add(Convolution2D(nb_filters+32, nb_conv, nb_conv))\n","# convout3 = Activation('relu')\n","# model.add(convout3)\n","# model.add(MaxPooling2D(pool_size=(nb_pool, nb_pool)))\n","# model.add(Dropout(0.5))\n","\n","# model.add(Flatten())\n","# model.add(Dense(257))\n","# model.add(Activation('relu'))\n","# model.add(Dropout(0.5))\n","# model.add(Dense(nb_classes))\n","# model.add(Activation('softmax'))\n","# model.compile(loss='categorical_crossentropy', optimizer='adadelta', metrics=['accuracy'])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"2Ows98rlO9Nx","colab_type":"code","outputId":"43407025-2f1b-4942-a893-1319015472a1","executionInfo":{"status":"ok","timestamp":1560933706374,"user_tz":-330,"elapsed":1797,"user":{"displayName":"Sudharsan B","photoUrl":"https://lh6.googleusercontent.com/-uepKUR74knY/AAAAAAAAAAI/AAAAAAAAxQg/axaKo_zOt9g/s64/photo.jpg","userId":"06739839580247062644"}},"colab":{"base_uri":"https://localhost:8080/","height":591}},"source":["\n","def plot_model_history(history):\n","    \"\"\"\n","    Function to plot training and validation data of model\n","    \n","    Parameters\n","    ----------\n","    history: dictionary\n","             history of training and validation of model\n","    Returns\n","    -------\n","    None\n","    \n","    \"\"\"\n","    print(history.history.keys())\n","    # summarize history for accuracy\n","    plt.plot(history.history['acc'])\n","    plt.plot(history.history['val_acc'])\n","    plt.title('model accuracy')\n","    plt.ylabel('accuracy')\n","    plt.xlabel('epoch')\n","    plt.legend(['train', 'test'], loc='upper left')\n","    plt.show()\n","    # summarize history for loss\n","    plt.plot(history.history['loss'])\n","    plt.plot(history.history['val_loss'])\n","    plt.title('model loss')\n","    plt.ylabel('loss')\n","    plt.xlabel('epoch')\n","    plt.legend(['train', 'test'], loc='upper left')\n","    plt.show()\n","    \n","plot_model_history(history)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYsAAAEWCAYAAACXGLsWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nVorIyrKyTkyWBip/L3jkjC1bT1CXbm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5bT019PScP7MY1x/RjWJ8sq5prim++gBeuhG6HwAX/hvhGPjpFYMDJ7lE4xyWN6XfD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\n+n+1h8kr5Xe16AAAAABJRU5ErkJggg==\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"yrYZCnEfVc8q","colab_type":"code","outputId":"e8587c15-a0ad-441c-f10b-40650bd8b3d0","executionInfo":{"status":"ok","timestamp":1560933721014,"user_tz":-330,"elapsed":2685,"user":{"displayName":"Sudharsan B","photoUrl":"https://lh6.googleusercontent.com/-uepKUR74knY/AAAAAAAAAAI/AAAAAAAAxQg/axaKo_zOt9g/s64/photo.jpg","userId":"06739839580247062644"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["score = model.evaluate(X_test, Y_test, verbose=0)\n","print(score)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["[1.2444444521793934, 0.4942056976730037]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"O5anfTa3o79a","colab_type":"code","outputId":"e0a8e158-903c-428c-a370-9a6e87234a65","executionInfo":{"status":"ok","timestamp":1560463986895,"user_tz":-330,"elapsed":1778,"user":{"displayName":"Aditya Jyoti Paul","photoUrl":"https://lh5.googleusercontent.com/-pUpj01En2hU/AAAAAAAAAAI/AAAAAAAABz0/srA9xDrjvII/s64/photo.jpg","userId":"18365019252305581582"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["from keras.utils import plot_model\n","img = plot_model(model, to_file='/content/gdrive/My Drive/model.png')\n","print (img)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["None\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"shY4fe27Ygr7","colab_type":"code","outputId":"40b8de9f-b371-4cc2-8ffb-60440ea45b06","executionInfo":{"status":"ok","timestamp":1560933731834,"user_tz":-330,"elapsed":1060,"user":{"displayName":"Sudharsan B","photoUrl":"https://lh6.googleusercontent.com/-uepKUR74knY/AAAAAAAAAAI/AAAAAAAAxQg/axaKo_zOt9g/s64/photo.jpg","userId":"06739839580247062644"}},"colab":{"base_uri":"https://localhost:8080/","height":53}},"source":["i = np.expand_dims(X_test[4], axis=0)\n","print(model.predict(i))\n","Y_test[4]"],"execution_count":0,"outputs":[{"output_type":"stream","text":["[[0.51322466 0.4895771 ]]\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["array([1., 0.], dtype=float32)"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"code","metadata":{"id":"9n92tP3N6cPs","colab_type":"code","outputId":"9f22e186-3334-4454-d307-fc004332994f","executionInfo":{"status":"ok","timestamp":1560933957313,"user_tz":-330,"elapsed":845,"user":{"displayName":"Sudharsan B","photoUrl":"https://lh6.googleusercontent.com/-uepKUR74knY/AAAAAAAAAAI/AAAAAAAAxQg/axaKo_zOt9g/s64/photo.jpg","userId":"06739839580247062644"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["Y_test[0][0]"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.0"]},"metadata":{"tags":[]},"execution_count":35}]},{"cell_type":"code","metadata":{"id":"hwrCjQ-v5tiw","colab_type":"code","colab":{}},"source":["countPX = 0\n","countX = 0\n","for i in range(2000):\n","  ans = model.predict(np.expand_dims(X_test[i], axis=0))\n","  if ans.argmax() == 1:\n","    countPX +=1\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"pBwO0Oke6sJ5","colab_type":"code","colab":{}},"source":["for i in range(2000):\n","  if Y_test[i][1] == 1:\n","    countX+=1"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"i4n3snLj633N","colab_type":"code","outputId":"d377a076-a42b-4415-dd94-ab2491259092","executionInfo":{"status":"ok","timestamp":1560934129534,"user_tz":-330,"elapsed":6080,"user":{"displayName":"Sudharsan B","photoUrl":"https://lh6.googleusercontent.com/-uepKUR74knY/AAAAAAAAAAI/AAAAAAAAxQg/axaKo_zOt9g/s64/photo.jpg","userId":"06739839580247062644"}},"colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["print(countX, countPX)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["1040 270\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"XX6wWOssYqH3","colab_type":"code","outputId":"906fc133-8a13-412f-e642-b8e1a10d1f3b","colab":{"base_uri":"https://localhost:8080/","height":287}},"source":["plt.imshow(X_test[0].reshape(256,256), cmap='gray')"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 0x7fc61f252978>"]},"metadata":{"tags":[]},"execution_count":24},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQYAAAD8CAYAAACVSwr3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNoUpqAFAAHj99ddRLBaRz+c9jVT8LNp0OkUkEvEsVAAeAlUkE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toITGsI4M1mM2m51nZw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\nKTkSAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"NEiQvEeHY7_p","colab_type":"code","outputId":"9003d347-fe18-47e8-cef7-f0e4d8d3ec21","colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["X_test[0].shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(256, 256, 1)"]},"metadata":{"tags":[]},"execution_count":38}]},{"cell_type":"code","metadata":{"id":"ZzHr7MqAaKZG","colab_type":"code","colab":{}},"source":["x = []\n","for i in range(1000):\n","  ans = model.predict(np.expand_dims(X_test[i], axis=0))\n","  print(ans)\n","  x.append(ans.argmax())  "],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"4U2Ci2t4ghT4","colab_type":"code","outputId":"654df8cb-10f4-41d3-dc6c-c9d6b972d721","colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["x = np.array(x)\n","np.unique(x)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 1, 2, 3])"]},"metadata":{"tags":[]},"execution_count":152}]},{"cell_type":"code","metadata":{"id":"RBpnSv2Vgij8","colab_type":"code","outputId":"d1f12c18-762e-4ae7-cd20-71c8024187cd","colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["newdf = pd.DataFrame()\n","type(newdf)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["pandas.core.frame.DataFrame"]},"metadata":{"tags":[]},"execution_count":60}]},{"cell_type":"code","metadata":{"id":"QWJxCZmAi2VM","colab_type":"code","colab":{}},"source":["\n","for index, value in df.iterrows():\n","    newdf.append(df['level']>0)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"OZWhsUD4kPZQ","colab_type":"code","outputId":"a10434ef-e887-498b-8ad6-fbd16a34a125","colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["df.columns"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['image', 'level', 'eq_img'], dtype='object')"]},"metadata":{"tags":[]},"execution_count":49}]},{"cell_type":"code","metadata":{"id":"IANNvV24nSXd","colab_type":"code","outputId":"55e68946-1ff5-4cbd-b697-97e9de17b975","colab":{"base_uri":"https://localhost:8080/","height":49}},"source":["newdf"],"execution_count":0,"outputs":[{"output_type":"execute_result","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>image</th>\n","      <th>level</th>\n","      <th>eq_img</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["Empty DataFrame\n","Columns: [image, level, eq_img]\n","Index: []"]},"metadata":{"tags":[]},"execution_count":58}]},{"cell_type":"code","metadata":{"id":"JfjpwPKanxBM","colab_type":"code","colab":{}},"source":["newdf=df[df['level']>0]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"TXSGXk-qpu4n","colab_type":"code","outputId":"a0b102c0-fb49-4bc5-d9e7-d33f74e4acba","colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["newdf.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(3898, 3)"]},"metadata":{"tags":[]},"execution_count":78}]},{"cell_type":"code","metadata":{"id":"QjTSbm3pp3DH","colab_type":"code","colab":{}},"source":["zerodf=df[df['level']==0][:802]"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"yz7okuo1qIlK","colab_type":"code","colab":{}},"source":["newdf = newdf.append(zerodf)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"uk1MSGSXqLjO","colab_type":"code","outputId":"e35f403b-5ebd-441f-dc54-a7754d2d2333","colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["newdf.shape"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(4700, 3)"]},"metadata":{"tags":[]},"execution_count":81}]},{"cell_type":"code","metadata":{"id":"BOtZDZrgsw-N","colab_type":"code","outputId":"863e9fa6-9bed-4b59-d513-4aa46d42c429","colab":{"base_uri":"https://localhost:8080/","height":89}},"source":["newdf.loc[0]"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["image                                               000001a\n","level                                                     0\n","eq_img    [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...\n","Name: 0, dtype: object"]},"metadata":{"tags":[]},"execution_count":106}]},{"cell_type":"code","metadata":{"id":"5NBAMeg-wJF2","colab_type":"code","outputId":"40ae1aed-69e4-469d-ca69-3077c0ae35b5","colab":{"base_uri":"https://localhost:8080/","height":35}},"source":["neww.shape\n"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(1650, 3)"]},"metadata":{"tags":[]},"execution_count":139}]},{"cell_type":"code","metadata":{"id":"QV-dhUMuwO7o","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}