2 from keras.models
import Sequential
3 from keras.optimizers
import Adam
4 from keras.layers
import Input, Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Reshape, BatchNormalization
6 model = keras.models.Sequential()
7 model.add(Reshape((16, 16, 1), input_shape = (256, )))
8 model.add(Conv2D(10, kernel_size = (3, 3), kernel_initializer =
'glorot_normal',activation =
'relu', padding =
'same'))
9 model.add(BatchNormalization())
10 model.add(Conv2D(10, kernel_size = (3, 3), kernel_initializer =
'glorot_normal',activation =
'relu', padding =
'same'))
11 model.add(MaxPooling2D(pool_size = (2, 2), strides = (1,1)))
13 model.add(Dense(256, activation =
'relu'))
14 model.add(Dense(2, activation =
'sigmoid'))
15 model.compile(loss =
'binary_crossentropy', optimizer = Adam(lr = 0.001), metrics = [
'accuracy'])
16 model.save(
'model_cnn.h5')
