preprocessing_train.py

from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D

batch_size = 64
num_classes = 2
epochs = 10

model = Sequential()
model.add(Conv2D(4, kernel_size=(3, 3),activation='relu',input_shape=input_shape))
model.add(Conv2D(8, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(16, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
    
model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
             metrics=['accuracy'])
             
datagen.fit(x_train)
history = model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
                        steps_per_epoch=x_train.shape[0] // batch_size,
                        epochs=20,
                        validation_data=(x_test, y_test))