Model.py

model = Sequential()
model.add(Reshape((64,64,3)))
model.add(Conv2D(32, (3, 3), padding = 'same', \
                 activation="relu",input_shape = (2000,64,64,3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.5))
model.add(Conv2D(32, (3, 3), padding = 'same',activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten()) 
model.add(Dropout(0.5))
model.add(Dense(32, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation = 'sigmoid'))


opt = keras.optimizers.rmsprop(lr=1e-4, decay=1e-6)
model.compile(loss="binary_crossentropy",optimizer=opt,metrics=['accuracy']) 

monitor = ModelCheckpoint('./model.h5', monitor='val_acc', verbose=0,\
                          save_best_only=True, save_weights_only=False,\
                          mode='auto', period=1)


history = model.fit(train_data, train_labels, epochs = 15,\
                    validation_data = (test_data, test_labels),callbacks=[monitor])

plot_acc(history)
plot_loss(history)