Dataaugment mnist

augment_mnist.py

datagen = ImageDataGenerator(
        featurewise_center=False,  
        samplewise_center=False,  
        featurewise_std_normalization=False,  
        samplewise_std_normalization=False,  
        zca_whitening=False,  
        rotation_range=10,  # randomly rotate images
        zoom_range = 0.2, # Randomly zoom image 
        width_shift_range=0.2,  # randomly shift images horizontally
        height_shift_range=0.2,  # randomly shift images vertically
        horizontal_flip=False,  # randomly flip images
        vertical_flip=False)  # randomly flip images

datagen.fit(X_train)
monitor_model = model.fit_generator(datagen.flow(X_train,Y_train, batch_size=batch_size),
                              epochs = epochs, validation_data = (X_val,Y_val),
                              verbose = 2, steps_per_epoch=X_train.shape[0] // batch_size
                              , callbacks=[learning_rate_reduction])