将每个批次或时期的验证准确性打印到控制台（Keras）

Question

I'm using ImageDataGenerator and flow_from_directory to generate my data, and using model.fit_generator to fit the data.

This defaults to outputting the accuracy for training data set only. There doesn't seem to be an option to output validation accuracy to the terminal.

Here is the relevant portion of my code:

#train data generator


print('Starting Preprocessing')

train_datagen = ImageDataGenerator(preprocessing_function = preprocess)

train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size = (img_height, img_width),
batch_size = batch_size, 
class_mode = 'categorical')  #class_mode = 'categorical'


#same for validation
val_datagen = ImageDataGenerator(preprocessing_function = preprocess)

validation_generator = val_datagen.flow_from_directory(
        validation_data_dir,
        target_size = (img_height, img_width),
        batch_size=batch_size,
        class_mode='categorical')





########################Model Creation###################################

#create the base pre-trained model
print('Finished Preprocessing, starting model creating \n')
base_model = InceptionV3(weights='imagenet', include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(12, activation='softmax')(x)
model = Model(input=base_model.input, output=predictions)




for layer in model.layers[:-34]:
   layer.trainable = False
for layer in model.layers[-34:]:
   layer.trainable = True


from keras.optimizers import SGD
model.compile(optimizer=SGD(lr=0.001, momentum=0.92),
              loss='categorical_crossentropy',
              metrics = ['accuracy'])



#############SAVE Model #######################################


file_name = str(datetime.datetime.now()).split(' ')[0] + '_{epoch:02d}.hdf5'
filepath = os.path.join(save_dir, file_name)



checkpoints =ModelCheckpoint(filepath, monitor='val_acc', verbose=1,
                                save_best_only=False, save_weights_only=False,
                                mode='auto', period=2)

###############Fit Model #############################

model.fit_generator(
train_generator,
steps_per_epoch =total_samples//batch_size,
epochs = epochs,
validation_data=validation_generator,
validation_steps=total_validation//batch_size,
callbacks = [checkpoints],
shuffle= True)

UPDATE OUTPUT:

Throughout training, I'm only getting the output of training accuracy, but at the end of training, I"m getting both training, validation accuracy.

Epoch 1/10

  1/363 [..............................] - ETA: 1:05:58 - loss: 2.4976 - acc: 0.0640
  2/363 [..............................] - ETA: 51:33 - loss: 2.4927 - acc: 0.0760  
  3/363 [..............................] - ETA: 48:55 - loss: 2.5067 - acc: 0.0787
  4/363 [..............................] - ETA: 47:26 - loss: 2.5110 - acc: 0.0770
  5/363 [..............................] - ETA: 46:30 - loss: 2.5021 - acc: 0.0824
  6/363 [..............................] - ETA: 45:56 - loss: 2.5063 - acc: 0.0820

Answer 1

Validation loss and validation accuracy gets printed for every epoch once you specify the validation_split.

model.fit(X, Y, epochs=1000, batch_size=10, validation_split=0.2)

I have used the above in my code, and val_loss and val_acc are getting printed for every epoch, but not after every batch.

Hope that answers your question.

Epoch 1/500
1267/1267 [==============================] - 0s 376us/step - loss: 0.6428        - acc: 0.6409 - val_loss: 0.5963 - val_acc: 0.6656

Answer 2

The idea is that you go through you validation set after each epoch, not after each batch. If after every batch, you had to evaluate the performances of the model on the whole validation set, you would loose a lot of time.

After each epoch, you will have the corresponding losses and accuracies both for training and validation. But during one epoch, you will only have access to the training loss and accuracy.

Answer 3

In fit_generator ,

fit_generator(generator, steps_per_epoch=None, epochs=1, verbose=1, callbacks=None, **validation_data=None, validation_steps=None**, validation_freq=1, class_weight=None, max_queue_size=10, workers=1, use_multiprocessing=False, shuffle=True, initial_epoch=0)

since there is no validation_split parameter, you can create two different ImageDataGenerator flow, one for training and one for validating and then place that 'validation_generator' in validation_data . Then it will print the validation loss and accuracy.

Answer 4

is it possible to print out parameters from the model.fit(.....) say for example I would like to print out the validation_split=0.2 parameter!

Why? I am trying to compute the percentage of videos taken from the training set automatically. every time I change my parameters I have to manually compute the size of the training set every time I change this validation parameter.