Керас: KeyError: 'val_a cc'

Question

Я пытаюсь провести мультиклассовую классификацию для 26 классов. Кажется, моя модель не проверяет данные, хотя я написал генератор данных проверки помимо генератора обучающих данных. Вот код:

datagen = ImageDataGenerator(
        rotation_range=0.2,
        width_shift_range=0.05,
        height_shift_range=0.05,
        shear_range=0.05,
        horizontal_flip=True,
        fill_mode='nearest',
        )

batch_size = 8 

train_generator = datagen.flow(
    train_images,
    train_labels,
    batch_size=batch_size,
    shuffle=True,
    subset='training', 
    seed=42)
    
valid_generator = datagen.flow(
    val_images,
    val_labels,
    batch_size=batch_size,
    shuffle=True,
    subset='validation', 
    seed=42)

Вот модель:

img_rows = 256
img_cols = 256

def get_net():

    inputs = Input((img_rows, img_cols, 1))
    print("inputs shape:",inputs.shape)

    #Convolution layers
    conv1 = Conv2D(24, 3, strides=(2, 2), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
    print("conv1 shape:",conv1.shape)
      
    conv2 = Conv2D(24, 3, strides=(2, 2), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
    print("conv2 shape:",conv2.shape)
    
    pool1 = MaxPooling2D(pool_size=(2, 2))(conv2)
    print("pool1 shape:",pool1.shape)
    
    drop1 = Dropout(0.25)(pool1)

    conv3 = Conv2D(36, 3, strides=(2, 2), activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(drop1)
    print("conv3 shape:",conv3.shape)

    conv4 = Conv2D(36, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
    print("conv4 shape:",conv4.shape)
    
    pool2 = MaxPooling2D(pool_size=(2, 2))(conv4)
    print("pool2 shape:",pool2.shape)
    
    drop2 = Dropout(0.25)(pool2)

    conv5 = Conv2D(48, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(drop2)
    print("conv5 shape:",conv5.shape)
    
    conv6 = Conv2D(48, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
    print("conv6 shape:",conv6.shape)
    
    pool3 = MaxPooling2D(pool_size=(2, 2))(conv6)
    print("pool3 shape:",pool3.shape)
    
    drop3 = Dropout(0.25)(pool3)

    #Flattening
    flat = Flatten()(drop3)

    #Fully connected layers
    dense1 = Dense(128, activation = 'relu', use_bias=True, kernel_initializer = 'he_normal')(flat)
    print("dense1 shape:",dense1.shape)
    drop4 = Dropout(0.5)(dense1)

    dense2 = Dense(128, activation = 'relu', use_bias=True, kernel_initializer = 'he_normal')(drop4)
    print("dense2 shape:",dense2.shape)
    drop5 = Dropout(0.5)(dense2)

    dense4 = Dense(26, activation = 'softmax', use_bias=True, kernel_initializer = 'he_normal')(drop5)
    print("dense4 shape:",dense4.shape)
    #drop7 = Dropout(0.25)(dense4)
    
    model = Model(input = inputs, output = dense4)

    optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=0.00000001, decay=0.0)

     model.compile(optimizer = optimizer, loss = 'categorical_crossentropy', metrics = ['accuracy'])

    return model

Вот код для обучения:

def train():
    
    model = get_net()
    print("got model")
    model.summary()

    model_checkpoint = ModelCheckpoint('seqnet.hdf5', monitor='loss',verbose=1, save_best_only=True)
    print('Fitting model...')
    
    #model.fit_generator(train_generator, validation_data=validation_generator, steps_per_epoch=len(train_generator), epochs=2)
    
    history = model.fit_generator(
    train_generator,
    steps_per_epoch = len(train_generator) // batch_size,
    validation_data = valid_generator, 
    validation_steps = len(valid_generator) // batch_size,
    epochs = 50)
    
    # list all data in history
    print(history.history.keys())
    # summarize history for accuracy
    plt.plot(history.history['acc'])
    plt.plot(history.history['val_acc'])
    plt.title('model accuracy')
    plt.ylabel('accuracy')
    plt.xlabel('epoch')
    plt.legend(['train', 'validation'], loc='upper left')
    plt.show()
    # summarize history for loss
    plt.plot(history.history['loss'])
    plt.plot(history.history['val_loss'])
    plt.title('model loss')
    plt.ylabel('loss')
    plt.xlabel('epoch')
    plt.legend(['train', 'validation'], loc='upper left')
    plt.show() 
    
    
    return model

model = train()

Журнал тренировок с несколькими последними эпохами - примечание он печатает информацию только о точности обучения и потере обучения, никакой информации о проверке.

Epoch 48/50
18/18 [==============================] - 12s 639ms/step - loss: 1.8327 - acc: 0.3125
Epoch 49/50
18/18 [==============================] - 11s 604ms/step - loss: 1.7274 - acc: 0.3840
Epoch 50/50
18/18 [==============================] - 11s 609ms/step - loss: 1.5989 - acc: 0.3542
dict_keys(['acc', 'loss'])
---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-72-387ae82a9daf> in <module>()
     39     return model
     40 
---> 41 model = train()

<ipython-input-72-387ae82a9daf> in train()
     21     # summarize history for accuracy
     22     plt.plot(history.history['acc'])
---> 23     plt.plot(history.history['val_acc'])
     24     plt.title('model accuracy')
     25     plt.ylabel('accuracy')

KeyError: 'val_acc'

Вот неполная кривая (согласно моему коду должны были быть созданы два графика - один - точность обучения против точности проверки, другой потеря обучения или потеря проверки):

введите описание изображения здесь