График отключается при попытке построить модель CNN с функциональным API Keras

Question

Я пытаюсь построить модель CNN, используя Keras Functional API, но всякий раз, когда я пытаюсь выполнить эту строку кода: model = CNN(settings, np.expand_dims(x_train, axis = 3)).build_network() Я продолжаю сталкиваться со следующей проблемой:

ValueError: График отключен: невозможно получить значение для тензорного тензора («input_11: 0», shape = (?, 28, 28, 1), dtype = float32) на слое «input_11».Следующие предыдущие слои были доступны без проблем: []

Вот мой код:

class CNN:

    def __init__(self, settings, data):

        self.flag = False
        self.settings = settings

        if self.check_network_settings() == False:
            self.flag = True
            return

        self.data = data

        if K.image_data_format() == "channels_first":
            self.data = self.data.reshape(data.shape[0], data.shape[3], data.shape[2], data.shape[1])

        self.build_network()

    def show_model_chart(self):

        if not os.path.isfile('model.png'):
            plot_model(self.model, to_file = 'model.png')

        model_pic = cv2.imread('model.png')
        plt.imshow(model_pic)
        plt.show()


    def build_network(self):
        print('Bulding Convolutional Neural Network ...')

        inputs = Input(shape = (self.data.shape[1], self.data.shape[2], self.data.shape[3]))
        final_output = None

        for layer_idx in range(self.settings['conv']['layers']):
            inputs = Conv2D(
                              filters = self.settings['conv']['filters'][layer_idx],
                              kernel_size = self.settings['conv']['kernel_size'][layer_idx],
                              strides = self.settings['conv']['strides'][layer_idx],
                              padding = self.settings['conv']['padding']
                            )(inputs)

            if self.settings['pooling']['apply'] == True:
                inputs = MaxPooling2D(
                                  pool_size = self.settings['pooling']['pool_size'][layer_idx],
                                  strides = self.settings['pooling']['strides'][layer_idx],
                                  padding = self.settings['pooling']['padding']
                                )(inputs)

            inputs = Activation(
                                activation = self.settings['detector_stage'][layer_idx]
                            )(inputs)

        inputs = Flatten()(inputs)

        for dense_layer_idx in range(self.settings['dense']['layers']):

            if self.settings['dense']['activations'][dense_layer_idx] != 'softmax':
                inputs = Dense(
                                units = self.settings['dense']['output_units'][dense_layer_idx],
                                activation = self.settings['dense']['activations'][dense_layer_idx]
                             )(inputs)
            else:
                final_output = Dense(
                                units = self.settings['dense']['output_units'][dense_layer_idx],
                                activation = self.settings['dense']['activations'][dense_layer_idx]
                             )(inputs)

        self.model = Model(inputs = inputs, outputs = final_output)

    def check_network_settings(self):

        for key in self.settings:

            if key == 'conv':

                if set(self.settings['conv'].keys()) != {'layers', 'filters', 'kernel_size', 'strides', 'padding'}:
                    print('[INCORRECT SETTINGS]: Convolutional layers ...')
                    return False

            elif key == 'pooling':

                if set(self.settings['pooling'].keys()) != {'apply', 'pool_size', 'strides', 'padding'}:
                    print('[INCORRECT SETTINGS]: Pooling layers ...')
                    return False

                if len(self.settings['pooling']['apply']) != self.settings['conv']['layers']:
                    print('Please specify wether or not to apply pooling for each convolutional layer')
                    return False

            elif key == 'detector_stage':

                if self.settings['conv']['layers'] != len(self.settings['detector_stage']):
                    print('Number of activation functions you have specified does not match the number of convolutional layers inside the network ...')
                    return False

            elif key == 'dense':

                if set(self.settings['dense'].keys()) != {'layers', 'output_units', 'activations'}:
                    print('[INCORRECT SETTINGS]: Dense layers ...')
                    return False

                if 'softmax' != self.settings['dense']['activations'][len(self.settings['dense']['activations'])-1]:
                    print('Your network must contain Softmax activation function at the last Dense layer in order to produce class probabilities ...')
                    return False

        print('Network settings have been correctly specified ...')
        return True

А вот параметры, которые я предоставил в качестве аргумента конструктору класса:

settings = {
    'conv':
        {
         'layers': 3,
         'filters': [32, 64, 128],
         'kernel_size':[(3,3), (5,5), (5,5)],
         'strides': [1, 1, 1],
         'padding': 'same',
        },
    'pooling':
        {
         'apply': [True, True, True],
         'pool_size': [(2,2), (3,3), (3,3)],
         'strides': [1, 1, 1],
         'padding': 'same'
        },
    'detector_stage': ['relu', 'relu', 'relu'],
    'dense':
        {
          'layers': 2,
          'output_units': [500, 10],
          'activations': ['relu', 'softmax'],
        },
    'batch_norm': [False, False, False, False]
}

Answer 1

Проблема заключалась в том, что переменная inputs имела выходной тензор первого плотного слоя вместо фактического ввода.