Graph Disconnected when trying to build CNN model with Keras Functional API

Question

I am trying to build a CNN model using Keras Functional API but whenever I try to execute this line of code: model = CNN(settings, np.expand_dims(x_train, axis = 3)).build_network() I keep running into the following issue:

ValueError: Graph disconnected: cannot obtain value for tensor Tensor("input_11:0", shape=(?, 28, 28, 1), dtype=float32) at layer "input_11". The following previous layers were accessed without issue: []

Here is my code:

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

And here are the settings I provided as an argument to the class constructor:

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]
}

Stefan Radonjic Stefan Radonjic · Accepted Answer · 2019-02-21T20:24:28

The problem was that inputs variable had the output tensor of the first Dense layer instead of the actual input.

Graph Disconnected when trying to build CNN model with Keras Functional API

1 Answers