keras/layers/convolutional.py [793:820]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                             '; Received input shape:', str(input_shape))
        if self.data_format == 'channels_first':
            channel_axis = 1
        else:
            channel_axis = -1
        if input_shape[channel_axis] is None:
            raise ValueError('The channel dimension of the inputs '
                             'should be defined. Found `None`.')
        input_dim = input_shape[channel_axis]

        if self.data_format == 'channels_first' and K.backend() == 'mxnet':
            kernel_shape = (input_dim, self.filters) + self.kernel_size
        else:
            kernel_shape = self.kernel_size + (self.filters, input_dim)

        self.kernel = self.add_weight(shape=kernel_shape,
                                      initializer=self.kernel_initializer,
                                      name='kernel',
                                      regularizer=self.kernel_regularizer,
                                      constraint=self.kernel_constraint)
        if self.use_bias:
            self.bias = self.add_weight(shape=(self.filters,),
                                        initializer=self.bias_initializer,
                                        name='bias',
                                        regularizer=self.bias_regularizer,
                                        constraint=self.bias_constraint)
        else:
            self.bias = None
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



keras/layers/convolutional.py [1069:1095]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
                             '; Received input shape:', str(input_shape))
        if self.data_format == 'channels_first':
            channel_axis = 1
        else:
            channel_axis = -1
        if input_shape[channel_axis] is None:
            raise ValueError('The channel dimension of the inputs '
                             'should be defined. Found `None`.')
        input_dim = input_shape[channel_axis]
        if self.data_format == 'channels_first' and K.backend() == 'mxnet':
            kernel_shape = (input_dim, self.filters) + self.kernel_size
        else:
            kernel_shape = self.kernel_size + (self.filters, input_dim)

        self.kernel = self.add_weight(shape=kernel_shape,
                                      initializer=self.kernel_initializer,
                                      name='kernel',
                                      regularizer=self.kernel_regularizer,
                                      constraint=self.kernel_constraint)
        if self.use_bias:
            self.bias = self.add_weight(shape=(self.filters,),
                                        initializer=self.bias_initializer,
                                        name='bias',
                                        regularizer=self.bias_regularizer,
                                        constraint=self.bias_constraint)
        else:
            self.bias = None
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -