023331ec2a7b0086abfc81eca16c84a1692ee653,keras/layers/convolutional.py,Convolution2D,build,#Convolution2D#Any#,420

Before Change

        super(Convolution2D, self).__init__(**kwargs)

    def build(self, input_shape):
        if self.data_format == "channels_first":
            stack_size = input_shape[1]
            self.W_shape = (self.nb_filter, stack_size, self.nb_row, self.nb_col)
        elif self.data_format == "channels_last":
            stack_size = input_shape[3]
            self.W_shape = (self.nb_row, self.nb_col, stack_size, self.nb_filter)
        else:
            raise ValueError("Invalid data_format:", self.data_format)
        self.W = self.add_weight(self.W_shape,
                                 initializer=self.init,
                                 name="{}_W".format(self.name),
                                 regularizer=self.W_regularizer,

After Change

        self.pointwise_constraint = pointwise_constraint

    def build(self, input_shape):
        if len(input_shape) < 4:
            raise ValueError("Inputs to `SeparableConv2D` should have rank 4. "
                             "Received input shape:", str(input_shape))
        if self.data_format == "channels_first":
            channel_axis = 1
        else:
            channel_axis = 3
        if input_shape[channel_axis] is None:
            raise ValueError("The channel dimension of the inputs to "
                             "`SeparableConv2D` "
                             "should be defined. Found `None`.")
        input_dim = int(input_shape[channel_axis])
        depthwise_kernel_shape = (self.kernel_size[0],
                                  self.kernel_size[1],
                                  input_dim,
                                  self.depth_multiplier)
        pointwise_kernel_shape = (1, 1,
                                  self.depth_multiplier * input_dim,
                                  self.filters)

        self.depthwise_kernel = self.add_weight(
            depthwise_kernel_shape,
            initializer=self.depthwise_initializer,
            name="depthwise_kernel",
            regularizer=self.depthwise_regularizer,
            constraint=self.depthwise_constraint)
        self.pointwise_kernel = self.add_weight(
            pointwise_kernel_shape,
            initializer=self.pointwise_initializer,
            name="pointwise_kernel",
            regularizer=self.pointwise_regularizer,
            constraint=self.pointwise_constraint)

        if self.use_bias:
            self.bias = self.add_weight((self.filters,),
                                        initializer=self.bias_initializer,

Instances