91efaaa1d7486df71be30a54d82b907443d80bf1,keras/layers/convolutional.py,_Conv,compute_output_shape,#_Conv#Any#,191

Before Change

        return outputs

    def compute_output_shape(self, input_shape):
        if self.data_format == "channels_last":
            space = input_shape[1:-1]
            new_space = []
            for i in range(len(space)):
                new_dim = conv_utils.conv_output_length(
                    space[i],
                    self.kernel_size[i],
                    padding=self.padding,
                    stride=self.strides[i],
                    dilation=self.dilation_rate[i])
                new_space.append(new_dim)
            return (input_shape[0],) + tuple(new_space) + (self.filters,)
        if self.data_format == "channels_first":
            space = input_shape[2:]
            new_space = []
            for i in range(len(space)):
                new_dim = conv_utils.conv_output_length(
                    space[i],
                    self.kernel_size[i],
                    padding=self.padding,
                    stride=self.strides[i],
                    dilation=self.dilation_rate[i])
                new_space.append(new_dim)
            return (input_shape[0], self.filters) + tuple(new_space)

    def get_config(self):
        config = {

After Change

    def compute_output_shape(self, input_shape):
        if self.data_format == "channels_last":
            space = input_shape[1:-1]
        elif self.data_format == "channels_first":
            space = input_shape[2:]
        new_space = []
        for i in range(len(space)):

Instances