shifted_conv2d

tfsnippet.layers.shifted_conv2d(*args, **kwargs)

2D convolution with shifted input.

This method first pads input according to the kernel_size and spatial_shift arguments, then do 2D convolution (using conv_fn) with “VALID” padding.

Parameters:

• input (Tensor) – The input tensor, at least 4-d.
• out_channels (int) – The channel numbers of the output.
• kernel_size (int or (int, int)) – Kernel size over spatial dimensions.
• spatial_shift –

  The spatial_shift should be a tuple with two elements (corresponding to height and width spatial axes), and the elements can only be -1, 0 or 1.

  If the shift for a specific axis is -1, then kernel_size - 1 zeros will be padded at the end of that axis. If the shift is 0, then (kernel_size - 1) // 2 zeros will be padded at the front, and kernel_size // 2 zeros will be padded at the end that axis. Otherwise if the shift is 1, then kernel_size + 1 zeros will be padded at the front of that axis.

• strides (int or (int, int)) – Strides over spatial dimensions.
• channels_last (bool) – Whether or not the channel axis is the last axis in input? (i.e., the data format is “NHWC”)
• conv_fn – The 2D convolution function. (default conv2d())
• name (str) – Default name of the variable scope. Will be uniquified. If not specified, generate one according to the class name.
• scope (str) – The name of the variable scope.
• **kwargs – Other named parameters passed to conv_fn.

Returns:

The output tensor.

Return type:

tf.Tensor