Transposed convolution layer (sometimes called Deconvolution).

Inherits From: Conv2D

Used in the notebooks

Used in the guide Used in the tutorials

The need for transposed convolutions generally arises from the desire to use a transformation going in the opposite direction of a normal convolution, i.e., from something that has the shape of the output of some convolution to something that has the shape of its input while maintaining a connectivity pattern that is compatible with said convolution.

When using this layer as the first layer in a model, provide the keyword argument input_shape (tuple of integers, does not include the sample axis), e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures in data_format="channels_last".

filters Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).