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Transposed 2D convolution layer (sometimes called 2D Deconvolution).
tf.compat.v1.layers.Conv2DTranspose( filters, kernel_size, strides=(1, 1), padding='valid', data_format='channels_last', activation=None, use_bias=True, kernel_initializer=None, bias_initializer=tf.zeros_initializer(), kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, trainable=True, name=None, **kwargs )
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.
||Integer, the dimensionality of the output space (i.e. the number of filters in the convolution).|
||A tuple or list of 2 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions.|
||A tuple or list of 2 positive integers specifying the strides of the convolution. Can be a single integer to specify the same value for all spatial dimensions.|