|TensorFlow 1 version||View source on GitHub|
Transposed convolution layer (sometimes called Deconvolution).
See Migration guide for more details.
tf.keras.layers.Conv2DTranspose( filters, kernel_size, strides=(1, 1), padding='valid', output_padding=None, data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs )
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
(tuple of integers, does not include the sample axis),
input_shape=(128, 128, 3) for 128x128 RGB pictures
||Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).|