|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 )
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
| ||Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).|
| ||An integer or tuple/list of 2 integers, specifying the height and width of the 2D convolution window. Can be a single integer to specify the same value for all spatial dimensions.|
| || An integer or tuple/list of 2 integers, specifying the strides of the convolution along the height and width. Can be a single integer to specify the same value for all spatial dimensions. Specifying any stride value != 1 is incompatible with specifying any |
| || one of |
| || An integer or tuple/list of 2 integers, specifying the amount of padding along the height and width of the output tensor. Can be a single integer to specify the same value for all spatial dimensions. The amount of output padding along a given dimension must be lower than the stride along that same dimension. If set to |
| || A string, one of |
| || an integer or tuple/list of 2 integers, specifying the dilation rate to use for dilated convolution. Can be a single integer to specify the same value for all spatial dimensions. Currently, specifying any |
| || Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: |
| ||Boolean, whether the layer uses a bias vector.|
| || Initializer for the |
| ||Initializer for the bias vector.|
| || Regularizer function applied to the |
| ||Regularizer function applied to the bias vector.|
| ||Regularizer function applied to the output of the layer (its "activation")..|
| ||Constraint function applied to the kernel matrix.|
| ||Constraint function applied to the bias vector.|
4D tensor with shape:
(batch, channels, rows, cols) if data_format='channels_first' or 4D tensor with shape:
(batch, rows, cols, channels) if data_format='channels_last'.
4D tensor with shape:
(batch, filters, new_rows, new_cols) if data_format='channels_first' or 4D tensor with shape:
(batch, new_rows, new_cols, filters) if data_format='channels_last'.
cols values might have changed due to padding.
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.