tf.keras.layers.Conv3DTranspose

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Transposed convolution layer (sometimes called Deconvolution).

Inherits From: Conv3D, Layer, Module

View aliases

Main aliases

tf.keras.layers.Convolution3DTranspose

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.layers.Conv3DTranspose, tf.compat.v1.keras.layers.Convolution3DTranspose

tf.keras.layers.Conv3DTranspose(
    filters,
    kernel_size,
    strides=(1, 1, 1),
    padding='valid',
    output_padding=None,
    data_format=None,
    dilation_rate=(1, 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 or None, does not include the sample axis), e.g. input_shape=(128, 128, 128, 3) for a 128x128x128 volume with 3 channels if data_format="channels_last".

Args
`filters`	Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).
`kernel_size`	An integer or tuple/list of 3 integers, specifying the depth, height and width of the 3D convolution window. Can be a single integer to specify the same value for all spatial dimensions.
`strides`	An integer or tuple/list of 3 integers, specifying the strides of the convolution along the depth, 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 `dilation_rate` value != 1.
`padding`	one of `"valid"` or `"same"` (case-insensitive). `"valid"` means no padding. `"same"` results in padding with zeros evenly to the left/right or up/down of the input such that output has the same height/width dimension as the input.
`output_padding`	An integer or tuple/list of 3 integers, specifying the amount of padding along the depth, height, and width. 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 `None` (default), the output shape is inferred.
`data_format`	A string, one of `channels_last` (default) or `channels_first`. The ordering of the dimensions in the inputs. `channels_last` corresponds to inputs with shape `(batch_size, depth, height, width, channels)` while `channels_first` corresponds to inputs with shape `(batch_size, channels, depth, height, width)`. It defaults to the `image_data_format` value found in your Keras config file at `~/.keras/keras.json`. If you never set it, then it will be "channels_last".
`dilation_rate`	an integer or tuple/list of 3 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 `dilation_rate` value != 1 is incompatible with specifying any stride value != 1.
`activation`	Activation function to use. If you don't specify anything, no activation is applied (see `keras.activations`).
`use_bias`	Boolean, whether the layer uses a bias vector.
`kernel_initializer`	Initializer for the `kernel` weights matrix (see `keras.initializers`). Defaults to 'glorot_uniform'.
`bias_initializer`	Initializer for the bias vector (see `keras.initializers`). Defaults to 'zeros'.
`kernel_regularizer`	Regularizer function applied to the `kernel` weights matrix (see `keras.regularizers`).
`bias_regularizer`	Regularizer function applied to the bias vector (see `keras.regularizers`).
`activity_regularizer`	Regularizer function applied to the output of the layer (its "activation") (see `keras.regularizers`).
`kernel_constraint`	Constraint function applied to the kernel matrix (see `keras.constraints`).
`bias_constraint`	Constraint function applied to the bias vector (see `keras.constraints`).

Input shape:

5D tensor with shape: (batch_size, channels, depth, rows, cols) if data_format='channels_first' or 5D tensor with shape: (batch_size, depth, rows, cols, channels) if data_format='channels_last'.

Output shape:

5D tensor with shape: (batch_size, filters, new_depth, new_rows, new_cols) if data_format='channels_first' or 5D tensor with shape: (batch_size, new_depth, new_rows, new_cols, filters) if data_format='channels_last'. depth and rows and cols values might have changed due to padding. If output_padding is specified::

new_depth = ((depth - 1) * strides[0] + kernel_size[0] - 2 * padding[0] +
output_padding[0])
new_rows = ((rows - 1) * strides[1] + kernel_size[1] - 2 * padding[1] +
output_padding[1])
new_cols = ((cols - 1) * strides[2] + kernel_size[2] - 2 * padding[2] +
output_padding[2])

Returns
A tensor of rank 5 representing `activation(conv3dtranspose(inputs, kernel) + bias)`.

Raises
`ValueError`	if `padding` is "causal".
`ValueError`	when both `strides` > 1 and `dilation_rate` > 1.

References:

Methods

`convolution_op`

View source

convolution_op(
    inputs, kernel
)

© 2022 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.9/api_docs/python/tf/keras/layers/Conv3DTranspose

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