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, 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".

Arguments
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 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.
bias_initializer	Initializer for the bias vector.
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:

• A guide to convolution arithmetic for deep learning
• Deconvolutional Networks