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Transposed 3D convolution layer (sometimes called 3D Deconvolution).
Inherits From: Conv3DTranspose
, Conv3D
, Layer
, Layer
, Module
tf.compat.v1.layers.Conv3DTranspose(
filters,
kernel_size,
strides=(1, 1, 1),
padding='valid',
data_format='channels_last',
activation=None,
use_bias=True,
kernel_initializer=None,
bias_initializer=tf.compat.v1.zeros_initializer(),
kernel_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
bias_constraint=None,
trainable=True,
name=None,
**kwargs
)
Migrate to TF2
This API is a legacy api that is only compatible with eager execution and
tf.function
if you combine it with
tf.compat.v1.keras.utils.track_tf1_style_variables
Please refer to tf.layers model mapping section of the migration guide to learn how to use your TensorFlow v1 model in TF2 with Keras.
The corresponding TensorFlow v2 layer is
tf.keras.layers.Conv3DTranspose
.
Structural Mapping to Native TF2
None of the supported arguments have changed name.
Before:
conv = tf.compat.v1.layers.Conv3DTranspose(filters=3, kernel_size=3)
After:
conv = tf.keras.layers.Conv3DTranspose(filters=3, kernels_size=3)
Description
Args | |
---|---|
filters
|
Integer, the dimensionality of the output space (i.e. the number of 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. |
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.
|
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, depth, height, width, channels) while channels_first
corresponds to inputs with shape
(batch, channels, depth, height, width) .
|
activation
|
Activation function. Set it to None to maintain a
linear activation.
|
use_bias
|
Boolean, whether the layer uses a bias. |
kernel_initializer
|
An initializer for the convolution kernel. |
bias_initializer
|
An initializer for the bias vector. If None , the
default initializer will be used.
|
kernel_regularizer
|
Optional regularizer for the convolution kernel. |
bias_regularizer
|
Optional regularizer for the bias vector. |
activity_regularizer
|
Optional regularizer function for the output. |
kernel_constraint
|
Optional projection function to be applied to the
kernel after being updated by an Optimizer (e.g. used to implement
norm constraints or value constraints for layer weights). The function
must take as input the unprojected variable and must return the
projected variable (which must have the same shape). Constraints are
not safe to use when doing asynchronous distributed training.
|
bias_constraint
|
Optional projection function to be applied to the
bias after being updated by an Optimizer .
|
trainable
|
Boolean, if True also add variables to the graph collection
GraphKeys.TRAINABLE_VARIABLES (see tf.Variable ).
|
name
|
A string, the name of the layer. |
Attributes | |
---|---|
graph
|
|
scope_name
|
Methods
apply
apply(
*args, **kwargs
)
convolution_op
convolution_op(
inputs, kernel
)
get_losses_for
get_losses_for(
inputs
)
Retrieves losses relevant to a specific set of inputs.
Args | |
---|---|
inputs
|
Input tensor or list/tuple of input tensors. |
Returns | |
---|---|
List of loss tensors of the layer that depend on inputs .
|
get_updates_for
get_updates_for(
inputs
)
Retrieves updates relevant to a specific set of inputs.
Args | |
---|---|
inputs
|
Input tensor or list/tuple of input tensors. |
Returns | |
---|---|
List of update ops of the layer that depend on inputs .
|