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Functional interface for transposed 3D convolution layer.
tf.compat.v1.layers.conv3d_transpose(
inputs,
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,
reuse=None
)
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:
y = tf.compat.v1.layers.conv3d_transpose(x, filters=3, kernel_size=3)
After:
To migrate code using TF1 functional layers use the Keras Functional API:
x = tf.keras.Input((28, 28, 1))
y = tf.keras.layers.Conv3DTranspose(filters=3, kernels_size=3)(x)
model = tf.keras.Model(x, y)
Description
Args | |
---|---|
inputs
|
Input tensor. |
filters
|
Integer, the dimensionality of the output space (i.e. the number of filters in the convolution). |
kernel_size
|
A tuple or list of 3 positive integers specifying the spatial dimensions of the filters. Can be a single integer to specify the same value for all spatial dimensions. |
strides
|
A tuple or list of 3 positive integers specifying the strides of the convolution. 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. |
reuse
|
Boolean, whether to reuse the weights of a previous layer by the same name. |
Returns | |
---|---|
Output tensor. |
Raises | |
---|---|
ValueError
|
if eager execution is enabled. |