Functional interface for transposed 2D convolution layer. (deprecated)
tf.compat.v1.layers.conv2d_transpose(
inputs, filters, kernel_size, strides=(1, 1), padding='valid',
data_format='channels_last', activation=None, use_bias=True,
kernel_initializer=None, bias_initializer=tf.zeros_initializer(),
kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None,
kernel_constraint=None, bias_constraint=None, trainable=True, name=None,
reuse=None
)
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.
Arguments |
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 2 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 2 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).
|
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, height, width, channels) while channels_first corresponds to
inputs with shape (batch, channels, 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.
|
Raises |
ValueError
|
if eager execution is enabled.
|