Transposed convolution layer (sometimes called Deconvolution).
Inherits From: Conv1D
, Layer
, Module
tf.keras.layers.Conv1DTranspose(
filters,
kernel_size,
strides=1,
padding='valid',
output_padding=None,
data_format=None,
dilation_rate=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, 3)
for data with 128 time steps and 3 channels.
Args |
filters
|
Integer, the dimensionality of the output space
(i.e. the number of output filters in the convolution).
|
kernel_size
|
An integer length of the 1D convolution window.
|
strides
|
An integer specifying the stride of the convolution along the
time dimension. Specifying a stride value != 1 is incompatible with
specifying a dilation_rate value != 1. Defaults to 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 specifying the amount of padding along
the time dimension of the output tensor.
The amount of output padding must be lower than the stride.
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, length, channels) while channels_first corresponds to
inputs with shape (batch_size, channels, length) .
|
dilation_rate
|
an integer, specifying
the dilation rate to use for dilated convolution.
Currently, specifying a dilation_rate value != 1 is
incompatible with specifying a stride value != 1.
Also dilation rate larger than 1 is not currently supported.
|
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 ).
|
|
3D tensor with shape:
(batch_size, steps, channels)
|
Output shape |
3D tensor with shape:
(batch_size, new_steps, filters)
If output_padding is specified:
new_timesteps = ((timesteps - 1) * strides + kernel_size -
2 * padding + output_padding)
|
Returns |
A tensor of rank 3 representing
activation(conv1dtranspose(inputs, kernel) + bias) .
|
Raises |
ValueError
|
if padding is "causal".
|
ValueError
|
when both strides > 1 and dilation_rate > 1.
|
Methods
convolution_op
View source
convolution_op(
inputs, kernel
)