Transposed convolution layer (sometimes called Deconvolution).
Inherits From: Conv2D
, Layer
, Module
tf.keras.layers.Conv2DTranspose(
filters,
kernel_size,
strides=(1, 1),
padding='valid',
output_padding=None,
data_format=None,
dilation_rate=(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 or None
, does not include the sample axis),
e.g. input_shape=(128, 128, 3)
for 128x128 RGB pictures
in data_format="channels_last"
.
Args |
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 2 integers, specifying the
height and width of the 2D convolution window.
Can be a single integer to specify the same value for
all spatial dimensions.
|
strides
|
An integer or tuple/list of 2 integers,
specifying the strides of the convolution along the 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 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 or tuple/list of 2 integers,
specifying the amount of padding along the height and width
of the output tensor.
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, height, width, channels) while channels_first
corresponds to inputs with shape
(batch_size, channels, 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, specifying the dilation rate for all spatial
dimensions for dilated convolution. Specifying different dilation rates
for different dimensions is not supported.
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
(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 ).
|
|
4D tensor with shape:
(batch_size, channels, rows, cols) if data_format='channels_first'
or 4D tensor with shape:
(batch_size, rows, cols, channels) if data_format='channels_last'.
|
Output shape |
4D tensor with shape:
(batch_size, filters, new_rows, new_cols) if
data_format='channels_first'
or 4D tensor with shape:
(batch_size, new_rows, new_cols, filters) if
data_format='channels_last'. rows and cols values might have changed
due to padding.
If output_padding is specified:
new_rows = ((rows - 1) * strides[0] + kernel_size[0] - 2 * padding[0] +
output_padding[0])
new_cols = ((cols - 1) * strides[1] + kernel_size[1] - 2 * padding[1] +
output_padding[1])
|
Returns |
A tensor of rank 4 representing
activation(conv2dtranspose(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
)