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TensorFlow 1 version
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View source on GitHub
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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
)
Used in the notebooks
| Used in the guide | Used in the tutorials |
|---|---|
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, does not include the sample axis),
e.g. input_shape=(128, 128, 3) for 128x128 RGB pictures
in data_format="channels_last".
Arguments | |
|---|---|
filters
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Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution). |
kernel_size
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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
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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.
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padding
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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.
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output_padding
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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.
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data_format
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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".
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dilation_rate
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an integer or tuple/list of 2 integers, specifying
the dilation rate to use for dilated convolution.
Can be a single integer to specify the same value for
all spatial dimensions.
Currently, specifying any dilation_rate value != 1 is
incompatible with specifying any stride value != 1.
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activation
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Activation function to use.
If you don't specify anything, no activation is applied (
see keras.activations).
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use_bias
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Boolean, whether the layer uses a bias vector. |
kernel_initializer
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Initializer for the kernel weights matrix (
see keras.initializers).
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bias_initializer
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Initializer for the bias vector (
see keras.initializers).
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kernel_regularizer
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Regularizer function applied to
the kernel weights matrix (see keras.regularizers).
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bias_regularizer
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Regularizer function applied to the bias vector (
see keras.regularizers).
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activity_regularizer
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Regularizer function applied to
the output of the layer (its "activation") (see keras.regularizers).
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kernel_constraint
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Constraint function applied to the kernel matrix (
see keras.constraints).
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bias_constraint
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Constraint function applied to the bias vector (
see keras.constraints).
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Input shape:
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 | |
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A tensor of rank 4 representing
activation(conv2dtranspose(inputs, kernel) + bias).
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Raises | |
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ValueError
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if padding is "causal".
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ValueError
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when both strides > 1 and dilation_rate > 1.
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