tf.keras.layers.Conv2DTranspose

2D transposed convolution layer.

Inherits From: Layer, Operation

View aliases

Main aliases

tf.keras.layers.Convolution2DTranspose

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
Customizing what happens in `fit()`	Convolutional Variational Autoencoder Deep Convolutional Generative Adversarial Network Intro to Autoencoders Learned data compression pix2pix: Image-to-image translation with a conditional GAN

The need for transposed convolutions generally arise 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.

Args
filters	int, the dimension of the output space (the number of filters in the transposed convolution).
kernel_size	int or tuple/list of 1 integer, specifying the size of the transposed convolution window.
strides	int or tuple/list of 1 integer, specifying the stride length of the transposed convolution. strides > 1 is incompatible with dilation_rate > 1.
padding	string, either "valid" or "same" (case-insensitive). "valid" means no padding. "same" results in padding evenly to the left/right or up/down of the input. When padding="same" and strides=1, the output has the same size 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	string, either "channels_last" 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 or tuple/list of 2 integers, 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. If None, no activation is applied.
use_bias	bool, if True, bias will be added to the output.
kernel_initializer	Initializer for the convolution kernel. If None, the default initializer ("glorot_uniform") will be used.
bias_initializer	Initializer for the bias vector. If None, the default initializer ("zeros") 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.

Input shape:

• If data_format="channels_last": A 4D tensor with shape: (batch_size, height, width, channels)
• If data_format="channels_first": A 4D tensor with shape: (batch_size, channels, height, width)

Output shape:

• If data_format="channels_last": A 4D tensor with shape: (batch_size, new_height, new_width, filters)
• If data_format="channels_first": A 4D tensor with shape: (batch_size, filters, new_height, new_width)

Returns
A 4D tensor representing activation(conv2d_transpose(inputs, kernel) + bias).

Raises
ValueError	when both strides > 1 and dilation_rate > 1.

References:

• A guide to convolution arithmetic for deep learning
• Deconvolutional Networks

Example:

x = np.random.rand(4, 10, 8, 128)
y = keras.layers.Conv2DTranspose(32, 2, 2, activation='relu')(x)
print(y.shape)
(4, 20, 16, 32)

Attributes
input	Retrieves the input tensor(s) of a symbolic operation. Only returns the tensor(s) corresponding to the first time the operation was called.
output	Retrieves the output tensor(s) of a layer. Only returns the tensor(s) corresponding to the first time the operation was called.

Methods

from_config

View source

@classmethod
from_config(
    config
)

Creates an operation from its config.

This method is the reverse of get_config, capable of instantiating the same operation from the config dictionary.

Note: If you override this method, you might receive a serialized dtype config, which is a dict. You can deserialize it as follows:

if "dtype" in config and isinstance(config["dtype"], dict):
    policy = dtype_policies.deserialize(config["dtype"])

Args
config	A Python dictionary, typically the output of get_config.

Returns
An operation instance.