tf.keras.layers.Conv1DTranspose

1D transposed convolution layer.

Inherits From: Layer, Operation

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 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.

filters int, the dimension of the output space (the number of filters in the transpose 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 such that output has the same height/width dimension as the input.
output_padding An integer tuple/list of 1 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 string, either "channels_last" or "channels_first". The ordering of the dimensions in the inputs. "channels_last" corresponds to inputs with shape (batch, steps, features) while "channels_first" corresponds to inputs with shape (batch, features, steps). 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 tuple/list of 1 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. 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 3D tensor with shape: (batch_shape, steps, channels)
  • If data_format="channels_first": A 3D tensor with shape: (batch_shape, channels, steps)

Output shape:

  • If data_format="channels_last": A 3D tensor with shape: (batch_shape, new_steps, filters)
  • If data_format="channels_first": A 3D tensor with shape: (batch_shape, filters, new_steps)

A 3D tensor representing activation(conv1d_transpose(inputs, kernel) + bias).

ValueError when both strides > 1 and dilation_rate > 1.

References:

Example:

x = np.random.rand(4, 10, 128)
y = keras.layers.Conv1DTranspose(32, 3, 2, activation='relu')(x)
print(y.shape)
(4, 21, 32)

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.

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.