tf.keras.layers.Conv1DTranspose

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Transposed convolution layer (sometimes called Deconvolution).

Inherits From: Conv1D

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, does not include the sample axis), e.g. input_shape=(128, 3) for data with 128 time steps and 3 channels.

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).
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.
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).
bias_initializer Initializer for the bias vector ( see keras.initializers).
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).

Input shape:

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)

A tensor of rank 3 representing activation(conv1dtranspose(inputs, kernel) + bias).

ValueError if padding is "causal".
ValueError when both strides > 1 and dilation_rate > 1.

References: