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.

layer_conv_1d_transpose(
  object,
  filters,
  kernel_size,
  strides = 1,
  padding = "valid",
  output_padding = NULL,
  data_format = NULL,
  dilation_rate = 1,
  activation = NULL,
  use_bias = TRUE,
  kernel_initializer = "glorot_uniform",
  bias_initializer = "zeros",
  kernel_regularizer = NULL,
  bias_regularizer = NULL,
  activity_regularizer = NULL,
  kernel_constraint = NULL,
  bias_constraint = NULL,
  input_shape = NULL,
  batch_input_shape = NULL,
  batch_size = NULL,
  dtype = NULL,
  name = NULL,
  trainable = NULL,
  weights = NULL
)

Arguments

object

Model or layer object

filters

Integer, the dimensionality of the output space (i.e. the number of output filters in the convolution).

kernel_size

An integer or list of a single integer, specifying the length of the 1D convolution window.

strides

An integer or list of a single integer, specifying the stride length of the convolution. Specifying any stride value != 1 is incompatible with specifying any dilation_rate value != 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 NULL (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, length, channels) (default format for temporal data in Keras) while "channels_first" corresponds to inputs with shape (batch, channels, length).

dilation_rate

an integer or list of a single integer, specifying the dilation rate to use for dilated convolution. Currently, specifying any dilation_rate value != 1 is incompatible with specifying any strides value != 1.

activation

Activation function to use. If you don't specify anything, no activation is applied (ie. "linear" activation: a(x) = x).

use_bias

Boolean, whether the layer uses a bias vector.

kernel_initializer

Initializer for the kernel weights matrix.

bias_initializer

Initializer for the bias vector.

kernel_regularizer

Regularizer function applied to the kernel weights matrix.

bias_regularizer

Regularizer function applied to the bias vector.

activity_regularizer

Regularizer function applied to the output of the layer (its "activation")..

kernel_constraint

Constraint function applied to the kernel matrix.

bias_constraint

Constraint function applied to the bias vector.

input_shape

Dimensionality of the input (integer) not including the samples axis. This argument is required when using this layer as the first layer in a model.

batch_input_shape

Shapes, including the batch size. For instance, batch_input_shape=c(10, 32) indicates that the expected input will be batches of 10 32-dimensional vectors. batch_input_shape=list(NULL, 32) indicates batches of an arbitrary number of 32-dimensional vectors.

batch_size

Fixed batch size for layer

dtype

The data type expected by the input, as a string (float32, float64, int32...)

name

An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.

trainable

Whether the layer weights will be updated during training.

weights

Initial weights for layer.

Input shape

3D tensor with shape: (batch, steps, channels)

Output shape

3D tensor with shape: (batch, new_steps, filters) If output_padding is specified:

new_timesteps = ((timesteps - 1) * strides + kernel_size - 2 * padding + output_padding)

References

See also