layer_conv_1d_transpose

Transposed 1D convolution layer (sometimes called Deconvolution).

Description

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.

Usage

 
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

Arguments	Description
object	What to compose the new `Layer` instance with. Typically a Sequential model or a Tensor (e.g., as returned by `layer_input()`). The return value depends on `object`. If `object` is: - missing or `NULL`, the `Layer` instance is returned. - a `Sequential` model, the model with an additional layer is returned. - a Tensor, the output tensor from `layer_instance(object)` is returned.
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.

Section

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

A guide to convolution arithmetic for deep learning