► Keras 2 API documentation / Layers API / Convolution layers / Conv1DTranspose layer

Conv1DTranspose layer

[source]

Conv1DTranspose class

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
)

Transposed convolution layer (sometimes called Deconvolution).

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 or None, does not include the sample axis), e.g. input_shape=(128, 3) for data with 128 time steps and 3 channels.

Arguments

  • 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). "valid" means no padding. "same" results in padding with zeros 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 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. Also dilation rate larger than 1 is not currently supported.
  • 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). Defaults to 'glorot_uniform'.
  • bias_initializer: Initializer for the bias vector (see keras.initializers). Defaults to 'zeros'.
  • 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)

Returns

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

Raises

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

References

Conv1DTranspose layer
Conv1DTranspose class