layer_upsampling_1d {keras}

Upsampling layer for 1D inputs.

Description

Repeats each temporal step size times along the time axis.

Usage

layer_upsampling_1d(
  object,
  size = 2L,
  batch_size = NULL,
  name = NULL,
  trainable = NULL,
  weights = NULL
)

Arguments

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.

size

integer. Upsampling factor.

batch_size

Fixed batch size for layer

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, features)⁠.

Output shape

3D tensor with shape: ⁠(batch, upsampled_steps, features)⁠.

See Also

Other convolutional layers: layer_conv_1d(), layer_conv_1d_transpose(), layer_conv_2d(), layer_conv_2d_transpose(), layer_conv_3d(), layer_conv_3d_transpose(), layer_conv_lstm_2d(), layer_cropping_1d(), layer_cropping_2d(), layer_cropping_3d(), layer_depthwise_conv_1d(), layer_depthwise_conv_2d(), layer_separable_conv_1d(), layer_separable_conv_2d(), layer_upsampling_2d(), layer_upsampling_3d(), layer_zero_padding_1d(), layer_zero_padding_2d(), layer_zero_padding_3d()