layer_upsampling_1d
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
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. |
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. |
Section
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_transpose()
, layer_conv_1d()
, layer_conv_2d_transpose()
, layer_conv_2d()
, layer_conv_3d_transpose()
, layer_conv_3d()
, 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()