Repeats each temporal step `size`

times along the time axis.

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

- 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.

3D tensor with shape: `(batch, steps, features)`

.

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

.

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()`