layer_cropping_1d {keras}

Cropping layer for 1D input (e.g. temporal sequence).

Description

It crops along the time dimension (axis 1).

Usage

layer_cropping_1d(
  object,
  cropping = c(1L, 1L),
  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.

cropping

int or list of int (length 2) How many units should be trimmed off at the beginning and end of the cropping dimension (axis 1). If a single int is provided, the same value will be used for both.

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, axis_to_crop, features)⁠

Output shape

3D tensor with shape ⁠(batch, cropped_axis, 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_2d(), layer_cropping_3d(), layer_depthwise_conv_1d(), layer_depthwise_conv_2d(), layer_separable_conv_1d(), layer_separable_conv_2d(), layer_upsampling_1d(), layer_upsampling_2d(), layer_upsampling_3d(), layer_zero_padding_1d(), layer_zero_padding_2d(), layer_zero_padding_3d()