Zero-padding layer for 1D input (e.g. temporal sequence).
layer_zero_padding_1d(
object,
padding = 1L,
batch_size = NULL,
name = NULL,
trainable = NULL,
weights = NULL
)
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.
int, or list of int (length 2)
If int: How many zeros to add at the beginning and end of the padding dimension (axis 1).
If list of int (length 2): How many zeros to add at the beginning and at the end of the padding dimension ((left_pad, right_pad)
).
Fixed batch size for layer
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.
Whether the layer weights will be updated during training.
Initial weights for layer.
3D tensor with shape (batch, axis_to_pad, features)
3D tensor with shape (batch, padded_axis, 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_1d()
,
layer_upsampling_2d()
,
layer_upsampling_3d()
,
layer_zero_padding_2d()
,
layer_zero_padding_3d()