"""Zero-padding layer for 1D input (e.g. temporal sequence).

Example:

>>> input_shape = (2, 2, 3)

>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)

>>> x

[[[ 0 1 2]

[ 3 4 5]]

[[ 6 7 8]

[ 9 10 11]]]

>>> y = keras.layers.ZeroPadding1D(padding=2)(x)

>>> y

[[[ 0 0 0]

[ 0 0 0]

[ 0 1 2]

[ 3 4 5]

[ 0 0 0]

[ 0 0 0]]

[[ 0 0 0]

[ 0 0 0]

[ 6 7 8]

[ 9 10 11]

[ 0 0 0]

[ 0 0 0]]]

Args:

padding: Int, or tuple of int (length 2), or dictionary.

- If int: how many zeros to add at the beginning and end of

the padding dimension (axis 1).

- If tuple of 2 ints: how many zeros to add at the beginning and the

end of the padding dimension (`(left_pad, right_pad)`).

Input shape:

3D tensor with shape `(batch_size, axis_to_pad, features)`

Output shape:

3D tensor with shape `(batch_size, padded_axis, features)`

"""

def __init__(self, padding=1, **kwargs):

super().__init__(**kwargs)

self.padding = argument_validation.standardize_tuple(

padding, 2, "padding", allow_zero=True

)

self.input_spec = InputSpec(ndim=3)

def compute_output_shape(self, input_shape):

output_shape = list(input_shape)

if output_shape[1] is not None:

output_shape[1] += self.padding[0] + self.padding[1]

return tuple(output_shape)

def call(self, inputs):

all_dims_padding = ((0, 0), self.padding, (0, 0))

return ops.pad(inputs, all_dims_padding)

def get_config(self):

config = {"padding": self.padding}

base_config = super().get_config()