83 lines
2.7 KiB
Python
83 lines
2.7 KiB
Python
from keras_core.api_export import keras_core_export
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from keras_core.layers.input_spec import InputSpec
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from keras_core.layers.layer import Layer
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from keras_core.utils import argument_validation
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@keras_core_export("keras_core.layers.Cropping1D")
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class Cropping1D(Layer):
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"""Cropping layer for 1D input (e.g. temporal sequence).
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It crops along the time dimension (axis 1).
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Examples:
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>>> input_shape = (2, 3, 2)
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>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
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>>> x
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[[[ 0 1]
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[ 2 3]
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[ 4 5]]
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[[ 6 7]
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[ 8 9]
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[10 11]]]
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>>> y = keras_core.layers.Cropping1D(cropping=1)(x)
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>>> y
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[[[2 3]]
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[[8 9]]]
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Args:
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cropping: Int, or tuple of int (length 2), or dictionary.
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- If int: how many units should be trimmed off at the beginning and
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end of the cropping dimension (axis 1).
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- If tuple of 2 ints: how many units should be trimmed off at the
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beginning and end of the cropping dimension
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(`(left_crop, right_crop)`).
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Input shape:
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3D tensor with shape `(batch_size, axis_to_crop, features)`
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Output shape:
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3D tensor with shape `(batch_size, cropped_axis, features)`
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"""
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def __init__(self, cropping=(1, 1), **kwargs):
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super().__init__(**kwargs)
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self.cropping = argument_validation.standardize_tuple(
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cropping, 2, "cropping", allow_zero=True
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)
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self.input_spec = InputSpec(ndim=3)
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def compute_output_shape(self, input_shape):
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if input_shape[1] is not None:
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length = input_shape[1] - self.cropping[0] - self.cropping[1]
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if length <= 0:
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raise ValueError(
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"`cropping` parameter of `Cropping1D` layer must be "
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"greater than the input length. Received: input_shape="
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f"{input_shape}, cropping={self.cropping}"
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)
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else:
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length = None
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return (input_shape[0], length, input_shape[2])
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def call(self, inputs):
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if (
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inputs.shape[1] is not None
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and sum(self.cropping) >= inputs.shape[1]
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):
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raise ValueError(
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"`cropping` parameter of `Cropping1D` layer must be "
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"greater than the input length. Received: inputs.shape="
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f"{inputs.shape}, cropping={self.cropping}"
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)
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if self.cropping[1] == 0:
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return inputs[:, self.cropping[0] :, :]
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else:
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return inputs[:, self.cropping[0] : -self.cropping[1], :]
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def get_config(self):
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config = {"cropping": self.cropping}
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base_config = super().get_config()
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return {**base_config, **config}
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