95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Keras zero-padding layer for 1D input."""
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import tensorflow.compat.v2 as tf
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from keras import backend
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from keras.engine.base_layer import Layer
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from keras.engine.input_spec import InputSpec
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from keras.utils import conv_utils
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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.layers.ZeroPadding1D")
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class ZeroPadding1D(Layer):
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"""Zero-padding layer for 1D input (e.g. temporal sequence).
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Examples:
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>>> input_shape = (2, 2, 3)
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>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
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>>> print(x)
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[[[ 0 1 2]
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[ 3 4 5]]
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[[ 6 7 8]
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[ 9 10 11]]]
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>>> y = tf.keras.layers.ZeroPadding1D(padding=2)(x)
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>>> print(y)
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tf.Tensor(
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[[[ 0 0 0]
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[ 0 0 0]
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[ 0 1 2]
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[ 3 4 5]
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[ 0 0 0]
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[ 0 0 0]]
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[[ 0 0 0]
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[ 0 0 0]
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[ 6 7 8]
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[ 9 10 11]
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[ 0 0 0]
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[ 0 0 0]]], shape=(2, 6, 3), dtype=int64)
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Args:
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padding: Int, or tuple of int (length 2), or dictionary.
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- If int:
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How many zeros to add at the beginning and end of
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the padding dimension (axis 1).
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- If tuple of int (length 2):
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How many zeros to add at the beginning and the end of
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the padding dimension (`(left_pad, right_pad)`).
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Input shape:
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3D tensor with shape `(batch_size, axis_to_pad, features)`
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Output shape:
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3D tensor with shape `(batch_size, padded_axis, features)`
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"""
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def __init__(self, padding=1, **kwargs):
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super().__init__(**kwargs)
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self.padding = conv_utils.normalize_tuple(
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padding, 2, "padding", 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.padding[0] + self.padding[1]
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else:
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length = None
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return tf.TensorShape([input_shape[0], length, input_shape[2]])
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def call(self, inputs):
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return backend.temporal_padding(inputs, padding=self.padding)
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def get_config(self):
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config = {"padding": self.padding}
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base_config = super().get_config()
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return dict(list(base_config.items()) + list(config.items()))
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