85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
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# 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 upsampling layer for 1D inputs."""
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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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# isort: off
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from tensorflow.python.util.tf_export import keras_export
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@keras_export("keras.layers.UpSampling1D")
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class UpSampling1D(Layer):
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"""Upsampling layer for 1D inputs.
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Repeats each temporal step `size` times along the time axis.
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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.UpSampling1D(size=2)(x)
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>>> print(y)
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tf.Tensor(
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[[[ 0 1 2]
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[ 0 1 2]
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[ 3 4 5]
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[ 3 4 5]]
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[[ 6 7 8]
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[ 6 7 8]
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[ 9 10 11]
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[ 9 10 11]]], shape=(2, 4, 3), dtype=int64)
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Args:
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size: Integer. Upsampling factor.
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Input shape:
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3D tensor with shape: `(batch_size, steps, features)`.
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Output shape:
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3D tensor with shape: `(batch_size, upsampled_steps, features)`.
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"""
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def __init__(self, size=2, **kwargs):
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super().__init__(**kwargs)
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self.size = int(size)
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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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input_shape = tf.TensorShape(input_shape).as_list()
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size = (
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self.size * input_shape[1] if input_shape[1] is not None else None
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)
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return tf.TensorShape([input_shape[0], size, input_shape[2]])
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def call(self, inputs):
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output = backend.repeat_elements(inputs, self.size, axis=1)
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return output
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def get_config(self):
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config = {"size": self.size}
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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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