82 lines
2.5 KiB
Python
82 lines
2.5 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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"""Leaky version of a Rectified Linear Unit activation layer."""
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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.utils import tf_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.LeakyReLU")
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class LeakyReLU(Layer):
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"""Leaky version of a Rectified Linear Unit.
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It allows a small gradient when the unit is not active:
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```
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f(x) = alpha * x if x < 0
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f(x) = x if x >= 0
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```
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Usage:
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>>> layer = tf.keras.layers.LeakyReLU()
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>>> output = layer([-3.0, -1.0, 0.0, 2.0])
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>>> list(output.numpy())
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[-0.9, -0.3, 0.0, 2.0]
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>>> layer = tf.keras.layers.LeakyReLU(alpha=0.1)
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>>> output = layer([-3.0, -1.0, 0.0, 2.0])
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>>> list(output.numpy())
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[-0.3, -0.1, 0.0, 2.0]
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Input shape:
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Arbitrary. Use the keyword argument `input_shape`
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(tuple of integers, does not include the batch axis)
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when using this layer as the first layer in a model.
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Output shape:
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Same shape as the input.
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Args:
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alpha: Float >= 0. Negative slope coefficient. Default to 0.3.
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"""
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def __init__(self, alpha=0.3, **kwargs):
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super().__init__(**kwargs)
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if alpha is None:
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raise ValueError(
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"The alpha value of a Leaky ReLU layer cannot be None, "
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f"Expecting a float. Received: {alpha}"
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)
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self.supports_masking = True
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self.alpha = backend.cast_to_floatx(alpha)
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def call(self, inputs):
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return backend.relu(inputs, alpha=self.alpha)
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def get_config(self):
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config = {"alpha": float(self.alpha)}
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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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@tf_utils.shape_type_conversion
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def compute_output_shape(self, input_shape):
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return input_shape
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