124 lines
4.1 KiB
Python
124 lines
4.1 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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"""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.ReLU")
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class ReLU(Layer):
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"""Rectified Linear Unit activation function.
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With default values, it returns element-wise `max(x, 0)`.
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Otherwise, it follows:
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```
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f(x) = max_value if x >= max_value
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f(x) = x if threshold <= x < max_value
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f(x) = negative_slope * (x - threshold) otherwise
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```
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Usage:
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>>> layer = tf.keras.layers.ReLU()
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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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[0.0, 0.0, 0.0, 2.0]
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>>> layer = tf.keras.layers.ReLU(max_value=1.0)
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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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[0.0, 0.0, 0.0, 1.0]
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>>> layer = tf.keras.layers.ReLU(negative_slope=1.0)
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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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[-3.0, -1.0, 0.0, 2.0]
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>>> layer = tf.keras.layers.ReLU(threshold=1.5)
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>>> output = layer([-3.0, -1.0, 1.0, 2.0])
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>>> list(output.numpy())
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[0.0, 0.0, 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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max_value: Float >= 0. Maximum activation value. Default to None, which
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means unlimited.
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negative_slope: Float >= 0. Negative slope coefficient. Default to 0.
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threshold: Float >= 0. Threshold value for thresholded activation. Default
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to 0.
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"""
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def __init__(
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self, max_value=None, negative_slope=0.0, threshold=0.0, **kwargs
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):
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super().__init__(**kwargs)
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if max_value is not None and max_value < 0.0:
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raise ValueError(
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"max_value of a ReLU layer cannot be a negative "
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f"value. Received: {max_value}"
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)
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if negative_slope is None or negative_slope < 0.0:
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raise ValueError(
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"negative_slope of a ReLU layer cannot be a negative "
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f"value. Received: {negative_slope}"
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)
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if threshold is None or threshold < 0.0:
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raise ValueError(
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"threshold of a ReLU layer cannot be a negative "
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f"value. Received: {threshold}"
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)
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self.supports_masking = True
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if max_value is not None:
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max_value = backend.cast_to_floatx(max_value)
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self.max_value = max_value
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self.negative_slope = backend.cast_to_floatx(negative_slope)
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self.threshold = backend.cast_to_floatx(threshold)
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def call(self, inputs):
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# alpha is used for leaky relu slope in activations instead of
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# negative_slope.
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return backend.relu(
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inputs,
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alpha=self.negative_slope,
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max_value=self.max_value,
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threshold=self.threshold,
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)
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def get_config(self):
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config = {
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"max_value": self.max_value,
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"negative_slope": self.negative_slope,
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"threshold": self.threshold,
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}
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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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