78 lines
2.4 KiB
Python
78 lines
2.4 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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"""Thresholded Rectified Linear Unit activation layer."""
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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.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.ThresholdedReLU")
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class ThresholdedReLU(Layer):
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"""Thresholded Rectified Linear Unit.
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It follows:
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```
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f(x) = x for x > theta
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f(x) = 0 otherwise`
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```
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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 samples 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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theta: Float >= 0. Threshold location of activation.
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"""
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def __init__(self, theta=1.0, **kwargs):
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super().__init__(**kwargs)
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if theta is None:
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raise ValueError(
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"Theta of a Thresholded ReLU layer cannot be None, expecting a "
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f"float. Received: {theta}"
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)
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if theta < 0:
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raise ValueError(
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"The theta value of a Thresholded ReLU layer "
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f"should be >=0. Received: {theta}"
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)
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self.supports_masking = True
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self.theta = backend.cast_to_floatx(theta)
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def call(self, inputs):
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dtype = self.compute_dtype
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return inputs * tf.cast(tf.greater(inputs, self.theta), dtype)
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def get_config(self):
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config = {"theta": float(self.theta)}
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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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