Intelegentny_Pszczelarz/.venv/Lib/site-packages/keras/layers/activation/thresholded_relu.py
2023-06-19 00:49:18 +02:00

78 lines
2.4 KiB
Python

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Thresholded Rectified Linear Unit activation layer."""
import tensorflow.compat.v2 as tf
from keras import backend
from keras.engine.base_layer import Layer
from keras.utils import tf_utils
# isort: off
from tensorflow.python.util.tf_export import keras_export
@keras_export("keras.layers.ThresholdedReLU")
class ThresholdedReLU(Layer):
"""Thresholded Rectified Linear Unit.
It follows:
```
f(x) = x for x > theta
f(x) = 0 otherwise`
```
Input shape:
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape:
Same shape as the input.
Args:
theta: Float >= 0. Threshold location of activation.
"""
def __init__(self, theta=1.0, **kwargs):
super().__init__(**kwargs)
if theta is None:
raise ValueError(
"Theta of a Thresholded ReLU layer cannot be None, expecting a "
f"float. Received: {theta}"
)
if theta < 0:
raise ValueError(
"The theta value of a Thresholded ReLU layer "
f"should be >=0. Received: {theta}"
)
self.supports_masking = True
self.theta = backend.cast_to_floatx(theta)
def call(self, inputs):
dtype = self.compute_dtype
return inputs * tf.cast(tf.greater(inputs, self.theta), dtype)
def get_config(self):
config = {"theta": float(self.theta)}
base_config = super().get_config()
return dict(list(base_config.items()) + list(config.items()))
@tf_utils.shape_type_conversion
def compute_output_shape(self, input_shape):
return input_shape