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

84 lines
2.8 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.
# ==============================================================================
"""Contains the GaussianDropout layer."""
import numpy as np
import tensorflow.compat.v2 as tf
from keras import backend
from keras.engine import base_layer
from keras.utils import tf_utils
# isort: off
from tensorflow.python.util.tf_export import keras_export
@keras_export("keras.layers.GaussianDropout")
class GaussianDropout(base_layer.BaseRandomLayer):
"""Apply multiplicative 1-centered Gaussian noise.
As it is a regularization layer, it is only active at training time.
Args:
rate: Float, drop probability (as with `Dropout`).
The multiplicative noise will have
standard deviation `sqrt(rate / (1 - rate))`.
seed: Integer, optional random seed to enable deterministic behavior.
Call arguments:
inputs: Input tensor (of any rank).
training: Python boolean indicating whether the layer should behave in
training mode (adding dropout) or in inference mode (doing nothing).
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 input.
"""
def __init__(self, rate, seed=None, **kwargs):
super().__init__(seed=seed, **kwargs)
self.supports_masking = True
self.rate = rate
self.seed = seed
def call(self, inputs, training=None):
if 0 < self.rate < 1:
def noised():
stddev = np.sqrt(self.rate / (1.0 - self.rate))
return inputs * self._random_generator.random_normal(
shape=tf.shape(inputs),
mean=1.0,
stddev=stddev,
dtype=inputs.dtype,
)
return backend.in_train_phase(noised, inputs, training=training)
return inputs
def get_config(self):
config = {"rate": self.rate, "seed": self.seed}
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