RDO-VAE work in progress

dnn/training_tf2/uniform_noise.py

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+# 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 UniformNoise layer."""
+
+
+import tensorflow.compat.v2 as tf
+
+from tensorflow.keras import backend
+
+from tensorflow.keras.layers import Layer
+
+class UniformNoise(Layer):
+    """Apply additive zero-centered uniform noise.
+
+    This is useful to mitigate overfitting
+    (you could see it as a form of random data augmentation).
+    Gaussian Noise (GS) is a natural choice as corruption process
+    for real valued inputs.
+
+    As it is a regularization layer, it is only active at training time.
+
+    Args:
+      stddev: Float, standard deviation of the noise distribution.
+      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 noise) 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, stddev=0.5, seed=None, **kwargs):
+        super().__init__(**kwargs)
+        self.supports_masking = True
+        self.stddev = stddev
+
+
+    def call(self, inputs, training=None):
+        def noised():
+            return inputs + backend.random_uniform(
+                shape=tf.shape(inputs),
+                minval=-self.stddev,
+                maxval=self.stddev,
+                dtype=inputs.dtype,
+            )
+
+        return backend.in_train_phase(noised, inputs, training=training)
+
+    def get_config(self):
+        config = {"stddev": self.stddev}
+        base_config = super().get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+    def compute_output_shape(self, input_shape):
+        return input_shape