[layers] gaussian noise layer/ v1.3.0

Author: zsdonghao

docs/conf.py

@@ -67,9 +67,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = '1.2.8'
+version = '1.3.0'
 # The full version, including alpha/beta/rc tags.
-release = '1.2.8'
+release = '1.3.0'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -143,7 +143,7 @@
 # The name for this set of Sphinx documents.
 # "<project> v<release> documentation" by default.
 #
-# html_title = 'TensorLayer v1.2.8'
+# html_title = 'TensorLayer v1.3.0'
 
 # A shorter title for the navigation bar.  Default is the same as html_title.
 #

docs/modules/layers.rst

@@ -260,14 +260,19 @@ Layer list
    get_variables_with_name
    set_name_reuse
    print_all_variables
+
    Layer
+
    InputLayer
    Word2vecEmbeddingInputlayer
    EmbeddingInputlayer
+
    DenseLayer
    ReconLayer
    DropoutLayer
+   GaussianNoiseLayer
    DropconnectDenseLayer
+
    Conv1dLayer
    Conv2dLayer
    DeConv2dLayer
@@ -277,28 +282,38 @@ Layer list
    UpSampling2dLayer
    AtrousConv2dLayer
    LocalResponseNormLayer
+
    Conv2d
    DeConv2d
    MaxPool2d
    MeanPool2d
+
    BatchNormLayer
    LocalResponseNormLayer
+
    RNNLayer
    BiRNNLayer
    advanced_indexing_op
    retrieve_seq_length_op
    retrieve_seq_length_op2
    DynamicRNNLayer
    BiDynamicRNNLayer
+
    FlattenLayer
    ReshapeLayer
    LambdaLayer
+
    ConcatLayer
    ElementwiseLayer
+
    SlimNetsLayer
+
    PReluLayer
+
    MultiplexerLayer
+
    EmbeddingAttentionSeq2seqWrapper
+   
    flatten_reshape
    clear_layers_name
    initialize_rnn_state
@@ -362,6 +377,10 @@ Dropout layer
 ^^^^^^^^^^^^^^^^
 .. autoclass:: DropoutLayer
 
+Gaussian noise layer
+^^^^^^^^^^^^^^^^^^^^^
+.. autoclass:: GaussianNoiseLayer
+
 Dropconnect + Dense layer
 ^^^^^^^^^^^^^^^^^^^^^^^^^^
 .. autoclass:: DropconnectDenseLayer

setup.py

@@ -10,7 +10,7 @@
 
 setup(
     name = "tensorlayer",
-    version = "1.2.8",
+    version = "1.3.0",
     include_package_data=True,
     author='TensorLayer Contributors',
     author_email='[email protected]',

tensorlayer/__init__.py

@@ -25,4 +25,4 @@
 from . import rein
 
 
-__version__ = "1.2.8"
+__version__ = "1.3.0"

tensorlayer/layers.py

@@ -880,6 +880,37 @@ def __init__(
         # value will be checked for compatibility with the placeholder.
         # If the key is a SparseTensor, the value should be a SparseTensorValue.
 
+class GaussianNoiseLayer(Layer):
+    """
+    The :class:`GaussianNoiseLayer` class is noise layer that adding noise with
+    normal distribution to the activation.
+
+    Parameters
+    ------------
+    layer : a :class:`Layer` instance
+        The `Layer` class feeding into this layer.
+    sigma : float
+        Scale value of gaussian noise.
+    name : a string or None
+        An optional name to attach to this layer.
+    """
+    def __init__(
+        self,
+        layer = None,
+        sigma = 0.1,
+        name = 'gaussian_noise_layer',
+    ):
+        Layer.__init__(self, name=name)
+        self.inputs = layer.outputs
+        print("  tensorlayer:Instantiate GaussianNoiseLayer %s: keep: %f" % (self.name, keep))
+        with tf.variable_scope(name) as vs:
+            noise = np.random.normal(0.0 , sigma , tf.to_int64(input_layer).get_shape())
+            self.inputs = self.inputs + noise
+        self.all_layers = list(layer.all_layers)
+        self.all_params = list(layer.all_params)
+        self.all_drop = dict(layer.all_drop)
+
+
 class DropconnectDenseLayer(Layer):
     """
     The :class:`DropconnectDenseLayer` class is ``DenseLayer`` with DropConnect
@@ -3742,27 +3773,7 @@ def __init__(
         self.all_layers.extend( [self.outputs] )
         self.all_params.extend( [W, b] )
 
-# noise
-class GaussianNoiseLayer(Layer):
-    """
-    Waiting for contribution
-    """
-    def __init__(
-        self,
-        layer = None,
-        sigma = 0.1,
-        name = 'gaussian_noise_layer',
-    ):
-        Layer.__init__(self, name=name)
-        self.inputs = layer.outputs
-        print("  tensorlayer:Instantiate GaussianNoiseLayer %s: keep: %f" % (self.name, keep))
-        print("    Waiting for contribution")
-        with tf.variable_scope(name) as vs:
-            noisy = np.random.normal(0.0 , sigma , tf.to_int64(input_layer).get_shape())
-            self.inputs = self.inputs + noisy
-        self.all_layers = list(layer.all_layers)
-        self.all_params = list(layer.all_params)
-        self.all_drop = dict(layer.all_drop)
+