release Binary Nets, release 1.8.1 (#423)

* update bnn cnn, htanh

* bconv example

* release binary

* release 1.8.2

docs/conf.py

@@ -67,9 +67,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = '1.8.1'
+version = '1.8.2'
 # The full version, including alpha/beta/rc tags.
-release = '1.8.1'
+release = '1.8.2'
 
 # 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 v1.8.1'
+# html_title = 'TensorLayer v1.8.2'
 
 # A shorter title for the navigation bar.  Default is the same as html_title.
 #

docs/modules/activation.rst

@@ -31,6 +31,7 @@ For more complex activation, TensorFlow API will be required.
    leaky_relu
    swish
    sign
+   hard_tanh
    pixel_wise_softmax
 
 Identity
@@ -53,6 +54,10 @@ Sign
 ---------------------
 .. autofunction:: sign
 
+Hard Tanh
+---------------------
+.. autofunction:: hard_tanh
+
 Pixel-wise softmax
 --------------------
 .. autofunction:: pixel_wise_softmax

docs/modules/layers.rst

@@ -152,7 +152,7 @@ At the end, for a layer with parameters, we also append the parameters into ``al
         name ='simple_dense',
     ):
         # check layer name (fixed)
-        Layer.__init__(self, name=name)
+        Layer.__init__(self, layer=layer, name=name)
 
         # the input of this layer is the output of previous layer (fixed)
         self.inputs = layer.outputs
@@ -169,11 +169,6 @@ At the end, for a layer with parameters, we also append the parameters into ``al
             # tensor operation
             self.outputs = act(tf.matmul(self.inputs, W) + b)
 
-        # get stuff from previous layer (fixed)
-        self.all_layers = list(layer.all_layers)
-        self.all_params = list(layer.all_params)
-        self.all_drop = dict(layer.all_drop)
-
         # update layer (customized)
         self.all_layers.extend( [self.outputs] )
         self.all_params.extend( [W, b] )
@@ -336,6 +331,11 @@ Layer list
 
    SlimNetsLayer
 
+   BinaryDenseLayer
+   BinaryConv2d
+   SignLayer
+   ScaleLayer
+
    PReluLayer
 
    MultiplexerLayer
@@ -799,6 +799,38 @@ see `Slim-model <https://github.com/tensorflow/models/tree/master/research/slim>
   .. autoclass:: KerasLayer
 
 
+Binary Nets
+------------------
+
+Read Me
+^^^^^^^^^^^^^^
+
+This is an experimental API package for building Binary Nets.
+We are using matrix multiplication rather than add-minus and bit-count operation at the moment.
+Therefore, these APIs would not speed up the inferencing, for production, you can train model via TensorLayer and deploy the model into other customized C/C++ implementation (We probably provide users an extra C/C++ binary net framework that can load model from TensorLayer).
+
+Note that, these experimental APIs can be changed in anytime.
+
+Binarized Dense
+^^^^^^^^^^^^^^^^^
+.. autoclass:: BinaryDenseLayer
+
+
+Binarized Conv2d
+^^^^^^^^^^^^^^^^^^
+.. autoclass:: BinaryConv2d
+
+
+Sign
+^^^^^^^^^^^^^^
+.. autoclass:: SignLayer
+
+
+Scale
+^^^^^^^^^^^^^^
+.. autoclass:: ScaleLayer
+
+
 Parametric activation layer
 ---------------------------
 

example/tutorial_binarynet_mnist_cnn.py

@@ -7,6 +7,7 @@
 
 X_train, y_train, X_val, y_val, X_test, y_test = \
                 tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
+# X_train, y_train, X_test, y_test = tl.files.load_cropped_svhn(include_extra=False)
 
 sess = tf.InteractiveSession()
 
@@ -17,25 +18,29 @@
 
 
 def model(x, is_train=True, reuse=False):
+    # In BNN, all the layers inputs are binary, with the exception of the first layer.
+    # ref: https://github.com/itayhubara/BinaryNet.tf/blob/master/models/BNN_cifar10.py
     with tf.variable_scope("binarynet", reuse=reuse):
         net = tl.layers.InputLayer(x, name='input')
         net = tl.layers.BinaryConv2d(net, 32, (5, 5), (1, 1), padding='SAME', name='bcnn1')
         net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool1')
+        net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn1')
 
-        net = tl.layers.BatchNormLayer(net, is_train=is_train, name='bn')
-        net = tl.layers.SignLayer(net, name='sign2')
+        net = tl.layers.SignLayer(net)
         net = tl.layers.BinaryConv2d(net, 64, (5, 5), (1, 1), padding='SAME', name='bcnn2')
         net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool2')
+        net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn2')
 
-        net = tl.layers.SignLayer(net, name='sign2')
         net = tl.layers.FlattenLayer(net, name='flatten')
-        net = tl.layers.DropoutLayer(net, 0.5, True, is_train, name='drop1')
-        # net = tl.layers.DenseLayer(net, 256, act=tf.nn.relu, name='dense')
+        net = tl.layers.DropoutLayer(net, 0.8, True, is_train, name='drop1')
+        net = tl.layers.SignLayer(net)
         net = tl.layers.BinaryDenseLayer(net, 256, name='dense')
-        net = tl.layers.DropoutLayer(net, 0.5, True, is_train, name='drop2')
-        # net = tl.layers.DenseLayer(net, 10, act=tf.identity, name='output')
+        net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn3')
+
+        net = tl.layers.DropoutLayer(net, 0.8, True, is_train, name='drop2')
+        net = tl.layers.SignLayer(net)
         net = tl.layers.BinaryDenseLayer(net, 10, name='bout')
-        # net = tl.layers.ScaleLayer(net, name='scale')
+        net = tl.layers.BatchNormLayer(net, is_train=is_train, name='bno')
     return net
 
 
@@ -66,7 +71,7 @@ def model(x, is_train=True, reuse=False):
 n_epoch = 200
 print_freq = 5
 
-# print(sess.run(net_test.all_params)) # print real value of parameters
+# print(sess.run(net_test.all_params)) # print real values of parameters
 
 for epoch in range(n_epoch):
     start_time = time.time()

setup.py

@@ -10,7 +10,7 @@
 
 setup(
     name="tensorlayer",
-    version="1.8.1",
+    version="1.8.2",
     include_package_data=True,
     author='TensorLayer Contributors',
     author_email='[email protected]',

tensorlayer/__init__.py

@@ -23,7 +23,7 @@
 act = activation
 vis = visualize
 
-__version__ = "1.8.1"
+__version__ = "1.8.2"
 
 global_flag = {}
 global_dict = {}

tensorlayer/activation.py

@@ -168,6 +168,28 @@ def sign(x):  # https://github.com/AngusG/tensorflow-xnor-bnn/blob/master/models
 #         return tf.sign(x), grad
 
 
+def hard_tanh(x, name='htanh'):
+    """Hard tanh activation function.
+
+    Which is a ramp function with low bound of -1 and upper bound of 1, shortcut is ``htanh`.
+
+    Parameters
+    ----------
+    x : Tensor
+        input.
+    name : str
+        The function name (optional).
+
+    Returns
+    -------
+    Tensor
+        A ``Tensor`` in the same type as ``x``.
+
+    """
+    # with tf.variable_scope("hard_tanh"):
+    return tf.clip_by_value(x, -1, 1, name=name)
+
+
 @deprecated("2018-06-30", "This API will be deprecated soon as tf.nn.softmax can do the same thing.")
 def pixel_wise_softmax(x, name='pixel_wise_softmax'):
     """Return the softmax outputs of images, every pixels have multiple label, the sum of a pixel is 1.
@@ -204,3 +226,4 @@ def pixel_wise_softmax(x, name='pixel_wise_softmax'):
 # Alias
 linear = identity
 lrelu = leaky_relu
+htanh = hard_tanh

tensorlayer/layers/binary.py

@@ -5,9 +5,9 @@
 
 __all__ = [
     'BinaryDenseLayer',
+    'BinaryConv2d',
     'SignLayer',
     'ScaleLayer',
-    'BinaryConv2d',
 ]
 
 
@@ -142,6 +142,18 @@ class BinaryConv2d(Layer):
     name : str
         A unique layer name.
 
+    Examples
+    ---------
+    >>> net = tl.layers.InputLayer(x, name='input')
+    >>> net = tl.layers.BinaryConv2d(net, 32, (5, 5), (1, 1), padding='SAME', name='bcnn1')
+    >>> net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool1')
+    >>> net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn1')
+    ...
+    >>> net = tl.layers.SignLayer(net)
+    >>> net = tl.layers.BinaryConv2d(net, 64, (5, 5), (1, 1), padding='SAME', name='bcnn2')
+    >>> net = tl.layers.MaxPool2d(net, (2, 2), (2, 2), padding='SAME', name='pool2')
+    >>> net = tl.layers.BatchNormLayer(net, act=tl.act.htanh, is_train=is_train, name='bn2')
+
     """
 
     def __init__(