release 1.8.3rc0 (#429)

* release 1.8.3rc0

* update links for examples

Author: zsdonghao

README.md

@@ -85,7 +85,7 @@ Examples can be found [in this folder](https://github.com/zsdonghao/tensorlayer/
  - VGG 19 (ImageNet). Classification task, see [tutorial_vgg19.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_vgg19.py).
  - InceptionV3 (ImageNet). Classification task, see [tutorial\_inceptionV3_tfslim.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_inceptionV3_tfslim.py).
  - SqueezeNet (ImageNet). Classification task, see [tutorial_squeezenet.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_squeezenet.py)
- - BinaryNet (MNIST). Classification task, see [tutorial_squeezenet.py](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_binarynet_mnist_cnn.py) 
+ - BinaryNet (MNIST). Classification task, see [tutorial_binarynet_mnist_cnn.py](https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_binarynet_mnist_cnn.py) 
  - Wide ResNet (CIFAR) by [ritchieng](https://github.com/ritchieng/wideresnet-tensorlayer).
  - More CNN implementations of [TF-Slim](https://github.com/tensorflow/models/tree/master/research/slim) can be connected to TensorLayer via SlimNetsLayer.
  - [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025) by [zsdonghao](https://github.com/zsdonghao/Spatial-Transformer-Nets).

docs/conf.py

@@ -67,9 +67,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = '1.8.2'
+version = '1.8.3rc0'
 # The full version, including alpha/beta/rc tags.
-release = '1.8.2'
+release = '1.8.3rc0'
 
 # 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 v1.8.2'
+# html_title = 'TensorLayer v1.8.3rc0'
 
 # A shorter title for the navigation bar.  Default is the same as html_title.
 #

docs/user/example.rst

@@ -22,7 +22,7 @@ Computer Vision
  - VGG 19 (ImageNet). Classification task, see `tutorial_vgg19.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_vgg19.py>`_.
  - InceptionV3 (ImageNet). Classification task, see `tutorial_inceptionV3_tfslim.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_inceptionV3_tfslim.py>`_.
  - SqueezeNet (ImageNet). Classification task, see `tutorial_squeezenet.py <https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_squeezenet.py>`_.
- - BinaryNet (MNIST). Classification task, see `tutorial_squeezenet.py <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_binarynet_mnist_cnn.py>`_.
+ - BinaryNet (MNIST). Classification task, see `tutorial_binarynet_mnist_cnn.py <https://github.com/tensorlayer/tensorlayer/blob/master/example/tutorial_binarynet_mnist_cnn.py>`_.
  - Wide ResNet (CIFAR) by `ritchieng <https://github.com/ritchieng/wideresnet-tensorlayer>`_.
  - More CNN implementations of `TF-Slim <https://github.com/tensorflow/models/tree/master/research/slim>`_ can be connected to TensorLayer via SlimNetsLayer.
  - `Spatial Transformer Networks <https://arxiv.org/abs/1506.02025>`_ by `zsdonghao <https://github.com/zsdonghao/Spatial-Transformer-Nets>`__.

example/tutorial_inceptionV3_tfslim.py

@@ -132,6 +132,7 @@ def print_prob(prob):
 img1 = load_image("data/puzzle.jpeg")  # test data in github: https://github.com/zsdonghao/tensorlayer/tree/master/example/data
 img1 = img1.reshape((1, 299, 299, 3))
 
+prob = sess.run(probs, feed_dict={x: img1})  # the 1st time need time to compile
 start_time = time.time()
 prob = sess.run(probs, feed_dict={x: img1})
 print("End time : %.5ss" % (time.time() - start_time))

example/tutorial_squeezenet.py

@@ -113,7 +113,7 @@ def squeezenet(x, is_train=True, reuse=False):
 
 img = tl.vis.read_image('data/tiger.jpeg', '')
 img = tl.prepro.imresize(img, (224, 224))
-prob = sess.run(softmax, feed_dict={x: [img]})[0]
+prob = sess.run(softmax, feed_dict={x: [img]})[0]  # the 1st time need time to compile
 start_time = time.time()
 prob = sess.run(softmax, feed_dict={x: [img]})[0]
 print("  End time : %.5ss" % (time.time() - start_time))

setup.py

@@ -10,7 +10,7 @@
 
 setup(
     name="tensorlayer",
-    version="1.8.2",
+    version="1.8.3rc0",
     include_package_data=True,
     author='TensorLayer Contributors',
     author_email='[email protected]',

tensorlayer/__init__.py

@@ -23,7 +23,7 @@
 act = activation
 vis = visualize
 
-__version__ = "1.8.2"
+__version__ = "1.8.3rc0"
 
 global_flag = {}
 global_dict = {}