[release] 1.8.4rc1

Author: zsdonghao

docs/conf.py

@@ -67,9 +67,9 @@
 # built documents.
 #
 # The short X.Y version.
-version = '1.8.4rc0'
+version = '1.8.4rc1'
 # The full version, including alpha/beta/rc tags.
-release = '1.8.4rc0'
+release = '1.8.4rc1'
 
 # 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 v1.8.4rc0'
+# html_title = 'TensorLayer v1.8.4rc1'
 
 # A shorter title for the navigation bar.  Default is the same as html_title.
 #

example/tutorial_mnist.py

@@ -119,13 +119,13 @@ def main_test_layers(model='relu'):
                 n_batch += 1
             print("   val loss: %f" % (val_loss / n_batch))
             print("   val acc: %f" % (val_acc / n_batch))
-            try:
-                # You can visualize the weight of 1st hidden layer as follow.
-                tl.vis.draw_weights(network.all_params[0].eval(), second=10, saveable=True, shape=[28, 28], name='w1_' + str(epoch + 1), fig_idx=2012)
-                # You can also save the weight of 1st hidden layer to .npz file.
-                # tl.files.save_npz([network.all_params[0]] , name='w1'+str(epoch+1)+'.npz')
-            except:  # pylint: disable=bare-except
-                print("You should change vis.draw_weights(), if you want to save the feature images for different dataset")
+            # try:
+            #     # You can visualize the weight of 1st hidden layer as follow.
+            #     tl.vis.draw_weights(network.all_params[0].eval(), second=10, saveable=True, shape=[28, 28], name='w1_' + str(epoch + 1), fig_idx=2012)
+            #     # You can also save the weight of 1st hidden layer to .npz file.
+            #     # tl.files.save_npz([network.all_params[0]] , name='w1'+str(epoch+1)+'.npz')
+            # except:  # pylint: disable=bare-except
+            #     print("You should change vis.draw_weights(), if you want to save the feature images for different dataset")
 
     print('Evaluation')
     test_loss, test_acc, n_batch = 0, 0, 0
@@ -306,11 +306,11 @@ def main_test_stacked_denoise_AE(model='relu'):
                 n_batch += 1
             print("   val loss: %f" % (val_loss / n_batch))
             print("   val acc: %f" % (val_acc / n_batch))
-            try:
-                # visualize the 1st hidden layer during fine-tune
-                tl.vis.draw_weights(network.all_params[0].eval(), second=10, saveable=True, shape=[28, 28], name='w1_' + str(epoch + 1), fig_idx=2012)
-            except:  # pylint: disable=bare-except
-                print("You should change vis.draw_weights(), if you want to save the feature images for different dataset")
+            # try:
+            #     # visualize the 1st hidden layer during fine-tune
+            #     tl.vis.draw_weights(network.all_params[0].eval(), second=10, saveable=True, shape=[28, 28], name='w1_' + str(epoch + 1), fig_idx=2012)
+            # except:  # pylint: disable=bare-except
+            #     print("You should change vis.draw_weights(), if you want to save the feature images for different dataset")
 
     print('Evaluation')
     test_loss, test_acc, n_batch = 0, 0, 0
@@ -451,10 +451,10 @@ def main_test_cnn_layer():
                 n_batch += 1
             print("   val loss: %f" % (val_loss / n_batch))
             print("   val acc: %f" % (val_acc / n_batch))
-            try:
-                tl.vis.CNN2d(network.all_params[0].eval(), second=10, saveable=True, name='cnn1_' + str(epoch + 1), fig_idx=2012)
-            except:  # pylint: disable=bare-except
-                print("You should change vis.CNN(), if you want to save the feature images for different dataset")
+            # try:
+            #     tl.vis.CNN2d(network.all_params[0].eval(), second=10, saveable=True, name='cnn1_' + str(epoch + 1), fig_idx=2012)
+            # except:  # pylint: disable=bare-except
+            #     print("You should change vis.CNN(), if you want to save the feature images for different dataset")
 
     print('Evaluation')
     test_loss, test_acc, n_batch = 0, 0, 0
@@ -474,7 +474,7 @@ def main_test_cnn_layer():
     sess = tf.InteractiveSession()
 
     # Dropout and Dropconnect
-    main_test_layers(model='relu')  # model = relu, dropconnect
+    # main_test_layers(model='relu')  # model = relu, dropconnect
 
     # Single Denoising Autoencoder
     # main_test_denoise_AE(model='sigmoid')       # model = relu, sigmoid
@@ -483,4 +483,4 @@ def main_test_cnn_layer():
     # main_test_stacked_denoise_AE(model='relu')  # model = relu, sigmoid
 
     # CNN
-    # main_test_cnn_layer()
+    main_test_cnn_layer()

setup.py

@@ -10,7 +10,7 @@
 
 setup(
     name="tensorlayer",
-    version="1.8.4rc0",
+    version="1.8.4rc1",
     include_package_data=True,
     author='TensorLayer Contributors',
     author_email='[email protected]',

tensorlayer/__init__.py

@@ -24,7 +24,7 @@
 act = activation
 vis = visualize
 
-__version__ = "1.8.4rc0"
+__version__ = "1.8.4rc1"
 
 global_flag = {}
 global_dict = {}