tensorlayer · luomai · Feb 18, 2018 · Feb 18, 2018 · Feb 18, 2018 · Feb 18, 2018
diff --git a/docs/index.rst b/docs/index.rst
@@ -51,13 +51,9 @@ method, this part of the documentation is for you.
   modules/rein
   modules/files
   modules/visualize
-  modules/ops
   modules/activation
   modules/distributed
 
-..
-  modules/db
-
 
 Command-line Reference
 ----------------------

diff --git a/docs/modules/activation.rst b/docs/modules/activation.rst
diff --git a/docs/modules/cost.rst b/docs/modules/cost.rst
diff --git a/docs/modules/distributed.rst b/docs/modules/distributed.rst
diff --git a/docs/modules/files.rst b/docs/modules/files.rst
diff --git a/docs/modules/iterate.rst b/docs/modules/iterate.rst
diff --git a/docs/modules/layers.rst b/docs/modules/layers.rst
diff --git a/docs/modules/nlp.rst b/docs/modules/nlp.rst
diff --git a/docs/modules/ops.rst b/docs/modules/ops.rst
diff --git a/docs/modules/prepro.rst b/docs/modules/prepro.rst
diff --git a/docs/modules/rein.rst b/docs/modules/rein.rst
diff --git a/docs/modules/utils.rst b/docs/modules/utils.rst
@@ -56,3 +56,19 @@ Convert list of string to dictionary
 Flatten a list
 ^^^^^^^^^^^^^^^^^^^
 .. autofunction:: flatten_list
+
+Close TF session and associated processes
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. autofunction:: exit_tensorflow
+
+Open TensorBoard
+^^^^^^^^^^^^^^^^^^^
+.. autofunction:: open_tensorboard
+
+Clear TensorFlow placeholder
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+.. autofunction:: clear_all_placeholder_variables
+
+Set GPU functions
+---------------------------
+.. autofunction:: set_gpu_fraction
diff --git a/docs/modules/visualize.rst b/docs/modules/visualize.rst
diff --git a/docs/user/development.rst b/docs/user/development.rst
diff --git a/docs/user/example.rst b/docs/user/example.rst
diff --git a/docs/user/installation.rst b/docs/user/installation.rst
diff --git a/docs/user/more.rst b/docs/user/more.rst
@@ -69,6 +69,15 @@ After you get the variable list, you can define your optimizer like that so as t
   train_op = tf.train.AdamOptimizer(0.001).minimize(cost, var_list= train_params)
 
 
+Logging
+-------
+
+TensorLayer adopts the `Python logging module <https://docs.python.org/3/library/logging.html>`__
+to log running information.
+The logging module would print logs to the console in default. 
+If you want to configure the logging module,
+you shall follow its `manual <https://docs.python.org/3/library/logging.html>`__.
+
 Visualization
 --------------
 

diff --git a/docs/user/tutorial.rst b/docs/user/tutorial.rst
diff --git a/example/tutorial_mnist_distributed.py b/example/tutorial_mnist_distributed.py
@@ -14,9 +14,6 @@
 import tensorflow as tf
 import tensorlayer as tl
 
-# set buffer mode to _IOLBF for stdout
-tl.ops.setlinebuf()
-
 # load environment for distributed training
 task_spec = tl.distributed.TaskSpec()
 task_spec.create_server()

diff --git a/tensorlayer/__init__.py b/tensorlayer/__init__.py
@@ -12,7 +12,6 @@
 from . import files
 from . import iterate
 from . import layers
-from . import ops
 from . import utils
 from . import visualize
 from . import prepro

diff --git a/tensorlayer/layers/time_distribution.py b/tensorlayer/layers/time_distribution.py
@@ -1,6 +1,5 @@
 # -*- coding: utf-8 -*-
 
-from .. import ops
 from .core import *
 
 
@@ -62,14 +61,13 @@ def __init__(
         timestep = input_shape[1]
         x = tf.unstack(self.inputs, axis=1)
 
-        with ops.suppress_stdout():
-            for i in range(0, timestep):
-                with tf.variable_scope(name, reuse=(set_keep['name_reuse'] if i == 0 else True)) as vs:
-                    set_name_reuse((set_keep['name_reuse'] if i == 0 else True))
-                    net = layer_class(InputLayer(x[i], name=args['name'] + str(i)), **args)
-                    # net = layer_class(InputLayer(x[i], name="input_"+args['name']), **args)
-                    x[i] = net.outputs
-                    variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
+        for i in range(0, timestep):
+            with tf.variable_scope(name, reuse=(set_keep['name_reuse'] if i == 0 else True)) as vs:
+                set_name_reuse((set_keep['name_reuse'] if i == 0 else True))
+                net = layer_class(InputLayer(x[i], name=args['name'] + str(i)), **args)
+                # net = layer_class(InputLayer(x[i], name="input_"+args['name']), **args)
+                x[i] = net.outputs
+                variables = tf.get_collection(TF_GRAPHKEYS_VARIABLES, scope=vs.name)
 
         self.outputs = tf.stack(x, axis=1, name=name)