readme

README.md

@@ -370,7 +370,7 @@ pip install . -e
 
 # Ways to Contribute
 
-TensorLayer begins as an internal repository at Imperial College Lodnon, helping researchers to test their new methods. It now encourage researches from all over the world to publish their new methods so as to promote the development of machine learning.
+TensorLayer begins as an internal repository at Imperial College London, helping researchers to test their new methods. It now encourage researches from all over the world to publish their new methods so as to promote the development of machine learning.
 
 Your method can be merged into TensorLayer, if you can prove it is better than the existing methods. Test script with detailed descriptions is required.
 

docs/user/development.rst

@@ -5,8 +5,8 @@ The TensorLayer project was started by Hao Dong, Imperial College London in Jun
 2016. It is developed by a core team (in alphabetical order:
 `Akara Supratak <https://akaraspt.github.io>`_,
 `Hao Dong <https://zsdonghao.github.io>`_,
-`Simiao Yu <https://github.com/zsdonghao>`_,)
-and numerous additional contributors on `GitHub`_,
+`Simiao Yu <https://github.com/zsdonghao>`_)
+and numerous additional contributors on `GitHub`_.
 
 As an open-source project by Researchers for Researchers and Engineers,
 we highly welcome contributions!

tensorlayer/files.py

@@ -31,6 +31,7 @@ def load_mnist_dataset(shape=(-1,784)):
     Examples
     --------
     >>> X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1,784))
+    >>> X_train, y_train, X_val, y_val, X_test, y_test = tl.files.load_mnist_dataset(shape=(-1, 28, 28, 1))
     """
     # We first define a download function, supporting both Python 2 and 3.
     if sys.version_info[0] == 2:
@@ -296,7 +297,11 @@ def load_ptb_dataset():
     after 14 epochs they start to reduce the learning rate by a factor of 1.15
     after each epoch. They clip the norm of the gradients (normalized by
     minibatch size) at 10.
-    
+
+    Returns
+    --------
+    train_data, valid_data, test_data, vocabulary size
+
     Examples
     --------
     >>> train_data, valid_data, test_data, vocab_size = tl.files.load_ptb_dataset()
@@ -491,7 +496,7 @@ def download_imbd(filename):
 def load_nietzsche_dataset():
     """Load Nietzsche dataset.
     Returns a string.
-    
+
     Examples
     --------
     >>> see tutorial_generate_text.py