Touch-ups in examples and doc

docs/sources/index.md

@@ -67,7 +67,7 @@ model.fit(X_train, Y_train, nb_epoch=5, batch_size=32)
 
 Alternatively, you can feed batches to your model manually:
 ```python
-model.train(X_batch, Y_batch)
+model.train_on_batch(X_batch, Y_batch)
 ```
 
 Evaluate your performance in one line:
@@ -81,7 +81,7 @@ classes = model.predict_classes(X_test, batch_size=32)
 proba = model.predict_proba(X_test, batch_size=32)
 ```
 
-Building a network of LSTMs, a deep CNN, a word2vec embedder or any other model is just as fast. The ideas behind deep learning are simple, so why should their implementation be painful?
+Building a network of LSTMs, a deep CNN, a Neural Turing Machine, a word2vec embedder or any other model is just as fast. The ideas behind deep learning are simple, so why should their implementation be painful?
 
 Have a look at the [examples](examples.md).
 
@@ -116,15 +116,15 @@ Keras welcomes all contributions from the community.
 - Keep a pragmatic mindset and avoid bloat. Only add to the source if that is the only path forward.
 - New features should be documented. Make sure you update the documentation along with your Pull Request.
 - The documentation for every new feature should include a usage example in the form of a code snippet. 
-- All changes should be tested. A formal test process will be introduced very soon.
+- All changes should be tested. Make sure any new feature you add has a corresponding unit test.
 - Even if you don't contribute to the Keras source code, if you have an application of Keras that is concise and powerful, please consider adding it to our collection of [examples](https://github.com/fchollet/keras/tree/master/examples).
 
 
 ## Why this name, Keras?
 
 Keras (κέρας) means _horn_ in Greek. It is a reference to a literary image from ancient Greek and Latin literature, first found in the _Odyssey_, where dream spirits (_Oneiroi_, singular _Oneiros_) are divided between those who deceive men with false visions, who arrive to Earth through a gate of ivory, and those who announce a future that will come to pass, who arrive through a gate of horn. It's a play on the words κέρας (horn) / κραίνω (fulfill), and ἐλέφας (ivory) / ἐλεφαίρομαι (deceive).
 
-Keras was developed as part of the research effort of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System).
+Keras was developed as part of the research effort of project __ONEIROS__ (*Open-ended Neuro-Electronic Intelligent Robot Operating System*).
 
 > _"Oneiroi are beyond our unravelling --who can be sure what tale they tell? Not all that men look for comes to pass. Two gates there are that give passage to fleeting Oneiroi; one is made of horn, one of ivory. The Oneiroi that pass through sawn ivory are deceitful, bearing a message that will not be fulfilled; those that come out through polished horn have truth behind them, to be accomplished for men who see them."_ 
 

docs/sources/layers/core.md

@@ -303,11 +303,11 @@ model.add(RepeatVector(2))  # output shape: (nb_samples, 2, 10)
 keras.layers.core.Merge(models, mode='sum')
 ```
 
-Merge the output of a list of models into a single tensor, following one of two modes: `sum` or `concat`. 
+Merge the output of a list of layers (or containers) into a single tensor, following one of two modes: `sum` or `concat`. 
 
 - __Arguments__:
-    - __models__: List of `Sequential` models.
-    - __mode__: String, one of `{'sum', 'concat'}`. `sum` will simply sum the outputs of the models (therefore all models should have an output with the same shape). `concat` will concatenate the outputs along the last dimension (therefore all models should have an output that only differ along the last dimension). 
+    - __layers__: List of layers or [containers](/layers/containers/).
+    - __mode__: String, one of `{'sum', 'concat'}`. `sum` will simply sum the outputs of the layers (therefore all layers should have an output with the same shape). `concat` will concatenate the outputs along the last dimension (therefore all layers should have an output that only differ along the last dimension). 
 
 - __Example__:
 

examples/kaggle_otto_nn.py

@@ -3,6 +3,7 @@
 
 import numpy as np
 import pandas as pd
+np.random.seed(1337) # for reproducibility
 
 from keras.models import Sequential
 from keras.layers.core import Dense, Dropout, Activation
@@ -17,7 +18,7 @@
     This demonstrates how to reach a score of 0.4890 (local validation)
     on the Kaggle Otto challenge, with a deep net using Keras.
 
-    Compatible Python 2.7-3.4 
+    Compatible Python 2.7-3.4. Requires Scikit-Learn and Pandas.
 
     Recommended to run on GPU: 
         Command: THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32 python kaggle_otto_nn.py
@@ -35,8 +36,6 @@
     Get the data from Kaggle: https://www.kaggle.com/c/otto-group-product-classification-challenge/data
 '''
 
-np.random.seed(1337) # for reproducibility
-
 def load_data(path, train=True):
     df = pd.read_csv(path)
     X = df.values.copy()
@@ -121,4 +120,3 @@ def make_submission(y_prob, ids, encoder, fname):
 
 proba = model.predict_proba(X_test)
 make_submission(proba, ids, encoder, fname='keras-otto.csv')
-

examples/mnist_irnn.py

@@ -20,7 +20,7 @@
     arXiv:1504.00941v2 [cs.NE] 7 Apr 201
     http://arxiv.org/pdf/1504.00941v2.pdf
 
-    Optimizer is replaced with RMSprop which give more stable and steady
+    Optimizer is replaced with RMSprop which yields more stable and steady
     improvement.
 
     0.80 train/test accuracy and 0.55 train/test loss after 70 epochs