Merge branch 'master' of github.com:fchollet/keras

Author: fchollet

docs/autogen.py

@@ -8,9 +8,7 @@
 - Getting started
     Getting started with the sequential model
     Getting started with the functional api
-    Examples
     FAQ
-    Installation guide
 
 - Models
     About Keras models
@@ -26,31 +24,39 @@
         explain common layer functions: get_weights, set_weights, get_config
         explain input_shape
         explain usage on non-Keras tensors
-    Core layers
-    Convolutional
-    Recurrent
-    Embeddings
-    Normalization
-    Advanced activations
-    Noise
+    Core Layers
+    Convolutional Layers
+    Pooling Layers
+    Locally-connected Layers
+    Recurrent Layers
+    Embedding Layers
+    Merge Layers
+    Advanced Activations Layers
+    Normalization Layers
+    Noise Layers
+    Layer Wrappers
+    Writing your own Keras layers
 
 - Preprocessing
-    Image preprocessing
-    Text preprocessing
-    Sequence preprocessing
+    Sequence Preprocessing
+    Text Preprocessing
+    Image Preprocessing
 
 Losses
 Metrics
 Optimizers
 Activations
 Callbacks
 Datasets
+Applications
 Backend
-Initializations
+Initializers
 Regularizers
 Constraints
 Visualization
 Scikit-learn API
+Utils
+Contributing
 
 '''
 from __future__ import print_function
@@ -509,3 +515,5 @@ def process_function_docstring(docstring):
     if not os.path.exists(subdir):
         os.makedirs(subdir)
     open(path, 'w').write(mkdown)
+
+shutil.copyfile('../CONTRIBUTING.md', 'sources/contributing.md')

docs/mkdocs.yml

@@ -51,3 +51,4 @@ pages:
 - Visualization: visualization.md
 - Scikit-learn API: scikit-learn-api.md
 - Utils: utils.md
+- Contributing: contributing.md

keras/callbacks.py

@@ -3,6 +3,7 @@
 
 import os
 import csv
+import six
 
 import numpy as np
 import time
@@ -861,16 +862,17 @@ def __init__(self, filename, separator=',', append=False):
         self.writer = None
         self.keys = None
         self.append_header = True
+        self.file_flags = 'b' if six.PY2 and os.name == 'nt' else ''
         super(CSVLogger, self).__init__()
 
     def on_train_begin(self, logs=None):
         if self.append:
             if os.path.exists(self.filename):
-                with open(self.filename) as f:
+                with open(self.filename, 'r' + self.file_flags) as f:
                     self.append_header = not bool(len(f.readline()))
-            self.csv_file = open(self.filename, 'a')
+            self.csv_file = open(self.filename, 'a' + self.file_flags)
         else:
-            self.csv_file = open(self.filename, 'w')
+            self.csv_file = open(self.filename, 'w' + self.file_flags)
 
     def on_epoch_end(self, epoch, logs=None):
         logs = logs or {}

keras/engine/training.py

@@ -1370,7 +1370,7 @@ def fit(self, x=None,
             batch_size: integer. Number of samples per gradient update.
             epochs: integer, the number of times to iterate
                 over the training data arrays.
-                verbose: 0, 1, or 2. Verbosity mode.
+            verbose: 0, 1, or 2. Verbosity mode.
                 0 = silent, 1 = verbose, 2 = one log line per epoch.
             callbacks: list of callbacks to be called during training.
                 See [callbacks](/callbacks).