Fix conflicts

docs/mkdocs.yml

@@ -38,4 +38,6 @@ pages:
 
 - [preprocessing/sequence.md, Preprocessing, Sequence Preprocessing]
 - [preprocessing/text.md, Preprocessing, Text Preprocessing]
-- [preprocessing/image.md, Preprocessing, Image Preprocessing]
+- [preprocessing/image.md, Preprocessing, Image Preprocessing]
+
+- [utils/visualization.md, Utils, Visualization Utilities]

docs/sources/models.md

@@ -39,8 +39,8 @@ model = keras.models.Sequential()
         - __Return__: loss over the data, or tuple `(loss, accuracy)` if `accuracy=True`.
     - __test__(X, y, accuracy=False): Single performance evaluation on one batch. if accuracy==False, return tuple (loss_on_batch, accuracy_on_batch). Else, return loss_on_batch.
         - __Return__: loss over the data, or tuple `(loss, accuracy)` if `accuracy=True`.
-    - __save_weights__(fname): Store the weights of all layers to a HDF5 file. 
-    - __load_weights__(fname): Sets the weights of a model, based to weights stored by __save_weights__. You can only __load_weights__ on a savefile from a model with an identical architecture. __load_weights__ can be called either before or after the __compile__ step.
+    - __save_weights__(fname, overwrite=False): Store the weights of all layers to a HDF5 file. If overwrite==False and the file already exists, an exception will be thrown.
+    - __load_weights__(fname): Sets the weights of a model, based to weights stored by __save__weights__. You can only __load__weights__ on a savefile from a model with an identical architecture. __load_weights__ can be called either before or after the __compile__ step.
 
 __Examples__:
 

docs/sources/utils/visualization.md

@@ -0,0 +1,28 @@
+## Grapher
+
+Creates a visualization of the model structure using `pydot`.
+
+```python
+grapher = keras.utils.dot_utils.Grapher()
+```
+- __Methods__:
+    - __plot__(model, to_file): creates a graph visualizing the structure of `model` and writes it to `to_file`.
+      - __Arguments__:
+        - __model__: an instance of a Keras model (e.g. `Sequential`)
+        - __to_file__: the filename to save the visualization png to.
+
+__Examples__:
+
+```python
+from keras.models import Sequential
+from keras.layers.core import Dense, Activation
+from keras.utils.dot_utils import Grapher
+
+grapher = Grapher()
+
+model = Sequential()
+model.add(Dense(64, 2, init='uniform'))
+model.add(Activation('softmax'))
+grapher.plot(model, 'model.png')
+```
+

examples/reuters_mlp.py

@@ -18,7 +18,7 @@ from keras.preprocessing.text import Tokenizer
 '''
 
 max_words = 10000
-batch_size = 16
+batch_size = 32
 
 print("Loading data...")
 (X_train, y_train), (X_test, y_test) = reuters.load_data(nb_words=max_words, test_split=0.2)
@@ -51,7 +51,8 @@ model.add(Activation('softmax'))
 
 model.compile(loss='categorical_crossentropy', optimizer='adam')
 
-model.fit(X_train, Y_train, nb_epoch=4, batch_size=batch_size, verbose=1, show_accuracy=True, validation_split=0.1)
+history = model.fit(X_train, Y_train, nb_epoch=3, batch_size=batch_size, verbose=1, show_accuracy=True, validation_split=0.1)
+print(history)
 score = model.evaluate(X_test, Y_test, batch_size=batch_size, verbose=1, show_accuracy=True)
 print('Test score:', score[0])
 print('Test accuracy:', score[1])

keras/models.py

@@ -382,10 +382,13 @@ class Sequential(Model):
             self.layers[i].set_weights(weights[:nb_param])
             weights = weights[nb_param:]
 
-    def save_weights(self, filepath):
+    def save_weights(self, filepath, overwrite=False):
         # Save weights from all layers to HDF5
         import h5py
-        # FIXME: fail if file exists, or add option to overwrite!
+        import os.path
+        # if file exists and should not be overwritten
+        if not overwrite and os.path.isfile(filepath):
+            raise IOError('%s already exists' % (filepath))
         f = h5py.File(filepath, 'w')
         f.attrs['nb_layers'] = len(self.layers)
         for k, l in enumerate(self.layers):
@@ -408,4 +411,4 @@ class Sequential(Model):
             weights = [g['param_{}'.format(p)] for p in range(g.attrs['nb_params'])]
             self.layers[k].set_weights(weights)
         f.close()
-        
+        

keras/utils/dot_utils.py

@@ -0,0 +1,51 @@
+import pydot
+from keras.layers.core import Merge
+from keras.models import Model
+from collections import Counter
+
+class Grapher(object):
+
+    def __init__(self):
+        self.names = {}
+        self.class_counts = Counter()
+
+    def get_name(self, model):
+        """
+        returns the name of the model instance. If model does not have a `name` attribute, then it will be assigned
+        a generic (and unique) identifier based on its class
+        """
+        if hasattr(model, 'name'):
+            return model.name
+        clz = model.__class__.__name__
+        if model not in self.names:
+            self.class_counts[clz] += 1
+            self.names[model] = clz + str(self.class_counts[clz])
+        return self.names[model]
+
+    def add_edge(self, f, t, graph):
+        if f: graph.add_edge(pydot.Edge(f, t))
+        return t
+
+    def add_model(self, model, graph, parent=None):
+        """
+        Recursively adds `model` and its components to the pydot graph
+        """
+        this = self.get_name(model)
+        if isinstance(model, Model):
+            parent = self.add_edge(parent, this, graph)
+            for child in reversed(model.layers):
+                parent = self.add_model(child, graph, parent)
+        elif isinstance(model, Merge):
+            for child in model.models:
+                self.add_model(child, graph, this)
+            return self.add_edge(parent, this, graph)
+        else:
+            return self.add_edge(parent, this, graph)
+
+    def plot(self, model, to_file):
+        """
+        creates a graph visualizing the structure of `model` and writes it to `to_file`
+        """
+        graph = pydot.Dot(graph_type='graph')
+        self.add_model(model, graph)
+        graph.write_png(to_file)

test/test_dot_utils.py

@@ -0,0 +1,27 @@
+from keras.utils.dot_utils import Grapher
+
+from keras.models import Sequential
+from keras.layers.core import Dense, Activation, Merge, Flatten
+from keras.layers.embeddings import Embedding
+from keras.layers.recurrent import GRU
+
+ent_lookup = Sequential()
+ent_lookup.add(Embedding(10, 2))
+ent_lookup.add(Flatten())
+
+rel_lookup = Sequential()
+rel_lookup.add(Embedding(20, 2))
+rel_lookup.add(Flatten())
+
+word_sequence = Sequential()
+word_sequence.add(Embedding(10, 5))
+word_sequence.add(GRU(5, 2))
+
+model = Sequential()
+model.add(Merge([word_sequence, ent_lookup, rel_lookup], mode='concat'))
+model.add(Activation('relu'))
+model.add(Dense(6, 2))
+model.add(Activation('softmax'))
+
+g = Grapher()
+g.plot(model, 'mymodel.png')