Added test script to save_weights/load_weights

keras/models.py

@@ -211,16 +211,16 @@ class Sequential(object):
             return tot_score/len(batches)
 
 
-    def save(self, filepath):
+    def save_weights(self, filepath):
         # Save weights from all layers to HDF5
         import h5py
         # FIXME: fail if file exists, or add option to overwrite!
         f = h5py.File(filepath, 'w')
-        f.attrs['n_layers'] = len(self.layers)
+        f.attrs['nb_layers'] = len(self.layers)
         for k, l in enumerate(self.layers):
             g = f.create_group('layer_{}'.format(k))
             weights = l.get_weights()
-            g.attrs['n_params'] = len(weights)
+            g.attrs['nb_params'] = len(weights)
             for n, param in enumerate(weights):
                 param_name = 'param_{}'.format(n)
                 param_dset = g.create_dataset(param_name, param.shape, dtype='float64')
@@ -228,13 +228,13 @@ class Sequential(object):
         f.flush()
         f.close()
 
-    def load(self, filepath):
+    def load_weights(self, filepath):
         # Loads weights from HDF5 file
         import h5py
         f = h5py.File(filepath)
-        for k in range(f.attrs['n_layers']):
+        for k in range(f.attrs['nb_layers']):
             g = f['layer_{}'.format(k)]
-            weights = [g['param_{}'.format(p)] for p in range(g.attrs['n_params'])]
+            weights = [g['param_{}'.format(p)] for p in range(g.attrs['nb_params'])]
             self.layers[k].set_weights(weights)
         f.close()
 

test/test_save_weights.py

@@ -0,0 +1,40 @@
+from keras.models import Sequential
+from keras.layers.core import Dense, Dropout, Activation
+from keras.optimizers import SGD
+
+import sys
+sys.setrecursionlimit(10000) # to be able to pickle Theano compiled functions
+
+import pickle, numpy
+
+def create_model():
+    model = Sequential()
+    model.add(Dense(256, 2048, init='uniform', activation='relu'))
+    model.add(Dropout(0.5))
+    model.add(Dense(2048, 2048, init='uniform', activation='relu'))
+    model.add(Dropout(0.5))
+    model.add(Dense(2048, 2048, init='uniform', activation='relu'))
+    model.add(Dropout(0.5))
+    model.add(Dense(2048, 2048, init='uniform', activation='relu'))
+    model.add(Dropout(0.5))
+    model.add(Dense(2048, 256, init='uniform', activation='linear'))
+    return model
+
+model = create_model()
+sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
+model.compile(loss='mse', optimizer=sgd)
+
+pickle.dump(model, open('/tmp/model.pkl', 'wb'))
+model.save_weights('/tmp/model_weights.hdf5')
+
+model_loaded = create_model()
+model_loaded.load_weights('/tmp/model_weights.hdf5')
+
+for k in range(len(model.layers)):
+    weights_orig = model.layers[k].get_weights()
+    weights_loaded = model_loaded.layers[k].get_weights()
+    for x, y in zip(weights_orig, weights_loaded):
+        if numpy.any(x != y):
+            raise ValueError('Loaded weights are different from pickled weights!')
+
+