在Keras中如何从HDF5文件加载模型？
我试过的保存模型的代码如下：
model = Sequential()
model.add(Dense(64, input_dim=14, init='uniform'))
model.add(LeakyReLU(alpha=0.3))
model.add(BatchNormalization(epsilon=1e-06, mode=0, momentum=0.9, weights=None))
model.add(Dropout(0.5))
model.add(Dense(64, init='uniform'))
model.add(LeakyReLU(alpha=0.3))
model.add(BatchNormalization(epsilon=1e-06, mode=0, momentum=0.9, weights=None))
model.add(Dropout(0.5))
model.add(Dense(2, init='uniform'))
model.add(Activation('softmax'))
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=sgd)
checkpointer = ModelCheckpoint(filepath="/weights.hdf5", verbose=1, save_best_only=True)
model.fit(X_train, y_train, nb_epoch=20, batch_size=16, show_accuracy=True, validation_split=0.2, verbose = 2, callbacks=[checkpointer])
上面的代码成功将最佳模型保存到名为weights.hdf5的文件中。然后，我要加载该模型。下面的代码显示了我的做法：
model2 = Sequential()
model2.load_weights("/Users/Desktop/SquareSpace/weights.hdf5")
这是我得到的错误：
IndexError Traceback (most recent call last)
<ipython-input-101-ec968f9e95c5> in <module>()
1 model2 = Sequential()
----> 2 model2.load_weights("/Users/Desktop/SquareSpace/weights.hdf5")
/Applications/anaconda/lib/python2.7/site-packages/keras/models.pyc in load_weights(self, filepath)
582 g = f['layer_{}'.format(k)]
583 weights = [g['param_{}'.format(p)] for p in range(g.attrs['nb_params'])]
--> 584 self.layers[k].set_weights(weights)
585 f.close()
586
IndexError: list index out of range
最佳办法
load_weights仅设置网络的权重。在调用load_weights之前，您仍然需要定义其体系结构：
def create_model():
model = Sequential()
model.add(Dense(64, input_dim=14, init='uniform'))
model.add(LeakyReLU(alpha=0.3))
model.add(BatchNormalization(epsilon=1e-06, mode=0, momentum=0.9, weights=None))
model.add(Dropout(0.5))
model.add(Dense(64, init='uniform'))
model.add(LeakyReLU(alpha=0.3))
model.add(BatchNormalization(epsilon=1e-06, mode=0, momentum=0.9, weights=None))
model.add(Dropout(0.5))
model.add(Dense(2, init='uniform'))
model.add(Activation('softmax'))
return model
def train():
model = create_model()
sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=sgd)
checkpointer = ModelCheckpoint(filepath="/tmp/weights.hdf5", verbose=1, save_best_only=True)
model.fit(X_train, y_train, nb_epoch=20, batch_size=16, show_accuracy=True, validation_split=0.2, verbose=2, callbacks=[checkpointer])
def load_trained_model(weights_path):
model = create_model()
model.load_weights(weights_path)
次佳办法
如果您将完整的模型(不仅是权重)存储在HDF5文件中，那么它就很简单了
from keras.models import load_model
model = load_model('model.h5')
第三种办法
请参阅以下示例代码，了解如何构建基本的Keras神经网络模型，保存模型(JSON)&权重(HDF5)并加载它们：
# create model
model = Sequential()
model.add(Dense(X.shape[1], input_dim=X.shape[1], activation='relu')) #Input Layer
model.add(Dense(X.shape[1], activation='relu')) #Hidden Layer
model.add(Dense(output_dim, activation='softmax')) #Output Layer
# Compile & Fit model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X,Y,nb_epoch=5,batch_size=100,verbose=1)
# serialize model to JSON
model_json = model.to_json()
with open("Data/model.json", "w") as json_file:
json_file.write(simplejson.dumps(simplejson.loads(model_json), indent=4))
# serialize weights to HDF5
model.save_weights("Data/model.h5")
print("Saved model to disk")
# load json and create model
json_file = open('Data/model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("Data/model.h5")
print("Loaded model from disk")
# evaluate loaded model on test data
# Define X_test & Y_test data first
loaded_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
score = loaded_model.evaluate(X_test, Y_test, verbose=0)
print ("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))
参考资料