如何从 Keras 中的 HDF5 文件加载模型？

Question

如何从 Keras 中的 HDF5 文件加载模型？

我尝试了什么：

 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])

上面的代码成功地将最佳模型保存到名为 weights.hdf5 的文件中。我想要做的是然后加载该模型。下面的代码显示了我是如何尝试这样做的：

 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

原文由 pr338 发布，翻译遵循 CC BY-SA 4.0 许可协议

Answer 1

load_weights 只设置网络的权重。在调用 load_weights 之前，你仍然需要定义它的架构：

 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)

原文由 Mikael Rousson 发布，翻译遵循 CC BY-SA 3.0 许可协议

Answer 2

如果您在 HDF5 文件中存储了完整的模型，而不仅仅是权重，那么它就像

from keras.models import load_model
model = load_model('model.h5')

原文由 Martin Thoma 发布，翻译遵循 CC BY-SA 3.0 许可协议