如何在 Keras 从一个 HDf5文件加载一个模型？

如何在 Keras 从一个 HDf5文件加载一个模型？

我试过:

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一个名为 weights.hdf5的文件中。我要做的就是加载这个模型。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")

我得到的错误是:

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
271470 次浏览
小开
最佳答案

load_weights只设置网络的权重。在调用 load_weights之前，您仍然需要定义其架构:

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)
小开

请参阅下面的示例代码，了解如何构建一个基本的 Keras 神经网络模型，保存模型(JSON)和权重(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))
小开

If you stored the complete model, not only the weights, in the HDF5 file, then it is as simple as

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

根据官方文件 Https://keras.io/getting-started/faq/#how-can-i-install-hdf5-or-h5py-to-save-my-models-in-keras

你可以:

第一个测试是否通过运行

import h5py

如果您在导入 h5py 时没有错误，那么最好保存:

from keras.models import load_model


model.save('my_model.h5')  # creates a HDF5 file 'my_model.h5'
del model  # deletes the existing model


# returns a compiled model
# identical to the previous one
model = load_model('my_model.h5')

如果您需要安装 h5py http://docs.h5py.org/en/latest/build.html

小开

我是这样做的

from keras.models import Sequential
from keras_contrib.losses import import crf_loss
from keras_contrib.metrics import crf_viterbi_accuracy


# To save model
model.save('my_model_01.hdf5')


# To load the model
custom_objects={'CRF': CRF,'crf_loss': crf_loss,'crf_viterbi_accuracy':crf_viterbi_accuracy}


# To load a persisted model that uses the CRF layer
model1 = load_model("/home/abc/my_model_01.hdf5", custom_objects = custom_objects)
小开

我为这个错误纠结了一会儿，然后意识到我不小心使用了

with open(f'path_to_filename/{filename.h5}', "rb") as file:
loaded_model = tf.keras.models.load_model(file)

鉴于此语法不适用于此负载模型函数,

正常的写作方式对我很有效

loaded_model = tf.keras.models.load_model('path_to_filename/filename.h5')

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