[英]How to load a model from an HDF5 file in Keras?
如何從 Keras 中的 HDF5 文件加載 model?
我嘗試了什么:
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])
上面的代碼成功地將最好的 model 保存到一個名為 weights.hdf5 的文件中。 我想要做的是加載 model。下面的代碼顯示了我是如何嘗試這樣做的:
model2 = Sequential()
model2.load_weights("/Users/Desktop/SquareSpace/weights.hdf5")
這是我得到的錯誤:
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
如果您在 HDF5 文件中存儲了完整的模型,而不僅僅是權重,那么它就像
from keras.models import load_model
model = load_model('model.h5')
load_weights
只設置網絡的權重。 您仍然需要在調用load_weights
之前定義其架構:
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)
請參閱以下示例代碼,了解如何構建基本的 Keras 神經網絡模型、保存模型 (JSON) 和權重 (HDF5) 並加載它們:
# 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))
根據官方文檔https://keras.io/getting-started/faq/#how-can-i-install-hdf5-or-h5py-to-save-my-models-in-keras
你可以做 :
首先測試您是否通過運行安裝了 h5py
import h5py
如果您在導入 h5py 時沒有錯誤,您可以保存:
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')
如果你需要安裝 h5py http://docs.h5py.org/en/latest/build.html
我為這個錯誤苦苦掙扎了一會兒,然后意識到我不小心使用了
with open(f'path_to_filename/{filename.h5}', "rb") as file:
loaded_model = tf.keras.models.load_model(file)
鑒於此語法不適用於此負載 model function,
寫這篇文章的正常方式對我有用
loaded_model = tf.keras.models.load_model('path_to_filename/filename.h5')
我是這樣做的
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)
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