load_weights
仅设置网络的权重。您仍然需要在调用load_weights
之前定义其体系结构:
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)
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如何在Keras中从HDF5文件加载模型?
我试过的
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如何尝试这样做:
这是我得到的错误: