Keras加载神经网络的权重/预测时出错

Question

我正在使用Keras库创建神经网络。我有一个iPython Notebook来加载训练数据，初始化网络并“适应”神经网络的权重。 最后，我使用save_weights（）方法保存了权重。 代码如下：

from keras.models import Sequential 
from keras.layers.core import Dense, Dropout, Activation 
from keras.optimizers import SGD 
from keras.regularizers import l2 
from keras.callbacks import History 

[...] 

input_size = data_X.shape[1] 
output_size = data_Y.shape[1] 
hidden_size = 100 
learning_rate = 0.01 
num_epochs = 100 
batch_size = 75 

model = Sequential() 
model.add(Dense(hidden_size, input_dim=input_size, init='uniform')) 
model.add(Activation('tanh')) 
model.add(Dropout(0.2)) 
model.add(Dense(hidden_size)) 
model.add(Activation('tanh')) 
model.add(Dropout(0.2)) 
model.add(Dense(output_size)) 
model.add(Activation('tanh')) 

sgd = SGD(lr=learning_rate, decay=1e-6, momentum=0.9, nesterov=True) 
model.compile(loss='mse', optimizer=sgd) 

model.fit(X_NN_part1, Y_NN_part1, batch_size=batch_size, nb_epoch=num_epochs, validation_data=(X_NN_part2, Y_NN_part2), callbacks=[history]) 

y_pred = model.predict(X_NN_part2) # works well 

model.save_weights('keras_w') 

然后，在另一个IPython的笔记本电脑，我只是想用这些权重和预测给出输入某些输出值。我初始化相同的神经网络，然后加载权重。

# same headers 
input_size = 37 
output_size = 40 
hidden_size = 100 

model = Sequential() 
model.add(Dense(hidden_size, input_dim=input_size, init='uniform')) 
model.add(Activation('tanh')) 
model.add(Dropout(0.2)) 
model.add(Dense(hidden_size)) 
model.add(Activation('tanh')) 
model.add(Dropout(0.2)) 
model.add(Dense(output_size)) 
model.add(Activation('tanh')) 

model.load_weights('keras_w') 
#no error until here 

y_pred = model.predict(X_nn) 

问题是，显然，load_weights方法不足以拥有功能模型。我得到一个错误：

--------------------------------------------------------------------------- 
AttributeError       Traceback (most recent call last) 
<ipython-input-17-e6d32bc0d547> in <module>() 
    1 
----> 2 y_pred = model.predict(X_nn) 
C:\XXXXXXX\Local\Continuum\Anaconda\lib\site-packages\keras\models.pyc in predict(self, X, batch_size, verbose) 
491  def predict(self, X, batch_size=128, verbose=0): 
492   X = standardize_X(X) 
--> 493   return self._predict_loop(self._predict, X, batch_size, verbose)[0] 
494 
495  def predict_proba(self, X, batch_size=128, verbose=1): 

AttributeError: 'Sequential' object has no attribute '_predict' 

任何想法？ 非常感谢。

Answer 1

您需要拨打model.compile。这可以在model.load_weights呼叫之前或之后完成，但必须在指定模型架构之后并且在model.predict呼叫之前完成。