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问题描述
我正在尝试使用此示例
我正在尝试使用此示例
I am trying a NN model using this example. I am fitting a list of values to a NN model. However, I am getting an AttributeError. This has been asked before and has been answered. Unfortunately, it is not working for me. As shown in the example, I created the following,
from keras.models import Sequential
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from keras.layers import Dense
def neuralnetmodel():
#Crete model
model = Sequential()
model.add(Dense(13, input_dim = 13, kernel_initializer = 'normal', activation = 'relu'))
model.add(Dense(1, kernel_initializer = 'normal', activation = 'relu'))
model.add(Dense(1, kernel_initializer = 'normal', activation = 'relu'))
## Output layer
model.add(Dense(1, kernel_initializer = 'normal'))
#Compile model
model.compile(loss = 'mean_squared_error', optimizer = 'adam')
return model
fit训练数据，
NNmodelList = []
for i,j in zip(X_train_scaled,y_train):
nn_model = KerasRegressor(build_fn= neuralnetmodel, nb_epoch = 50, batch_size = 10, verbose = 0)
NNmodelList.append(nn_model.fit(i,j))
predict来自测试数据，
PredList = []
for val in X_test_scaled:
for mod in NNmodelList:
pred = mod.predict(val)
PredList.append(pred)
现在，我得到了错误:
在以前的线程 ，这似乎是火车集不是predict之前的模型的fit.但是，在我的系统中，我将它们放入第二个代码段中.有什么想法我还会犯其他错误吗?
In previous threads , it seems to be the train set was not fit to the model before predict. However, in mine, I fit them in the second code snippet. Any ideas what other possible mistakes I am making?
model.fit()不返回Keras模型，而是一个History对象，其中包含训练的损失和度量值.所以在这段代码中:
model.fit() does not return the Keras model, but a History object containing loss and metric values of your training. So in this code:
NNmodelList.append(nn_model.fit(i,j))
您正在创建历史记录对象列表，而不是模型.一个简单的解决方法是:
you're creating a list of History objects, not models. A simple fix would be:
NNmodelList.append(nn_model)
nn_model.fit(i,j)
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