Question

我正在尝试使用python中的Keras / Theanos后端对时间序列数据进行预测，其中我将过去几天的因素用于预测第二天。我能够使用其他算法（如xgboost）生成预测但是想尝试ANN但在预测步骤中遇到了索引越界错误

部分代码是这样的：

clfnn = Sequential()
clfnn.add(Dense(32, input_dim=9,init='uniform',activation='tanh'))
clfnn.add(Dense(9,init='uniform',activation='tanh'))
clfnn.add(Dense(1,activation='tanh'))
clfnn.compile(loss='mse', optimizer=sgd, metrics=['accuracy'])

imp = Imputer(missing_values='NaN', strategy='mean', axis=0)
imp.fit(testsample[factorsnew])
testsample[factorsnew]=imp.transform(testsample[factorsnew])
validationsample[factorsnew]=imp.transform(validationsample[factorsnew])

models={'prediction':clfnn}

for key in models:
    try:
        models[key].fit(testsample[factorsnew].as_matrix(),testsample['returnranksnn'].as_matrix(),verbose=1) 
        validationsample[key]=models[key].predict_proba(validationsample[factorsnew].as_matrix(),verbose=1)[:,1]           
    except:
        print sys.exc_info()[0]
        print sys.exc_info()[1]
        pass

该模型似乎没有任何问题，但预测步骤给出了错误。输出看起来像这样：

Epoch 1/10
32240/32240 [==============================] - 0s - loss: 0.2506 - acc: 0.5980     
Epoch 2/10
32240/32240 [==============================] - 0s - loss: 0.2504 - acc: 0.6054     
Epoch 3/10
32240/32240 [==============================] - 0s - loss: 0.2504 - acc: 0.6069     
Epoch 4/10
32240/32240 [==============================] - 0s - loss: 0.2505 - acc: 0.6028     
Epoch 5/10
32240/32240 [==============================] - 0s - loss: 0.2504 - acc: 0.6015     
Epoch 6/10
32240/32240 [==============================] - 0s - loss: 0.2503 - acc: 0.6067     
Epoch 7/10
32240/32240 [==============================] - 0s - loss: 0.2504 - acc: 0.6020     
Epoch 8/10
32240/32240 [==============================] - 0s - loss: 0.2505 - acc: 0.5999     
Epoch 9/10
32240/32240 [==============================] - 0s - loss: 0.2504 - acc: 0.6040     
Epoch 10/10
32240/32240 [==============================] - 0s - loss: 0.2505 - acc: 0.6024     

32/40 [=======================>......] - ETA: 0s<type 'exceptions.IndexError'>
index 1 is out of bounds for axis 1 with size 1

注意：数据在没有任何NaN的情况下被标准化，预测变量是int类型，只有两个结果0或1，结果应该只是一个概率数

尝试更改优化器的设置，数据中的不同因素但是徒劳无功。如您在输出中看到的，大多数样品都会停留在 32 / 40或 32 / ***。关于我在这里缺少什么的想法？感谢

在keras / python的预测步骤中索引越界错误

0 个答案: