Question

我试图在theano中总结多次失利，但我无法使其发挥作用。我使用了分类的crossentroy。

这是我的代码：

RegexOptions.Singleline

我得到的错误是

import numpy as np

import theano
import theano.tensor as T


answers = T.ivector()
temp = T.scalar()
predictions = T.matrix()

def loss_acc(curr_ans,curr_pred, loss):
    temp= T.nnet.categorical_crossentropy(curr_pred.dimshuffle('x',0), T.stack([curr_ans]))[0]
    return temp + loss



outputs, updates = theano.scan(fn = loss_acc, 
                               sequences = [answers, predictions], 
                               outputs_info = [np.float64(0.0)], 
                                               n_steps = 5)

loss = outputs[-1]

loss_cal = theano.function(inputs = [answers, predictions], outputs = [loss])

#Here I'm just generating some random data to see if I can make the code work
max_nbr = 5
pred = []
for i in range(0, max_nbr):
    temp = np.ones(8)
    temp[i] = temp[i] + 5
    temp = temp/sum(temp)
    pred.append(temp)


answers = []
for i in range(0, max_nbr):
    answers.append(pred[i].argmax())

loss = loss_cal(answers, predictions)
print(loss)

我不知道为什么我的代码不起作用，有人可以向我解释一下吗？非常感谢！

Answer 1

我发现了我的问题，这真的是一个愚蠢的问题。

loss = loss_cal(answers, predictions)

这是错误的，因为预测是theano矩阵，我本应该使用pred。

pred = []
for i in range(0, max_nbr):
    temp = np.ones(8)
    temp[i] = temp[i] + 5
    temp = temp/sum(temp)
    pred.append(temp)

现在可以使用了 loss = loss_cal(answers, pred)无论如何，谢谢

Theano＆＃39;期待一个类似于数组的对象，但找到了一个变量＆＃39;：Using scan＆amp; categorical_crossentropy

1 个答案: