"sequences" 参数在 theano.scan 中接受哪些参数以及它们是如何解释的

Question

我从http://deeplearning.net/software/theano/library/scan.html

中获取了以下代码

    import numpy

    coefficients = theano.tensor.vector("coefficients")
    x = T.scalar("x")

    max_coefficients_supported = 10000

    # Generate the components of the polynomial
    components, updates = theano.scan(fn=lambda coefficient, power, free_variable: coefficient * (free_variable ** power),
                              outputs_info=None,
                              sequences=[coefficients, theano.tensor.arange(max_coefficients_supported)],
                              non_sequences=x)

这里的代码是为了解释"sequences"参数。这是我的问题：

序列是如何馈送的？第一项 "coefficients" 是一个张量变量。第二项 "theano.tensor.arange(max_coefficients)" 是一个张量变量，它在使用 eval() 时给出一个包含 [0......999] 的列表。教程说-
```
"The tensor(s) to be looped over should be provided to scan using the sequence keyword argument."
```
根据 "sequences" 中提供的参数，这里的循环是如何发生的？

Answer 1

参数的顺序是：sequence[t],outputs_infor,non_sequence

 coefficients[t]
 theano.tensor.arange(max_coefficients_supported)[t]
 x

outputs_infor保存上一次迭代的结果

"sequences" 参数在 theano.scan 中接受哪些参数以及它们是如何解释的

What arguments does "sequences" paramater take in theano.scan and how are they interpreted

python-2.7

theano

theano.scan