Question

在Python中，我可以从main函数调用变量吗？使用全局变量？任何帮助赞赏！

def main(dataset, n_h, n_y, batch_size, dev_split, n_epochs):
    input_to_state = Linear(name='input_to_state',
                            input_dim=seq_u.shape[-1],
                            output_dim=n_h)
    global RNN # correct?
    RNN = SimpleRecurrent(activation=Tanh(),
                          dim=n_h, name="RNN")


def predict(dev_X):
    dev_transform = main.input_to_state.apply(dev_X) #?  call  "input_to_state", which one is correct?
    dev_transform = input_to_state.apply(dev_X) #?
    dev_h = main.RNN.apply(dev_transform) #? call "RNN", which one is correct?
    dev_h = RNN.apply(dev_transform) #?

if __name__ == "__main__":    
    def predict(dev_X): #  one more question: can predict function be added here?
    dataset =  ....
    main(dataset, n_h, n_y, batch_size, dev_split, 5000)
    get_predictions = theano.function([dev_X], predict) # call predict function

Answer 1

你必须在'main'函数之外定义'input_to_state'和'RNN'，然后再修改它们。像这样：

input_to_state = None
RNN = None
def main(dataset, n_h, n_y, batch_size, dev_split, n_epochs):
    # Calling 'global' allows you to modify these variables
    global input_to_state
    global RNN
    input_to_state = Linear(name='input_to_state',
                            input_dim=seq_u.shape[-1],
                            output_dim=n_h)
    RNN = SimpleRecurrent(activation=Tanh(),
                          dim=n_h, name="RNN")


def predict(dev_X):
    dev_transform = input_to_state.apply(dev_X)
    dev_h = RNN.apply(dev_transform)

if __name__ == "__main__":   
    main(args) 
    predict(dev_X)

Howerver，我不建议这样做，全局变量应该尽量少用。 more detail here

更好的解决方案是在main函数的末尾返回'input_to_state'和'RNN'，如下所示：

def main(dataset, n_h, n_y, batch_size, dev_split, n_epochs):
    input_to_state = Linear(name='input_to_state',
                            input_dim=seq_u.shape[-1],
                            output_dim=n_h)
    RNN = SimpleRecurrent(activation=Tanh(),
                          dim=n_h, name="RNN")
    return input_to_state, RNN

def predict(dev_X, input_to_state, RNN):
    dev_transform = input_to_state.apply(dev_X)
    dev_h = RNN.apply(dev_transform)

if __name__ == "__main__":   
    input_to_state, RNN = main(args) 
    predict(dev_X, input_to_state, RNN)

Answer 2

试试这个。

<强> main.py

__dataset__ = main(dataset, n_h, n_y, batch_size, dev_split, 5000)

<强> sub.py

import sys, main
__dataset__ = sys.modules['__main__'].__dataset__

修改
另一种方法是使用带有静态变量的类。

<强> mclass.py

class MClass: i = 0 MClass.i = 1

<强> main.py

import sub from mclass import MClass # In the main file print(MClass.i) # Outputs 1 MClass.i = 3 print(MClass.i) # Outputs 3 # In a subfile sub.mPrint() # Outputs 3 sub.set(10) sub.mPrint() # Outputs 10 # And back in the main print(MClass.i) # Outputs 10

<强> sub.py

from mclass import MClass def mPrint(): print(MClass.i) def set(n): MClass.i = n

在Python中，我可以从main函数调用变量 - 使用全局变量吗？

2 个答案: