TypeError: uniform() got an unexpected keyword argument 'low'/'size'-CodePudding

visualizing a neural network result and this is what shows up:

def apply_net(y_in):
  global w, b

  z=dot(w, y_in) b
  return(1/(1 exp(-z)))

N0=2
N1=1 

w=random.uniform(low=-10,high= 10,size=(N1,N0)) # random weights: N1xN0
b=random.uniform(low=-1,high= 1,size=N1) #biases: N1 vector

TypeError Traceback (most recent call last) in () 2 N1=1 3 ----> 4 w=random.uniform(low=-10,high= 10,size=(N1,N0)) # random weights: N1xN0 5 b=random.uniform(low=-1,high= 1,size=N1) #biases: N1 vector

TypeError: uniform() got an unexpected keyword argument 'low'

___ If I remove low and high and keep it (-10, 10, size=(N1,N0)), it says: TypeError: uniform() got an unexpected keyword argument 'size'

If I remove size then it says: TypeError: uniform() takes 3 positional arguments but 4 were given

CodePudding user response：

must use random.uniform while importing the related class with an alias (using 'as') or else just use import numpy while importing

An example for using alias is :

from numpy import random as np_random

Then utilize np_random.uniform()

(figured it out and had to use this to solve the problem - note I ran it on colab)

CodePudding user response：

You must declare the alias library or the library name directly

        import numpy as np
        
        def apply_net(y_in):
          global w, b
        
          z=np.dot(w, y_in) b
          return(1/(1 np.exp(-z)))
        
        N0=2
        N1=1 
        
        w=np.random.uniform(low=-10,high= 10,size=(N1,N0)) # random weights: N1xN0
        b=np.random.uniform(low=-1,high= 1,size=N1) #biases: N1 vector