关于python中的 take no arguments 的解决方法

针对第四章编写的代码出现的错误做一个总结

Traceback (most recent call last):

File "H:\image\chapter4\p81_chongxie.py", line 160, in <module>
l1 = Linear(X, W1, b1)

TypeError: Linear() takes no arguments

出问题时的init方法的图片

可以看出init两边只有一个下划线  _.

解决办法：把init的两边改成两个下划线 __。即可。

代码运行环境：win7系统 + anaconda3_2020

第四章的代码如下：

 #######################数据结构部分#############################################
 import numpy as np
 import matplotlib.pyplot  as  plt
 
 #    %matplotlib inline
 
 class Node(object):
     def __init__(self, inbound_nodes = []):
         self.inbound_nodes = inbound_nodes
         self.value = None
         self.outbound_nodes = []
 
         self.gradients = {}
 
         for node in inbound_nodes:
             node.outbound_nodes.append(self)
 
     def forward(self):
         raise NotImplementedError
 
     def backward(self):
         raise NotImplementedError
 
 class Input(Node):
     def __init__(self):
         Node.__init__(self)
 
     def forward(self):
         pass
 
     def backward(self):
         self.gradients = {self : 0}
         for n in self.outbound_nodes:
             self.gradients[self] += n.gradients[self]
 
 ##################################################################################
 class Linear(Node):
     def __init__(self, X, W, b):
         Node.__init__(self, [X, W, b])
 
     def forward(self):
         X = self.inbound_nodes[0].value
         W = self.inbound_nodes[1].value
         b = self.inbound_nodes[2].value
         self.value = np.dot(X, W) + b 
 
     def backward(self):
         self.gradients = {n: np.zeros_like(n.value) for n in self.inbound_nodes }
         for n in self.outbound_nodes:
             grad_cost = n.gradients[self]
             self.gradients[self.inbound_nodes[0]] += np.dot(grad_cost, self.inbound_nodes[1].value.T)
             self.gradients[self.inbound_nodes[1]] += np.dot(self.inbound_nodes[0].value.T, grad_cost)
             self.gradients[self.inbound_nodes[2]] += np.sum(grad_cost, axis = 0, keepdims = False)
 
 ###################################################################################
 class Sigmoid(Node):
     def __init__(self, node):
         Node.__init__(self, [node])
 
     def _sigmoid(self, x):
         return 1. / (1. +  np.exp(-x))    #exp() 方法返回x的指数,e的x次幂
 
     def forward(self):
         input_value = self.inbound_nodes[0].value
         self.value = self._sigmoid(input_value)
 
     def backward(self):
         self.gradients = {n: np.zeros_like(n.value) for n in self.inbound_nodes}
         for n in self.outbound_nodes:
             grad_cost = n.gradients[self]
             sigmoid = self.value
             self.gradients[self.inbound_nodes[0]] += sigmoid * (1 - sigmoid) * grad_cost
 
 class MSE(Node):
     def __init__(self, y, a):
         Node.__init__(self, [y, a])
 
     def forward(self):
         y = self.inbound_nodes[0].value.reshape(-1, 1)
         a = self.inbound_nodes[1].value.reshape(-1, 1)
 
         self.m = self.inbound_nodes[0].value.shape[0]
         self.diff = y - a
         self.value = np.mean(self.diff**2)
 
     def backward(self):
         self.gradients[self.inbound_nodes[0]] = (2 / self.m) * self.diff
         self.gradients[self.inbound_nodes[1]] = (-2 / self.m) * self.diff
 
 ##########################计算图部分#############################################
 def topological_sort(feed_dict):
     input_nodes = [n for n in feed_dict.keys()]
     G = {}
     nodes = [n for n in input_nodes]
     while len(nodes) > 0:
         n = nodes.pop(0)
         if n not in G:
             G[n] = {'in' : set(), 'out' : set()}
         for m in n.outbound_nodes:
             if m not in G:
                 G[m] = {'in' : set(), 'out' : set()}
             G[n]['out'].add(m)
             G[m]['in'].add(n)
             nodes.append(m)
 
     L = []
     S = set(input_nodes)
     while len(S) > 0 :
         n = S.pop()
         if isinstance(n, Input):
             n.value = feed_dict[n]
         L.append(n)
         for m in n.outbound_nodes:
             G[n]['out'].remove(m)
             G[m]['in'].remove(n)
             if len(G[m]['in'])  == 0 :
                 S.add(m)
     return L 
 
 #######################使用方法##############################################
 #首先由图的定义执行顺序
  #graph  = topological_sort(feed_dict)
 def forward_and_backward(graph):
     for n in graph :
         n.forward()
 
     for n in graph[:: -1]:
         n.backward()
 
 #对各个模块进行正向计算和反向求导
  #forward_and_backward(graph)
 
 #########################介绍梯度下降################
 def sgd_update(trainables, learning_rate = 1e-2):
     for t in trainables :
         t.value = t.value - learning_rate * t.gradients[t]
 
 ###########使用这个模型#################################
 from sklearn.utils import resample
 from sklearn import datasets
 
 #   %matplotlib inline
 
 data = datasets.load_iris()
 X_  = data.data
 y_  = data.target
 y_[y_ == 2] = 1             # 0 for virginica, 1 for not virginica
 print(X_.shape, y_.shape)   # out (150,4) (150,)
 
 ########################用写的模块来定义这个神经网络#########################
 
 np.random.seed(0)
 n_features = X_.shape[1]
 n_class = 1
 n_hidden = 3
 
 X, y = Input(), Input()
 W1, b1  = Input(), Input()
 W2, b2  = Input(), Input()
 
 l1 = Linear(X, W1, b1)
 s1 = Sigmoid(l1)
 l2 = Linear(s1, W2, b2)
 t1 = Sigmoid(l2)
 cost = MSE(y, t1)
 
 ###########训练模型###########################################
 #随即初始化参数值
 W1_0 = np.random.random(X_.shape[1] * n_hidden).reshape([X_.shape[1], n_hidden])
 W2_0 = np.random.random(n_hidden * n_class).reshape([n_hidden, n_class])
 b1_0 = np.random.random(n_hidden)
 b2_0 = np.random.random(n_class)
 
 #将输入值带入算子
 feed_dict = {
     X: X_, y: y_,
     W1:W1_0, b1: b1_0,
     W2:W2_0, b2: b2_0
 }
 
 #训练参数
 #这里训练100轮(eprochs),每轮抽4个样本(batch_size),训练150/4次(steps_per_eproch),学习率 0.1
 epochs = 100
 m = X_.shape[0]
 batch_size = 4
 steps_per_eproch = m // batch_size
 lr = 0.1
 
 graph = topological_sort(feed_dict)
 trainables = [W1, b1,W2, b2]
 
 l_Mat_W1 = [W1_0]
 l_Mat_W2 = [W2_0]
 
 l_loss = []
 for i in range(epochs):
     loss = 0
     for j in range(steps_per_eproch):
         X_batch, y_batch = resample(X_, y_, n_samples = batch_size)
         X.value = X_batch
         y.value = y_batch
 
         forward_and_backward(graph)
         sgd_update(trainables, lr)
         loss += graph[-1].value
 
     l_loss.append(loss)
     if i % 10 ==9 :
         print("Eproch %d, Loss = %1.5f" % (i, loss))
 
 #图形化显示
 plt.plot(l_loss)
 plt.title("Cross Entropy value")
 plt.xlabel("Eproch")
 plt.ylabel("Loss")
 plt.show()
 
 ##########最后用模型预测所有的数据的情况
 X.value = X_
 y.value = y_
 for n in  graph:
     n.forward()
 
 plt.plot(graph[-2].value.ravel())
 plt.title("predict for all 150 Iris data")
 plt.xlabel("Sample ID")
 plt.ylabel("Probability for not a virginica")
 plt.show()