如何修复numpy中的“ ValueError:形状(1,3)和(1,1)不对齐:3(dim 1)!= 1(dim 0)”错误

时间:2019-04-14 07:17:39

标签: arrays python-3.x numpy matrix neural-network

我目前正在学习如何在numpy / python中编码神经网络。我使用了this tutorial中的代码,并试图将其改编成可导入的模块。但是,当我尝试使用自己的数据集时。它引发了一个小错误ValueError: shapes (1,3) and (1,1) not aligned: 3 (dim 1) != 1 (dim 0)

我已经尝试过将所有矩阵从(x,)重塑为(x,1),但是没有成功。经过一番阅读之后,转置数组也意味着可以解决此问题,但我也尝试过这样做,但也没有成功。

这里是模块(称为hidden_​​net):

import numpy as np
class network:
    def __init__(self,layer_num,learning_rate=0.7,seed=None,logistic_coefficent=0.9):
        self.logistic_coefficent=logistic_coefficent
        self.learning_rate=learning_rate
        self.w0 = np.random.random((layer_num[0],layer_num[1]))
        self.w1 = np.random.random((layer_num[1],layer_num[2]))

        np.random.seed(seed)
    def sigmoid(self,x,reverse=False):
            if(reverse==True):
                return x*(1-x)
            return 1/(1+np.exp(-x*self.logistic_coefficent))

    def train(self,inps,outs):
        inps=np.array(inps)
        layer0 = inps
        layer1 = self.sigmoid(np.dot(layer0,self.w0))
        layer2 = self.sigmoid(np.dot(layer1,self.w1))
        layer2_error = outs - layer2
        layer2_delta = layer2_error*self.sigmoid(layer2,reverse=True)#*self.learning_rate
        layer1_error = layer2_delta.dot(self.w1.T)
        layer1_delta = layer1_error * self.sigmoid(layer1,reverse=True)#*self.learning_rate

        layer1= np.reshape(layer1, (layer1.shape[0], 1))
        layer2= np.reshape(layer2, (layer2.shape[0], 1))
        layer1_delta= np.reshape(layer1_delta, (layer1_delta.shape[0], 1))  #Other attempts to reshape to avoid this error
        layer2_delta= np.reshape(layer2_delta, (layer2_delta.shape[0], 1))

        self.w1 += layer1.T.dot(layer2_delta)
        self.w0 += layer0.T.dot(layer1_delta)

这是导入该模块的程序:

import hidden_net
op=open('Mall_Customers_Mod.txt','r')
full=op.read()
op.close()
full_lines=full.split('\n')
training_lines=[]
for i in range(174):
    training_lines.append(full_lines[0])
    del full_lines[0]
training_inputs=[]
training_outputs=[]
for j in training_lines:
    training_inputs.append([float(j.split(',')[0]),float(j.split(',')[1])])
    training_outputs.append(float(j.split(',')[2]))
testing_lines=full_lines
testing_inputs=[]
testing_outputs=[]
for l in testing_lines:
    testing_inputs.append([float(l.split(',')[0]),float(l.split(',')[1])])
    testing_outputs.append(float(l.split(',')[2]))
nn=hidden_net.network([2,3,1],seed=10)
for i in range(1000):
    for cur in range(len(training_inputs)):
        nn.train(training_inputs[cur],training_outputs[cur])

这是我的数据集(Mall_Customers_Mod.txt)的一部分

-1,19,15
-1,21,15
1,20,16
1,23,16
1,31,17
1,22,17
1,35,18
1,23,18
-1,64,19
1,30,19
-1,67,19
1,35,19
1,58,20
1,24,20
-1,37,20
-1,22,20
1,35,21
-1,20,21
-1,52,23

错误在第30行:

self.w1 += layer1.T.dot(layer2_delta)
ValueError: shapes (1,3) and (1,1) not aligned: 3 (dim 1) != 1 (dim 0)

也很抱歉,我知道我本来是要避免粘贴整个文件的,但是在这里看来这是不可避免的

1 个答案:

答案 0 :(得分:1)

下面的行是错误的,layer0是输入层,不包含任何神经元。

self.w1 += layer1.T.dot(layer2_delta)
self.w0 += layer0.T.dot(layer1_delta)

它们应该是:

self.w1 += layer2.T.dot(layer2_delta)
self.w0 += layer1.T.dot(layer1_delta)

所有重塑操作也应删除。更新的train函数

def train(self,inps,outs):
    inps=np.array(inps)
    layer0 = inps
    layer1 = self.sigmoid(np.dot(layer0,self.w0))
    layer2 = self.sigmoid(np.dot(layer1,self.w1))
    layer2_error = outs - layer2
    layer2_delta = layer2_error*self.sigmoid(layer2,reverse=True)#*self.learning_rate
    layer1_error = layer2_delta.dot(self.w1.T)
    layer1_delta = layer1_error * self.sigmoid(layer1,reverse=True)#*self.learning_rate

    self.w1 += layer2.T.dot(layer2_delta)
    self.w0 += layer1.T.dot(layer1_delta)