Question

我正在创建一个神经网络来打印患者是否患有心脏病。有6个输入属性和1个输出（0或1）。数据集有318行，我正在使用70-30拆分，因此x_train和y_train有222行。因此，在运行我在互联网上发现的代码时，它会给出尺寸错误：

Traceback (most recent call last):
File "nptestmedium.py", line 43, in <module>
nn.backprop()
File "nptestmedium.py", line 29, in backprop
d_weights1 = np.dot(self.input.T,  (np.dot(2*(self.y - self.output) *   sigmoid_derivative(self.output), self.weights2.T) * sigmoid_derivative(self.layer1)))
ValueError: shapes (222,222) and (1,4) not aligned: 222 (dim 1) != 1 (dim 0)

错误在于代码的权重部分，但我不知道rand（）函数的参数是什么。有人可以帮我吗。谢谢

import numpy as np
from sklearn.model_selection import train_test_split
np.random.seed(2)
dataset = np.loadtxt("heartorig1.csv", delimiter=",")
X = dataset[:,0:6]
Y = dataset[:,6]
x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.3,          random_state=42)
def sigmoid(x):
    return 1.0/(1+ np.exp(-x))

def sigmoid_derivative(x):
    return x * (1.0 - x)

class NeuralNetwork:
    def __init__(self, x, y):
        self.input      = x
        self.weights1   = np.random.rand(self.input.shape[1],4) 
        self.weights2   = np.random.rand(4,1)                 
        self.y          = y
        self.output     = np.zeros(self.y.shape)

    def feedforward(self):
        self.layer1 = sigmoid(np.dot(self.input, self.weights1))
        self.output = sigmoid(np.dot(self.layer1, self.weights2))

    def backprop(self):
        # application of the chain rule to find derivative of the loss function with respect to weights2 and weights1
        d_weights2 = np.dot(self.layer1.T, (2*(self.y - self.output) * sigmoid_derivative(self.output)))
        d_weights1 = np.dot(self.input.T,  (np.dot(2*(self.y - self.output) * sigmoid_derivative(self.output), self.weights2.T) * sigmoid_derivative(self.layer1)))

        # update the weights with the derivative (slope) of the loss function
        self.weights1 += d_weights1
        self.weights2 += d_weights2


if __name__ == "__main__":
    X = x_train
    y = y_train
    nn = NeuralNetwork(X,y)

    for i in range(1500):
        nn.feedforward()
        nn.backprop()

    print(nn.output)

创建我的神经网络时输入尺寸错误

0 个答案: