介绍：
## Flow chart for a simple neural network:
#(1)Take inputs 输入
#(2)Add bias (if required)
#(3)Assign random weights to input features 随机一个权重
#(4)Run the code for training. 训练集训练
#(5)Find the error in prediction. 找预测损失
#(6)Update the weight by gradient descent algorithm. 根据梯度下降更新权重
#(7)Repeat the training phase with updated weights. 重复训练更新权重
#(8)Make predictions. 做预测
参考： 深度学习使用python建立最简单的神经元neuron-CSDN博客
数据：
# Import the required libraries
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt# Load the data
df = pd.read_csv('Lesson44-data.csv')
df
一、
# Separate the features and label
x = df[['Glucose','BloodPressure']]#特征值
y = df['Outcome']#标签三、
np.random.seed(10)#初始化
label = y.values.reshape(y.shape[0],1)
weights = np.random.rand(2,1)#随机一个权重
bias = np.random.rand(1)
learning_rate = 0.0000004#梯度下降步长
epochs = 1000 #迭代次数四~七、
# Define the sigmoid function
def sigmoid(input): output = 1 / (1 + np.exp(-input))return output# Define the sigmoid derivative function基于sigmoid导数
def sigmoid_derivative(input):return sigmoid(input) * (1.0 - sigmoid(input))def train_network(x,y,weights,bias,learning_rate,epochs): #Epochs. 来回 One Epoch is when an ENTIRE dataset is passed forward and backward through the neural network only ONCE.j=0 #weights 权重k=[] #learning_rate梯度下降的步长l=[]for epoch in range(epochs): dot_prod = np.dot(x, weights) + bias#np.dot矩阵乘积# using sigmoidpreds = sigmoid(dot_prod)# Calculating the errorerrors = preds - y #计算错误，预测-实际# sigmoid derivativederiva_preds = sigmoid_derivative(preds)deriva_product = errors * deriva_preds#update the weightsweights = weights - np.dot(x.T, deriva_product) * learning_rateloss = errors.sum()j=j+1k.append(j)l.append(loss)print(j,loss)for i in deriva_product:bias = bias - i * learning_rateplt.plot(k,l)return weights,biasweights_final, bias_final = train_network(x,label,weights,bias,learning_rate,epochs)
八、
weights_final
'''结果：
array([[ 0.06189634],[-0.12595182]])
'''bias_final
#结果：array([0.633647])# Prediction
inputs = [[101,76]]
dot_prod = np.dot(inputs, weights_final) + bias_final
preds = sigmoid(dot_prod) >= 1/2
preds
#结果：array([[False]])inputs = [[137,40]]
dot_prod = np.dot(inputs, weights_final) + bias_final
preds = sigmoid(dot_prod) >= 1/2
preds
#结果：array([[ True]])
