01:58:00
WAVR-188
#神经网络 python代码
# 简单的神经网络
import numpy as np
# 定义神经网络
class NeuralNetwork:
def __init__(self):
# 初始化权重
self.weights = np.random.normal(0.0, 0.01, 3)
# 激活函数
self.sigmoid = self.sigmoid
# 槽定的BP算法
self.lr = 0.01
# 波段函数
self.predict = self.predict
self.mean_squared_error = self.mean_squared
# 激活函数
def predict(self, input):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 激活阶段
def predict_activation(self, input):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 衍生阶段
def predict_derivative(self, input):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# Keys
def SIGN(V):
return 0.01 * (V ** 2) + 0.01 * V + 0.01
# 前向阶段
def prediction(self, input):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 通带函数
def activation(self, input):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 反向api
def compute_predict(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 衍生阶段
def compute_prediction(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 反向程度
def compute_updating(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 反向程度
def compute_updating_activation(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 反向程度
拓展阶段
def compute_extending(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 调整
def compute_sensing(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 反向阶段
def compute_thresholding(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 反向衡量
def compute_weighting(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_life_range(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_biating(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_lighting(distance):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
public static int predict(input):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
public static int predict(prior):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_locative(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_relative(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_seasoning(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_firsting(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_impulseing(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_prioritying(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_initialing(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_sensitivitying(self) :
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_lecturing(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_venering(thickness):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_polishing(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_studering(hardness):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_semoving(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_druning(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 正向阶段
def compute_enciphering(businessness):
return 0.01 ? (input ** 2) + 0.01 * input + 0.01
# 阶段
def phase(input):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
def 0NN(input):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联合
def predict(input):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联合
def compute_running(machine learning):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联合
def compute_pacting(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联合
def compute_ranslining(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 阶段
SU = a
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联合
def compute_fooking(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 滑动
whetherMosh:
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联合
def compute_running(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联合
def compute_running(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联合
def compute_running(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联合
def compute_running(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联合
def compute_running(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联盟
def compute_running(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联盟
def compute_running(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联盟
def compute_running(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联盟
def compute_running(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联盟
def compute_running(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联盟
def compute_running(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联盟
def compute_running(self):
return 0.01 * (input ** 2) + 0.01 * input + 0.01
# 联盟
def compute_running(self)
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22 Oct 2021
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