bp神经网络的soc估计代码

BP神经网络的SoC（System on Chip）估计是指在集成电路中实现BP神经网络的功能，将其作为芯片的一部分来实现。下面给出一个简单的BP神经网络的SoC估计代码示例：

import numpy as np

def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# 设定BP神经网络的超参数
input_size = 2
hidden_size = 3
output_size = 1

# 初始化权重和偏置
W1 = np.random.randn(input_size, hidden_size)
b1 = np.zeros(hidden_size)
W2 = np.random.randn(hidden_size, output_size)
b2 = np.zeros(output_size)

# 定义前向传播函数
def forward(X):
    Z1 = np.dot(X, W1) + b1
    A1 = sigmoid(Z1)
    Z2 = np.dot(A1, W2) + b2
    A2 = sigmoid(Z2)
    return A2

# 定义损失函数
def loss(Y_pred, Y_true):
    return np.mean((Y_pred - Y_true) ** 2)

# 定义反向传播函数
def backward(X, Y_true, learning_rate):
    # 前向传播
    Z1 = np.dot(X, W1) + b1
    A1 = sigmoid(Z1)
    Z2 = np.dot(A1, W2) + b2
    A2 = sigmoid(Z2)
    
    # 反向传播
    dZ2 = A2 - Y_true
    dW2 = np.dot(A1.T, dZ2)
    db2 = np.sum(dZ2, axis=0)
    dZ1 = np.dot(dZ2, W2.T) * (A1 * (1 - A1))
    dW1 = np.dot(X.T, dZ1)
    db1 = np.sum(dZ1, axis=0)
    
    # 权重和偏置更新
    W2 -= learning_rate * dW2
    b2 -= learning_rate * db2
    W1 -= learning_rate * dW1
    b1 -= learning_rate * db1

# 定义训练函数
def train(X, Y_true, learning_rate, epochs):
    for i in range(epochs):
        Y_pred = forward(X)
        loss_val = loss(Y_pred, Y_true)
        backward(X, Y_true, learning_rate)
        if i % 100 == 0:
            print(f"Epoch {i+1}, Loss: {loss_val:.4f}")

# 构造训练数据
X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
Y = np.array([[0], [1], [1], [0]])

# 启动训练
train(X, Y, learning_rate=0.1, epochs=1000)

# 进行预测
print("Predictions:")
print(forward(X))

这是一个简单的使用numpy库实现的BP神经网络的SoC估计代码。其中包括了前向传播、反向传播、权重和偏置的更新以及训练和预测的功能。通过训练数据的输入和输出，对神经网络进行训练，并利用训练好的网络进行预测。