bp Python伪代码

以下是一个简单的BP神经网络的Python伪代码示例：

# 初始化权重和偏置
w1 = initialize_weights()
w2 = initialize_weights()
b1 = initialize_bias()
b2 = initialize_bias()

# 定义激活函数
def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# 前向传播
def forward_propagation(x):
    hidden_layer_input = np.dot(w1, x) + b1
    hidden_layer_output = sigmoid(hidden_layer_input)
    output_layer_input = np.dot(w2, hidden_layer_output) + b2
    output_layer_output = sigmoid(output_layer_input)
    return output_layer_output

# 反向传播
def backward_propagation(x, y, output):
    error = y - output
    output_error = error * sigmoid_derivative(output)
    hidden_error = np.dot(w2.T, output_error) * sigmoid_derivative(hidden_layer_output)
    w2 += learning_rate * np.dot(output_error, hidden_layer_output.T)
    b2 += learning_rate * output_error
    w1 += learning_rate * np.dot(hidden_error, x.T)
    b1 += learning_rate * hidden_error

# 训练模型
for epoch in range(num_epochs):
    for x, y in training_data:
        output = forward_propagation(x)
        backward_propagation(x, y, output)

# 使用训练好的模型进行预测
prediction = forward_propagation(test_data)