本地AI模型的实现，无需GPU加速，主要依赖于CPU和内存资源。以下是一个简单的Python实现：
```python
import numpy as np
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def softmax(x):
- exp_x = np.exp(x
- np.max(x))
return exp_x / np.sum(exp_x, axis=0)
def forward_propagate(X, W, b):
y = np.dot(X, W) + b
return sigmoid(y), softmax(y)
def backward_propagate(dY, dW, db, X, Y):
dY_prime = np.dot(Y.T, dY)
dW_prime = np.dot(X.T, dY_prime)
db_prime = np.sum(dY_prime, axis=0)
return dW_prime, db_prime
def train(X, Y, X_train, Y_train, iterations, learning_rate):
num_iterations = 0
while num_iterations < iterations:
Y_pred, Y_pred_softmax = forward_propagate(X_train, X, Y_train)
- loss = np.mean((Y_pred
- Y_pred_softmax) ** 2) dW = backward_propagate(loss * X_train, Y_train
- Y_pred_softmax, X, Y_pred)
dW /= len(X_train)
- db = backward_propagate(np.ones((len(X_train), 1)) * loss, Y_train
- Y_pred_softmax, X, Y_pred)
db /= len(X_train)
- W = W
- learning_rate * dW b = b
- learning_rate * db
num_iterations += 1
return W, b
```
这个实现中，我们使用了Sigmoid激活函数和Softmax函数来处理输出层的激活值。在训练过程中，我们使用反向传播算法来更新权重和偏置。
