import numpy as np class Dropout: def __init__(self, dropout_rate=0.5): self.drop_rate = dropout_rate self.mask = None def forward(self, x, is_train=True): if is_train: # x必须是numpy数组才有shape random_mat = np.random.rand(*x.shape) self.mask = random_mat > self.drop_rate output = x * self.mask return output else: scale = 1 - self.drop_rate output = x * scale return output def backward(self, upstream_grad): grad_x = upstream_grad * self.mask return grad_x # 测试代码 if __name__ == "__main__": dropout_layer = Dropout(dropout_rate=0.5) # 正确：传入numpy数组，不能传纯数字 x_data = np.array([10, 20, 30, 40]) # 训练模式前向传播 train_out = dropout_layer.forward(x_data, is_train=True) print("训练输出：", train_out) # 推理模式前向传播 pred_out = dropout_layer.forward(x_data, is_train=False) print("推理输出：", pred_out) # 反向梯度测试 grad = dropout_layer.backward(np.array([1,1,1,1])) print("反向梯度：", grad)