pytorch机器学习人工智能示例代码
代码语言:python
所属分类:人工智能
代码描述:pytorch机器学习人工智能示例代码
下面为部分代码预览,完整代码请点击下载或在bfwstudio webide中打开
import torch device = torch.device('cpu') # CPU环境 # device = torch.device('cuda') # Uncomment this to run on GPU GPU环境 # N is batch size; D_in is input dimension; # H is hidden dimension; D_out is output dimension. N, D_in, H, D_out = 64, 1000, 100, 10 # Create random input and output data x = torch.randn(N, D_in, device=device) # 输入 (64,1000) y = torch.randn(N, D_out, device=device) # 输出 (64,10) # Randomly initialize weights w1 = torch.randn(D_in, H, device=device) # 输入层-隐藏层 权重 (1000,100) w2 = torch.randn(H, D_out, device=device) # 隐藏层-输出层 权重 (100,10) learning_rate = 1e-6 # 学习率 for t in range(500): # Forward pass: compute predicted y 前向传播:计算预测的y h = x.mm(w1) # 点乘 得到隐藏层 (64,100) # torch.mm()矩阵相乘 # torch.mul() 矩阵位相乘 h_relu = h.clamp(min=0) # 计算relu激活函数 # torch.clamp(input, min, max, out=None) → Tensor 将输入input张量每个元素的夹紧到区间 [min,max][min,max],并返回结果到一个新张量。 y_pred = h_relu.mm(w2) # 点乘 得到输出层 (64,10) # Compute and print loss; loss is a sc.........完整代码请登录后点击上方下载按钮下载查看
网友评论0