- 论坛徽章:
- 24
|
其实这个就是 pytorch 底层的 C 模块，最近这一个月主要是在移植和调试 win10 mingw 环境下的 pytorch。
今天终于调通了。  
我终于把 C 模块和 python 连上了。
- import time
- import torch
- from torch.autograd import Variable
- torch.set_num_threads(2)
- num_threads = torch.get_num_threads()
- print("num_threads {}".format(num_threads))
- #dtype = torch.FloatTensor
- dtype = torch.DoubleTensor
- # dtype = torch.cuda.FloatTensor # Uncomment this to run on GPU
- # 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 Tensors to hold input and outputs, and wrap them in Variables.
- # Setting requires_grad=False indicates that we do not need to compute gradients
- # with respect to these Variables during the backward pass.
- x = Variable(torch.randn(N, D_in).type(dtype), requires_grad=False)
- y = Variable(torch.randn(N, D_out).type(dtype), requires_grad=False)
- # Create random Tensors for weights, and wrap them in Variables.
- # Setting requires_grad=True indicates that we want to compute gradients with
- # respect to these Variables during the backward pass.
- w1 = Variable(torch.randn(D_in, H).type(dtype), requires_grad=True)
- w2 = Variable(torch.randn(H, D_out).type(dtype), requires_grad=True)
- timer_up = time.time()
- learning_rate = 1e-6
- for t in range(1000):
- # Forward pass: compute predicted y using operations on Variables; these
- # are exactly the same operations we used to compute the forward pass using
- # Tensors, but we do not need to keep references to intermediate values since
- # we are not implementing the backward pass by hand.
- y_pred = x.mm(w1).clamp(min=0).mm(w2)
- # Compute and print loss using operations on Variables.
- # Now loss is a Variable of shape (1,) and loss.data is a Tensor of shape
- # (1,); loss.data[0] is a scalar value holding the loss.
- loss = (y_pred - y).pow(2).sum()
- #print(t, loss.data[0])
- print(t, loss.item())
- # Use autograd to compute the backward pass. This call will compute the
- # gradient of loss with respect to all Variables with requires_grad=True.
- # After this call w1.grad and w2.grad will be Variables holding the gradient
- # of the loss with respect to w1 and w2 respectively.
- loss.backward()
- # Update weights using gradient descent; w1.data and w2.data are Tensors,
- # w1.grad and w2.grad are Variables and w1.grad.data and w2.grad.data are
- # Tensors.
- w1.data -= learning_rate * w1.grad.data
- w2.data -= learning_rate * w2.grad.data
- # Manually zero the gradients after updating weights
- w1.grad.data.zero_()
- w2.grad.data.zero_()
- timer = time.time() - timer_up
- print("timer : {0:.3}s".format(timer))
|
|
免费注册
发表于 2018-02-01 00:32
