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xuming06
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add pytorch demo.
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‎20pytorch/01.tensor.py‎

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# -*- coding: utf-8 -*-
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# Author: XuMing <[email protected]>
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# Brief: http://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html
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import torch
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x = torch.Tensor(5, 2)
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print(x)
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y = torch.rand(3, 4)
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print(y)
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print(len(y))
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print(y.size())
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z = torch.rand(3, 1)
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print(z)
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o = torch.add(y, z)
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print(o)
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k = torch.Tensor(3, 4)
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torch.add(y, z, out=k)
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print(k)
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a = torch.ones(3)
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print(a)
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b = torch.FloatTensor(3, 4)
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print(b)
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b = a.add_(2)
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print(b)
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import numpy as np
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a = np.ones(3)
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b = torch.from_numpy(a)
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np.add(a, 1, out=a)
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print(a)
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print(b)

‎20pytorch/02.variable.py‎

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# -*- coding: utf-8 -*-
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# Author: XuMing <[email protected]>
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# Brief: http://pytorch.org/tutorials/beginner/blitz/autograd_tutorial.html#variable
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import torch
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from torch.autograd import Variable
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a = Variable(torch.ones(3, 3), requires_grad=True)
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print(a)
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b = a + 2
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print(b)
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c = b * b * 2
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out = c.mean()
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print(c, out)
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print('a.grad:')
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print(a.grad)
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print('a.grad after backward:')
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out.backward()
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print(a.grad)
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print('y = y * 2 result:')
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x = torch.randn(3)
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print('raw x:',x)
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x = Variable(x, requires_grad=True)
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y = x * 2
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while y.data.norm() < 1000:
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y = y * 2
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print(y)

‎20pytorch/03.neural_network.py‎

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# -*- coding: utf-8 -*-
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# Author: XuMing <[email protected]>
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# Brief: http://pytorch.org/tutorials/beginner/blitz/neural_networks_tutorial.html#define-the-network
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# 1. define network
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import torch
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from torch.autograd import Variable
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import torch.nn as nn
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import torch.nn.functional as F
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class Network(nn.Module):
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def __init__(self):
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super(Network, self).__init__()
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self.conv1 = nn.Conv2d(1, 6, 5)
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self.conv2 = nn.Conv2d(6, 16, 5)
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self.fc1 = nn.Linear(16 * 5 * 5, 120)
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self.fc2 = nn.Linear(120, 84)
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self.fc3 = nn.Linear(84, 10)
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def forward(self, x):
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x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
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x = F.max_pool2d(F.relu(self.conv2(x)), 2)
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x = x.view(-1, self.num_flat_features(x))
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc2(x))
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x = self.fc3(x)
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return x
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def num_flat_features(self, x):
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size = x.size()[1:]
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num_features = 1
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for s in size:
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num_features *= s
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return num_features
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network = Network()
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print(network)
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params = list(network.parameters())
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print(len(params))
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print(params[0].size())
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input = Variable(torch.randn(1, 1, 32, 32))
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out = network(input)
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print(out)
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network.zero_grad()
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out.backward(torch.randn(1, 10))
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# loss function
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output = network(input)
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target = Variable(torch.arange(1, 11))
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target = target.view(1, -1)
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criterion = nn.MSELoss()
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loss = criterion(output, target)
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print('loss:', loss)

‎20pytorch/__init__.py‎

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# -*- coding: utf-8 -*-
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# Author: XuMing <[email protected]>
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# Brief:

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