Merge branch 'master' of github.com:SherlockLiao/pytorch-beginner

Author: L1aoXingyu

.gitignore

@@ -1,3 +1,5 @@
 data
 .ipynb_checkpoints
 *.pth
+__pycache__
+.vscode

03-Neural Network/neural_network.py

@@ -2,7 +2,7 @@
 
 import torch
 from torch import nn, optim
-import torch.nn.functional as F
+
 from torch.autograd import Variable
 from torch.utils.data import DataLoader
 from torchvision import transforms
@@ -17,7 +17,7 @@
                                transform=transforms.ToTensor(), 
                                download=True)
 
-test_dataset = datasets.MNIST(root='./data', train=False,
+test_dataset = datasets.MNIST(root='./data', train=False, 
                               transform=transforms.ToTensor())
 
 train_loader = DataLoader(train_dataset, batch_size=batch_size, 

11-backward/backward.py

@@ -0,0 +1,66 @@
+import torch as t
+from torch.autograd import Variable as v
+
+# simple gradient
+a = v(t.FloatTensor([2, 3]), requires_grad=True)
+b = a + 3
+c = b * b * 3
+out = c.mean()
+out.backward()
+print('*'*10)
+print('=====simple gradient======')
+print('input')
+print(a.data)
+print('compute result is')
+print(out.data[0])
+print('input gradients are')
+print(a.grad.data)
+
+# backward on non-scalar output
+m = v(t.FloatTensor([[2, 3]]), requires_grad=True)
+n = v(t.zeros(1, 2))
+n[0, 0] = m[0, 0] ** 2
+n[0, 1] = m[0, 1] ** 3
+n.backward(t.FloatTensor([[1, 1]]))
+print('*'*10)
+print('=====non scalar output======')
+print('input')
+print(m.data)
+print('input gradients are')
+print(m.grad.data)
+
+# jacobian
+j = t.zeros(2 ,2)
+k = v(t.zeros(1, 2))
+m.grad.data.zero_()
+k[0, 0] = m[0, 0] ** 2 + 3 * m[0 ,1]
+k[0, 1] = m[0, 1] ** 2 + 2 * m[0, 0]
+k.backward(t.FloatTensor([[1, 0]]), retain_variables=True)
+j[:, 0] = m.grad.data
+m.grad.data.zero_()
+k.backward(t.FloatTensor([[0, 1]]))
+j[:, 1] = m.grad.data
+print('jacobian matrix is')
+print(j)
+
+# compute jacobian matrix
+x = t.FloatTensor([2, 1]).view(1, 2)
+x = v(x, requires_grad=True)
+y = v(t.FloatTensor([[1, 2], [3, 4]]))
+
+z = t.mm(x, y)
+jacobian = t.zeros((2, 2))
+z.backward(t.FloatTensor([[1, 0]]), retain_variables=True)  # dz1/dx1, dz2/dx1
+jacobian[:, 0] = x.grad.data
+x.grad.data.zero_()
+z.backward(t.FloatTensor([[0, 1]]))  # dz1/dx2, dz2/dx2
+jacobian[:, 1] = x.grad.data
+print('=========jacobian========')
+print('x')
+print(x.data)
+print('y')
+print(y.data)
+print('compute result')
+print(z.data)
+print('jacobian matrix is')
+print(jacobian)