Add Autograd.

pytorch/Basic/Autograd/autograd.py

@@ -2,9 +2,12 @@
 from matplotlib import pyplot as plt
 
 x = torch.linspace(0, 5, 100, requires_grad=True)
-
 y = (x**2).cos()
 
+print(x)
+print(y)
+print(y.sum())
+
 dydx = torch.autograd.grad(y.sum(), [x])[0]
 
 plt.plot(x.detach(), y.detach(), label='y')

pytorch/Basic/Autograd/autograd_1.py

@@ -0,0 +1,16 @@
+import torch 
+from matplotlib import pyplot as plt
+
+x = torch.linspace(0, 5, 100, requires_grad=True)
+y = (x**3 - 6 * x**2 + 8 * x)
+
+print(x)
+print(y)
+print(y.sum())
+
+dydx = torch.autograd.grad(y.sum(), [x])[0]
+
+plt.plot(x.detach(), y.detach(), label='y')
+plt.plot(x.detach(), dydx, label='dy/dx')
+plt.legend()
+plt.show()

pytorch/Basic/Autograd/backprop.py

@@ -0,0 +1,13 @@
+import torch
+from matplotlib import pyplot as plt
+
+x = torch.linspace(0, 5, 100, requires_grad=True)
+print(x.grad)
+y = (x**2).cos()
+y.sum().backward()
+print(x.grad)
+
+plt.plot(x.detach(), y.detach(), label='y')
+plt.plot(x.detach(), x.grad, label='dy/dx')
+plt.legend()
+plt.show()

pytorch/Basic/Autograd/backprop_1.py

@@ -0,0 +1,14 @@
+import torch
+from matplotlib import pyplot as plt
+
+x = torch.linspace(0, 5, 100, requires_grad=True)
+x2 = torch.linspace(0, 5, 100, requires_grad=True)
+
+y = x2**3 - 6 * x**2 + 8 * x
+y.sum().backward()
+
+plt.plot(x.detach(), y.detach(), label='y')
+plt.plot(x.detach(), x.grad, label='dy/dx')
+plt.plot(x2.detach(), x2.grad, label='dy/dx2')
+plt.legend()
+plt.show()

pytorch/Basic/Autograd/backprop_2.py

@@ -0,0 +1,17 @@
+import torch
+from matplotlib import pyplot as plt
+
+x = torch.linspace(0, 5, 100, requires_grad=True)
+
+y = x**3 - 6 * x**2 + 8 * x
+y.sum().backward(create_graph=True)
+
+dy = x.grad.clone()
+x.grad.zero_()
+dy.sum().backward()
+
+plt.plot(x.detach(), y.detach(), label='y')
+#plt.plot(x.detach(), x.grad.detach(), label='dy/dx')
+plt.plot(x.detach(), x.grad.detach(), label='d2y/dx')
+plt.legend()
+plt.show()