add readme, chapter 03 finished

02_tensors_playground.py

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+import torch
+
+# empty, zeros, ones of different sizes, specify datatype
+print('empty, zeros, ones of different sizes, specify datatype')
+x = torch.empty(1)
+print(x)
+x = torch.empty(3)
+print(x)
+x = torch.zeros(2, 3)
+print(x)
+x = torch.ones(2,3,4)
+print(x)
+x = torch.ones(2, 5, dtype=torch.float64)
+print(x.dtype)
+print()
+
+# from data
+print('from data')
+x = torch.tensor([2.5, 0.1])
+print(x)
+print()
+
+#basic ops
+print('basic ops')
+x = torch.rand(2,2)
+y = torch.rand(2,2)
+
+print('add')
+z1 = x + y
+z2 = torch.add(x,y)
+print(x)
+print(y)
+print(z1)
+print(z2)
+# in place addition
+x.add_(y)
+print(x)
+
+print('sub')
+z1 = x - y
+z2 = torch.sub(x,y)
+print(x)
+print(y)
+print(z1)
+print(z2)
+# in place addition
+x.sub_(y)
+print(x)
+
+print('mul')
+z1 = x * y
+z2 = torch.mul(x,y)
+print(x)
+print(y)
+print(z1)
+print(z2)
+# in place addition
+x.mul_(y)
+print(x)
+
+print('div')
+z1 = x / y
+z2 = torch.div(x,y)
+print(x)
+print(y)
+print(z1)
+print(z2)
+# in place addition
+x.div_(y)
+print(x)
+
+print()
+
+#slicing
+print('slicing, item')
+x = torch.rand(2,3,2)
+print(x)
+print(x[:,2,:])
+print(x[1, 2, 1])
+print(x[1, 2, 1].item()) # for single element tensors only
+
+print()
+
+# reshaping
+print('reshaping')
+
+x = torch.rand(4,4)
+print(x)
+y = x.view(16)
+print(y)
+y = x.view(-1, 8)
+print(y)
+y = x.view(2, -1)
+print(y)
+
+# y = x.view(3, -1) # fails 'shape is invalid'
+# print(y)
+
+print()
+
+# transposing
+print('transposing')
+x = torch.rand(2, 3)
+print(x.size())
+x = torch.transpose(x, 0, 1)
+print(x.size())
+x = torch.t(x)
+print(x.size())
+x = torch.rand(2, 3, 4)
+print(f'Original: {x.size()}')
+x = torch.transpose(x, 0, 1)
+print(f'01: {x.size()}')
+x = torch.rand(2, 3, 4)
+x = torch.transpose(x, 1, 2)
+print(f'12: {x.size()}')
+x = torch.rand(2, 3, 4)
+x = torch.transpose(x, 0, 2)
+print(f'02: {x.size()}')
+
+print()
+
+# numpy
+import numpy as np
+print('numpy')
+
+a = torch.ones(5)
+print(a)
+b = a.numpy()
+print(b)
+print(type(b))
+
+a.add_(1) # vectors/tensors share same memory
+print(a)
+print(b)
+
+c = np.ones(5)
+print(c)
+d = torch.from_numpy(c)
+print(d)
+e = d.to(dtype=torch.float32) # e has its own memory
+print(e)
+
+c += 1 # c and d share same memory
+print(c)
+print(d)
+print(e)
+
+# device
+print('device')
+if torch.cuda.is_available():
+    device = torch.device('cuda')
+else:
+    device = torch.device('cpu')
+print(device)
+x = torch.ones(5, device=device)
+y = torch.ones(5)
+y = y.to(device)
+z = x+y
+print(z)
+z = z.to('cpu') #move tensor back to cpu for conversion into numopy vector
+print(z)
+a = z.numpy()
+print(a)
+
+x = torch.ones(5, requires_grad=True) # enable grad for autograd
+print(x)