Pytorch Tutorial 1

import torch
import numpy as np

t1 = torch.tensor(4.)
print(t1)
print(t1.dtype)

t2 = torch.tensor([1., 2, 3, 4])
print(t2)
print(t2.dtype)
# in this case the all data will be transformed to same data type
# [1., 2., 3., 4.]

t3 = torch.tensor([1., 2, 3, 4])
print(t3)
print(t3.dtype)

t4 = torch.tensor([[1, 2], [1., 4], [4, 3], [5, 6]])
print(t4)
print(t4.dtype)

print(t1.shape)
print(t2.shape)
print(t3.shape)
print(t4.shape)

# ---
x = torch.tensor(3., requires_grad=True)
w = torch.tensor(4., requires_grad=True)
b = torch.tensor(5., requires_grad=True)

y = w * x + b
print(y)
y.backward()

print(x.grad)
print(w.grad)
print(b.grad)

#  convert numpy to torch
x = np.array([[1, 2], [2, 4]])

# use shared memory space, not copy
y = torch.from_numpy(x)

#  copy data
y = torch.tensor(x)

print(y)
print(y.dtype)

# convert torch to numpy
z = y.numpy()
print(z)