张量简单来说就是对矩阵的一种称呼。如果熟悉NumPy数组，理解和使用PyTorch张量将会非常容易。标量值由一个零维张量表示。类似地，列/行矩阵使用一维张量表示，以此类推。下面给出了一些不同维度的张量示例，供理解：
测试代码：PyTorch_for_Beginners
3.1 构建张量
import torch
# Create a Tensor with just ones in a column
a = torch.ones(5)
# Print the tensor we created
print(a)
# tensor([1., 1., 1., 1., 1.])
# Create a Tensor with just zeros in a column
b = torch.zeros(5)
print(b)
# tensor([0., 0., 0., 0., 0.])
c = torch.tensor([1.0, 2.0, 3.0, 4.0, 5.0])
print(c)
# tensor([1., 2., 3., 4., 5.])
d = torch.zeros(3,2)
print(d)
# tensor([[0., 0.],
# [0., 0.],
# [0., 0.]])
e = torch.ones(3,2)
print(e)
# tensor([[1., 1.],
# [1., 1.],
# [1., 1.]])
f = torch.tensor([[1.0, 2.0],[3.0, 4.0]])
print(f)
# tensor([[1., 2.],
# [3., 4.]])
# 3D Tensor
g = torch.tensor([[[1., 2.], [3., 4.]], [[5., 6.], [7., 8.]]])
print(g)
# tensor([[[1., 2.],
# [3., 4.]],
#
# [[5., 6.],
# [7., 8.]]])
print(f.shape)
# torch.Size([2, 2])
print(e.shape)
# torch.Size([3, 2])
print(g.shape)
# torch.Size([2, 2, 2])
3.2 访问张量元素
- 1D
# Get element at index 2
print(c[2])
# tensor(3.)
- 2D/3D
# All indices starting from 0
# Get element at row 1, column 0
print(f[1,0])
# We can also use the following
print(f[1][0])
# tensor(3.)
# Similarly for 3D Tensor
print(g[1,0,0])
print(g[1][0][0])
# tensor(5.)
- 访问部分张量
# All elements
print(f[:])
# All elements from index 1 to 2 (inclusive)
print(c[1:3])
# All elements till index 4 (exclusive)
print(c[:4])
# First row
print(f[0,:])
# Second column
print(f[:,1])
3.3 张量元素类型
int_tensor = torch.tensor([[1,2,3],[4,5,6]])
print(int_tensor.dtype)
# torch.int64
# What if we changed any one element to floating point number?
int_tensor = torch.tensor([[1,2,3],[4.,5,6]])
print(int_tensor.dtype)
# torch.float32
print(int_tensor)
# tensor([[1., 2., 3.],
# [4., 5., 6.]])
# This can be overridden as follows
int_tensor = torch.tensor([[1,2,3],[4.,5,6]], dtype=torch.int32)
print(int_tensor.dtype)
# torch.int32
print(int_tensor)
# tensor([[1, 2, 3],
# [4, 5, 6]], dtype=torch.int32)
3.4 张量转换（NumPy Array）
# Import NumPy
import numpy as np
# Tensor to Array
f_numpy = f.numpy()
print(f_numpy)
# array([[1., 2.],
# [3., 4.]], dtype=float32)
# Array to Tensor
h = np.array([[8,7,6,5],[4,3,2,1]])
h_tensor = torch.from_numpy(h)
print(h_tensor)
# tensor([[8, 7, 6, 5],
# [4, 3, 2, 1]])
3.5 张量运算
# Create tensor
tensor1 = torch.tensor([[1,2,3],[4,5,6]])
tensor2 = torch.tensor([[-1,2,-3],[4,-5,6]])
# Addition
print(tensor1+tensor2)
# We can also use
print(torch.add(tensor1,tensor2))
# tensor([[ 0, 4, 0],
# [ 8, 0, 12]])
# Subtraction
print(tensor1-tensor2)
# We can also use
print(torch.sub(tensor1,tensor2))
# tensor([[ 2, 0, 6],
# [ 0, 10, 0]])
# Multiplication
# Tensor with Scalar
print(tensor1 * 2)
# tensor([[ 2, 4, 6],
# [ 8, 10, 12]])
# Tensor with another tensor
# Elementwise Multiplication
print(tensor1 * tensor2)
# tensor([[ -1, 4, -9],
# [ 16, -25, 36]])
# Matrix multiplication
tensor3 = torch.tensor([[1,2],[3,4],[5,6]])
print(torch.mm(tensor1,tensor3))
# tensor([[22, 28],
# [49, 64]])
# Division
# Tensor with scalar
print(tensor1/2)
# tensor([[0, 1, 1],
# [2, 2, 3]])
# Tensor with another tensor
# Elementwise division
print(tensor1/tensor2)
# tensor([[-1, 1, -1],
# [ 1, -1, 1]])
3.6 CPU v/s GPU 张量
PyTorch针对CPU和GPU有不同的Tensor实现。可以将每个张量转换为GPU，以执行大规模并行、快速的计算。所有对张量执行的操作都将使用PyTorch提供的专用于GPU的例程进行。
# Create a tensor for CPU
# This will occupy CPU RAM
tensor_cpu = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], device='cpu')
# Create a tensor for GPU
# This will occupy GPU RAM
tensor_gpu = torch.tensor([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], device='cuda')
CPU v/s GPU张量转换
# Move GPU tensor to CPU
tensor_gpu_cpu = tensor_gpu.to(device='cpu')
# Move CPU tensor to GPU
tensor_cpu_gpu = tensor_cpu.to(device='cuda')
测试代码：001 PyTorch for Beginners