PyTorch Get Started

Install

Can install from pip

pip install torch

Programming

Before starting, you need to import PyTorch Library

import torch

PyTorch uses Tensor (unique class for PyTorch) for calculation, PyTorch supports auto differential calculation

Basic Programming

  • Create 0 dim tensor
  • Create 1 dim tensor from numpy array
  • Create 2 dim tensor from numpy matrix (array of array)
  • Create 3 dim tensor from normal distribution
  • Create 4 dim tensor from torch.ones
  • Convert PyTorch Tensor to Numpy
  • view (reshape)

Create 0 dim tensor

import numpy as np

np1 = np.array([1,3,5,7,9])
# Convert tensor from Numpy
tensor1 = torch.tensor(np1).float()

Create 1 dim tensor from numpy array

np2 = np.array([[1,2,3], [4,5,6]])
tensor2 = torch.tensor(np2).float()

Create 2 dim tensor from numpy matrix (array of array)

# Random
torch.manual_seed(234)
# create shape=[3,2,2] normal distribution tensor 3 x 2 x 2
tensor3 = torch.randn([3,2,2]) 

# shape
print(tensor3.shape) # torch.Size([3, 2, 2])
# data
print(tensor3.data)

Create 4 dim tensor from torch.ones

# create shape=[2,3,2,2] all one
tensor4 = torch.ones([2,3,2,2])
# shape
print(tensor4.shape) # torch.Size([2, 3, 2, 2])
# data
print(tensor4.data)

Convert PyTorch Tensor to Numpy

Use .data.numpy()

np1 = tensor1.data.numpy()

# Check Type
print(type(np1)) # <class 'numpy.ndarray'>

# Check Value
print(np1)

View

# create shape=[3,2,2] normal distribution tensor 3 x 2 x 2
tensor3 = torch.randn([3,2,2]) 

# set element -1, adjust auto
tensor2 = tensor3.view(3, -1) # 2 dim

# shape
print(tensor2.shape) # 3 x 4 (12 elements)

# value
print(tensor2.data)

Tensor Attributes

dtypeGet Tensor data type (torch.float32)
shapeGet shape (same as Numpy shape)
dataGet value
required_gradWhether this tensor supports grad or not
deviceSupport device for calculation (CPU, GPU)
itemGet value (scalar tensor can return actual value)
Only 0 dim tensor
maxGet max value (return tensor)

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