Machine learning pytorch platform code study notes (1) - numpy and Pytorch

1. numpy & pytorch

Torch calls itself the Numpy of the neural network world, because it can put the tensor generated by torch in the GPU to accelerate the operation (provided you have a suitable GPU), just like Numpy will put the array in the CPU to accelerate the operation.

Convert numpy array and torch tensor freely in pytorch.

import torch
import numpy as np

np_data = np.arange(6).reshape((2, 3))
torch_data = torch.from_numpy(np_data)
tensor2array = torch_data.numpy()
print(
    '\nnumpy array:', np_data,          # [[0 1 2], [3 4 5]]
    '\ntorch tensor:', torch_data,      #  0  1  2 \n 3  4  5    [torch.LongTensor of size 2x3]
    '\ntensor to array:', tensor2array, # [[0 1 2], [3 4 5]]
)

2. Mathematical operations in Torch 

# abs absolute value calculation
data = [-1, -2, 1, 2]
tensor = torch.FloatTensor(data) # Convert to 32-bit floating point tensor
print(
    '\nabs',
    '\nnumpy: ', np.abs(data),          # [1 2 1 2]
    '\ntorch: ', torch.abs(tensor)      # [1 2 1 2]
)

# sin trigonometric function sin
print(
    '\ nsin',
    '\nnumpy: ', np.sin(data),      # [-0.84147098 -0.90929743  0.84147098  0.90929743]
    '\ntorch: ', torch.sin(tensor)  # [-0.8415 -0.9093  0.8415  0.9093]
)

# mean mean
print(
    '\nmean',
    '\nnumpy: ', np.mean(data),         # 0.0
    '\ntorch: ', torch.mean(tensor)     # 0.0
)

# matrix multiplication matrix dot multiplication
data = [[1,2], [3,4]]
tensor = torch.FloatTensor(data) # Convert to 32-bit floating point tensor
# correct method
print(
    '\ nmatrix multiplication (matmul)',
    '\nnumpy: ', np.matmul(data, data),     # [[7, 10], [15, 22]]
    '\ntorch: ', torch.mm(tensor, tensor)   # [[7, 10], [15, 22]]
)

# !!!! BELOW IS THE WRONG WAY!!!!
data = np.array(data)
print(
    '\nmatrix multiplication (dot)',
    '\nnumpy: ', data.dot(data), # [[7, 10], [15, 22]] works in numpy
    '\ntorch: ', tensor.dot(tensor) # torch will be converted to [1,2,3,4].dot([1,2,3,4) = 30.0
)

In the new version (>=0.3.0), there are new changes to tensor.dot(), which can only be used for one-dimensional arrays. So the above will be wrong.

Reference: https://morvanzhou.github.io/tutorials/machine-learning/torch/2-01-torch-numpy/

https://www.jianshu.com/p/5ae644748f21