人工智慧

Data Science and Robots by Brandon Rohrer

https://brohrer.mcknote.com/

A Neural Network Playground

https://playground.tensorflow.org/

安裝PyTorch

https://pytorch.org/

如有安裝NVIDIA GPU，請選CUDA ，否則請選None

PyTorch與Numpy轉換

import torch

import numpy as np

np_data = np.arange(6).reshape((2, 3)) # [[0 1 2], [3 4 5]]

torch_from_numpy = torch.from_numpy(np_data)

numpy_from_torch = torch_from_numpy.numpy()

print('numpy array:', np_data)

print('torch tensor:', torch_from_numpy)

print('tensor to numpy array:', numpy_from_torch)

數學運算

data = [-1, -2, -3, -4]

tensor = torch.FloatTensor(data) # 轉換32位元浮點數tensor

print('abs絕對值')

print('numpy: ', np.abs(data))

print('torch: ', torch.abs(tensor))

print('sin三角函數')

print('numpy: ', np.sin(data))

print('torch: ', torch.sin(tensor))

print('mean平均數')

print('numpy: ', np.mean(data))

print('torch: ', torch.mean(tensor))

矩陣運算

data = [[1,2], [3,4]]

tensor = torch.FloatTensor(data) # 轉換32位元浮點數tensor

#正確方法

print('matrix multiplication 矩陣相乘')

print('numpy: ', np.matmul(data, data))

print('torch: ', torch.mm(tensor, tensor))

#torch使用dot問題

data = np.array(data)

print('matrix multiplication (使用dot)')

print('numpy: ', data.dot(data)) # numpy 中可行

#print('torch: ', tensor.dot(tensor)) # torch 不可行