PyTorch-基本數據操作(Numpy)
硬件:NVIDIA-GTX1080
軟件:Windows7、python3.6.5、pytorch-gpu-0.4.1
一、基礎知識
1、Torch 爲神經網絡界的 Numpy,torch.from_numpy() 與 torch_data.numpy() 即可完成torch數據和numpy數據的相互轉化
2、Torch 浮點數接收方式,torch.FloatTensor(),數據計算方式和numpy相似,如abs, sin, mean...
3、Torch 矩陣點乘方式,torch.mm(tensor, tensor),與numpy.matmul(data, data) 類似
二、代碼展示
Example1:
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]]
)
Example2:
import torch
import numpy as np
# abs 絕對值計算
data = [-1, -2, 1, 2]
tensor = torch.FloatTensor(data) # 轉換成32位浮點 tensor
print(
'\nabs',
'\nnumpy: ', np.abs(data), # [1 2 1 2]
'\ntorch: ', torch.abs(tensor) # [1 2 1 2]
)
# sin 三角函數 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 均值
print(
'\nmean',
'\nnumpy: ', np.mean(data), # 0.0
'\ntorch: ', torch.mean(tensor) # 0.0
)
Example3:
import torch
import numpy as np
# matrix multiplication 矩陣點乘
data = [[1,2], [3,4]]
tensor = torch.FloatTensor(data) # 轉換成32位浮點 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]]
)
三、參考:
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