动手学深度学习3月20日
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import torch
x = torch.arange(12)
x
tensor([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
x.shape
torch.Size([12])
x.numel()#元素种数
12
X = x.reshape(3,4)
X
tensor([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
torch.zeros(2,3,4)
tensor([[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]],
[[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]]])
torch.ones(2,3,4)
tensor([[[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]],
[[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.]]])
torch.tensor([[[2,1,3,4],[1,2,3,4],[4,3,2,1]]]).shape
torch.Size([1, 3, 4])
x = torch.tensor([1.0,2,4,8])
y = torch.tensor([2,2,2,2])
x + y, x - y, x * y, x / y, x**y
(tensor([ 3., 4., 6., 10.]),
tensor([-1., 0., 2., 6.]),
tensor([ 2., 4., 8., 16.]),
tensor([0.5000, 1.0000, 2.0000, 4.0000]),
tensor([ 1., 4., 16., 64.]))
torch.exp(x)
tensor([2.7183e+00, 7.3891e+00, 5.4598e+01, 2.9810e+03])
X = torch.arange(12, dtype=torch.float32).reshape((3,4))
Y = torch.tensor([[2,1,3,4],[1,2,3,4],[4,3,2,1]])
torch.cat((X, Y), dim=0), torch.cat((X, Y), dim=1)
(tensor([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[ 2., 1., 3., 4.],
[ 1., 2., 3., 4.],
[ 4., 3., 2., 1.]]),
tensor([[ 0., 1., 2., 3., 2., 1., 3., 4.],
[ 4., 5., 6., 7., 1., 2., 3., 4.],
[ 8., 9., 10., 11., 4., 3., 2., 1.]]))
X == Y
tensor([[False, True, False, False],
[False, False, False, False],
[False, False, False, False]])
X.sum()
tensor(66.)
a = torch.arange(3).reshape((3,1))
b = torch.arange(2).reshape((1,2))
a, b
(tensor([[0],
[1],
[2]]),
tensor([[0, 1]]))
广播机制慎重，可能会得到意想不到的结果
a + b
tensor([[0, 1],
[1, 2],
[2, 3]])
X[-1],X[1:3]#第一行和第二行 左闭右开
(tensor([ 8., 9., 10., 11.]),
tensor([[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]]))
X
tensor([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]])
X[1,2] = 9
X
tensor([[ 0., 1., 2., 3.],
[ 4., 5., 9., 7.],
[ 8., 9., 10., 11.]])
X[0:2,:] = 12
X
tensor([[12., 12., 12., 12.],
[12., 12., 12., 12.],
[ 8., 9., 10., 11.]])
before = id(Y)
Y = Y + X
id(Y) == before
#运行一些操作可能会导致为新结果分配内存
False
Z = torch.zeros_like(Y)#原地操作
print('id(Z):', id(Z))
Z[:] = X + Y
print('id(Z):', id(Z))
print('id(Y):', id(Y))
id(Z): 140696439911232
id(Z): 140696439911232
id(Y): 140696439824000
before = id(X)
X += Y
id(X) == before
True
A = X.numpy()
B = torch.tensor(A)
type(A),type(B)
(numpy.ndarray, torch.Tensor)
a = torch.tensor([3.5])
a, a.item(), float(a), int(a)
(tensor([3.5000]), 3.5, 3.5, 3)
数据预处理
import os
os.makedirs(os.path.join('..','data'),exist_ok=True)
data_file = os.path.join('..','data','house_tiny.csv')
with open(data_file,'w') as f:
f.write("NumRooms,Alley,Prince\n") # 列名
f.write('NA,Pave,127500\n') # 每行表示一个数据样本
f.write('2,NA,106000\n')
f.write('4,NA,178100\n')
f.write('NA,NA,140000\n')
import pandas as pd
data = pd.read_csv(data_file)
print(data)
NumRooms Alley Prince
0 NaN Pave 127500
1 2.0 NaN 106000
2 4.0 NaN 178100
3 NaN NaN 140000
data
| NumRooms | Alley | Prince | |
|---|---|---|---|
| 0 | NaN | Pave | 127500 |
| 1 | 2.0 | NaN | 106000 |
| 2 | 4.0 | NaN | 178100 |
| 3 | NaN | NaN | 140000 |
处理缺失数据，插值或者删除
inputs, outputs = data.iloc[:,0:2],data.iloc[:,2]
inputs = inputs.fillna(inputs.mean())
inputs
| NumRooms | Alley | |
|---|---|---|
| 0 | 3.0 | Pave |
| 1 | 2.0 | NaN |
| 2 | 4.0 | NaN |
| 3 | 3.0 | NaN |
inputs = pd.get_dummies(inputs,dummy_na=True)
inputs
| NumRooms | Alley_Pave | Alley_nan | |
|---|---|---|---|
| 0 | 3.0 | 1 | 0 |
| 1 | 2.0 | 0 | 1 |
| 2 | 4.0 | 0 | 1 |
| 3 | 3.0 | 0 | 1 |
import torch
X, y = torch.tensor(inputs.values), torch.tensor(outputs.values)
X, y
(tensor([[3., 1., 0.],
[2., 0., 1.],
[4., 0., 1.],
[3., 0., 1.]], dtype=torch.float64),
tensor([127500, 106000, 178100, 140000]))