下面為官方文檔學習筆記 http://pytorch.org/docs/0.3.0/index.html
1、torch.Tensor
from __future__ import print_function import torch import numpy as np import pandas as pd from pandas import Series,DataFrame ################Tensors Tensors Tensors##################################
x=torch.Tensor(5,3) torch.is_tensor(x) torch.is_storage(x) torch.numel(x) #int
torch.eye(2) torch.eye(2,3) torch.from_numpy(np.array([1,2,3])) #torch.IntTensor of size 3
torch.from_numpy(np.array([1.0,2,3])) #torch.DoubleTensor of size 3
print(torch.linspace(0,10,steps=3)) #torch.FloatTensor of size 3 0 5 10
print(torch.linspace(0,10,3)) #torch.FloatTensor of size 3 0 5 10 # steps大於1的整數，start end中間樣本點的個數
print(torch.logspace(0,1,4)) #torch.FloatTensor of size 4,類似於linspace 以10為底，10的0次方到10的1次方之間
print(torch.ones(2,2)) #torch.FloatTensor of size 2x2
print(torch.ones(2,2,1,5,8,2,8,10)) #torch.FloatTensor of size 2x2x1x5x8x2x8x10
print(torch.ones_like(x)) #torch.FloatTensor of size 5x3
print(torch.arange(0,3,1)) #torch.FloatTensor of size 3 0 1 2 #可迭代對象
print(torch.zeros(2)) #torch.FloatTensor of size 2 0 0
print(torch.zeros_like(x)) #torch.FloatTensor of size 5x3
##############Indexing,Slicing,Joining,Mutating Ops##############################
print(torch.cat((x,x,x),0)) #torch.FloatTensor of size 15x3 #concat 連接Tensor
print(torch.cat((x,x,x),1)) #torch.FloatTensor of size 5x9
print(torch.chunk(x,2)) #截斷，結果為元組，元組內元素個數為截斷個數 # ( # 0 0 0 # 0 0 0 # 0 0 0 # [torch.FloatTensor of size 3x3] # , # 1.00000e-37 * # 0.0000 0.0000 0.0000 # 0.0000 7.4058 0.0000 # [torch.FloatTensor of size 2x3] # )
t=torch.Tensor([[1,2],[3,4]]) # torch.gather(input, dim, index, out=None) → Tensor dim選擇橫向還是縱向的
print(torch.gather(t,0,torch.LongTensor([[0,0],[0,0]]))) #torch.FloatTensor of size 2x2 # 1 2 # 1 2
print(torch.gather(t,1,torch.LongTensor([[0,0],[0,0]]))) #torch.FloatTensor of size 2x2 # 1 1 # 3 3
# torch.index_select(input, dim, index, out=None) → Tensor #選擇幾行或者幾列，如果要同時選列和行，可以分兩步 #注意和前面gather一樣，index數據類型為LongTensor
print(torch.index_select(t,0,torch.LongTensor([0]))) #torch.FloatTensor of size 1x2 1 2
print(torch.index_select(t,1,torch.LongTensor([0]))) #torch.FloatTensor of size 2x1 1 3
print(torch.index_select(t,1,torch.LongTensor([0,1]))) #torch.FloatTensor of size 2x2 # 1 2 # 3 4
print(t.ge(1)) #torch.ByteTensor of size 2x2 判斷t中元素是否大於等於1 # 1 1 # 1 1
print(t.ge(0.5)) #torch.ByteTensor of size 2x2 # 1 1 # 1 1
print(t.ge(2.5)) #torch.ByteTensor of size 2x2 # 0 0 # 1 1
print(t.ge(5)) #torch.ByteTensor of size 2x2 # 0 0 # 0 0
#torch.masked_select(input, mask, out=None) → Tensor 標記選擇，即定向選擇
print(torch.masked_select(t,t.ge(2.5))) #torch.FloatTensor of size 2 # 3 4
for i in torch.masked_select(t,t.ge(1.5)): #可迭代對象
print(i) # 2.0 # 3.0 # 4.0
# torch.nonzero(input, out=None) → LongTensor 得到非0元素所在位置N*2，其中N為非0元素個數
print(torch.nonzero(torch.Tensor([1,2,3,0,4]))) #torch.LongTensor of size 4x1
# 0
# 1
# 2
# 4
non_zero=torch.nonzero(torch.Tensor([[0.6, 0.0, 0.0, 0.0], [0.0, 0.4, 0.0, 0.0], [0.0, 0.0, 1.2, 0.0], [0.0, 0.0, 0.0,-0.4]])) print(non_zero) #torch.LongTensor of size 4x2 # 0 0 # 1 1 # 2 2 # 3 3
non_zero=torch.nonzero(torch.Tensor([[0.6, 0.0, 0.0, 0.0], [2, 0.4, 0.0, 0.0], [3, 0.0, 1.2, 0.0], [4, 0.0, 0.0,-0.4]])) print(non_zero) #torch.LongTensor of size 4x2 # 0 0 # 1 0 # 1 1 # 2 0 # 2 2 # 3 0
#torch.split(tensor, split_size, dim=0) 與前面torch.chunk類似
print(torch.split(x,2)) # ( # 0 0 0 # 0 0 0 # [torch.FloatTensor of size 2x3] # , # 1.00000e-42 * # 0.0000 0.0000 0.0000 # 0.0000 2.0011 0.0000 # [torch.FloatTensor of size 2x3] # , # 1.00000e-38 * # 0.0000 1.1112 0.0000 # [torch.FloatTensor of size 1x3] # )
#torch.squeeze(input, dim=None, out=None) # 將某一方向全部相同數據壓縮掉，想想一下，x、y、z是三維的，如果z全為0，則可轉化為二維。
torch_squeeze=torch.zeros(2,1,2,1,2) print(torch_squeeze) #torch.FloatTensor of size 2x1x2x1x2
print(torch_squeeze.size()) #torch.Size([2, 1, 2, 1, 2])
squeeze_one=torch.squeeze(torch_squeeze) print(squeeze_one) #torch.FloatTensor of size 2x2x2
squeeze_one=torch.squeeze(torch_squeeze,-2) #可用[-5, 4]等
print(squeeze_one) #torch.FloatTensor of size 2x1x2x2
squeeze_one=torch.squeeze(torch_squeeze,1) print(squeeze_one) #torch.FloatTensor of size 2x2x1x2
#torch.stack(sequence, dim=0, out=None) # Concatenates sequence of tensors along a new dimension.
print(x) print(torch.stack((x,x),1));print(torch.stack([x,x],dim=2)
#torch.t(input,out=None) → Tensor 轉置
print(torch.t(x)) #torch.FloatTensor of size 3x5 #print(torch.t(torch.Tensor(1,2,3,4))) #RuntimeError: t() expects a 2D tensor, but self is 4D
#torch.take(input,indices) → Tensor 把input當做1D tensor，按照indices來選擇元素
print(torch.take(x,torch.LongTensor([0,2,5]))) #torch.FloatTensor of size 3
#torch.transpose(input,dim0,dim1,out=None) → Tensor
y=torch.Tensor(1,2,3,4) print(y) #torch.FloatTensor of size 1x2x3x4
print(torch.transpose(y,1,3)) #torch.FloatTensor of size 1x4x3x2 對調，從1*2*3*4——>1*4*3*2
#torch.unbind(tensor,dim=0) 移出tensor中的一維 removes a tensor dimension #返回移出后的元組
print(torch.unbind(y)) #torch.FloatTensor of size 2x3x4 默認dim=0
print(torch.unbind(y,2)) #torch.FloatTensor of size 1x2x4
#torch.unsqueeze(input,dim,out=None)
m=torch.Tensor([1,2,3,4]) print(m) #torch.FloatTensor of size 4
m_zero=torch.unsqueeze(m,0) print(m_zero) #torch.FloatTensor of size 1x4
m_one=torch.unsqueeze(m,1) print(m_one) #torch.FloatTensor of size 4x1
m_zero_to_m=torch.squeeze(m_zero) print(m_zero_to_m) #torch.FloatTensor of size 4
print(m==m_zero_to_m) #torch.ByteTensor of size 4 # 1 # 1 # 1 # 1
print(m.equal(m_zero_to_m)) #True
2、Random sampling
import torch ################ Random sampling ##################################
print(torch.manual_seed(1)) #<torch._C.Generator object at 0x0000023ED56BD470>
print(torch.manual_seed(2)) #<torch._C.Generator object at 0x0000023F7532D470>
print(torch.initial_seed()) #2 返回初始生成的隨機數字
print(torch.get_rng_state()) #torch.ByteTensor of size 5048
#torch.set_rng_state(new_state)
print(torch.set_rng_state(torch.get_rng_state())) #設置隨機數生成狀態,返回為None
#torch.bernoulli(input,out=None) → Tensor 從伯努利分布中刻畫二項分布（0或1）隨機數
a=torch.Tensor(3,3).uniform_(0,1) #torch.FloatTensor of size 3x3 #先生成隨機3*3Tensor，再通過uniform轉換
print(torch.bernoulli(a)) #torch.FloatTensor of size 3x3 # 1 1 1 # 0 1 0 # 0 0 1
a=torch.ones(3,3) print(torch.bernoulli(a)) #torch.FloatTensor of size 3x3 # 1 1 1 # 1 1 1 # 1 1 1
#torch.multinomial(input, num_samples, replacement=False, out=None) → LongTensor #返回一個張量，每行包含num_samples指數多項式概率分布位於張量輸入相應的行采樣。 #輸入行不需要求和（在這種情況下，我們使用值作為權重），但必須是非負的，並且有一個非零和。 #看了半天覺得比較復雜，等以后用時再看
print(torch.multinomial(a,3)) #torch.LongTensor of size 3x3 # 2 0 1 # 2 0 1 # 0 2 1
print(torch.multinomial(torch.Tensor([1,2,3]),3)) #torch.LongTensor of size 3 # 2 1 0
print(torch.multinomial(torch.Tensor([1,2,3,3]),4)) #torch.LongTensor of size 3 # 3 1 2 0
print(torch.multinomial(torch.Tensor([1.0,2,3]),3)) #torch.LongTensor of size 3 # 1 2 0
print(torch.multinomial(torch.Tensor([1,2.0,3,3]),4)) #torch.LongTensor of size 3 # 3 1 2 0
#torch.normal(means, std, out=None)
print(torch.normal(means=torch.arange(1,6),std=torch.arange(0.85,0,-0.2))) #torch.FloatTensor of size 5 #注意要保持means和std兩個size相同 # 1.2742 # 2.5393 # 3.3374 # 4.2307 # 4.9896
print(torch.normal(mean=0,std=torch.arange(0.85,0,-0.2))) #torch.FloatTensor of size 5 #注意前面是means后面是mean # -0.7768 # -0.1913 # -0.3296 # 0.3402 # 0.0021
x=torch.normal(std=torch.arange(0.85,0,-0.2)) print(x) #torch.FloatTensor of size 5
print(x.mean()) #0.4534170083701611
x=torch.normal(means=torch.arange(0.85,0,-0.2)) print(x) #torch.FloatTensor of size 5
print(x.mean()) #0.5901669651269913
#torch.rand(*sizes, out=None) → Tensor #0到1之間均勻分布
print(torch.rand(4)) #torch.FloatTensor of size 4 # 0.6558 # 0.2958 # 0.0541 # 0.6938
print(torch.rand(2,3)) #torch.FloatTensor of size 2x3 # 0.7529 0.6873 0.0716 # 0.9869 0.4623 0.0241
#torch.randperm(n, out=None) → LongTensor #返回隨機置換后的整數。
print(torch.randperm(4)) #torch.LongTensor of size 4 # 1 # 3 # 2 # 0
#還有一些在張量上定義的隨機抽樣函數。 # torch.Tensor.bernoulli_() - in-place version of torch.bernoulli() # torch.Tensor.cauchy_() - numbers drawn from the Cauchy distribution # torch.Tensor.exponential_() - numbers drawn from the exponential distribution # torch.Tensor.geometric_() - elements drawn from the geometric distribution # torch.Tensor.log_normal_() - samples from the log-normal distribution # torch.Tensor.normal_() - in-place version of torch.normal() # torch.Tensor.random_() - numbers sampled from the discrete uniform distribution # torch.Tensor.uniform_() - numbers sampled from the uniform distribution
3、Serialization 序列化、Parallelism平行運算和Math operations 數學運算
from __future__ import print_function import torch import numpy as np import pandas as pd from pandas import Series,DataFrame ################ Serialization ################################## #torch.save(the_model,PATH) #torch.load('tensors.pt') #torch.load(PATH) ################ Parallelism ################################## #torch.get_num_threads() → int #torch.set_num_threads(int) ################ Math operations ################################## #torch.abs(input, out=None) → Tensor 求絕對值 #torch.add(input, value, out=None) out=tensor+value #torch.add(input, value=1, other, out=None) out=input+(other∗value) #torch.mul(input,value,out=None) #相乘，value可以為數字，也可以為與t1元素個數相同Tensor
a_tensor=torch.IntTensor([1,2,3]) #[torch.IntTensor of size 3]
print(torch.dot(a_tensor,a_tensor)) #14 a_tensor不能為size numb*numb，只能為size numb
print(torch.mul(a_tensor,a_tensor)) #1 4 9 [torch.IntTensor of size 3]
#torch.div(input, value, out=None) #與mul類似 相除，如果是IntTensor類型，只保留整數部分，小數部分舍去（注意不是四舍五入） #torch.ceil(input, out=None) → Tensor 向上取整，input類型不能為IntTensor，FloatTensor可以 #torch.erfinv(tensor, out=None) → Tensor 反誤差函數，x區間為[-inf,inf],y區間為[-1,1],即給的是y，求x值 #torch.fmod(input, divisor, out=None) → Tensor 求元素mod，即求余數 % 即可 與下面torch.remainder相同，沒有取余計算，// 不可以 # torch.frac(tensor, out=None) → Tensor 求每個元素小數部分 # torch.exp(tensor, out=None) → Tensor 求每個元素的指數值 #torch.log(input, out=None) → Tensor 求每個元素對數值 #torch.log1p(input, out=None) → Tensor yi=log(xi+1) #torch.neg(input, out=None) → Tensor out=−1∗input 可以直接在前面加負號 如： -a_tensor #torch.pow(input, exponent, out=None) outi=x(i)的exponent次方 或者out(i)=x(i)的exponent(i)次方 torch.pow(torch.Tensor([1,3]),torch.Tensor([1,2])) #1 9 numpy中為np.power() #torch.pow(base, input, out=None) #outi=base的input(i) 次方 base (float)，input (Tensor) 例如:torch.pow(2,torch.Tensor([1,2]) #2 4 #torch.reciprocal(input, out=None) → Tensor 1.0/x 求Tensor的倒數 也可以1/input #torch.remainder(input, divisor, out=None) → Tensor 求余數 input (Tensor)，The dividend divisor (Tensor or float) #torch.round(input, out=None) → Tensor 四舍五入 #torch.sqrt(input, out=None) → Tensor 求元素的平方根 #torch.rsqrt(input, out=None) → Tensor 求元素平方根倒數，負數返回結果為nan #torch.sigmoid(input, out=None) → Tensor 求元素的sigmod值，位於0到1之間，S型函數 #torch.trunc(input, out=None) → Tensor 取元素整數部分 x=torch.Tensor([-1,-2,3]) t=torch.ones(3,2) t1=torch.ones(1,6) t2=torch.ones(6,1) #torch.add(input, value, out=None) out=tensor+value print(torch.add(x,20))#每一個都加20 # 19 # 18 # 23 #torch.add(input, value=1, other, out=None) out=input+(other∗value) #input (Tensor) – the first input Tensor # value (Number) – the scalar multiplier for other # other (Tensor) – the second input Tensor print(torch.add(x,1,x)) x+1*x 中間1為配的系數 # -2 # -4 # 6 #torch.mul(input,value,out=None) #value可以為數字，也可以為與 t1元素個數相同Tensor 相乘 print(torch.mul(t1,10)) #torch.FloatTensor of size 1x6 print(torch.mul(t1,t2)) #torch.FloatTensor of size 6x6 print(torch.mul(t2,t1)) #torch.FloatTensor of size 6x6 t2=torch.ones(7,1) print(torch.mul(t1,t2)) #torch.FloatTensor of size 7x6 t2=torch.ones(7,2) #看來也不能亂乘 #print(torch.mul(t1,t2)) #RuntimeError: inconsistent tensor size t1=torch.Tensor([1,2,3,4]) t2=torch.Tensor([[0,1],[0,1]]) #下面這個也報警告 # print(torch.mul(t1,t2)) #torch.FloatTensor of size 4 # print(torch.mul(t2,t1)) #torch.FloatTensor of size 2x2 #torch.div(input, value, out=None) #out=tensor/value outi=inputi/otheri print(torch.div(t1,2)) #torch.FloatTensor of size 4 # 0.5000 # 1.0000 # 1.5000 # 2.0000 #下面除法也會出現警告 UserWarning: self and other not broadcastable #print(torch.div(t1,t2)) #torch.FloatTensor of size 4 # inf # 2 # inf # 4 #print(torch.div(t2,t1)) #[torch.FloatTensor of size 2x2] # 0.0000 0.5000 # 0.0000 0.2500 #torch.addcdiv(tensor, value=1, tensor1, tensor2, out=None) → Tensor # tensor (Tensor) – the tensor to be added # value (Number, optional) – multiplier for tensor1 ./ tensor2 # tensor1 (Tensor) – Numerator tensor # tensor2 (Tensor) – Denominator tensor t=torch.ones(3,2) t1=torch.ones(1,6) t2=torch.ones(6,1) #加上后面兩個相除，加上后面兩個相乘，中間還可以配個系數，不過報警告 #UserWarning: self, tensor1, and tensor2 not broadcastable, but have the same number of elements #print(torch.addcdiv(t,0.1,t1,t1)) #torch.FloatTensor of size 3x2 # 1.1000 1.1000 # 1.1000 1.1000 # 1.1000 1.1000 #print(torch.addcmul(t,0.1,t1,t1)) #torch.FloatTensor of size 3x2 # 1.1000 1.1000 # 1.1000 1.1000 # 1.1000 1.1000 # torch.lerp(start, end, weight, out=None) # outi=starti+weight∗(endi−starti) print(t1,t2) print(torch.lerp(t1,t2,0.5)) # 0.1000 # 0.9000 # 1.6000 # -2.4000 # [torch.FloatTensor of size 4] # # 0 # 1 # 0 # 1 # [torch.FloatTensor of size 2x2] # # 0.0500 # 0.9500 # 0.8000 # -0.7000 # [torch.FloatTensor of size 4] #torch.floor(input, out=None) → Tensor #與ceil相對應的，floor #torch.clamp(input, min, max, out=None) → Tensor # | min, if x_i < min # y_i = | x_i, if min <= x_i <= max # | max, if x_i > max print(torch.clamp(t1,0,1)) #torch.FloatTensor of size 4 # 0.1000 # 0.9000 # 1.0000 # 0.0000 #必須要輸入min或者max print(torch.clamp(t1,min=0)) #torch.FloatTensor of size 4 # 0.1000 # 0.9000 # 1.6000 # 0.0000 print(torch.clamp(t1,max=1)) #torch.FloatTensor of size 4 # 0.1000 # 0.9000 # 1.0000 # -2.4000 print(torch.clamp(torch.randn(5,5),min=0,max=1)) #torch.FloatTensor of size 5x5 # 0.9985 0.4794 0.0000 0.1223 0.0000 # 0.0000 0.0000 0.0000 0.0000 0.1613 # 0.0527 0.1433 0.6362 0.0000 0.0000 # 0.4906 0.0000 0.0000 0.9332 0.0000 # 0.0000 0.0000 1.0000 0.3525 0.9937 #torch.erf(tensor, out=None) → Tensor #Computes the error function of each element 計算每個元素的誤差函數，S型，-1到1之間 print(torch.erf(torch.Tensor([-100,-10,-2,-1,0,0.5,1,2,10,100]))) #torch.FloatTensor of size 10 # -1.0000 # -1.0000 # -0.9953 # -0.8427 # 0.0000 # 0.5205 # 0.8427 # 0.9953 # 1.0000 # 1.0000
4、Math operations 數學運算
import torch ################################### Reduction operation ################################## #注意：dim=0，按照列來處理的，dim=1，按照行來處理的，python數據處理列比行方便，0在1前 #torch.sum(input) → float 得到一個值，類加值 #torch.sum(input, dim, keepdim=False, out=None) → Tensor 對行或列加總，得到行或者列 #torch.cumprod(input, dim, out=None) → Tensor 累乘 #torch.cumsum(input, dim, out=None) → Tensor 累加 #torch.dist(input, other, p=2) → float 求 p范數 input:(Tensor), other:(Tensor), p:(float, optional) #torch.mean(input) → float 求元素均值,得到一個值 #torch.mean(input, dim, keepdim=False, out=None) → Tensor #torch.median(input) → float #torch.median(input, dim=-1, keepdim=False, values=None, indices=None) -> (Tensor, LongTensor) #median與mean類似，整體和部分處理 #torch.mode(input, dim=-1, keepdim=False, values=None, indices=None) -> (Tensor, LongTensor) #返回元組，兩個元素，每行或者每列最小值和元素所在位置 #torch.norm(input, p=2) → float 返回p范數值 注意與torch.normal()區別 #torch.norm(input, p, dim, keepdim=False, out=None) → Tensor 與前面類似 #torch.prod(input) → float 返回一個值，所有元素累乘值 #torch.prod(input, dim, keepdim=False, out=None) → Tensor #torch.std(input, unbiased=True) → floa 生成無偏的標准差 #torch.std(input, dim, keepdim=False, unbiased=True, out=None) → Tenso #torch.var(input, unbiased=True) → float #torch.var(input, dim, keepdim=False, unbiased=True, out=None) → Tensor ################################### Comparison operation ################################## #torch.eq(input, other, out=None) → Tensor other可以為Tensor或者float，判斷兩個是否相等，得到0 1 Tensor #torch.equal(tensor1, tensor2) → bool True 或者False #torch.ge(input, other, out=None) → Tensor 與eq類似，判斷是否大於等於other，返回0 1 Tensor #torch.gt(input, other, out=None) → Tensor 判斷是否大於other，返回0 1 Tensor #torch.le(input, other, out=None) → Tensor 判斷是否小於等於other，返回0 1 Tensor #torch.lt(input, other, out=None) → Tensor 判斷是否小於other，返回0 1 Tensor #torch.max(input) → float #torch.max(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor) #torch.max(input, other, out=None) → Tensor 取兩個Tensor中較大的元素組成Tensor #torch.min(input) → float #torch.min(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor) #torch.min(input, other, out=None) → Tensor 取兩個Tensor中較小的元素組成Tensor #torch.ne(input, other, out=None) → Tensor 不等於，other可以為Tensor或者float #torch.sort(input, dim=None, descending=False, out=None) -> (Tensor, LongTensor) 返回Tuple x=torch.randn(3,3) sorted,indices=torch.sort(x) print(sorted,indices) # -1.7012 0.2619 0.3892 # -1.8940 -0.7567 1.2057 # -0.8224 0.7787 1.3752 # [torch.FloatTensor of size 3x3] # 0 2 1 # 2 1 0 # 2 1 0 # [torch.LongTensor of size 3x3] #torch.kthvalue(input, k, dim=None, keepdim=False, out=None) -> (Tensor, LongTensor) # k：第k個最小元素,返回第k個最小元素 #torch.topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor) #返回前k個最大元素，注意是前k個，largest=False，返回前k個最小元素 ################################### Comparison operation ################################## #torch.cross(input, other, dim=-1, out=None) → Tensor 向量積、叉乘與mul點乘對應 x=torch.Tensor([1,2,3]) y=torch.Tensor([2,5,1]) print(torch.cross(x,y)) #https://baike.baidu.com/item/%E5%90%91%E9%87%8F%E7%A7%AF/4601007?fr=aladdin # -13 2*1-3*5 # 5 3*2-1*1 # 1 1*5-2*2 # [torch.FloatTensor of size 3] #torch.trace(input) → float 得到主對角線和 #torch.diag(input, diagonal=0, out=None) → Tensor 1、一維到二維Tensor 2、二維到一維Tensor，一維就是主對角線上，有參數可以調節 a=torch.randn(3) print(a) # 2.1126 # -1.4150 # 0.4451 [torch.FloatTensor of size 3] print(torch.diag(a)) # 2.1126 0.0000 0.0000 # 0.0000 -1.4150 0.0000 # 0.0000 0.0000 0.4451 # [torch.FloatTensor of size 3x3] a=torch.randn(3,3) print(a) # 0.6810 -2.1620 -0.3158 # 0.0545 1.1060 1.3524 # 0.1481 -1.1468 -0.0113 # [torch.FloatTensor of size 3x3] print(torch.diag(a,0)) # 1.0596 # 1.2221 # 0.2311 # [torch.FloatTensor of size 3] print(torch.diag(a,1)) # 1.8588 # -0.2285 # [torch.FloatTensor of size 2] #torch.tril(input, diagonal=0, out=None) → Tensor 刪除Tensor部分對角線數據 #torch.triu(input, diagonal=0, out=None) → Tensor 與tril類似 #torch.inverse(input, out=None) → Tensor 求Tensor的逆 #torch.mm(mat1, mat2, out=None) → Tensor 兩個Tensor的矩陣乘積 data=[[1,2],[3,4]]
tensor=torch.FloatTensor(data)
tensor
Out[23]:
tensor([[ 1., 2.],
[ 3., 4.]])
np.matmul(data,data)
Out[24]:
array([[ 7, 10],
[15, 22]])
torch.mm(tensor,tensor)
Out[25]:
tensor([[ 7., 10.],
[ 15., 22.]])
#torch.mv(mat, vec, out=None) → Tensor mat = torch.randn(2, 3) # [torch.FloatTensor of size 2x3] vec = torch.randn(3) # [torch.FloatTensor of size 3] torch.mv(mat, vec) # -2.0939 # -2.2950 # [torch.FloatTensor of size 2] # #torch.qr(input, out=None) -> (Tensor, Tensor) 齊次分解