cost = np.array([[4, 1, 3], [2, 0, 5], [3, 2, 2]])
from scipy.optimize import linear_sum_assignment
row_ind, col_ind = linear_sum_assignment(cost)
###col_ind
#array([1, 0, 2])
###cost[row_ind, col_ind].sum()
#5

from collections import deque
def find_free_partner(boys, girls, sort_boy_to_girl, sort_girl_to_boy):
    # 当前选择的舞伴
    current_boys = dict(zip(boys, [None]*len(boys)))
    current_girls = dict(zip(girls, [None]*len(girls)))
    # current_boys = {boys[0]:None, boys[1]:None, boys[2]:None, boys[3]:None}
    # current_girls = {girls[0]:None, girls[1]:None, girls[2]:None, boys[3]:None}
    count = len(boys)

    # 建立队列，男孩下一次选择的女孩
    next_select = dict(zip(boys, [None]*len(boys)))
    for i in range(count):
        temp = [girls[m-1] for m in sort_boy_to_girl[i]]
        next_select[boys[i]] = deque(temp)

    # 女孩选择男孩字典
    sort_girl = dict(zip(girls, [None]*len(boys)))
    for i in range(count):
        # 通过题目给出的sort_girl_to_boy字典,排在前面的名字好感度比较高
        temp = [[boys[m-1], 4-ind] for ind, m in enumerate(sort_girl_to_boy[i])]
        name, love = [], []
        for t in temp:
            name.append(t[0])
            love.append(t[1])
        sort_girl[girls[i]] = dict(zip(name, love))

    while None in current_boys.values():
        for i in range(count):
            bid = boys[i]
            if current_boys[bid]:
                # 男孩有对象，跳过
                continue
            else:
                # 优先选择的女孩
                select = next_select[bid][0]
                if current_girls[select] == None:
                    # 女孩没对象，两者结合
                    current_boys[bid] = select
                    current_girls[select] = bid
                    next_select[bid].popleft()
                else:
                    # 和女孩的对象好感度对比,如果对现任的好感度,大于第三者,不动
                    if sort_girl[select][current_girls[select]] > sort_girl[select][bid]:
                        next_select[bid].popleft()
                    # 如果与上面相反
                    # 现任男孩失恋,第三者男孩选择了当前女孩,当前女孩选择了第三者男孩
                    # 第三者男孩失去对当前女孩的追求权(本算法不能对同一女士追求两次)
                    else:
                        current_boys[current_girls[select]] = None
                        current_boys[bid] = select
                        current_girls[select] = bid
                        next_select[bid].popleft()
    return current_boys

## 初始化
boys = ["Alex", "David", "Bob", "Chris"]
girls = ["Ada", "Becky", "Cindy", "Diana"]

# 偏爱列表
sort_boy_to_girl = [[1, 4, 3, 2], [3, 1, 2, 4],
                    [1, 2, 3, 4], [2, 4, 3, 1]]
sort_girl_to_boy = [[4, 1, 3, 2], [1, 2, 4, 3],
                    [3, 2, 4, 1], [2, 3, 1, 4]]

print(find_free_partner(boys, girls, sort_boy_to_girl, sort_girl_to_boy))

import torch as t
import torch.nn as nn
import torch.nn.functional as F
  
class LeNet(nn.Module):
    def __init__(self):
        super(LeNet,self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.conv2 = nn.Conv2d(6,16,5)
        self.fc1 = nn.Linear(16*5*5,120)
        self.fc2 = nn.Linear(120,84)
        self.fc3 = nn.Linear(84,10)
  
    def forward(self,x):
        x = F.max_pool2d(F.relu(self.conv1(x)),(2,2))
        x = F.max_pool2d(F.relu(self.conv2(x)),2)
        x = x.view(x.size()[0], -1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

inputs = torch.rand(64, 3, 244, 244)
conv = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1)
out = conv(inputs)

weight = torch.rand(64,3,3,3)
bias = torch.rand(64) 
out = nn.functional.conv2d(inputs, weight, bias, padding=1)

fm_layer = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(num_features=64),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2),
            nn.Dropout(0.2)
  )

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        
        self.cnn1 = nn.Conv2d(in_channels=1,  out_channels=16, kernel_size=5,padding=0)
        self.relu1 = nn.ReLU()
        self.maxpool1 = nn.MaxPool2d(kernel_size=2)
        
        self.cnn2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5,  padding=0)
        self.relu2 = nn.ReLU()
        self.maxpool2 = nn.MaxPool2d(kernel_size=2)
        
        self.linear1 = nn.Linear(4 * 4 * 32, 10)
        
    def forward(self, x):
        x = x.view(x.size(0), -1)
        out = self.maxpool1(self.relu1(self.cnn1(x)))
        out = self.maxpool2(self.relu2(self.cnn2(out)))
        out = self.linear1(out.view(x.size(0), -1))
        return out

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        
        self.cnn1_weight = nn.Parameter(torch.rand(16, 1, 5, 5))
        self.bias1_weight = nn.Parameter(torch.rand(16))
        
        self.cnn2_weight = nn.Parameter(torch.rand(32, 16, 5, 5))
        self.bias2_weight = nn.Parameter(torch.rand(32))
        
        self.linear1_weight = nn.Parameter(torch.rand(4 * 4 * 32, 10))
        self.bias3_weight = nn.Parameter(torch.rand(10))
        
    def forward(self, x):
        x = x.view(x.size(0), -1)
        out = F.conv2d(x, self.cnn1_weight, self.bias1_weight)
        out = F.relu(out)
        out = F.max_pool2d(out)
        
        out = F.conv2d(x, self.cnn2_weight, self.bias2_weight)
        out = F.relu(out)
        out = F.max_pool2d(out)
        
        out = F.linear(x, self.linear1_weight, self.bias3_weight)
        return out

class net1(nn.Module):
    def __init__(self):
        super(net1, self).__init__()
        self.linears = nn.ModuleList([nn.Linear(10,10) for i in range(2)])
    def forward(self, x):
        for m in self.linears:
            x = m(x)
        return x

net = net1()
print(net)
# net1(
#   (modules): ModuleList(
#     (0): Linear(in_features=10, out_features=10, bias=True)
#     (1): Linear(in_features=10, out_features=10, bias=True)
#   )
# )

for param in net.parameters():
    print(type(param.data), param.size())
# <class 'torch.Tensor'> torch.Size([10, 10])
# <class 'torch.Tensor'> torch.Size([10])
# <class 'torch.Tensor'> torch.Size([10, 10])
# <class 'torch.Tensor'> torch.Size([10])

class net2(nn.Module):
    def __init__(self):
        super(net2, self).__init__()
        self.linears = [nn.Linear(10,10) for i in range(2)]
    def forward(self, x):
        for m in self.linears:
            x = m(x)
        return x

net = net2()
print(net)
# net2()
print(list(net.parameters()))
# []

class net3(nn.Module):
    def __init__(self):
        super(net3, self).__init__()
        self.linears = nn.ModuleList([nn.Linear(10,20), nn.Linear(20,30), nn.Linear(5,10)])
    def forward(self, x):
        x = self.linears[2](x)
        x = self.linears[0](x)
        x = self.linears[1](x) 
        return x

net = net3()
print(net)
# net3(
#   (linears): ModuleList(
#     (0): Linear(in_features=10, out_features=20, bias=True)
#     (1): Linear(in_features=20, out_features=30, bias=True)
#     (2): Linear(in_features=5, out_features=10, bias=True)
#   )
# )
input = torch.randn(32, 5)
print(net(input).shape)
# torch.Size([32, 30])

class net4(nn.Module):
    def __init__(self):
        super(net4, self).__init__()
        self.linears = nn.ModuleList([nn.Linear(5, 10), nn.Linear(10, 10)])
    def forward(self, x):
        x = self.linears[0](x)
        x = self.linears[1](x)
        x = self.linears[1](x)
        return x

net = net4()
print(net)
# net4(
#   (linears): ModuleList(
#     (0): Linear(in_features=5, out_features=10, bias=True)
#     (1): Linear(in_features=10, out_features=10, bias=True)
#   )
# )
for name, param in net.named_parameters():
    print(name, param.size())
# linears.0.weight torch.Size([10, 5])
# linears.0.bias torch.Size([10])
# linears.1.weight torch.Size([10, 10])
# linears.1.bias torch.Size([10])

class net5(nn.Module):
    def __init__(self):
        super(net5, self).__init__()
        self.block = nn.Sequential(nn.Conv2d(1,20,5),
                                    nn.ReLU(),
                                    nn.Conv2d(20,64,5),
                                    nn.ReLU())
    def forward(self, x):
        x = self.block(x)
        return x

net = net5()
print(net)
# net5(
#   (block): Sequential(
#     (0): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
#     (1): ReLU()
#     (2): Conv2d(20, 64, kernel_size=(5, 5), stride=(1, 1))
#     (3): ReLU()
#   )
# )

# Example of using Sequential
model1 = nn.Sequential(
          nn.Conv2d(1,20,5),
          nn.ReLU(),
          nn.Conv2d(20,64,5),
          nn.ReLU()
        )
print(model1)
# Sequential(
#   (0): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
#   (1): ReLU()
#   (2): Conv2d(20, 64, kernel_size=(5, 5), stride=(1, 1))
#   (3): ReLU()
# )

# Example of using Sequential with OrderedDict
import collections
model2 = nn.Sequential(collections.OrderedDict([
          ('conv1', nn.Conv2d(1,20,5)),
          ('relu1', nn.ReLU()),
          ('conv2', nn.Conv2d(20,64,5)),
          ('relu2', nn.ReLU())
        ]))
print(model2)
# Sequential(
#   (conv1): Conv2d(1, 20, kernel_size=(5, 5), stride=(1, 1))
#   (relu1): ReLU()
#   (conv2): Conv2d(20, 64, kernel_size=(5, 5), stride=(1, 1))
#   (relu2): ReLU()
# )

layers = [nn.Linear(10, 10) for i in range(5)]

class net6(nn.Module):
    def __init__(self):
        super(net6, self).__init__()
        self.linears = nn.ModuleList([nn.Linear(10, 10) for i in range(3)])

    def forward(self, x):
        for layer in self.linears:
            x = layer(x)
        return x

net = net6()
print(net)
# net6(
#   (linears): ModuleList(
#     (0): Linear(in_features=10, out_features=10, bias=True)
#     (1): Linear(in_features=10, out_features=10, bias=True)
#     (2): Linear(in_features=10, out_features=10, bias=True)
#   )
# )

class net7(nn.Module):
    def __init__(self):
        super(net7, self).__init__()
        self.linear_list = [nn.Linear(10, 10) for i in range(3)]
        self.linears = nn.Sequential(*self.linears_list)

    def forward(self, x):
        self.x = self.linears(x)
        return x

net = net7()
print(net)
# net7(
#   (linears): Sequential(
#     (0): Linear(in_features=10, out_features=10, bias=True)
#     (1): Linear(in_features=10, out_features=10, bias=True)
#     (2): Linear(in_features=10, out_features=10, bias=True)
#   )
# )

class net8(nn.Module):
    def __init__(self):
        super(net8, self).__init__()
        self.linears = nn.ModuleList([nn.Linear(10, 20), nn.Linear(20, 30), nn.Linear(30, 50)])
        self.trace = []

    def forward(self, x):
        for layer in self.linears:
            x = layer(x)
            self.trace.append(x)
        return x

net = net8()
input  = torch.randn(32, 10) # input batch size: 32
output = net(input)
for each in net.trace:
    print(each.shape)
# torch.Size([32, 20])
# torch.Size([32, 30])
# torch.Size([32, 50])

for i,(feature,target) in enumerate(train_loader):
    outputs = model(feature)  # 前向传播
    loss = criterion(outputs,target)  # 计算损失

    optimizer.zero_grad()   # 清空梯度
    loss.backward()  # 计算梯度
    optimizer.step()  # 反向传播， 更新网络参数

for i,(features,target) in enumerate(train_loader):
    outputs = model(images)  # 前向传播
    loss = criterion(outputs,target)  # 计算损失
    loss = loss/accumulation_steps   # 可选，如果损失要在训练样本上取平均

    loss.backward()  # 计算梯度
    if((i+1)%accumulation_steps)==0:
        optimizer.step()        # 反向传播，更新网络参数
        optimizer.zero_grad()   # 清空梯度

>>> element = 2*np.arange(4).reshape((2, 2))
>>> element
array([[0, 2],
       [4, 6]])
>>> test_elements = [1, 2, 4, 8]
>>> mask = np.isin(element, test_elements)
>>> mask
array([[ False,  True],
       [ True,  False]])
>>> element[mask]
array([2, 4])
>>> mask = np.isin(element, test_elements, invert=True)
>>> mask
array([[ True, False],
       [ False, True]])
>>> element[mask]
array([0, 6])

>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[5, 6]])
>>> np.concatenate((a, b), axis=0)
array([[1, 2],
       [3, 4],
       [5, 6]])
>>> np.concatenate((a, b.T), axis=1)
array([[1, 2, 5],
       [3, 4, 6]])
>>> np.concatenate((a, b), axis=None)
array([1, 2, 3, 4, 5, 6])