import numpy as np

A = np.array([[2, 1, 1], [1, 3, 2], [1, 0, 0]]) 

B = np.array([4, 5, 6]) 

# linalg.solve is the function of NumPy to solve a system of linear scalar equations
print ("Solutions:\n",np.linalg.solve(A, B ) )

Solutions:
[  6.  15. -23.]

nn = nn.LSTM(input_size=10, hidden_size=20, num_layers=2)#(input_size,hidden_size,num_layers)
input = torch.randn(5, 3, 10)#(seq_len, batch, input_size)
h0 = torch.randn(2, 3, 20) #(num_layers,batch,output_size)
c0 = torch.randn(2, 3, 20) #(num_layers,batch,output_size)
output, (hn, cn) = rnn(input, (h0, c0))

output.shape #(seq_len, batch, output_size)
torch.Size([5, 3, 20])
hn.shape #(num_layers, batch, output_size)
torch.Size([2, 3, 20])
cn.shape #(num_layers, batch, output_size)
torch.Size([2, 3, 20])

rnn = nn.LSTM(input_size=10, hidden_size=20, num_layers=2,bidirectional=True)#(input_size,hidden_size,num_layers)
input = torch.randn(5, 3, 10)#(seq_len, batch, input_size)
h0 = torch.randn(4, 3, 20) #(num_layers,batch,output_size)
c0 = torch.randn(4, 3, 20) #(num_layers,batch,output_size)
output, (hn, cn) = rnn(input, (h0, c0))

output.shape #(seq_len, batch, output_size*2)
torch.Size([5, 3, 40])
hn.shape #(num_layers*2, batch, output_size)
torch.Size([4, 3, 20])
cn.shape #(num_layers*2, batch, output_size)
torch.Size([4, 3, 20])

# https://www.pytorchtutorial.com/pytorch-sequence-model-and-lstm-networks/

import torch
import torch.autograd as autograd # torch中自动计算梯度模块
import torch.nn as nn             # 神经网络模块
import torch.nn.functional as F   # 神经网络模块中的常用功能 
import torch.optim as optim       # 模型优化器模块
 
torch.manual_seed(1)

######################
### 定义训练数据
training_data = [
    ("The dog ate the apple".split(), ["DET", "NN", "V", "DET", "NN"]),
    ("Everybody read that book".split(), ["NN", "V", "DET", "NN"])
]

######################
### 创建词典：单词词典用于特征生成；词性词典用于label生成
# 创建索引字典（这里是根据训练数据创建的，一般的应该是有一个大而全的词典）
word_to_ix = {} # 单词的索引字典
for sent, tags in training_data:
    for word in sent:
        if word not in word_to_ix:
            word_to_ix[word] = len(word_to_ix)
print(word_to_ix)

# 创建词性的词典
tag_to_ix = {"DET": 0, "NN": 1, "V": 2} # 手工设定词性标签数据字典

# 根据序列和对应的词典，生成对应序列在词典中的index
# sentence+单词词典 =》sentence顺序每个单词的index
# tags+词性词典 =》每个词的词性对应的index
def prepare_sequence(seq, to_ix):
    idxs = [to_ix[w] for w in seq]
    tensor = torch.LongTensor(idxs)
    return autograd.Variable(tensor)

######################
### 定义LSTM网络
class LSTMTagger(nn.Module):
 
    def __init__(self, embedding_dim, hidden_dim, vocab_size, tagset_size):
        super(LSTMTagger, self).__init__()
        self.hidden_dim = hidden_dim
 
        self.word_embeddings = nn.Embedding(vocab_size, embedding_dim)
 
        self.lstm = nn.LSTM(embedding_dim, hidden_dim)
 
        self.hidden2tag = nn.Linear(hidden_dim, tagset_size)
        self.hidden = self.init_hidden()
 
    def init_hidden(self):
        return (autograd.Variable(torch.zeros(1, 1, self.hidden_dim)),
                autograd.Variable(torch.zeros(1, 1, self.hidden_dim)))
 
    def forward(self, sentence):
        embeds = self.word_embeddings(sentence)
        lstm_out, self.hidden = self.lstm(
            embeds.view(len(sentence), 1, -1), self.hidden)
        tag_space = self.hidden2tag(lstm_out.view(len(sentence), -1))
        tag_scores = F.log_softmax(tag_space)
        return tag_scores
    
######################
### 定义一个具体的LSTM网络：主要是对网络参数进行传值
model = LSTMTagger(2, 3, len(word_to_ix), len(tag_to_ix))
# 定义损失函数
loss_function = nn.NLLLoss()
# 定义优化器
optimizer = optim.SGD(model.parameters(), lr=0.1)
 
######################
### 这里是还没有使用训练数据进行模型训练的结果
# inputs = prepare_sequence(training_data[0][0], word_to_ix)
# tag_scores = model(inputs)
# print(training_data[0][0])
# print(inputs)
# print(tag_scores)
# ['The', 'dog', 'ate', 'the', 'apple']
# tensor([0, 1, 2, 3, 4])
# tensor([[-1.6456, -1.0384, -0.7917],
#         [-1.6287, -1.1794, -0.7004],
#         [-1.6278, -1.2331, -0.6689],
#         [-1.6497, -1.1290, -0.7245],
#         [-1.6244, -1.2177, -0.6791]], grad_fn=<LogSoftmaxBackward>)

######################
### 使用训练数据进行模型训练
for epoch in range(300): # 我们要训练300次，可以根据任务量的大小酌情修改次数。
    print("ecpo:",epoch)
    for sentence, tags in training_data:
        # 清除网络先前的梯度值，梯度值是Pytorch的变量才有的数据，Pytorch张量没有
        model.zero_grad()
        # 重新初始化隐藏层数据，避免受之前运行代码的干扰
        model.hidden = model.init_hidden()
        # 准备网络可以接受的的输入数据和真实标签数据，这是一个监督式学习
        # 这里的模型的输入x就是sentence_in，转换后就是句子里每个词在词典里面的index
        sentence_in = prepare_sequence(sentence, word_to_ix)
        targets = prepare_sequence(tags, tag_to_ix)
        # 运行我们的模型，直接将模型名作为方法名看待即可
        tag_scores = model(sentence_in)
        # 计算损失，反向传递梯度及更新模型参数
        loss = loss_function(tag_scores, targets)
        loss.backward()
        optimizer.step()
 
######################
### 检验模型效果：这里用的是训练数据，只为了演示
inputs = prepare_sequence(training_data[0][0], word_to_ix)
tag_scores = model(inputs)
print(tag_scores)
# tensor([[-0.9031, -0.5412, -4.3697],
#         [-2.3633, -0.1098, -4.6217],
#         [-2.7825, -3.1968, -0.1085],
#         [-0.6320, -0.8947, -2.8180],
#         [-2.3956, -0.1101, -4.3304]], grad_fn=<LogSoftmaxBackward>)

# CNN：https://github.com/pytorch/examples/blob/master/mnist/main.py
# RNN LSTM：https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/02-intermediate/recurrent_neural_network/main.py

from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms


device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5, 1)
        self.conv2 = nn.Conv2d(20, 50, 5, 1)
        self.fc1 = nn.Linear(4*4*50, 500)
        self.fc2 = nn.Linear(500, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2, 2)
        x = F.relu(self.conv2(x))
        x = F.max_pool2d(x, 2, 2)
        x = x.view(-1, 4*4*50)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_classes)
    
    def forward(self, x):
        # Set initial hidden and cell states 
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) 
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        
        # Forward propagate LSTM
        out, _ = self.lstm(x, (h0, c0))  # out: tensor of shape (batch_size, seq_length, hidden_size)
        
        # Decode the hidden state of the last time step
        out = self.fc(out[:, -1, :])
        return out

# model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)
    
def train(args, model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))

def train_LSTM(args, model, device, train_loader, optimizer, epoch, sequence_length, input_size):
    model.train()
    criterion = nn.CrossEntropyLoss()
    for batch_idx, (data, target) in enumerate(train_loader):
        data = data.reshape(-1, sequence_length, input_size).to(device)
        target = target.to(device)
        optimizer.zero_grad()
        output = model(data)
        # loss = F.nll_loss(output, target) # if use nll_loss for LSTM, will be negative number
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item()))
            
def test(args, model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
            pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))

def test_LSTM(args, model, device, test_loader, sequence_length, input_size):
    model.eval()
    test_loss = 0
    correct = 0
    criterion = nn.CrossEntropyLoss()
    with torch.no_grad():
        for data, target in test_loader:
            data = data.reshape(-1, sequence_length, input_size).to(device)
            target = target.to(device)
            output = model(data)
            # test_loss += F.nll_loss(output, target, reduction='sum').item() # sum up batch loss
            test_loss += criterion(output, target)
            pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)

    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))
    
def main():
    # Training settings
    parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
    parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                        help='input batch size for training (default: 64)')
    parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                        help='input batch size for testing (default: 1000)')
    parser.add_argument('--epochs', type=int, default=10, metavar='N',
                        help='number of epochs to train (default: 10)')
    parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
                        help='learning rate (default: 0.01)')
    parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                        help='SGD momentum (default: 0.5)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='disables CUDA training')
    parser.add_argument('--seed', type=int, default=1, metavar='S',
                        help='random seed (default: 1)')
    parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                        help='how many batches to wait before logging training status')
    
    parser.add_argument('--save-model', action='store_true', default=False,
                        help='For Saving the current Model')
    args = parser.parse_args()
    use_cuda = not args.no_cuda and torch.cuda.is_available()

    torch.manual_seed(args.seed)

#     device = torch.device("cuda" if use_cuda else "cpu")
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    kwargs = {'num_workers': 1, 'pin_memory': True} if use_cuda else {}
    train_loader = torch.utils.data.DataLoader(
        datasets.MNIST('../../data', train=True, download=True,
                       transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.1307,), (0.3081,))
                       ])),
        batch_size=args.batch_size, shuffle=True, **kwargs)
    test_loader = torch.utils.data.DataLoader(
        datasets.MNIST('../../data', train=False, transform=transforms.Compose([
                           transforms.ToTensor(),
                           transforms.Normalize((0.1307,), (0.3081,))
                       ])),
        batch_size=args.test_batch_size, shuffle=True, **kwargs)


    # CNN
#     model = Net().to(device)
#     optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
#     for epoch in range(1, args.epochs + 1):
#         train(args, model, device, train_loader, optimizer, epoch)
#         test(args, model, device, test_loader)
    
    # RNN LSTM
    sequence_length = 28
    input_size = 28
    hidden_size = 128
    num_layers = 2
    num_classes = 10
    model = RNN(input_size=input_size, hidden_size=hidden_size, num_layers=num_layers, num_classes=num_classes).to(device) # RNN LSTM
#     optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)
    # SGD works not well for LSTM
    optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
    for epoch in range(1, args.epochs + 1):
        train_LSTM(args, model, device, train_loader, optimizer, epoch, sequence_length, input_size)
        test_LSTM(args, model, device, test_loader, sequence_length, input_size)

    if (args.save_model):
        torch.save(model.state_dict(),"mnist_cnn.pt")
        
if __name__ == '__main__':
    main()

# CNN
➜  LSTM git:(master) ✗ CUDA_VISIBLE_DEVICES=3 python mnist.py
Train Epoch: 1 [0/60000 (0%)]   Loss: 2.300039
Train Epoch: 1 [640/60000 (1%)] Loss: 2.213470
Train Epoch: 1 [1280/60000 (2%)]        Loss: 2.170460
Train Epoch: 1 [1920/60000 (3%)]        Loss: 2.076699
...
Train Epoch: 1 [57600/60000 (96%)]      Loss: 0.117136
Train Epoch: 1 [58240/60000 (97%)]      Loss: 0.191664
Train Epoch: 1 [58880/60000 (98%)]      Loss: 0.204842
Train Epoch: 1 [59520/60000 (99%)]      Loss: 0.064107

Test set: Average loss: 0.1015, Accuracy: 9661/10000 (97%)

Train Epoch: 2 [0/60000 (0%)]   Loss: 0.145626
Train Epoch: 2 [640/60000 (1%)] Loss: 0.119849
Train Epoch: 2 [1280/60000 (2%)]        Loss: 0.101394
Train Epoch: 2 [1920/60000 (3%)]        Loss: 0.068628
...
Train Epoch: 2 [57600/60000 (96%)]      Loss: 0.037703
Train Epoch: 2 [58240/60000 (97%)]      Loss: 0.166170
Train Epoch: 2 [58880/60000 (98%)]      Loss: 0.035417
Train Epoch: 2 [59520/60000 (99%)]      Loss: 0.069601
Test set: Average loss: 0.0610, Accuracy: 9829/10000 (98%)

# RNN LSTM
➜  LSTM git:(master) ✗ CUDA_VISIBLE_DEVICES=2 python mnist.py
Train Epoch: 1 [0/60000 (0%)]   Loss: -0.016249
Train Epoch: 1 [640/60000 (1%)] Loss: -0.056388
Train Epoch: 1 [1280/60000 (2%)]        Loss: -0.091170
Train Epoch: 1 [1920/60000 (3%)]        Loss: -0.088119
...
Train Epoch: 1 [57600/60000 (96%)]      Loss: -161.295212
Train Epoch: 1 [58240/60000 (97%)]      Loss: -165.509781
Train Epoch: 1 [58880/60000 (98%)]      Loss: -168.507706
Train Epoch: 1 [59520/60000 (99%)]      Loss: -168.760498

Test set: Average loss: -171.4964, Accuracy: 1135/10000 (11%)

Train Epoch: 2 [0/60000 (0%)]   Loss: -172.674515
Train Epoch: 2 [640/60000 (1%)] Loss: -172.516800
Train Epoch: 2 [1280/60000 (2%)]        Loss: -179.091690
Train Epoch: 2 [1920/60000 (3%)]        Loss: -181.107941
...
Train Epoch: 2 [57600/60000 (96%)]      Loss: -406.059845
Train Epoch: 2 [58240/60000 (97%)]      Loss: -407.615234
Train Epoch: 2 [58880/60000 (98%)]      Loss: -407.243866
Train Epoch: 2 [59520/60000 (99%)]      Loss: -412.519165

Test set: Average loss: -414.2386, Accuracy: 1135/10000 (11%)