lstm in pytorch

Posted 2022-11-22 机器学习的小学生

下面是lstm进行手写字符分类示例，将大小为28×28的图像，每一列或者每一行看作是特征维度，那么其对应的每一行或者每一列就是“视频序列帧”。例如看作：28个视频帧，每个视频帧的长度为28维。最后损失函数采用：整个“视频序列帧”即28×28大小的图像，对应单个类别，具体实现为最后一个“视频帧”对应的隐含状态进行后续损失计算（直接参加）。与lstm in caffe中的“ lisa-caffe-public-lstm_video_deploy” 中视频行为识别不一样，在其中将视频序列中的每个图像，都对应一个类别，然后参加损失函数的计算，具体实现是每个“视频帧”对应的隐含状态都参加到后续损失的计算（直接参加）。

import torch
import torch.nn as nn
import torchvision.transforms as transforms
import torchvision.datasets as dsets
from torch.autograd import Variable

'''
STEP 1: LOADING DATASET
'''
train_dataset = dsets.MNIST(root='./data', 
                            train=True, 
                            transform=transforms.ToTensor(),
                            download=True)

test_dataset = dsets.MNIST(root='./data', 
                           train=False, 
                           transform=transforms.ToTensor())

'''
STEP 2: MAKING DATASET ITERABLE
'''


batch_size = 64
n_iters = 3000
num_epochs = n_iters / (len(train_dataset) / batch_size)
num_epochs = int(num_epochs)

train_loader = torch.utils.data.DataLoader(dataset=train_dataset, 
                                           batch_size=batch_size, 
                                           shuffle=True)

test_loader = torch.utils.data.DataLoader(dataset=test_dataset, 
                                          batch_size=batch_size, 
                                          shuffle=False)

'''
STEP 3: CREATE MODEL CLASS
'''

class LSTMModel(nn.Module):
    def __init__(self, input_dim, hidden_dim, layer_dim, output_dim):
        super(LSTMModel, self).__init__()
        # Hidden dimensions
        self.hidden_dim = hidden_dim
        
        # Number of hidden layers
        self.layer_dim = layer_dim
        
        # Building your LSTM
        # batch_first=True causes input/output tensors to be of shape
        # (batch_dim, seq_dim, feature_dim)
        self.lstm = nn.LSTM(input_dim, hidden_dim, layer_dim, batch_first=True)
        
        # Readout layer
        self.fc = nn.Linear(hidden_dim, output_dim)
    
    def forward(self, x):
        # Initialize hidden state with zeros
        #######################
        #  USE GPU FOR MODEL  #
        #######################

        if torch.cuda.is_available():
            h0 = Variable(torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).cuda())
        else:
            h0 = Variable(torch.zeros(self.layer_dim, x.size(0), self.hidden_dim))
        
        # Initialize cell state
        if torch.cuda.is_available():
            c0 = Variable(torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).cuda())
        else:
            c0 = Variable(torch.zeros(self.layer_dim, x.size(0), self.hidden_dim))
        
        # One time step
        out, (hn, cn) = self.lstm(x, (h0,c0))
        
        # Index hidden state of last time step
        # out.size() --> 100, 28, 100
        # out[:, -1, :] --> 100, 100 --> just want last time step hidden states! 
        out = self.fc(out[:, -1, :]) 
        # out.size() --> 100, 10
        return out

'''
STEP 4: INSTANTIATE MODEL CLASS
'''
input_dim = 28
hidden_dim = 100
layer_dim = 3  # ONLY CHANGE IS HERE FROM ONE LAYER TO TWO LAYER
output_dim = 10

model = LSTMModel(input_dim, hidden_dim, layer_dim, output_dim)

#######################
#  USE GPU FOR MODEL  #
#######################

if torch.cuda.is_available():
    model.cuda()
    
'''
STEP 5: INSTANTIATE LOSS CLASS
'''
criterion = nn.CrossEntropyLoss()

'''
STEP 6: INSTANTIATE OPTIMIZER CLASS
'''
learning_rate = 0.1

optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  

'''
STEP 7: TRAIN THE MODEL
'''

# Number of steps to unroll
seq_dim = 28  

iter = 0
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # Load images as Variable
        #######################
        #  USE GPU FOR MODEL  #
        #######################
        print('orginal shape: ')
        print(images.shape)
        print(labels.shape)
        if torch.cuda.is_available():
            images = Variable(images.view(-1, seq_dim, input_dim).cuda())
            
            #print(images.shape)
            labels = Variable(labels.cuda())
        else:
            images = Variable(images.view(-1, seq_dim, input_dim))
            labels = Variable(labels)
        
        print('after shape: ')
        print(images.shape)
        print(labels.shape)
        # Clear gradients w.r.t. parameters
        optimizer.zero_grad()
        
        # Forward pass to get output/logits
        # outputs.size() --> 100, 10
        outputs = model(images)
        
        print(outputs.shape)
        # Calculate Loss: softmax --> cross entropy loss
        loss = criterion(outputs, labels)
        
        # Getting gradients w.r.t. parameters
        loss.backward()
        
        # Updating parameters
        optimizer.step()
        
        iter += 1
        
        if iter % 500 == 0:
            # Calculate Accuracy         
            correct = 0
            total = 0
            # Iterate through test dataset
            for images, labels in test_loader:
                #######################
                #  USE GPU FOR MODEL  #
                #######################
                if torch.cuda.is_available():
                    images = Variable(images.view(-1, seq_dim, input_dim).cuda())
                else:
                    images = Variable(images.view(-1, seq_dim, input_dim))
                
                # Forward pass only to get logits/output
                outputs = model(images)
                
                # Get predictions from the maximum value
                _, predicted = torch.max(outputs.data, 1)
                
                # Total number of labels
                total += labels.size(0)
                
                # Total correct predictions
                #######################
                #  USE GPU FOR MODEL  #
                #######################
                if torch.cuda.is_available():
                    correct += (predicted.cpu() == labels.cpu()).sum()
                else:
                    correct += (predicted == labels).sum()
            
            accuracy = 100 * correct / total
            
            # Print Loss
            print('Iteration: . Loss: . Accuracy: '.format(iter, loss.data[0], accuracy))

输出测试形状：

orginal shape: 
torch.Size([64, 1, 28, 28]) #images
torch.Size([64]) #labels
after shape: 
torch.Size([64, 28, 28]) #images
torch.Size([64]) #labels
torch.Size([64, 10])#outputs

参考文献：

1. https://github.com/MagaliDrumare/How-to-learn-PyTorch-NN-CNN-RNN-LSTM