Pytorch-基础入门之LSTM
学习Pytorch的目的就是用LSTM来对舆情的数据进行处理,之后那个项目全部做好会发布出来。LSTM也是很经典的网络了,一种RNN网络,在这里也不做赘述了。
某型的一些说明:
hidden layer dimension is 100 number of hidden layer is 1
这一块的话与上一篇逻辑斯蒂回归使用的是相同的数据集MNIST。
第一部分:构造模型
# Import Libraries import torch import torch.nn as nn from torch.autograd import Variable 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 # LSTM self.lstm = nn.LSTM(input_dim, hidden_dim, layer_dim, batch_first=True) # batch_first=True (batch_dim, seq_dim, feature_dim) # Readout layer self.fc = nn.Linear(hidden_dim, output_dim) def forward(self, x): # Initialize hidden state with zeros h0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_() # Initialize cell state c0 = torch.zeros(self.layer_dim, x.size(0), self.hidden_dim).requires_grad_() # 28 time steps # We need to detach as we are doing truncated backpropagation through time (BPTT) # If we don't, we'll backprop all the way to the start even after going through another batch out, (hn, cn) = self.lstm(x, (h0.detach(), c0.detach())) # 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 input_dim = 28 hidden_dim = 100 layer_dim = 1 output_dim = 10 model = LSTMModel(input_dim, hidden_dim, layer_dim, output_dim) error = nn.CrossEntropyLoss() learning_rate = 0.1 optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
第二部分:训练模型
# Number of steps to unroll seq_dim = 28 loss_list = [] iteration_list = [] accuracy_list = [] count = 0 for epoch in range(num_epochs): for i, (images, labels) in enumerate(train_loader): # Load images as a torch tensor with gradient accumulation abilities images = images.view(-1, seq_dim, input_dim).requires_grad_() # Clear gradients w.r.t. parameters optimizer.zero_grad() # Forward pass to get output/logits # outputs.size 100, 10 outputs = model(images) # Calculate Loss: softmax --> cross entropy loss loss = error(outputs, labels) # Getting gradients loss.backward() # Updating parameters optimizer.step() count += 1 if count % 500 == 0: # Calculate Accuracy correct = 0 total = 0 for images, labels in test_loader: images = 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 correct += (predicted == labels).sum() accuracy = 100 * correct / total loss_list.append(loss.data.item()) iteration_list.append(count) accuracy_list.append(accuracy) # Print Loss print('Iteration: {}. Loss: {}. Accuracy: {}'.format(count, loss.data.item(), accuracy))
结果:
Iteration: 500. Loss: 2.2601425647735596. Accuracy: 19 Iteration: 1000. Loss: 0.9044000506401062. Accuracy: 71 Iteration: 1500. Loss: 0.33562779426574707. Accuracy: 88 Iteration: 2000. Loss: 0.29831066727638245. Accuracy: 92 Iteration: 2500. Loss: 0.20772598683834076. Accuracy: 94 Iteration: 3000. Loss: 0.13703776895999908. Accuracy: 95 Iteration: 3500. Loss: 0.1824885755777359. Accuracy: 95 Iteration: 4000. Loss: 0.021043945103883743. Accuracy: 96 Iteration: 4500. Loss: 0.13939177989959717. Accuracy: 96 Iteration: 5000. Loss: 0.032742198556661606. Accuracy: 96 Iteration: 5500. Loss: 0.1308797001838684. Accuracy: 96
第三部分:可视化展示
# visualization loss plt.plot(iteration_list,loss_list) plt.xlabel("Number of iteration") plt.ylabel("Loss") plt.title("LSTM: Loss vs Number of iteration") plt.show() # visualization accuracy plt.plot(iteration_list,accuracy_list,color = "red") plt.xlabel("Number of iteration") plt.ylabel("Accuracy") plt.title("LSTM: Accuracy vs Number of iteration") plt.savefig('graph.png') plt.show()
结果: