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人工智能系统实战第三期/实战代码/深度学习项目实战/神经网络实现鸢尾花分类/fully_connected_network.py

+import os 
+import os 
+import argparse 
+import sys
+from torch.utils.data import DataLoader
+from tqdm import tqdm 
+import torch
+import torch.optim as optim  
+import torch.nn as nn 
+from data_loader import iris_dataload
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--num_classes', type=int, default=100, help='the number of classes')
+parser.add_argument('--epochs', type=int, default=20, help='the number of training epoch')
+parser.add_argument('--batch_size', type=int, default=16, help='batch_size for training')
+parser.add_argument('--lr', type=float, default=0.005, help='star learning rate')   
+parser.add_argument('--data_path', type=str, default="/mnt/d/Codes/GNN/NN/Iris_data.txt") 
+parser.add_argument('--device', default='cuda', help='device id (i.e. 0 or 0,1 or cpu)')
+opt = parser.parse_args()  
+
+# 初始化神经网络
+class Neuralnetwork(nn.Module):
+    def __init__(self, in_dim, n_hidden_1, n_hidden_2, out_dim):
+        super(Neuralnetwork, self).__init__()
+        self.layer1 = nn.Linear(in_dim, n_hidden_1)
+        self.layer2 = nn.Linear(n_hidden_1, n_hidden_2)
+        self.layer3 = nn.Linear(n_hidden_2, out_dim)
+
+    def forward(self, x):
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        return x
+
+# 定义当前模型的训练环境
+device = torch.device(opt.device if torch.cuda.is_available() else "cpu")
+# device = torch.device("cpu")
+
+# 划分数据集并加载
+custom_dataset = iris_dataload( "./Iris_data.txt") 
+train_size = int(len(custom_dataset) * 0.7)
+validate_size = int(len(custom_dataset) * 0.2)
+test_size = len(custom_dataset) - validate_size - train_size
+train_dataset, validate_dataset, test_dataset = torch.utils.data.random_split(custom_dataset, [train_size, validate_size, test_size])
+ 
+train_loader = DataLoader(train_dataset, batch_size=opt.batch_size, shuffle=False )
+validate_loader = DataLoader(validate_dataset, batch_size=1, shuffle=False )
+test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False ) 
+print("Training set data size:", len(train_loader)*opt.batch_size, ",Validating set data size:", len(validate_loader), ",Testing set data size:", len(test_loader)) 
+
+# 定义推理过程，返回准确率。用于验证阶段和测试阶段
+def infer(model, dataset, device):
+    model.eval()
+    acc_num = 0.0  
+    with torch.no_grad(): 
+        for data in dataset:
+            datas, labels = data
+            outputs = model(datas.to(device))
+            predict_y = torch.max(outputs, dim=1)[1]
+            acc_num += torch.eq(predict_y, labels.to(device)).sum().item()  
+    accuratcy = acc_num / len(dataset)
+    return accuratcy 
+
+# 定义训练，验证和测试过程
+def main(args): 
+    print(args)
+ 
+    model = Neuralnetwork(4, 12, 6, 3).to(device)  # 实例化模型
+    loss_function = nn.CrossEntropyLoss()  # 定义损失函数
+    pg = [p for p in model.parameters() if p.requires_grad]  # 定义模型参数
+    optimizer = optim.Adam(pg, lr=args.lr)  # 定义优化器
+ 
+    # 定义模型权重存储地址
+    save_path = os.path.join(os.getcwd(), 'results/weights')
+    if os.path.exists(save_path) is False:
+        os.makedirs(save_path)
+
+    # 开始训练过程
+    for epoch in range(opt.epochs):
+        ############################################################## train ######################################################
+        model.train() 
+        acc_num = torch.zeros(1).to(device)    # 初始化，用于计算训练过程中预测正确的数量
+        sample_num = 0                         # 初始化，用于记录当前迭代中，已经计算了多少个样本
+        # tqdm是一个进度条显示器，可以在终端打印出现在的训练进度
+        train_bar = tqdm(train_loader, file=sys.stdout, ncols=100)
+        for datas in train_bar :
+            data, label = datas
+            label = label.squeeze(-1)
+            sample_num += data.shape[0]
+
+            optimizer.zero_grad()
+            outputs = model(data.to(device)) # output_shape: [batch_size, num_classes]
+            pred_class = torch.max(outputs, dim=1)[1] # torch.max 返回值是一个tuple，第一个元素是max值，第二个元素是max值的索引。
+            acc_num += torch.eq(pred_class, label.to(device)).sum()
+ 
+            loss = loss_function(outputs, label.to(device)) # 求损失
+            loss.backward() # 自动求导
+            optimizer.step() # 梯度下降
+
+            # print statistics 
+            train_acc = acc_num.item() / sample_num  
+            train_bar.desc = "train epoch[{}/{}] loss:{:.3f}".format(epoch + 1,  opt.epochs, loss)
+
+        ############################################################## validate ###################################################### 
+        val_accurate = infer(model = model, dataset=validate_loader, device=device)
+        print('[epoch %d] train_loss: %.3f  train_acc: %.3f  val_accuracy: %.3f' %  (epoch + 1, loss, train_acc, val_accurate))    
+        torch.save(model.state_dict(), os.path.join(save_path, "NN.pth") )
+
+        # 每次迭代后清空这些指标，重新计算 
+        train_acc = 0.0
+        val_accurate = 0.0 
+    print('Finished Training')
+
+    ################################################################# test ############################################################  
+    test_accurate = infer(model = model, dataset = test_loader, device= device)
+    print(' test_accuracy: %.3f' %  ( test_accurate))  
+
+if __name__ == '__main__': 
+    main(opt)
+
+
+
+ 
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