111 lines
3.3 KiB
Python
111 lines
3.3 KiB
Python
import time
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import numpy as np
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import torch
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from torch.nn.functional import *
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from torch.utils.data import Dataset, DataLoader
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from torch import nn
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from torchvision import datasets, transforms
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from tqdm import tqdm
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from utils import *
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import ipdb
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class Model_3_1(nn.Module):
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def __init__(self, num_classes):
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super().__init__()
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self.flatten = nn.Flatten()
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self.fc1 = nn.Linear(in_features=28 * 28, out_features=1024)
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self.fc2 = nn.Linear(in_features=1024, out_features=256)
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self.fc3 = nn.Linear(in_features=256, out_features=num_classes)
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self.activate_fn = relu
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def forward(self, x: torch.Tensor):
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x = self.flatten(x)
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x = self.fc1(x)
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x = self.activate_fn(x)
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x = self.fc2(x)
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x = self.activate_fn(x)
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x = self.fc3(x)
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x = self.activate_fn(x)
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return x
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class Model_3_2(nn.Module):
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def __init__(self, num_classes):
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super().__init__()
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self.flatten = nn.Flatten()
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self.fc1 = nn.Linear(in_features=28 * 28, out_features=1024)
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self.fc2 = nn.Linear(in_features=1024, out_features=256)
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self.fc3 = nn.Linear(in_features=256, out_features=num_classes)
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self.activate_fn = sigmoid
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def forward(self, x: torch.Tensor):
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x = self.flatten(x)
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x = self.fc1(x)
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x = self.activate_fn(x)
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x = self.fc2(x)
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x = self.activate_fn(x)
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x = self.fc3(x)
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x = self.activate_fn(x)
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return x
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class Model_3_3(nn.Module):
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def __init__(self, num_classes):
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super().__init__()
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self.flatten = nn.Flatten()
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self.fc1 = nn.Linear(in_features=28 * 28, out_features=1024)
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self.fc2 = nn.Linear(in_features=1024, out_features=256)
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self.fc3 = nn.Linear(in_features=256, out_features=num_classes)
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self.activate_fn = tanh
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def forward(self, x: torch.Tensor):
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x = self.flatten(x)
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x = self.fc1(x)
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x = self.activate_fn(x)
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x = self.fc2(x)
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x = self.activate_fn(x)
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x = self.fc3(x)
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x = self.activate_fn(x)
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return x
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class Model_3_4(nn.Module):
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def __init__(self, num_classes):
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super().__init__()
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self.flatten = nn.Flatten()
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self.fc1 = nn.Linear(in_features=28 * 28, out_features=1024)
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self.fc2 = nn.Linear(in_features=1024, out_features=256)
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self.fc3 = nn.Linear(in_features=256, out_features=num_classes)
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self.activate_fn = leaky_relu
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def forward(self, x: torch.Tensor):
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x = self.flatten(x)
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x = self.fc1(x)
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x = self.activate_fn(x)
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x = self.fc2(x)
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x = self.activate_fn(x)
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x = self.fc3(x)
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x = self.activate_fn(x)
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return x
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if __name__ == "__main__":
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print("模型1开始训练，激活函数为relu：")
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train_loss_3_1, test_acc_3_1 = train_MNIST_CLS(Model=Model_3_1) # 激活函数为relu
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print("模型2开始训练，激活函数为sigmoid：")
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train_loss_3_2, test_acc_3_2 = train_MNIST_CLS(Model=Model_3_2) # 激活函数为sigmoid
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print("模型3开始训练，激活函数为tanh：")
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train_loss_3_3, test_acc_3_3 = train_MNIST_CLS(Model=Model_3_3) # 激活函数为tanh
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print("模型4开始训练，激活函数为leaky_relu：")
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train_loss_3_4, test_acc_3_4 = train_MNIST_CLS(Model=Model_3_4) # 激活函数为leaky_relu
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