From 2624236aea073b50b4c4ed2dfa47c81b68d26382 Mon Sep 17 00:00:00 2001
From: Gao Enhao <gaoeh@lamda.nju.edu.cn>
Date: Fri, 31 Mar 2023 15:14:39 +0800
Subject: [PATCH] [MNT] move nn.py to examples/models

---
 examples/models/nn.py | 98 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 98 insertions(+)
 create mode 100644 examples/models/nn.py

diff --git a/examples/models/nn.py b/examples/models/nn.py
new file mode 100644
index 0000000..1977734
--- /dev/null
+++ b/examples/models/nn.py
@@ -0,0 +1,98 @@
+# coding: utf-8
+# ================================================================#
+#   Copyright (C) 2021 Freecss All rights reserved.
+#
+#   File Name     ：lenet5.py
+#   Author        ：freecss
+#   Email         ：karlfreecss@gmail.com
+#   Created Date  ：2021/03/03
+#   Description   ：
+#
+# ================================================================#
+
+
+import torch
+import numpy as np
+from torch import nn
+from torch.nn import functional as F
+
+
+class LeNet5(nn.Module):
+    def __init__(self, num_classes=10, image_size=(28, 28)):
+        super().__init__()
+        self.conv1 = nn.Conv2d(1, 6, 3, padding=1)
+        self.conv2 = nn.Conv2d(6, 16, 3)
+        self.conv3 = nn.Conv2d(16, 16, 3)
+
+        feature_map_size = (np.array(image_size) // 2 - 2) // 2 - 2
+        num_features = 16 * feature_map_size[0] * feature_map_size[1]
+
+        self.fc1 = nn.Linear(num_features, 120)
+        self.fc2 = nn.Linear(120, 84)
+        self.fc3 = nn.Linear(84, num_classes)
+
+    def forward(self, x):
+        """前向传播函数"""
+        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
+        x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2))
+        x = F.relu(self.conv3(x))
+        x = x.view(-1, self.num_flat_features(x))
+        # print(x.size())
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+    def num_flat_features(self, x):
+        size = x.size()[1:]
+        num_features = 1
+        for s in size:
+            num_features *= s
+        return num_features
+
+
+class SymbolNet(nn.Module):
+    def __init__(self, num_classes=4, image_size=(28, 28, 1)):
+        super(SymbolNet, self).__init__()
+        self.conv1 = nn.Sequential(
+            nn.Conv2d(1, 32, 5, stride=1),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+            nn.BatchNorm2d(32, momentum=0.99, eps=0.001),
+        )
+        self.conv2 = nn.Sequential(
+            nn.Conv2d(32, 64, 5, padding=2, stride=1),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2),
+            nn.BatchNorm2d(64, momentum=0.99, eps=0.001),
+        )
+
+        num_features = 64 * (image_size[0] // 4 - 1) * (image_size[1] // 4 - 1)
+        self.fc1 = nn.Sequential(nn.Linear(num_features, 120), nn.ReLU())
+        self.fc2 = nn.Sequential(nn.Linear(120, 84), nn.ReLU())
+        self.fc3 = nn.Sequential(nn.Linear(84, num_classes), nn.Softmax(dim=1))
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.conv2(x)
+        x = torch.flatten(x, 1)
+        x = self.fc1(x)
+        x = self.fc2(x)
+        x = self.fc3(x)
+        return x
+
+
+class SymbolNetAutoencoder(nn.Module):
+    def __init__(self, num_classes=4, image_size=(28, 28, 1)):
+        super(SymbolNetAutoencoder, self).__init__()
+        self.base_model = SymbolNet(num_classes, image_size)
+        self.fc1 = nn.Sequential(nn.Linear(num_classes, 100), nn.ReLU())
+        self.fc2 = nn.Sequential(
+            nn.Linear(100, image_size[0] * image_size[1]), nn.ReLU()
+        )
+
+    def forward(self, x):
+        x = self.base_model(x)
+        x = self.fc1(x)
+        x = self.fc2(x)
+        return x