Upload a6_model_yasir.py

a6_model_yasir.py

@@ -0,0 +1,67 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision
+import torchvision.transforms as transforms
+from torchvision import datasets, transforms
+import os
+from torchvision.datasets import ImageFolder
+ 
+ 
+class model(nn.Module):
+    def __init__(self):
+        super(model, self).__init__()  # Use the correct class name
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=5, padding=2, stride=1)
+        self.relu1 = nn.ReLU()
+        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
+ 
+        self.conv2 = nn.Conv2d(64, 128, kernel_size=5, padding=2)
+        self.relu2 = nn.ReLU()
+        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+ 
+        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
+        self.relu3 = nn.ReLU()
+        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
+ 
+        self.conv4 = nn.Conv2d(256, 384, kernel_size=3, padding=1)
+        self.relu4 = nn.ReLU()
+ 
+        self.conv5 = nn.Conv2d(384, 256, kernel_size=3, padding=1)
+        self.relu5 = nn.ReLU()
+ 
+        self.adaptive_pool = nn.AdaptiveAvgPool2d((3, 3))
+ 
+        self.flatten = nn.Flatten()
+ 
+        self.dropout1 = nn.Dropout(p=0.1)
+        self.fc1 = nn.Linear(256 * 3 * 3, 1024)
+        self.relu6 = nn.ReLU()
+ 
+        self.dropout2 = nn.Dropout(p=0.1)
+        self.fc2 = nn.Linear(1024, 512)
+        self.relu7 = nn.ReLU()
+ 
+        self.fc3 = nn.Linear(512, 200)
+ 
+        self.apply(self._init_weights)
+ 
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=0.01)
+            if module.bias is not None:
+                module.bias.data.zero_()
+ 
+    def forward(self, x):
+        x = self.relu1(self.conv1(x))
+        x = self.pool1(x)
+        x = self.relu2(self.conv2(x))
+        x = self.pool2(x)
+        x = self.relu3(self.conv3(x))
+        x = self.pool3(x)
+        x = self.relu4(self.conv4(x))
+        x = self.relu5(self.conv5(x))
+        x = self.adaptive_pool(x)
+        x = self.flatten(x)
+        x = self.dropout1(self.relu6(self.fc1(x)))
+        x = self.dropout2(self.relu7(self.fc2(x)))
+        return x