Initial commit

.gitignore

@@ -0,0 +1 @@
+.venv

.idea/.gitignore

@@ -0,0 +1,8 @@
+# Default ignored files
+/shelf/
+/workspace.xml
+# Editor-based HTTP Client requests
+/httpRequests/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml

.idea/XD.iml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.13" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>

.idea/inspectionProfiles/profiles_settings.xml

@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>

.idea/misc.xml

@@ -0,0 +1,7 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="Black">
+    <option name="sdkName" value="Python 3.13" />
+  </component>
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.13" project-jdk-type="Python SDK" />
+</project>

.idea/modules.xml

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/XD.iml" filepath="$PROJECT_DIR$/.idea/XD.iml" />
+    </modules>
+  </component>
+</project>

.idea/vcs.xml

@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$" vcs="Git" />
+  </component>
+</project>

BN.py

@@ -0,0 +1,21 @@
+import torch.nn as nn
+import torch.optim as optim
+
+# Define CNN Model
+class CatDogClassifier(nn.Module):
+    def __init__(self, img_size=128):
+        super(CatDogClassifier, self).__init__()
+        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
+        self.pool = nn.MaxPool2d(2, 2)
+        self.fc1 = nn.Linear(64 * (img_size//4) * (img_size//4), 128)  # Flatten size depends on image size
+        self.fc2 = nn.Linear(128, 2)  # Output 2 classes (cat or dog)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.pool(self.relu(self.conv1(x)))
+        x = self.pool(self.relu(self.conv2(x)))
+        x = x.view(x.size(0), -1)  # Flatten
+        x = self.relu(self.fc1(x))
+        x = self.fc2(x)
+        return x

caca.py

@@ -0,0 +1,7 @@
+import torch
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+tensor = torch.randn(3, 3).to(device)  # Moves the tensor to GPU
+
+
+print(tensor)
+print(device)