Create README.md

README.md

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+class CNN(nn.Module):
+  def __init__(self):
+    super(CNN, self).__init__()
+    self.relu = nn.ReLU()
+    self.maxpool = nn.MaxPool2d(kernel_size = 2, stride = 2)
+    self.conv1 = nn.Conv2d(3,32,3,stride = 1, padding = 1)
+    self.conv2 = nn.Conv2d(32,64,3,stride = 1, padding = 1)
+    self.conv3 = nn.Conv2d(64,128,3,stride = 1, padding = 1)
+    self.conv4 = nn.Conv2d(128,256,3,stride = 1, padding = 1)
+
+    self.dropout = nn.Dropout(p = 0.5)
+    self.fc1 = nn.Linear(14*14*256, 4096)
+    self.fc2 = nn.Linear(4096,1024)
+    self.fc3 = nn.Linear(1024, 10)
+    
+  def forward(self, x):
+    x = self.maxpool(self.relu(self.conv1(x)))
+    x = self.maxpool(self.relu(self.conv2(x)))
+    x = self.maxpool(self.relu(self.conv3(x)))
+    x = self.maxpool(self.relu(self.conv4(x)))
+
+    x = x.view(-1, 14*14*256)
+    x = self.dropout(self.relu(self.fc1(x)))
+    x = self.dropout(self.relu(self.fc2(x)))
+    x = self.fc3(x)
+
+    return x
+
+model = CNN().to(device)
+
+criterion = nn.CrossEntropyLoss()
+
+optimizer = torch.optim.Adam(model.parameters(), lr = learning_rate)
+
+############## TENSORBOARD ########################
+writer.add_graph(model, example_data.to(device))
+writer.close()