From 996f6115ad0d1fa01baa2ce88265bdc6eea27cf0 Mon Sep 17 00:00:00 2001
From: Hari Krishnan U <harikrishnan6336@cet.ac.in>
Date: Wed, 17 Jun 2020 22:09:10 +0530
Subject: [PATCH] Create task4.py

---
 Tasks/daily tasks/Hari Krishnan/task4.py | 141 +++++++++++++++++++++++
 1 file changed, 141 insertions(+)
 create mode 100644 Tasks/daily tasks/Hari Krishnan/task4.py

diff --git a/Tasks/daily tasks/Hari Krishnan/task4.py b/Tasks/daily tasks/Hari Krishnan/task4.py
new file mode 100644
index 0000000..7b5d7b6
--- /dev/null
+++ b/Tasks/daily tasks/Hari Krishnan/task4.py	
@@ -0,0 +1,141 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+import torchvision.transforms as transforms
+import torch.optim as optim
+
+if __name__ == '__main__':
+    transform = transforms.Compose(
+        [
+            transforms.ToTensor(),
+            transforms.Normalize(
+                (0.5, 0.5, 0.5),
+                (0.5, 0.5, 0.5)
+            )
+        ]
+    )
+
+    trainset = torchvision.datasets.CIFAR10(
+        root='./data',
+        train=True,
+        download=False,
+        transform=transform
+    )
+
+    testset = torchvision.datasets.CIFAR10(
+        root='./data',
+        train=False,
+        download=False,
+        transform=transform
+    )
+
+    trainloader = torch.utils.data.DataLoader(
+        trainset,
+        batch_size=4,
+        shuffle=True,
+        num_workers=2
+    )
+
+    testloader = torch.utils.data.DataLoader(
+        testset,
+        batch_size=4,
+        shuffle=False,
+        num_workers=2
+    )
+
+    classes = (
+        'plane', 'car', 'bird', 'cat',
+        'deer', 'dog', 'frog', 'horse', 'ship', 'truck'
+    )
+
+    class Net(nn.Module):
+        def __init__(self):
+            super(Net, self).__init__()
+            self.conv1 = nn.Conv2d(3, 192, 3)
+            self.pool = nn.MaxPool2d(2, 2)
+            self.conv2 = nn.Conv2d(192, 512, 3)
+            self.fc1 = nn.Linear(512 * 6 * 6, 120)
+            self.fc2 = nn.Linear(120, 84)
+            self.fc3 = nn.Linear(84, 10)
+
+        def forward(self, x):
+            x = self.pool(F.relu(self.conv1(x)))
+            x = self.pool(F.relu(self.conv2(x)))
+            x = x.view(-1, 512 * 6 * 6)
+            x = F.relu(self.fc1(x))
+            x = F.relu(self.fc2(x))
+            x = self.fc3(x)
+            return x
+
+    net = Net()
+
+    loss_function = nn.CrossEntropyLoss()
+    optimizer = optim.SGD(
+        net.parameters(),
+        lr=0.03
+
+    )
+
+    for epoch in range(2):
+        running_loss = 0.0
+        for i, data in enumerate(trainloader, 0):
+            # data = (inputs, labels)
+            inputs, labels = data
+            optimizer.zero_grad()
+
+            outputs = net(inputs)
+            loss = loss_function(outputs, labels)
+            loss.backward()
+            optimizer.step()
+
+            running_loss = running_loss + loss.item()
+            if i % 2000 == 1999:
+                print(
+                    '[%d, %5d] loss: %.3f' %
+                    (epoch + 1, i+1, running_loss/2000)
+                )
+                running_loss = 0.0
+
+    correct = 0
+    total = 0
+    with torch.no_grad():
+        for data in testloader:
+            images, labels = data
+            outputs = net(images)
+            _, predicted = torch.max(outputs.data, 1)
+            total += labels.size(0)
+            correct += (predicted == labels).sum().item()
+
+    print('Accuracy of the network on the 10000 test images: %d %%' % (
+        100 * correct / total))
+
+"""
+
+With the given code
+Accuracy : 37%          Loss : 1.789
+With a Learning Rate of 0.0001
+Accuracy : 10%          Loss : 2.302
+With output channels of Conv layers made 4x.
+Accuracy : 40%          Loss : 1.620
+With output channels of Conv layers made 4x and learning rate = 0.003
+Accuracy : 53%          Loss : 1.258
+With output channels of Conv layers made 4x and learning rate = 0.007
+Accuracy : 60%          Loss : 1.105
+With output channels of Conv layers made 4x and learning rate = 0.01
+Accuracy : 24%          Loss : 1.979
+With output channels of Conv layers made 8x and learning rate = 0.008
+Accuracy : 64%          Loss : 1.021
+With output channels of Conv layers made 16x and learning rate = 0.009
+Accuracy : 67%          Loss : 0.932
+With output channels of Conv layers made 32x and learning rate = 0.009
+Accuracy : 68%          Loss : 0.930
+With output channels of Conv layers made 32x and learning rate = 0.009 and kernel size (3,3)
+Accuracy : 68%          Loss : 0.915
+With output channels of Conv layers made 32x and learning rate = 0.009 and kernel size (3,3)
+Accuracy : 67%          Loss : 0.950
+With output channels of Conv layers made 32x and learning rate = 0.01 and kernel size (3,3)
+Accuracy : 68%          Loss : 0.900
+With output channels of Conv layers made 32x and learning rate = 0.03 and kernel size (3,3)
+Accuracy : 69%          Loss : 0.892
+"""