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268 changes: 268 additions & 0 deletions Tasks/daily tasks/Jamcey_V_P/task3.py
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# keeping kernel size to 3 in the convultional layer
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

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, 6, 3)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 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, 16 * 5 * 5)
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=0.001
)

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
print("vola")
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs, 1)
c = (predicted == labels).squeeze()
for i in range(4):
label = labels[i]
class_correct[label] += c[i].item()
class_total[label] += 1


for i in range(10):
print('Accuracy of %5s : %2d %%' % (
classes[i], 100 * class_correct[i] / class_total[i]))


#Accuracy of plane : 53 %
#Accuracy of car : 71 %
#Accuracy of bird : 44 %
#Accuracy of cat : 52 %
#Accuracy of deer : 41 %
#Accuracy of dog : 32 %
#Accuracy of frog : 65 %
#Accuracy of horse : 65 %
#Accuracy of ship : 76 %
#Accuracy of truck : 36 % These accuray are changed to

#Accuracy of plane : 42 %
#Accuracy of car : 39 %
#Accuracy of bird : 18 %
#Accuracy of cat : 3 %
#Accuracy of deer : 6 %
#Accuracy of dog : 27 %
#Accuracy of frog : 64 %
#Accuracy of horse : 31 %
#Accuracy of ship : 46 %
#Accuracy of truck : 47 %

# keeping kernel size to 4 in the convultional layer
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

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, 6, 3)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 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, 16 * 5 * 5)
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=0.001
)

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
print("vola")
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs, 1)
c = (predicted == labels).squeeze()
for i in range(4):
label = labels[i]
class_correct[label] += c[i].item()
class_total[label] += 1


for i in range(10):
print('Accuracy of %5s : %2d %%' % (
classes[i], 100 * class_correct[i] / class_total[i]))


#Accuracy of plane : 40 %
#Accuracy of car : 48 %
#Accuracy of bird : 8 %
#Accuracy of cat : 9 %
#Accuracy of deer : 14 %
#Accuracy of dog : 37 %
#Accuracy of frog : 57 %
#Accuracy of horse : 40 %
#Accuracy of ship : 40 %
Accuracy of truck : 41 %