diff --git a/Tasks/daily tasks/Jaseem ck/Task_1.py b/Tasks/daily tasks/Jaseem ck/Task_2.py similarity index 100% rename from Tasks/daily tasks/Jaseem ck/Task_1.py rename to Tasks/daily tasks/Jaseem ck/Task_2.py diff --git a/Tasks/daily tasks/Jaseem ck/Task_2.ipynb b/Tasks/daily tasks/Jaseem ck/Task_3.ipynb similarity index 100% rename from Tasks/daily tasks/Jaseem ck/Task_2.ipynb rename to Tasks/daily tasks/Jaseem ck/Task_3.ipynb diff --git a/Tasks/daily tasks/Jaseem ck/Task_4.py b/Tasks/daily tasks/Jaseem ck/Task_4.py new file mode 100644 index 0000000..e4cb2c2 --- /dev/null +++ b/Tasks/daily tasks/Jaseem ck/Task_4.py @@ -0,0 +1,152 @@ +#Task 4 +#Run these codes in colab + +#------------------------------------------------------------------------------------ +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, 64, 3) + self.conv2 = nn.Conv2d(64, 128, 3) + self.conv3 = nn.Conv2d(128, 256, 3) + self.pool = nn.MaxPool2d(2, 2) + self.fc1 = nn.Linear(64 * 4 * 4, 128) + self.fc2 = nn.Linear(128, 256) + self.fc3 = nn.Linear(256, 10) + + def forward(self, x): + x = self.pool(F.relu(self.conv1(x))) + x = self.pool(F.relu(self.conv2(x))) + x = self.pool(F.relu(self.conv3(x))) + x = x.view(-1, 64 * 4 * 4) + 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=0.01 +) + +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") + +#------------------------------------------------------------------------------------ + +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)) + +#------------------------------------------------------------------------------------ + +''' +Epochs = 2; Batch_size = 4; lr = 0.001; loss = 1.818; accuracy = 34 + +Epochs = 2; Batch_size = 10; lr = 0.001; loss = 2.268; accuracy = 22 + +Epochs = 2; Batch_size = 1; lr = 0.001; loss = 1.392; accuracy = 51 + +Epochs = 2; Batch_size = 4; lr = 0.01; loss = 1.259; accuracy = 55 + +Epochs = 2; Batch_size = 4; lr = 0.1; loss = 1.985; accuracy = 22 + +Epochs = 10; Batch_size = 4; lr = 0.01; loss = 0.848; accuracy = 62 + +Epochs = 20; Batch_size = 4; lr = 0.01; loss = 0.726; accuracy = 58 + + +--- + +with adam optimizer: +Epochs = 20; Batch_size = 4; lr = 0.01; loss = 2.307; accuracy = 10 + + + +--- +Epochs = 2; Batch_size = 4; lr = 0.01; output_channel=24; kernel_size=5; Accuracy = 63 + +Epochs = 2; Batch_size = 4; lr = 0.01; output_channel=32; kernel_size=5; Accuracy = 59 + +Epochs = 2; Batch_size = 4; lr = 0.01; output_channel=256; kernel_size=3; Accuracy = 59 +''' diff --git a/Tasks/daily tasks/Jaseem ck/Task_5.py b/Tasks/daily tasks/Jaseem ck/Task_5.py new file mode 100644 index 0000000..25cfe9a --- /dev/null +++ b/Tasks/daily tasks/Jaseem ck/Task_5.py @@ -0,0 +1,52 @@ +#Task 5 +#Run these codes in colab + +#-------------------------------------------------------------------------------- +import os +os.chdir('/content/drive/My Drive/Colab Notebooks') +print(os.getcwd()) +#-------------------------------------------------------------------------------- + +#upload an image in the following directory in your google drive +path = "/content/drive/My Drive/Colab Notebooks/photo.jpg" + +#-------------------------------------------------------------------------------- + +import torch +from PIL import Image +from torchvision import transforms +import torchvision.transforms.functional as F + +transform = transforms.Compose([ +transforms.Resize(300), +transforms.CenterCrop(200), +transforms.ColorJitter(brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5), +transforms.RandomRotation((-60,60), resample=False, expand=False, center=None, fill=None), +transforms.RandomHorizontalFlip(), +transforms.RandomVerticalFlip(), + transforms.ToTensor(), + transforms.Normalize( + (0.5, 0.5, 0.5), + (0.5, 0.5, 0.5) + ), + + +]) + + +img=Image.open(path) + +img = transform(img) + +a = F.to_pil_image(img) +b = F.to_grayscale(a, num_output_channels=1) + +#-------------------------------------------------------------------------------- + +#a.show() +a + +#-------------------------------------------------------------------------------- + +#b.show() +b