Added task 4 and 5

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
+'''

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