Added expModel.py with 75.58% accuracy at 33 epochs

.gitignore

@@ -0,0 +1,163 @@
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expModel.py

@@ -0,0 +1,244 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader, Dataset
+
+from cifar10 import Cifar10
+from CustomCIFAR10Dataset import CustomCIFAR10Dataset
+
+
+# create dataset builder instance
+cifar10_builder = Cifar10()
+# downloads the dataset
+cifar10_builder.download_and_prepare()
+
+# generate the dataset ('train', 'test' portion)
+train_data = cifar10_builder.as_dataset(split='train')
+test_data = cifar10_builder.as_dataset(split='test')
+
+train_images = train_data["img"]
+train_labels = train_data["label"]
+
+test_images = test_data["img"]
+test_labels = test_data["label"]
+
+# Cifar10 classes
+classes =  ("airplane", "automobile", "bird", "cat", "deer",
+            "dog", "frog", "horse", "ship", "truck")
+
+# PARAMETERS
+# batch size during training
+batch_size = 128
+
+# image size
+img_size = 32
+
+# number of channels in image (3, because RGB in this case)
+nc = 3
+
+# output size (10 classes)
+output = len(classes)
+
+# Num of GPUs (pick 0 for CPU)
+ngpu = 1
+
+# number of workers
+nw = 0
+
+# number of training epochs
+num_epochs = 33 
+
+# learning rate
+learning_rate = 0.0022
+
+# chooses which device to use
+device = torch.device("cuda:0" if (torch.cuda.is_available()) and (ngpu > 0) else "cpu")
+
+# transforms for image. CONVERT TO TENSOR VERY IMPORTANT, OTHERWISE DATALOADER WON"T ACCEPT IMAGE
+transform = transforms.Compose([
+    transforms.Resize((32, 32)),                # Resize the image to 32x32 (required for CIFAR-10)
+    transforms.RandomResizedCrop(32, scale=(0.8, 0.8)),    # Random crop (image augmentation)
+    transforms.RandomHorizontalFlip(),          # Random hoizontal flip (image augmentation) 
+    transforms.ToTensor(),                      # Convert PIL Image to a tensor
+    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),  # Normalize the image to [-1, 1]
+])
+
+# We use our custsom cifar10 dataset class to convert the dataset to a format that the torch dataloader can use
+train_ds = CustomCIFAR10Dataset(train_data["img"], train_data["label"], transform=transform)
+test_ds = CustomCIFAR10Dataset(test_data["img"], test_data["label"], transform=transform)
+
+# LOADERS FOR DATASET
+train_loader = DataLoader(train_ds, batch_size, shuffle=True, num_workers=nw)
+test_loader = DataLoader(test_ds, batch_size, shuffle=True, num_workers=nw)
+
+
+# The nueral net class
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+
+        self.network = nn.Sequential(
+            # first 2 concolutional layers
+            nn.Conv2d(nc, 16, kernel_size=3, stride=1, padding=1),          # a convoltional layer with 3 input channels, 16 output channels,
+                                                                            # a kernel size of 3, a stride of 1, and padding of 1
+            nn.BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=1, stride=1),
+            nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),
+            nn.ReLU(),
+            nn.MaxPool2d(1, 1),
+            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=1, stride=1),
+            nn.Conv2d(64, 32, kernel_size=3, stride=1, padding=1),
+            nn.BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=1, stride=1),
+            nn.Conv2d(32, 16, kernel_size=3, stride=1, padding=1),
+
+            # max pooling layers
+            nn.MaxPool2d(kernel_size=2, stride=2),                          # a max pooling layer with kernel size of 3 and stride of 2
+                                                                            # helps reduce spatial dimensions of feature maps
+            nn.Flatten(),
+            nn.Linear(16 * 16 * 16, 64),                                    # adjust the input size based on the output of the last conv layer
+            nn.Linear(64, 128),
+            nn.Dropout(0.2),
+            nn.Linear(128, 16),
+            nn.Linear(16, output),
+        )
+
+
+    def forward(self, x):
+        return self.network(x)
+    
+# creates instance of the model
+model = Net()
+
+print("model parameters: ", model.parameters)
+
+# create the optimizer and criterion
+criterion = nn.CrossEntropyLoss()
+# Adam optimizer yields much better results than SGD
+optimizer = optim.Adam(model.parameters(), lr=learning_rate, weight_decay=0.001)
+
+# moves model to device (ie. cpu/gpu)
+model.to(device)
+
+# accuracy array (for plotting)
+accuracyArr = []
+
+print("started training")
+for epoch in range(num_epochs):
+    model.train()           # set model to training mode (important when using dropout or batch normalization)
+
+    running_loss = 0.0
+    for batch_idx, (images, labels) in enumerate(train_loader):
+        inputs = images.to(device)
+        labels = labels.to(device)
+        # print("print inputs shape: ", inputs.shape)
+
+        optimizer.zero_grad()       # reset gradients
+
+        # forward pass
+        predictions = model(inputs)
+
+        # compute loss
+        loss = criterion(predictions, labels)
+
+        # Backpropogation
+        loss.backward()
+
+        # update models parameters
+        optimizer.step()
+
+        # print statistics
+        running_loss += loss.item()
+        
+    print(f"epoch: {epoch + 1}/{num_epochs} Loss: {running_loss}")
+
+    # After training, evaluate the model on the test dataset to get final performance metrics
+    model.eval()  # Set the model to evaluation mode (important when using dropout or batch normalization)
+    correct = 0
+    total = 0
+
+    with torch.no_grad():
+        for batch_idx, (images, labels) in enumerate(test_loader):
+            images = images.to(device)
+            labels = labels.to(device)
+
+            # Forward pass
+            predictions = model(images)
+
+            # Compute evaluation metrics (e.g., accuracy, precision, recall, etc.)
+            # get predicted class for each image
+            _, predicted = torch.max(predictions.data, 1)
+
+            # Count the total number of labels in the test dataset
+            total += labels.size(0)
+
+            # Count the number of correct predictions
+            correct += (predicted == labels).sum().item()
+
+    # calculate the accuracy
+    accuracy = correct/total
+    accuracyArr.append(accuracy)
+    print(f"Accuracy on the test dataset: {accuracy:.2%}, epoch: {epoch + 1}")
+
+
+# After training, evaluate the model on the test dataset to get final performance metrics
+model.eval()  # Set the model to evaluation mode (important when using dropout or batch normalization)
+correct = 0
+total = 0
+
+with torch.no_grad():
+    for batch_idx, (images, labels) in enumerate(test_loader):
+        images = images.to(device)
+        labels = labels.to(device)
+
+        # Forward pass
+        predictions = model(images)
+
+        # Compute evaluation metrics (e.g., accuracy, precision, recall, etc.)
+        # get predicted class for each image
+        _, predicted = torch.max(predictions.data, 1)
+
+        # Count the total number of labels in the test dataset
+        total += labels.size(0)
+
+        # Count the number of correct predictions
+        correct += (predicted == labels).sum().item()
+
+# calculate the accuracy
+accuracy = correct/total
+print(f"Accuracy on the test dataset: {accuracy:.2%}")
+
+
+## IMPROVEMENTS/DEGREDATIONS ##
+# BASELINE: ~51-54%
+
+# After AutoAugment(CIFAR10):  ~40%
+
+# After Dropout: ~51-52%
+
+# After adding another fully connected layer (64 in, 16 out): ~50-51%
+
+# After adding weight decay to optimizer: (0.01): ~51+%
+
+# ADDED: After adding all layers to nn.Sequential: ~55-57%
+
+# ADDED: After using optim.Adam instead of optim.SGD: ~61-62%
+
+# ADDED: After mimicking VGG16 architecture with: Conv2d -> ReLU -> MaxPool -> REPEAT
+
+# MAX CURRENT ACCURACY: 72.71%
+
+# import numpy as np
+# import matplotlib.pyplot as plt
+# y = np.array(accuracyArr)
+# x = num_epochs
+# plt.plot(x, y)
+# plt.show()

model.py

@@ -1,12 +1,9 @@
-import os
-import datasets
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import torchvision.transforms as transforms
 from torch.utils.data import DataLoader, Dataset
-from PIL import Image
 
 from cifar10 import Cifar10
 from CustomCIFAR10Dataset import CustomCIFAR10Dataset
@@ -148,7 +145,7 @@ for epoch in range(num_epochs):
             print(f'[{epoch + 1}, {batch_idx + 1:5d}] loss: {running_loss / 2000:.3f}')
             running_loss = 0.0
         
-    print(f"epoch: {epoch}/{num_epochs}")
+    print(f"epoch: {epoch + 1}/{num_epochs}")
         
 print("finished training")
 
@@ -196,4 +193,4 @@ print(f"Accuracy on the test dataset: {accuracy:.2%}")
 
 # ADDED: After using optim.Adam instead of optim.SGD: ~61-62%
 
-# ADDED: After adding a nn.Linear(64, 16): ~62-63+%
+# MAX Observed accuracy: 63.20%