MNIST classification CNN.

computer_vision/image_classification_example_mnist_cnn.py

@@ -0,0 +1,169 @@
+"""
+This Script contains the default EMNIST code for comparison.
+
+Execution Details are available here:
+https://www.kaggle.com/code/dipuk0506/mnist-cnn
+
+The code is improved from:
+    nextjournal.com/gkoehler/pytorch-mnist
+
+@author: Dipu Kabir
+"""
+
+! pip install torch
+
+import torch
+import torchvision
+import torch.nn as nn
+import torch.nn.functional as functional
+import torch.optim as optim
+
+n_epochs = 8
+batch_size_train = 64
+batch_size_test = 1000
+learning_rate = 0.01
+momentum = 0.5
+log_interval = 100
+
+
+torch.backends.cudnn.enabled = False
+
+
+train_loader = torch.utils.data.DataLoader(
+    torchvision.datasets.MNIST(
+        "/files/",
+        train=True,
+        download=True,
+        transform=torchvision.transforms.Compose(
+            [
+                torchvision.transforms.ToTensor(),
+                torchvision.transforms.Normalize((0.1307,), (0.3081,)),
+            ]
+        ),
+    ),
+    batch_size=batch_size_train,
+    shuffle=True,
+)
+
+test_loader = torch.utils.data.DataLoader(
+    torchvision.datasets.MNIST(
+        "/files/",
+        train=False,
+        download=True,
+        transform=torchvision.transforms.Compose(
+            [
+                torchvision.transforms.ToTensor(),
+                torchvision.transforms.Normalize((0.1307,), (0.3081,)),
+            ]
+        ),
+    ),
+    batch_size=batch_size_test,
+    shuffle=True,
+)
+
+examples = enumerate(test_loader)
+batch_idx, (example_data, example_targets) = next(examples)
+
+print(example_data.shape)
+
+import matplotlib.pyplot as plt
+
+fig = plt.figure()
+for i in range(6):
+    plt.subplot(2, 3, i + 1)
+    plt.tight_layout()
+    plt.imshow(example_data[i][0], cmap="gray", interpolation="none")
+    plt.title("Ground Truth: {}".format(example_targets[i]))
+    plt.xticks([])
+    plt.yticks([])
+fig
+
+
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
+        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
+        self.conv2_drop = nn.Dropout2d()
+        self.fc1 = nn.Linear(320, 50)
+        self.fc2 = nn.Linear(50, 10)
+
+    def forward(self, x):
+        x = functional.relu(functional.max_pool2d(self.conv1(x), 2))
+        x = functional.relu(functional.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
+        x = x.view(-1, 320)
+        x = functional.relu(self.fc1(x))
+        x = functional.dropout(x, training=self.training)
+        x = self.fc2(x)
+        return functional.log_softmax(x)
+
+
+network = Net()
+optimizer = optim.SGD(network.parameters(), lr=learning_rate, momentum=momentum)
+
+
+train_losses = []
+train_counter = []
+test_losses = []
+test_counter = [i * len(train_loader.dataset) for i in range(n_epochs + 1)]
+
+
+def train(epoch):
+    network.train()
+    for batch_idx, (data, target) in enumerate(train_loader):
+        optimizer.zero_grad()
+        output = network(data)
+        loss = functional.nll_loss(output, target)
+        loss.backward()
+        optimizer.step()
+        if batch_idx % log_interval == 0:
+            print(
+                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
+                    epoch,
+                    batch_idx * len(data),
+                    len(train_loader.dataset),
+                    100.0 * batch_idx / len(train_loader),
+                    loss.item(),
+                )
+            )
+            train_losses.append(loss.item())
+            train_counter.append(
+                (batch_idx * 64) + ((epoch - 1) * len(train_loader.dataset))
+            )
+
+
+def test():
+    network.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for data, target in test_loader:
+            output = network(data)
+            test_loss += functional.nll_loss(output, target, size_average=False).item()
+            pred = output.data.max(1, keepdim=True)[1]
+            correct += pred.eq(target.data.view_as(pred)).sum()
+    test_loss /= len(test_loader.dataset)
+    test_losses.append(test_loss)
+    print(
+        "\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format(
+            test_loss,
+            correct,
+            len(test_loader.dataset),
+            100.0 * correct / len(test_loader.dataset),
+        )
+    )
+
+
+test()
+for epoch in range(1, n_epochs + 1):
+    train(epoch)
+    test()
+# %%
+
+fig = plt.figure()
+plt.plot(train_counter, train_losses, color="blue")
+plt.scatter(test_counter, test_losses, color="red")
+plt.legend(["Train Loss", "Test Loss"], loc="upper right")
+plt.xlabel("number of training examples seen")
+plt.ylabel("negative log likelihood loss")
+fig