Added examples and README.md

Author: aaharr

convolutional-neural-networks-pytorch/README.md

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+### Examples from "Convolutional Neural Networks using PyTorch"
+
+- examples.py

convolutional-neural-networks-pytorch/examples.py

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+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+from torch.autograd import Variable
+from torchvision.datasets import FashionMNIST
+
+transform = transforms.Compose([transforms.ToTensor(),
+                                transforms.Normalize((0.1307,), (0.3081,))])
+train_dataset = FashionMNIST(root='./data',
+                             train=True,
+                             transform=transform,
+                             download=True
+                             )
+test_dataset = FashionMNIST(root='./data',
+                            train=False,
+                            transform=transform, )
+
+batch_size = 100
+n_iters = 5500
+num_epochs = n_iters / (len(train_dataset) / batch_size)
+num_epochs = int(num_epochs)
+train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
+                                           batch_size=batch_size,
+                                           shuffle=True)
+test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
+                                          batch_size=batch_size,
+                                          shuffle=False)
+
+
+class CNNModel(nn.Module):
+
+    def __init__(self):
+        super(CNNModel, self).__init__()
+        self.cnn1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=5, stride=1, padding=0)
+        self.relu1 = nn.ReLU()
+        self.maxpool1 = nn.MaxPool2d(kernel_size=2)
+        self.cnn2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=5, stride=1, padding=0)
+        self.relu2 = nn.ReLU()
+        self.maxpool2 = nn.MaxPool2d(kernel_size=2)
+        self.dropout = nn.Dropout(p=0.5)
+        self.fc1 = nn.Linear(32 * 4 * 4, 10)
+
+    def forward(self, x):
+        out = self.cnn1(x)
+        out = self.relu1(out)
+        out = self.maxpool1(out)
+        out = self.cnn2(out)
+        out = self.relu2(out)
+        out = self.maxpool2(out)
+        out = out.view(out.size(0), -1)
+        out = self.dropout(out)
+        out = self.fc1(out)
+        return out
+
+
+model = CNNModel()
+criterion = nn.CrossEntropyLoss()
+learning_rate = 0.001
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+
+iter = 0
+losses = []
+for epoch in range(num_epochs):
+    for i, (images, labels) in enumerate(train_loader):
+        images = Variable(images)
+        labels = Variable(labels)
+
+        optimizer.zero_grad()
+
+        outputs = model(images)
+
+        loss = criterion(outputs, labels)
+        losses.append(loss)
+
+        loss.backward()
+        optimizer.step()
+
+        iter += 1
+
+        if iter % 500 == 0:
+            correct = 0
+            total = 0
+
+            for images, labels in test_loader:
+                images = Variable(images)
+
+                outputs = model(images)
+
+                _, predicted = torch.max(outputs.data, 1)
+                total += labels.size(0)
+                correct += (predicted == labels).sum()
+
+            accuracy = 100 * correct / total
+            print('Iteration: {}. Loss: {}. Accuracy: {}'.format(iter, loss.data, accuracy))