Add demo code of CNN Mnist

demo_code/3_CNN_MNIST.py

@@ -0,0 +1,99 @@
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+from torchvision import datasets, transforms
+
+# Training settings
+batch_size = 64
+
+# MNIST Dataset
+dataset_path = "../data/mnist"
+train_dataset = datasets.MNIST(root=dataset_path,
+                               train=True,
+                               transform=transforms.ToTensor(),
+                               download=True)
+
+test_dataset = datasets.MNIST(root=dataset_path,
+                              train=False,
+                              transform=transforms.ToTensor())
+
+# Data Loader (Input Pipeline)
+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)
+
+
+# Define the network
+class Net_CNN(nn.Module):
+    def __init__(self):
+        super(Net_CNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 6, 5)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.fc1   = nn.Linear(16*4*4, 120)
+        self.fc2   = nn.Linear(120, 84)
+        self.fc3   = nn.Linear(84, 10)
+
+    def forward(self, x):
+        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
+        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
+        x = x.view(x.size()[0], -1)
+        x = F.relu(self.fc1(x))
+        x = F.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+
+# define optimizer & criterion
+model = Net_CNN()
+optim = torch.optim.Adam(model.parameters(), 0.01)
+criterion = nn.CrossEntropyLoss()
+
+
+# train the network
+for e in range(100):
+    # train
+    model.train()
+    for batch_idx, (data, target) in enumerate(train_loader):
+        data, target = Variable(data), Variable(target)
+
+        out = model(data)
+        loss = criterion(out, target)
+        optim.zero_grad()
+        loss.backward()
+        optim.step()
+
+        if batch_idx % 100 == 0:
+            pred = out.data.max(1, keepdim=True)[1]
+            c = float(pred.eq(target.data.view_as(pred)).cpu().sum() ) /out.size(0)
+
+            print("epoch: %5d, loss: %f, acc: %f" %
+                  ( e +1, loss.data[0], c))
+
+    # test
+    model.eval()
+
+    test_loss = 0.0
+    correct = 0.0
+    for data, target in test_loader:
+        data, target = Variable(data), Variable(target)
+        output = model(data)
+
+        # sum up batch loss
+        test_loss += criterion(output, target).data[0]
+
+        # get the index of the max
+        pred = output.data.max(1, keepdim=True)[1]
+        correct += float(pred.eq(target.data.view_as(pred)).cpu().sum())
+
+    test_loss /= len(test_loader.dataset)
+    print("\nTest set: Average loss: %.4f, Accuracy: %6d/%6d (%4.2f %%)\n" %
+          (test_loss,
+           correct, len(test_loader.dataset),
+           100.0*correct / len(test_loader.dataset)) )

demo_code/3_CNN_MNIST_dropout.py

@@ -0,0 +1,99 @@
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+
+from torchvision import datasets, transforms
+
+# Training settings
+batch_size = 64
+
+# MNIST Dataset
+dataset_path = "../data/mnist"
+train_dataset = datasets.MNIST(root=dataset_path,
+                               train=True,
+                               transform=transforms.ToTensor(),
+                               download=True)
+
+test_dataset = datasets.MNIST(root=dataset_path,
+                              train=False,
+                              transform=transforms.ToTensor())
+
+# Data Loader (Input Pipeline)
+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)
+
+
+# Define the network
+class Net_CNN(nn.Module):
+    def __init__(self):
+        super(Net_CNN, self).__init__()
+        self.conv1 = nn.Conv2d(1, 6, 5)
+        self.conv2 = nn.Conv2d(6, 16, 5)
+        self.conv2_drop = nn.Dropout2d()
+        self.fc1   = nn.Linear(16*4*4, 120)
+        self.fc2   = nn.Linear(120, 10)
+
+    def forward(self, x):
+        x = F.relu(F.max_pool2d(F.relu(self.conv1(x)), (2, 2)))
+        x = F.relu(F.max_pool2d(F.relu(self.conv2_drop(self.conv2(x))), 2))
+        x = x.view(x.size()[0], -1)
+        x = F.relu(self.fc1(x))
+        x = F.dropout(x, training=self.training)
+        x = self.fc2(x)
+        return x
+
+
+# define optimizer & criterion
+model = Net_CNN()
+optim = torch.optim.Adam(model.parameters(), 0.01)
+criterion = nn.CrossEntropyLoss()
+
+
+# train the network
+for e in range(100):
+    # train
+    model.train()
+    for batch_idx, (data, target) in enumerate(train_loader):
+        data, target = Variable(data), Variable(target)
+
+        out = model(data)
+        loss = criterion(out, target)
+        optim.zero_grad()
+        loss.backward()
+        optim.step()
+
+        if batch_idx % 100 == 0:
+            pred = out.data.max(1, keepdim=True)[1]
+            c = float(pred.eq(target.data.view_as(pred)).cpu().sum() ) /out.size(0)
+
+            print("epoch: %5d, loss: %f, acc: %f" %
+                  ( e +1, loss.data[0], c))
+
+    # test
+    model.eval()
+
+    test_loss = 0.0
+    correct = 0.0
+    for data, target in test_loader:
+        data, target = Variable(data), Variable(target)
+        output = model(data)
+
+        # sum up batch loss
+        test_loss += criterion(output, target).data[0]
+
+        # get the index of the max
+        pred = output.data.max(1, keepdim=True)[1]
+        correct += float(pred.eq(target.data.view_as(pred)).cpu().sum())
+
+    test_loss /= len(test_loader.dataset)
+    print("\nTest set: Average loss: %.4f, Accuracy: %6d/%6d (%4.2f %%)\n" %
+          (test_loss,
+           correct, len(test_loader.dataset),
+           100.0*correct / len(test_loader.dataset)) )

demo_code/3_NN_FC_2.py

@@ -0,0 +1,66 @@
+ 
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torch.autograd import Variable
+
+from torchvision import datasets, transforms
+
+# Training settings
+batch_size = 64
+
+# MNIST Dataset
+dataset_path = "../data/mnist"
+train_dataset = datasets.MNIST(root=dataset_path,
+                               train=True,
+                               transform=transforms.ToTensor(),
+                               download=True)
+
+test_dataset = datasets.MNIST(root=dataset_path,
+                              train=False,
+                              transform=transforms.ToTensor())
+
+# Data Loader (Input Pipeline)
+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)
+
+
+# define Network
+seq_net = nn.Sequential(
+    nn.Linear(28*28, 300),
+    nn.ReLU(),
+    nn.Linear(300, 100),
+    nn.ReLU(),
+    nn.Linear(100, 10)
+)
+
+# define optimizer & criterion
+param = seq_net.parameters()
+optim = torch.optim.Adam(param, 0.01)
+criterion = nn.CrossEntropyLoss()
+
+# train the network
+for e in range(100):
+    for batch_idx, (data, target) in enumerate(train_loader):
+        data, target = Variable(data), Variable(target)
+        data = data.view(-1, 784)
+
+        out = seq_net(data)
+        loss = criterion(out, target)
+        optim.zero_grad()
+        loss.backward()
+        optim.step()
+
+        if batch_idx % 100 == 0:
+            pred = out.data.max(1, keepdim=True)[1]
+            c = float(pred.eq(target.data.view_as(pred)).cpu().sum())/out.size(0)
+
+            print("epoch: %5d, loss: %f, acc: %f" %
+                  (e+1, loss.data[0], c))
+