Update tutorials for pytorch 0.4.0

tutorials/01-basics/feedforward_neural_network/main.py

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+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Hyper-parameters 
+input_size = 784
+hidden_size = 500
+num_classes = 10
+num_epochs = 5
+batch_size = 100
+learning_rate = 0.001
+
+# MNIST dataset 
+train_dataset = torchvision.datasets.MNIST(root='../../data', 
+                                           train=True, 
+                                           transform=transforms.ToTensor(),  
+                                           download=True)
+
+test_dataset = torchvision.datasets.MNIST(root='../../data', 
+                                          train=False, 
+                                          transform=transforms.ToTensor())
+
+# Data loader
+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)
+
+# Fully connected neural network with one hidden layer
+class NeuralNet(nn.Module):
+    def __init__(self, input_size, hidden_size, num_classes):
+        super(NeuralNet, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size) 
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(hidden_size, num_classes)  
+    
+    def forward(self, x):
+        out = self.fc1(x)
+        out = self.relu(out)
+        out = self.fc2(out)
+        return out
+
+model = NeuralNet(input_size, hidden_size, num_classes).to(device)
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)  
+
+# Train the model
+total_step = len(train_loader)
+for epoch in range(num_epochs):
+    for i, (images, labels) in enumerate(train_loader):  
+        # Move tensors to the configured device
+        images = images.reshape(-1, 28*28).to(device)
+        labels = labels.to(device)
+        
+        # Forward pass
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+        
+        # Backward and optimize
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        
+        if (i+1) % 100 == 0:
+            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
+                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))
+
+# Test the model
+# In test phase, we don't need to compute gradients (for memory efficiency)
+with torch.no_grad():
+    correct = 0
+    total = 0
+    for images, labels in test_loader:
+        images = images.reshape(-1, 28*28).to(device)
+        labels = labels.to(device)
+        outputs = model(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: {} %'.format(100 * correct / total))
+
+# Save the model checkpoint
+torch.save(model.state_dict(), 'model.ckpt')

tutorials/01-basics/linear_regression/main.py

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+import torch
+import torch.nn as nn
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+# Hyper-parameters
+input_size = 1
+output_size = 1
+num_epochs = 60
+learning_rate = 0.001
+
+# Toy dataset
+x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168], 
+                    [9.779], [6.182], [7.59], [2.167], [7.042], 
+                    [10.791], [5.313], [7.997], [3.1]], dtype=np.float32)
+
+y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573], 
+                    [3.366], [2.596], [2.53], [1.221], [2.827], 
+                    [3.465], [1.65], [2.904], [1.3]], dtype=np.float32)
+
+# Linear regression model
+model = nn.Linear(input_size, output_size)
+
+# Loss and optimizer
+criterion = nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  
+
+# Train the model
+for epoch in range(num_epochs):
+    # Convert numpy arrays to torch tensors
+    inputs = torch.from_numpy(x_train)
+    targets = torch.from_numpy(y_train)
+
+    # Forward pass
+    outputs = model(inputs)
+    loss = criterion(outputs, targets)
+    
+    # Backward and optimize
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+    
+    if (epoch+1) % 5 == 0:
+        print ('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))
+
+# Plot the graph
+predicted = model(torch.from_numpy(x_train)).detach().numpy()
+plt.plot(x_train, y_train, 'ro', label='Original data')
+plt.plot(x_train, predicted, label='Fitted line')
+plt.legend()
+plt.show()
+
+# Save the model checkpoint
+torch.save(model.state_dict(), 'model.ckpt')

tutorials/01-basics/logistic_regression/main.py

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+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+
+
+# Hyper-parameters 
+input_size = 784
+num_classes = 10
+num_epochs = 5
+batch_size = 100
+learning_rate = 0.001
+
+# MNIST dataset (images and labels)
+train_dataset = torchvision.datasets.MNIST(root='../../data', 
+                                           train=True, 
+                                           transform=transforms.ToTensor(),
+                                           download=True)
+
+test_dataset = torchvision.datasets.MNIST(root='../../data', 
+                                          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)
+
+# Logistic regression model
+model = nn.Linear(input_size, num_classes)
+
+# Loss and optimizer
+# nn.CrossEntropyLoss() computes softmax internally
+criterion = nn.CrossEntropyLoss()  
+optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  
+
+# Train the model
+total_step = len(train_loader)
+for epoch in range(num_epochs):
+    for i, (images, labels) in enumerate(train_loader):
+        # Reshape images to (batch_size, input_size)
+        images = images.reshape(-1, 28*28)
+        
+        # Forward pass
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+        
+        # Backward and optimize
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        
+        if (i+1) % 100 == 0:
+            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
+                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))
+
+# Test the model
+# In test phase, we don't need to compute gradients (for memory efficieny)
+with torch.no_grad():
+    correct = 0
+    total = 0
+    for images, labels in test_loader:
+        images = images.reshape(-1, 28*28)
+        outputs = model(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum()
+
+    print('Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
+
+# Save the model checkpoint
+torch.save(model.state_dict(), 'model.ckpt')

tutorials/01-basics/pytorch_basics/main.py

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+import torch 
+import torchvision
+import torch.nn as nn
+import numpy as np
+import torchvision.transforms as transforms
+
+
+# ================================================================== #
+#                         Table of Contents                          #
+# ================================================================== #
+
+# 1. Basic autograd example 1               (Line 25 to 39)
+# 2. Basic autograd example 2               (Line 46 to 83)
+# 3. Loading data from numpy                (Line 90 to 97)
+# 4. Input pipline                          (Line 104 to 129)
+# 5. Input pipline for custom dataset       (Line 136 to 156)
+# 6. Pretrained model                       (Line 163 to 176)
+# 7. Save and load model                    (Line 183 to 189) 
+
+
+# ================================================================== #
+#                     1. Basic autograd example 1                    #
+# ================================================================== #
+
+# Create tensors.
+x = torch.tensor(1, requires_grad=True)
+w = torch.tensor(2, requires_grad=True)
+b = torch.tensor(3, requires_grad=True)
+
+# Build a computational graph.
+y = w * x + b    # y = 2 * x + 3
+
+# Compute gradients.
+y.backward()
+
+# Print out the gradients.
+print(x.grad)    # x.grad = 2 
+print(w.grad)    # w.grad = 1 
+print(b.grad)    # b.grad = 1 
+
+
+# ================================================================== #
+#                    2. Basic autograd example 2                     #
+# ================================================================== #
+
+# Create tensors of shape (10, 3) and (10, 2).
+x = torch.randn(10, 3)
+y = torch.randn(10, 2)
+
+# Build a fully connected layer.
+linear = nn.Linear(3, 2)
+print ('w: ', linear.weight)
+print ('b: ', linear.bias)
+
+# Build loss function and optimizer.
+criterion = nn.MSELoss()
+optimizer = torch.optim.SGD(linear.parameters(), lr=0.01)
+
+# Forward pass.
+pred = linear(x)
+
+# Compute loss.
+loss = criterion(pred, y)
+print('loss: ', loss.item())
+
+# Backward pass.
+loss.backward()
+
+# Print out the gradients.
+print ('dL/dw: ', linear.weight.grad) 
+print ('dL/db: ', linear.bias.grad)
+
+# 1-step gradient descent.
+optimizer.step()
+
+# You can also perform gradient descent at the low level.
+# linear.weight.data.sub_(0.01 * linear.weight.grad.data)
+# linear.bias.data.sub_(0.01 * linear.bias.grad.data)
+
+# Print out the loss after 1-step gradient descent.
+pred = linear(x)
+loss = criterion(pred, y)
+print('loss after 1 step optimization: ', loss.item())
+
+
+# ================================================================== #
+#                     3. Loading data from numpy                     #
+# ================================================================== #
+
+# Create a numpy array.
+x = np.array([[1, 2], [3, 4]])
+
+# Convert the numpy array to a torch tensor.
+y = torch.from_numpy(x)
+
+# Convert the torch tensor to a numpy array.
+z = y.numpy()
+
+
+# ================================================================== #
+#                         4. Input pipline                           #
+# ================================================================== #
+
+# Download and construct CIFAR-10 dataset.
+train_dataset = torchvision.datasets.CIFAR10(root='../../data/',
+                                             train=True, 
+                                             transform=transforms.ToTensor(),
+                                             download=True)
+
+# Fetch one data pair (read data from disk).
+image, label = train_dataset[0]
+print (image.size())
+print (label)
+
+# Data loader (this provides queues and threads in a very simple way).
+train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
+                                           batch_size=64, 
+                                           shuffle=True)
+
+# When iteration starts, queue and thread start to load data from files.
+data_iter = iter(train_loader)
+
+# Mini-batch images and labels.
+images, labels = data_iter.next()
+
+# Actual usage of the data loader is as below.
+for images, labels in train_loader:
+    # Training code should be written here.
+    pass
+
+
+# ================================================================== #
+#                5. Input pipline for custom dataset                 #
+# ================================================================== #
+
+# You should your build your custom dataset as below.
+class CustomDataset(torch.utils.data.Dataset):
+    def __init__(self):
+        # TODO
+        # 1. Initialize file paths or a list of file names. 
+        pass
+    def __getitem__(self, index):
+        # TODO
+        # 1. Read one data from file (e.g. using numpy.fromfile, PIL.Image.open).
+        # 2. Preprocess the data (e.g. torchvision.Transform).
+        # 3. Return a data pair (e.g. image and label).
+        pass
+    def __len__(self):
+        # You should change 0 to the total size of your dataset.
+        return 0 
+
+# You can then use the prebuilt data loader. 
+custom_dataset = CustomDataset()
+train_loader = torch.utils.data.DataLoader(dataset=custom_dataset,
+                                           batch_size=64, 
+                                           shuffle=True)
+
+
+# ================================================================== #
+#                        6. Pretrained model                         #
+# ================================================================== #
+
+# Download and load the pretrained ResNet-18.
+resnet = torchvision.models.resnet18(pretrained=True)
+
+# If you want to finetune only the top layer of the model, set as below.
+for param in resnet.parameters():
+    param.requires_grad = False
+
+# Replace the top layer for finetuning.
+resnet.fc = nn.Linear(resnet.fc.in_features, 100)  # 100 is an example.
+
+# Forward pass.
+images = torch.randn(64, 3, 224, 224)
+outputs = resnet(images)
+print (outputs.size())     # (64, 100)
+
+
+# ================================================================== #
+#                      7. Save and load the model                    #
+# ================================================================== #
+
+# Save and load the entire model.
+torch.save(resnet, 'model.ckpt')
+model = torch.load('model.ckpt')
+
+# Save and load only the model parameters (recommended).
+torch.save(resnet.state_dict(), 'params.ckpt')
+resnet.load_state_dict(torch.load('params.ckpt'))

tutorials/02-intermediate/bidirectional_recurrent_neural_network/main.py

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+import torch 
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Hyper-parameters
+sequence_length = 28
+input_size = 28
+hidden_size = 128
+num_layers = 2
+num_classes = 10
+batch_size = 100
+num_epochs = 2
+learning_rate = 0.003
+
+# MNIST dataset
+train_dataset = torchvision.datasets.MNIST(root='../../data/',
+                                           train=True, 
+                                           transform=transforms.ToTensor(),
+                                           download=True)
+
+test_dataset = torchvision.datasets.MNIST(root='../../data/',
+                                          train=False, 
+                                          transform=transforms.ToTensor())
+
+# Data loader
+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)
+
+# Bidirectional recurrent neural network (many-to-one)
+class BiRNN(nn.Module):
+    def __init__(self, input_size, hidden_size, num_layers, num_classes):
+        super(BiRNN, self).__init__()
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bidirectional=True)
+        self.fc = nn.Linear(hidden_size*2, num_classes)  # 2 for bidirection
+    
+    def forward(self, x):
+        # Set initial states
+        h0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(device) # 2 for bidirection 
+        c0 = torch.zeros(self.num_layers*2, x.size(0), self.hidden_size).to(device)
+        
+        # Forward propagate LSTM
+        out, _ = self.lstm(x, (h0, c0))  # out: tensor of shape (batch_size, seq_length, hidden_size*2)
+        
+        # Decode the hidden state of the last time step
+        out = self.fc(out[:, -1, :])
+        return out
+
+model = BiRNN(input_size, hidden_size, num_layers, num_classes).to(device)
+
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+    
+# Train the model
+total_step = len(train_loader)
+for epoch in range(num_epochs):
+    for i, (images, labels) in enumerate(train_loader):
+        images = images.reshape(-1, sequence_length, input_size).to(device)
+        labels = labels.to(device)
+        
+        # Forward pass
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+        
+        # Backward and optimize
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        
+        if (i+1) % 100 == 0:
+            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
+                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))
+
+# Test the model
+with torch.no_grad():
+    correct = 0
+    total = 0
+    for images, labels in test_loader:
+        images = images.reshape(-1, sequence_length, input_size).to(device)
+        labels = labels.to(device)
+        outputs = model(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+    print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total)) 
+
+# Save the model checkpoint
+torch.save(model.state_dict(), 'model.ckpt')

tutorials/02-intermediate/convolutional_neural_network/main.py

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+import torch 
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+
+
+# Device configuration
+device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
+
+# Hyper parameters
+num_epochs = 5
+num_classes = 10
+batch_size = 100
+learning_rate = 0.001
+
+# MNIST dataset
+train_dataset = torchvision.datasets.MNIST(root='../../data/',
+                                           train=True, 
+                                           transform=transforms.ToTensor(),
+                                           download=True)
+
+test_dataset = torchvision.datasets.MNIST(root='../../data/',
+                                          train=False, 
+                                          transform=transforms.ToTensor())
+
+# Data loader
+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)
+
+# Convolutional neural network (two convolutional layers)
+class ConvNet(nn.Module):
+    def __init__(self, num_classes=10):
+        super(ConvNet, self).__init__()
+        self.layer1 = nn.Sequential(
+            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
+            nn.BatchNorm2d(16),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2))
+        self.layer2 = nn.Sequential(
+            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
+            nn.BatchNorm2d(32),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2))
+        self.fc = nn.Linear(7*7*32, num_classes)
+        
+    def forward(self, x):
+        out = self.layer1(x)
+        out = self.layer2(out)
+        out = out.reshape(out.size(0), -1)
+        out = self.fc(out)
+        return out
+
+model = ConvNet(num_classes).to(device)
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+
+# Train the model
+total_step = len(train_loader)
+for epoch in range(num_epochs):
+    for i, (images, labels) in enumerate(train_loader):
+        images = images.to(device)
+        labels = labels.to(device)
+        
+        # Forward pass
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+        
+        # Backward and optimize
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        
+        if (i+1) % 100 == 0:
+            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
+                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))
+
+# Test the model
+model.eval()  # eval mode (batchnorm uses moving mean/variance instead of mini-batch mean/variance)
+with torch.no_grad():
+    correct = 0
+    total = 0
+    for images, labels in test_loader:
+        images = images.to(device)
+        labels = labels.to(device)
+        outputs = model(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+    print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))
+
+# Save the model checkpoint
+torch.save(model.state_dict(), 'model.ckpt')

tutorials/02-intermediate/deep_residual_network/main.py

@@ -0,0 +1,169 @@
+# ---------------------------------------------------------------------------- #
+# An implementation of https://arxiv.org/pdf/1512.03385.pdf                    #
+# See section 4.2 for the model architecture on CIFAR-10                       #
+# Some part of the code was referenced from below                              #
+# https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py   #
+# ---------------------------------------------------------------------------- #
+
+import torch
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Hyper-parameters
+num_epochs = 80
+learning_rate = 0.001
+
+# Image preprocessing modules
+transform = transforms.Compose([
+    transforms.Pad(4),
+    transforms.RandomHorizontalFlip(),
+    transforms.RandomCrop(32),
+    transforms.ToTensor()])
+
+# CIFAR-10 dataset
+train_dataset = torchvision.datasets.CIFAR10(root='../../data/',
+                                             train=True, 
+                                             transform=transform,
+                                             download=True)
+
+test_dataset = torchvision.datasets.CIFAR10(root='../../data/',
+                                            train=False, 
+                                            transform=transforms.ToTensor())
+
+# Data loader
+train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
+                                           batch_size=100, 
+                                           shuffle=True)
+
+test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
+                                          batch_size=100, 
+                                          shuffle=False)
+
+# 3x3 convolution
+def conv3x3(in_channels, out_channels, stride=1):
+    return nn.Conv2d(in_channels, out_channels, kernel_size=3, 
+                     stride=stride, padding=1, bias=False)
+
+# Residual block
+class ResidualBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
+        super(ResidualBlock, self).__init__()
+        self.conv1 = conv3x3(in_channels, out_channels, stride)
+        self.bn1 = nn.BatchNorm2d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(out_channels, out_channels)
+        self.bn2 = nn.BatchNorm2d(out_channels)
+        self.downsample = downsample
+        
+    def forward(self, x):
+        residual = x
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        out = self.conv2(out)
+        out = self.bn2(out)
+        if self.downsample:
+            residual = self.downsample(x)
+        out += residual
+        out = self.relu(out)
+        return out
+
+# ResNet
+class ResNet(nn.Module):
+    def __init__(self, block, layers, num_classes=10):
+        super(ResNet, self).__init__()
+        self.in_channels = 16
+        self.conv = conv3x3(3, 16)
+        self.bn = nn.BatchNorm2d(16)
+        self.relu = nn.ReLU(inplace=True)
+        self.layer1 = self.make_layer(block, 16, layers[0])
+        self.layer2 = self.make_layer(block, 32, layers[0], 2)
+        self.layer3 = self.make_layer(block, 64, layers[1], 2)
+        self.avg_pool = nn.AvgPool2d(8)
+        self.fc = nn.Linear(64, num_classes)
+        
+    def make_layer(self, block, out_channels, blocks, stride=1):
+        downsample = None
+        if (stride != 1) or (self.in_channels != out_channels):
+            downsample = nn.Sequential(
+                conv3x3(self.in_channels, out_channels, stride=stride),
+                nn.BatchNorm2d(out_channels))
+        layers = []
+        layers.append(block(self.in_channels, out_channels, stride, downsample))
+        self.in_channels = out_channels
+        for i in range(1, blocks):
+            layers.append(block(out_channels, out_channels))
+        return nn.Sequential(*layers)
+    
+    def forward(self, x):
+        out = self.conv(x)
+        out = self.bn(out)
+        out = self.relu(out)
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = self.avg_pool(out)
+        out = out.view(out.size(0), -1)
+        out = self.fc(out)
+        return out
+    
+model = ResNet(ResidualBlock, [2, 2, 2, 2]).to(device)
+
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+
+# For updating learning rate
+def update_lr(optimizer, lr):    
+    for param_group in optimizer.param_groups:
+        param_group['lr'] = lr
+
+# Train the model
+total_step = len(train_loader)
+curr_lr = learning_rate
+for epoch in range(num_epochs):
+    for i, (images, labels) in enumerate(train_loader):
+        images = images.to(device)
+        labels = labels.to(device)
+        
+        # Forward pass
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+        
+        # Backward and optimize
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        
+        if (i+1) % 100 == 0:
+            print ("Epoch [{}/{}], Step [{}/{}] Loss: {:.4f}"
+                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))
+
+    # Decay learning rate
+    if (epoch+1) % 20 == 0:
+        curr_lr /= 3
+        update_lr(optimizer, curr_lr)
+
+# Test the model
+model.eval()
+with torch.no_grad():
+    correct = 0
+    total = 0
+    for images, labels in test_loader:
+        images = images.to(device)
+        labels = labels.to(device)
+        outputs = model(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+    print('Accuracy of the model on the test images: {} %'.format(100 * correct / total))
+
+# Save the model checkpoint
+torch.save(model.state_dict(), 'resnet.ckpt')

tutorials/02-intermediate/language_model/data_utils.py

@@ -0,0 +1,46 @@
+import torch
+import os
+
+
+class Dictionary(object):
+    def __init__(self):
+        self.word2idx = {}
+        self.idx2word = {}
+        self.idx = 0
+    
+    def add_word(self, word):
+        if not word in self.word2idx:
+            self.word2idx[word] = self.idx
+            self.idx2word[self.idx] = word
+            self.idx += 1
+    
+    def __len__(self):
+        return len(self.word2idx)
+
+
+class Corpus(object):
+    def __init__(self):
+        self.dictionary = Dictionary()
+
+    def get_data(self, path, batch_size=20):
+        # Add words to the dictionary
+        with open(path, 'r') as f:
+            tokens = 0
+            for line in f:
+                words = line.split() + ['<eos>']
+                tokens += len(words)
+                for word in words: 
+                    self.dictionary.add_word(word)  
+        
+        # Tokenize the file content
+        ids = torch.LongTensor(tokens)
+        token = 0
+        with open(path, 'r') as f:
+            for line in f:
+                words = line.split() + ['<eos>']
+                for word in words:
+                    ids[token] = self.dictionary.word2idx[word]
+                    token += 1
+        num_batches = ids.size(0) // batch_size
+        ids = ids[:num_batches*batch_size]
+        return ids.view(batch_size, -1)

tutorials/02-intermediate/language_model/main.py

@@ -0,0 +1,120 @@
+# Some part of the code was referenced from below.
+# https://github.com/pytorch/examples/tree/master/word_language_model 
+import torch
+import torch.nn as nn
+import numpy as np
+from torch.nn.utils import clip_grad_norm
+from data_utils import Dictionary, Corpus
+
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Hyper-parameters
+embed_size = 128
+hidden_size = 1024
+num_layers = 1
+num_epochs = 5
+num_samples = 1000     # number of words to be sampled
+batch_size = 20
+seq_length = 30
+learning_rate = 0.002
+
+# Load "Penn Treebank" dataset
+corpus = Corpus()
+ids = corpus.get_data('data/train.txt', batch_size)
+vocab_size = len(corpus.dictionary)
+num_batches = ids.size(1) // seq_length
+
+
+# RNN based language model
+class RNNLM(nn.Module):
+    def __init__(self, vocab_size, embed_size, hidden_size, num_layers):
+        super(RNNLM, self).__init__()
+        self.embed = nn.Embedding(vocab_size, embed_size)
+        self.lstm = nn.LSTM(embed_size, hidden_size, num_layers, batch_first=True)
+        self.linear = nn.Linear(hidden_size, vocab_size)
+        
+    def forward(self, x, h):
+        # Embed word ids to vectors
+        x = self.embed(x)
+        
+        # Forward propagate LSTM
+        out, (h, c) = self.lstm(x, h)
+        
+        # Reshape output to (batch_size*sequence_length, hidden_size)
+        out = out.reshape(out.size(0)*out.size(1), out.size(2))
+        
+        # Decode hidden states of all time steps
+        out = self.linear(out)
+        return out, (h, c)
+
+model = RNNLM(vocab_size, embed_size, hidden_size, num_layers).to(device)
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+
+# Truncated backpropagation
+def detach(states):
+    return [state.detach() for state in states] 
+
+# Train the model
+for epoch in range(num_epochs):
+    # Set initial hidden and cell states
+    states = (torch.zeros(num_layers, batch_size, hidden_size).to(device),
+              torch.zeros(num_layers, batch_size, hidden_size).to(device))
+    
+    for i in range(0, ids.size(1) - seq_length, seq_length):
+        # Get mini-batch inputs and targets
+        inputs = ids[:, i:i+seq_length].to(device)
+        targets = ids[:, (i+1):(i+1)+seq_length].to(device)
+        
+        # Forward pass
+        states = detach(states)
+        outputs, states = model(inputs, states)
+        loss = criterion(outputs, targets.reshape(-1))
+        
+        # Backward and optimize
+        model.zero_grad()
+        loss.backward()
+        clip_grad_norm(model.parameters(), 0.5)
+        optimizer.step()
+
+        step = (i+1) // seq_length
+        if step % 100 == 0:
+            print ('Epoch [{}/{}], Step[{}/{}], Loss: {:.4f}, Perplexity: {:5.2f}'
+                   .format(epoch+1, num_epochs, step, num_batches, loss.item(), np.exp(loss.item())))
+
+# Test the model
+with torch.no_grad():
+    with open('sample.txt', 'w') as f:
+        # Set intial hidden ane cell states
+        state = (torch.zeros(num_layers, 1, hidden_size).to(device),
+                 torch.zeros(num_layers, 1, hidden_size).to(device))
+
+        # Select one word id randomly
+        prob = torch.ones(vocab_size)
+        input = torch.multinomial(prob, num_samples=1).unsqueeze(1).to(device)
+
+        for i in range(num_samples):
+            # Forward propagate RNN 
+            output, state = model(input, state)
+
+            # Sample a word id
+            prob = output.exp()
+            word_id = torch.multinomial(prob, num_samples=1).item()
+
+            # Fill input with sampled word id for the next time step
+            input.fill_(word_id)
+
+            # File write
+            word = corpus.dictionary.idx2word[word_id]
+            word = '\n' if word == '<eos>' else word + ' '
+            f.write(word)
+
+            if (i+1) % 100 == 0:
+                print('Sampled [{}/{}] words and save to {}'.format(i+1, num_samples, 'sample.txt'))
+
+# Save the model checkpoints
+torch.save(model.state_dict(), 'model.ckpt')

tutorials/02-intermediate/recurrent_neural_network/main.py

@@ -0,0 +1,102 @@
+import torch 
+import torch.nn as nn
+import torchvision
+import torchvision.transforms as transforms
+
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Hyper-parameters
+sequence_length = 28
+input_size = 28
+hidden_size = 128
+num_layers = 2
+num_classes = 10
+batch_size = 100
+num_epochs = 2
+learning_rate = 0.01
+
+# MNIST dataset
+train_dataset = torchvision.datasets.MNIST(root='../../data/',
+                                           train=True, 
+                                           transform=transforms.ToTensor(),
+                                           download=True)
+
+test_dataset = torchvision.datasets.MNIST(root='../../data/',
+                                          train=False, 
+                                          transform=transforms.ToTensor())
+
+# Data loader
+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)
+
+# Recurrent neural network (many-to-one)
+class RNN(nn.Module):
+    def __init__(self, input_size, hidden_size, num_layers, num_classes):
+        super(RNN, self).__init__()
+        self.hidden_size = hidden_size
+        self.num_layers = num_layers
+        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
+        self.fc = nn.Linear(hidden_size, num_classes)
+    
+    def forward(self, x):
+        # Set initial hidden and cell states 
+        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device) 
+        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
+        
+        # Forward propagate LSTM
+        out, _ = self.lstm(x, (h0, c0))  # out: tensor of shape (batch_size, seq_length, hidden_size)
+        
+        # Decode the hidden state of the last time step
+        out = self.fc(out[:, -1, :])
+        return out
+
+model = RNN(input_size, hidden_size, num_layers, num_classes).to(device)
+
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+
+# Train the model
+total_step = len(train_loader)
+for epoch in range(num_epochs):
+    for i, (images, labels) in enumerate(train_loader):
+        images = images.reshape(-1, sequence_length, input_size).to(device)
+        labels = labels.to(device)
+        
+        # Forward pass
+        outputs = model(images)
+        loss = criterion(outputs, labels)
+        
+        # Backward and optimize
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        
+        if (i+1) % 100 == 0:
+            print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' 
+                   .format(epoch+1, num_epochs, i+1, total_step, loss.item()))
+
+# Test the model
+with torch.no_grad():
+    correct = 0
+    total = 0
+    for images, labels in test_loader:
+        images = images.reshape(-1, sequence_length, input_size).to(device)
+        labels = labels.to(device)
+        outputs = model(images)
+        _, predicted = torch.max(outputs.data, 1)
+        total += labels.size(0)
+        correct += (predicted == labels).sum().item()
+
+    print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total)) 
+
+# Save the model checkpoint
+torch.save(model.state_dict(), 'model.ckpt')

tutorials/03-advanced/generative_adversarial_network/main.py

@@ -0,0 +1,144 @@
+import os
+import torch
+import torchvision
+import torch.nn as nn
+from torchvision import transforms
+from torchvision.utils import save_image
+
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Hyper-parameters
+latent_size = 64
+hidden_size = 256
+image_size = 784
+num_epochs = 200
+batch_size = 100
+sample_dir = 'samples'
+
+# Create a directory if not exists
+if not os.path.exists(sample_dir):
+    os.makedirs(sample_dir)
+
+# Image processing
+transform = transforms.Compose([
+                transforms.ToTensor(),
+                transforms.Normalize(mean=(0.5, 0.5, 0.5),   # 3 for RGB channels
+                                     std=(0.5, 0.5, 0.5))])
+
+# MNIST dataset
+mnist = torchvision.datasets.MNIST(root='../../data/',
+                                   train=True,
+                                   transform=transform,
+                                   download=True)
+
+# Data loader
+data_loader = torch.utils.data.DataLoader(dataset=mnist,
+                                          batch_size=batch_size, 
+                                          shuffle=True)
+
+# Discriminator
+D = nn.Sequential(
+    nn.Linear(image_size, hidden_size),
+    nn.LeakyReLU(0.2),
+    nn.Linear(hidden_size, hidden_size),
+    nn.LeakyReLU(0.2),
+    nn.Linear(hidden_size, 1),
+    nn.Sigmoid())
+
+# Generator 
+G = nn.Sequential(
+    nn.Linear(latent_size, hidden_size),
+    nn.ReLU(),
+    nn.Linear(hidden_size, hidden_size),
+    nn.ReLU(),
+    nn.Linear(hidden_size, image_size),
+    nn.Tanh())
+
+# Device setting
+D = D.to(device)
+G = G.to(device)
+
+# Binary cross entropy loss and optimizer
+criterion = nn.BCELoss()
+d_optimizer = torch.optim.Adam(D.parameters(), lr=0.0002)
+g_optimizer = torch.optim.Adam(G.parameters(), lr=0.0002)
+
+def denorm(x):
+    out = (x + 1) / 2
+    return out.clamp(0, 1)
+
+def reset_grad():
+    d_optimizer.zero_grad()
+    g_optimizer.zero_grad()
+
+# Start training
+total_step = len(data_loader)
+for epoch in range(num_epochs):
+    for i, (images, _) in enumerate(data_loader):
+        images = images.reshape(batch_size, -1).to(device)
+        
+        # Create the labels which are later used as input for the BCE loss
+        real_labels = torch.ones(batch_size, 1).to(device)
+        fake_labels = torch.zeros(batch_size, 1).to(device)
+
+        # ================================================================== #
+        #                      Train the discriminator                       #
+        # ================================================================== #
+
+        # Compute BCE_Loss using real images where BCE_Loss(x, y): - y * log(D(x)) - (1-y) * log(1 - D(x))
+        # Second term of the loss is always zero since real_labels == 1
+        outputs = D(images)
+        d_loss_real = criterion(outputs, real_labels)
+        real_score = outputs
+        
+        # Compute BCELoss using fake images
+        # First term of the loss is always zero since fake_labels == 0
+        z = torch.randn(batch_size, latent_size).to(device)
+        fake_images = G(z)
+        outputs = D(fake_images)
+        d_loss_fake = criterion(outputs, fake_labels)
+        fake_score = outputs
+        
+        # Backprop and optimize
+        d_loss = d_loss_real + d_loss_fake
+        reset_grad()
+        d_loss.backward()
+        d_optimizer.step()
+        
+        # ================================================================== #
+        #                        Train the generator                         #
+        # ================================================================== #
+
+        # Compute loss with fake images
+        z = torch.randn(batch_size, latent_size).to(device)
+        fake_images = G(z)
+        outputs = D(fake_images)
+        
+        # We train G to maximize log(D(G(z)) instead of minimizing log(1-D(G(z)))
+        # For the reason, see the last paragraph of section 3. https://arxiv.org/pdf/1406.2661.pdf
+        g_loss = criterion(outputs, real_labels)
+        
+        # Backprop and optimize
+        reset_grad()
+        g_loss.backward()
+        g_optimizer.step()
+        
+        if (i+1) % 200 == 0:
+            print('Epoch [{}/{}], Step [{}/{}], d_loss: {:.4f}, g_loss: {:.4f}, D(x): {:.2f}, D(G(z)): {:.2f}' 
+                  .format(epoch, num_epochs, i+1, total_step, d_loss.item(), g_loss.item(), 
+                          real_score.mean().item(), fake_score.mean().item()))
+    
+    # Save real images
+    if (epoch+1) == 1:
+        images = images.reshape(images.size(0), 1, 28, 28)
+        save_image(denorm(images), os.path.join(sample_dir, 'real_images.png'))
+    
+    # Save sampled images
+    fake_images = fake_images.reshape(fake_images.size(0), 1, 28, 28)
+    save_image(denorm(fake_images), os.path.join(sample_dir, 'fake_images-{}.png'.format(epoch+1)))
+
+# Save the model checkpoints 
+torch.save(G.state_dict(), 'G.ckpt')
+torch.save(D.state_dict(), 'D.ckpt')

tutorials/03-advanced/image_captioning/README.md

@@ -0,0 +1,59 @@
+# Image Captioning
+The goal of image captioning is to convert a given input image into a natural language description. The encoder-decoder framework is widely used for this task. The image encoder is a convolutional neural network (CNN). In this tutorial, we used [resnet-152](https://arxiv.org/abs/1512.03385) model pretrained on the [ILSVRC-2012-CLS](http://www.image-net.org/challenges/LSVRC/2012/) image classification dataset. The decoder is a long short-term memory (LSTM) network. 
+
+![alt text](png/model.png)
+
+#### Training phase
+For the encoder part, the pretrained CNN extracts the feature vector from a given input image. The feature vector is linearly transformed to have the same dimension as the input dimension of the LSTM network. For the decoder part, source and target texts are predefined. For example, if the image description is **"Giraffes standing next to each other"**, the source sequence is a list containing **['\<start\>', 'Giraffes', 'standing', 'next', 'to', 'each', 'other']** and the target sequence is a list containing **['Giraffes', 'standing', 'next', 'to', 'each', 'other', '\<end\>']**. Using these source and target sequences and the feature vector, the LSTM decoder is trained as a language model conditioned on the feature vector.
+
+#### Test phase
+In the test phase, the encoder part is almost same as the training phase. The only difference is that batchnorm layer uses moving average and variance instead of mini-batch statistics. This can be easily implemented using [encoder.eval()](https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/03-advanced/image_captioning/sample.py#L43). For the decoder part, there is a significant difference between the training phase and the test phase. In the test phase, the LSTM decoder can't see the image description. To deal with this problem, the LSTM decoder feeds back the previosly generated word to the next input. This can be implemented using a [for-loop](https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/03-advanced/image_captioning/model.py#L48).
+
+
+
+## Usage 
+
+
+#### 1. Clone the repositories
+```bash
+$ git clone https://github.com/pdollar/coco.git
+$ cd coco/PythonAPI/
+$ make
+$ python setup.py build
+$ python setup.py install
+$ cd ../../
+$ git clone https://github.com/yunjey/pytorch-tutorial.git
+$ cd pytorch-tutorial/tutorials/03-advanced/image_captioning/
+```
+
+#### 2. Download the dataset
+
+```bash
+$ pip install -r requirements.txt
+$ chmod +x download.sh
+$ ./download.sh
+```
+
+#### 3. Preprocessing
+
+```bash
+$ python build_vocab.py   
+$ python resize.py
+```
+
+#### 4. Train the model
+
+```bash
+$ python train.py    
+```
+
+#### 5. Test the model 
+
+```bash
+$ python sample.py --image='png/example.png'
+```
+
+<br>
+
+## Pretrained model
+If you do not want to train the model from scratch, you can use a pretrained model. You can download the pretrained model [here](https://www.dropbox.com/s/ne0ixz5d58ccbbz/pretrained_model.zip?dl=0) and the vocabulary file [here](https://www.dropbox.com/s/26adb7y9m98uisa/vocap.zip?dl=0). You should extract pretrained_model.zip to `./models/` and vocab.pkl to `./data/` using `unzip` command.

tutorials/03-advanced/image_captioning/build_vocab.py

@@ -0,0 +1,76 @@
+import nltk
+import pickle
+import argparse
+from collections import Counter
+from pycocotools.coco import COCO
+
+
+class Vocabulary(object):
+    """Simple vocabulary wrapper."""
+    def __init__(self):
+        self.word2idx = {}
+        self.idx2word = {}
+        self.idx = 0
+
+    def add_word(self, word):
+        if not word in self.word2idx:
+            self.word2idx[word] = self.idx
+            self.idx2word[self.idx] = word
+            self.idx += 1
+
+    def __call__(self, word):
+        if not word in self.word2idx:
+            return self.word2idx['<unk>']
+        return self.word2idx[word]
+
+    def __len__(self):
+        return len(self.word2idx)
+
+def build_vocab(json, threshold):
+    """Build a simple vocabulary wrapper."""
+    coco = COCO(json)
+    counter = Counter()
+    ids = coco.anns.keys()
+    for i, id in enumerate(ids):
+        caption = str(coco.anns[id]['caption'])
+        tokens = nltk.tokenize.word_tokenize(caption.lower())
+        counter.update(tokens)
+
+        if (i+1) % 1000 == 0:
+            print("[{}/{}] Tokenized the captions.".format(i+1, len(ids)))
+
+    # If the word frequency is less than 'threshold', then the word is discarded.
+    words = [word for word, cnt in counter.items() if cnt >= threshold]
+
+    # Create a vocab wrapper and add some special tokens.
+    vocab = Vocabulary()
+    vocab.add_word('<pad>')
+    vocab.add_word('<start>')
+    vocab.add_word('<end>')
+    vocab.add_word('<unk>')
+
+    # Add the words to the vocabulary.
+    for i, word in enumerate(words):
+        vocab.add_word(word)
+    return vocab
+
+def main(args):
+    vocab = build_vocab(json=args.caption_path, threshold=args.threshold)
+    vocab_path = args.vocab_path
+    with open(vocab_path, 'wb') as f:
+        pickle.dump(vocab, f)
+    print("Total vocabulary size: {}".format(len(vocab)))
+    print("Saved the vocabulary wrapper to '{}'".format(vocab_path))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--caption_path', type=str, 
+                        default='data/annotations/captions_train2014.json', 
+                        help='path for train annotation file')
+    parser.add_argument('--vocab_path', type=str, default='./data/vocab.pkl', 
+                        help='path for saving vocabulary wrapper')
+    parser.add_argument('--threshold', type=int, default=4, 
+                        help='minimum word count threshold')
+    args = parser.parse_args()
+    main(args)

tutorials/03-advanced/image_captioning/data_loader.py

@@ -0,0 +1,105 @@
+import torch
+import torchvision.transforms as transforms
+import torch.utils.data as data
+import os
+import pickle
+import numpy as np
+import nltk
+from PIL import Image
+from build_vocab import Vocabulary
+from pycocotools.coco import COCO
+
+
+class CocoDataset(data.Dataset):
+    """COCO Custom Dataset compatible with torch.utils.data.DataLoader."""
+    def __init__(self, root, json, vocab, transform=None):
+        """Set the path for images, captions and vocabulary wrapper.
+        
+        Args:
+            root: image directory.
+            json: coco annotation file path.
+            vocab: vocabulary wrapper.
+            transform: image transformer.
+        """
+        self.root = root
+        self.coco = COCO(json)
+        self.ids = list(self.coco.anns.keys())
+        self.vocab = vocab
+        self.transform = transform
+
+    def __getitem__(self, index):
+        """Returns one data pair (image and caption)."""
+        coco = self.coco
+        vocab = self.vocab
+        ann_id = self.ids[index]
+        caption = coco.anns[ann_id]['caption']
+        img_id = coco.anns[ann_id]['image_id']
+        path = coco.loadImgs(img_id)[0]['file_name']
+
+        image = Image.open(os.path.join(self.root, path)).convert('RGB')
+        if self.transform is not None:
+            image = self.transform(image)
+
+        # Convert caption (string) to word ids.
+        tokens = nltk.tokenize.word_tokenize(str(caption).lower())
+        caption = []
+        caption.append(vocab('<start>'))
+        caption.extend([vocab(token) for token in tokens])
+        caption.append(vocab('<end>'))
+        target = torch.Tensor(caption)
+        return image, target
+
+    def __len__(self):
+        return len(self.ids)
+
+
+def collate_fn(data):
+    """Creates mini-batch tensors from the list of tuples (image, caption).
+    
+    We should build custom collate_fn rather than using default collate_fn, 
+    because merging caption (including padding) is not supported in default.
+
+    Args:
+        data: list of tuple (image, caption). 
+            - image: torch tensor of shape (3, 256, 256).
+            - caption: torch tensor of shape (?); variable length.
+
+    Returns:
+        images: torch tensor of shape (batch_size, 3, 256, 256).
+        targets: torch tensor of shape (batch_size, padded_length).
+        lengths: list; valid length for each padded caption.
+    """
+    # Sort a data list by caption length (descending order).
+    data.sort(key=lambda x: len(x[1]), reverse=True)
+    images, captions = zip(*data)
+
+    # Merge images (from tuple of 3D tensor to 4D tensor).
+    images = torch.stack(images, 0)
+
+    # Merge captions (from tuple of 1D tensor to 2D tensor).
+    lengths = [len(cap) for cap in captions]
+    targets = torch.zeros(len(captions), max(lengths)).long()
+    for i, cap in enumerate(captions):
+        end = lengths[i]
+        targets[i, :end] = cap[:end]        
+    return images, targets, lengths
+
+def get_loader(root, json, vocab, transform, batch_size, shuffle, num_workers):
+    """Returns torch.utils.data.DataLoader for custom coco dataset."""
+    # COCO caption dataset
+    coco = CocoDataset(root=root,
+                       json=json,
+                       vocab=vocab,
+                       transform=transform)
+    
+    # Data loader for COCO dataset
+    # This will return (images, captions, lengths) for each iteration.
+    # images: a tensor of shape (batch_size, 3, 224, 224).
+    # captions: a tensor of shape (batch_size, padded_length).
+    # lengths: a list indicating valid length for each caption. length is (batch_size).
+    data_loader = torch.utils.data.DataLoader(dataset=coco, 
+                                              batch_size=batch_size,
+                                              shuffle=shuffle,
+                                              num_workers=num_workers,
+                                              collate_fn=collate_fn)
+    return data_loader

tutorials/03-advanced/image_captioning/download.sh

@@ -0,0 +1,11 @@
+mkdir data
+wget http://msvocds.blob.core.windows.net/annotations-1-0-3/captions_train-val2014.zip -P ./data/
+wget http://msvocds.blob.core.windows.net/coco2014/train2014.zip -P ./data/
+wget http://msvocds.blob.core.windows.net/coco2014/val2014.zip -P ./data/
+
+unzip ./data/captions_train-val2014.zip -d ./data/
+rm ./data/captions_train-val2014.zip
+unzip ./data/train2014.zip -d ./data/
+rm ./data/train2014.zip 
+unzip ./data/val2014.zip -d ./data/ 
+rm ./data/val2014.zip 

tutorials/03-advanced/image_captioning/model.py

@@ -0,0 +1,56 @@
+import torch
+import torch.nn as nn
+import torchvision.models as models
+from torch.nn.utils.rnn import pack_padded_sequence
+
+
+class EncoderCNN(nn.Module):
+    def __init__(self, embed_size):
+        """Load the pretrained ResNet-152 and replace top fc layer."""
+        super(EncoderCNN, self).__init__()
+        resnet = models.resnet152(pretrained=True)
+        modules = list(resnet.children())[:-1]      # delete the last fc layer.
+        self.resnet = nn.Sequential(*modules)
+        self.linear = nn.Linear(resnet.fc.in_features, embed_size)
+        self.bn = nn.BatchNorm1d(embed_size, momentum=0.01)
+        
+    def forward(self, images):
+        """Extract feature vectors from input images."""
+        with torch.no_grad():
+            features = self.resnet(images)
+        features = features.reshape(features.size(0), -1)
+        features = self.bn(self.linear(features))
+        return features
+
+
+class DecoderRNN(nn.Module):
+    def __init__(self, embed_size, hidden_size, vocab_size, num_layers, max_seq_length=20):
+        """Set the hyper-parameters and build the layers."""
+        super(DecoderRNN, self).__init__()
+        self.embed = nn.Embedding(vocab_size, embed_size)
+        self.lstm = nn.LSTM(embed_size, hidden_size, num_layers, batch_first=True)
+        self.linear = nn.Linear(hidden_size, vocab_size)
+        self.max_seg_length = max_seq_length
+        
+    def forward(self, features, captions, lengths):
+        """Decode image feature vectors and generates captions."""
+        embeddings = self.embed(captions)
+        embeddings = torch.cat((features.unsqueeze(1), embeddings), 1)
+        packed = pack_padded_sequence(embeddings, lengths, batch_first=True) 
+        hiddens, _ = self.lstm(packed)
+        outputs = self.linear(hiddens[0])
+        return outputs
+    
+    def sample(self, features, states=None):
+        """Generate captions for given image features using greedy search."""
+        sampled_ids = []
+        inputs = features.unsqueeze(1)
+        for i in range(self.max_seg_length):
+            hiddens, states = self.lstm(inputs, states)          # hiddens: (batch_size, 1, hidden_size)
+            outputs = self.linear(hiddens.squeeze(1))            # outputs:  (batch_size, vocab_size)
+            _, predicted = outputs.max(1)                        # predicted: (batch_size)
+            sampled_ids.append(predicted)
+            inputs = self.embed(predicted)                       # inputs: (batch_size, embed_size)
+            inputs = inputs.unsqueeze(1)                         # inputs: (batch_size, 1, embed_size)
+        sampled_ids = torch.stack(sampled_ids, 1)                # sampled_ids: (batch_size, max_seq_length)
+        return sampled_ids

tutorials/03-advanced/image_captioning/requirements.txt

@@ -0,0 +1,5 @@
+matplotlib
+nltk
+numpy
+Pillow
+argparse

tutorials/03-advanced/image_captioning/resize.py

@@ -0,0 +1,42 @@
+import argparse
+import os
+from PIL import Image
+
+
+def resize_image(image, size):
+    """Resize an image to the given size."""
+    return image.resize(size, Image.ANTIALIAS)
+
+def resize_images(image_dir, output_dir, size):
+    """Resize the images in 'image_dir' and save into 'output_dir'."""
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    images = os.listdir(image_dir)
+    num_images = len(images)
+    for i, image in enumerate(images):
+        with open(os.path.join(image_dir, image), 'r+b') as f:
+            with Image.open(f) as img:
+                img = resize_image(img, size)
+                img.save(os.path.join(output_dir, image), img.format)
+        if (i+1) % 100 == 0:
+            print ("[{}/{}] Resized the images and saved into '{}'."
+                   .format(i+1, num_images, output_dir))
+
+def main(args):
+    image_dir = args.image_dir
+    output_dir = args.output_dir
+    image_size = [args.image_size, args.image_size]
+    resize_images(image_dir, output_dir, image_size)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--image_dir', type=str, default='./data/train2014/',
+                        help='directory for train images')
+    parser.add_argument('--output_dir', type=str, default='./data/resized2014/',
+                        help='directory for saving resized images')
+    parser.add_argument('--image_size', type=int, default=256,
+                        help='size for image after processing')
+    args = parser.parse_args()
+    main(args)

tutorials/03-advanced/image_captioning/sample.py

@@ -0,0 +1,81 @@
+import torch
+import matplotlib.pyplot as plt
+import numpy as np 
+import argparse
+import pickle 
+import os
+from torchvision import transforms 
+from build_vocab import Vocabulary
+from model import EncoderCNN, DecoderRNN
+from PIL import Image
+
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+def load_image(image_path, transform=None):
+    image = Image.open(image_path)
+    image = image.resize([224, 224], Image.LANCZOS)
+    
+    if transform is not None:
+        image = transform(image).unsqueeze(0)
+    
+    return image
+
+def main(args):
+    # Image preprocessing
+    transform = transforms.Compose([
+        transforms.ToTensor(), 
+        transforms.Normalize((0.485, 0.456, 0.406), 
+                             (0.229, 0.224, 0.225))])
+    
+    # Load vocabulary wrapper
+    with open(args.vocab_path, 'rb') as f:
+        vocab = pickle.load(f)
+
+    # Build models
+    encoder = EncoderCNN(args.embed_size).eval()  # eval mode (batchnorm uses moving mean/variance)
+    decoder = DecoderRNN(args.embed_size, args.hidden_size, len(vocab), args.num_layers)
+    encoder = encoder.to(device)
+    decoder = decoder.to(device)
+
+    # Load the trained model parameters
+    encoder.load_state_dict(torch.load(args.encoder_path))
+    decoder.load_state_dict(torch.load(args.decoder_path))
+
+    # Prepare an image
+    image = load_image(args.image, transform)
+    image_tensor = image.to(device)
+    
+    # Generate an caption from the image
+    feature = encoder(image_tensor)
+    sampled_ids = decoder.sample(feature)
+    sampled_ids = sampled_ids[0].cpu().numpy()          # (1, max_seq_length) -> (max_seq_length)
+    
+    # Convert word_ids to words
+    sampled_caption = []
+    for word_id in sampled_ids:
+        word = vocab.idx2word[word_id]
+        sampled_caption.append(word)
+        if word == '<end>':
+            break
+    sentence = ' '.join(sampled_caption)
+    
+    # Print out the image and the generated caption
+    print (sentence)
+    image = Image.open(args.image)
+    plt.imshow(np.asarray(image))
+    
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--image', type=str, required=True, help='input image for generating caption')
+    parser.add_argument('--encoder_path', type=str, default='models/encoder-2-1000.ckpt', help='path for trained encoder')
+    parser.add_argument('--decoder_path', type=str, default='models/decoder-2-1000.ckpt', help='path for trained decoder')
+    parser.add_argument('--vocab_path', type=str, default='data/vocab.pkl', help='path for vocabulary wrapper')
+    
+    # Model parameters (should be same as paramters in train.py)
+    parser.add_argument('--embed_size', type=int , default=256, help='dimension of word embedding vectors')
+    parser.add_argument('--hidden_size', type=int , default=512, help='dimension of lstm hidden states')
+    parser.add_argument('--num_layers', type=int , default=1, help='number of layers in lstm')
+    args = parser.parse_args()
+    main(args)

tutorials/03-advanced/image_captioning/train.py

@@ -0,0 +1,101 @@
+import argparse
+import torch
+import torch.nn as nn
+import numpy as np
+import os
+import pickle
+from data_loader import get_loader 
+from build_vocab import Vocabulary
+from model import EncoderCNN, DecoderRNN
+from torch.nn.utils.rnn import pack_padded_sequence
+from torchvision import transforms
+
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+def main(args):
+    # Create model directory
+    if not os.path.exists(args.model_path):
+        os.makedirs(args.model_path)
+    
+    # Image preprocessing, normalization for the pretrained resnet
+    transform = transforms.Compose([ 
+        transforms.RandomCrop(args.crop_size),
+        transforms.RandomHorizontalFlip(), 
+        transforms.ToTensor(), 
+        transforms.Normalize((0.485, 0.456, 0.406), 
+                             (0.229, 0.224, 0.225))])
+    
+    # Load vocabulary wrapper
+    with open(args.vocab_path, 'rb') as f:
+        vocab = pickle.load(f)
+    
+    # Build data loader
+    data_loader = get_loader(args.image_dir, args.caption_path, vocab, 
+                             transform, args.batch_size,
+                             shuffle=True, num_workers=args.num_workers) 
+
+    # Build the models
+    encoder = EncoderCNN(args.embed_size).to(device)
+    decoder = DecoderRNN(args.embed_size, args.hidden_size, len(vocab), args.num_layers).to(device)
+    
+    # Loss and optimizer
+    criterion = nn.CrossEntropyLoss()
+    params = list(decoder.parameters()) + list(encoder.linear.parameters()) + list(encoder.bn.parameters())
+    optimizer = torch.optim.Adam(params, lr=args.learning_rate)
+    
+    # Train the models
+    total_step = len(data_loader)
+    for epoch in range(args.num_epochs):
+        for i, (images, captions, lengths) in enumerate(data_loader):
+            
+            # Set mini-batch dataset
+            images = images.to(device)
+            captions = captions.to(device)
+            targets = pack_padded_sequence(captions, lengths, batch_first=True)[0]
+            
+            # Forward, backward and optimize
+            features = encoder(images)
+            outputs = decoder(features, captions, lengths)
+            loss = criterion(outputs, targets)
+            decoder.zero_grad()
+            encoder.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            # Print log info
+            if i % args.log_step == 0:
+                print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}, Perplexity: {:5.4f}'
+                      .format(epoch, args.num_epochs, i, total_step, loss.item(), np.exp(loss.item()))) 
+                
+            # Save the model checkpoints
+            if (i+1) % args.save_step == 0:
+                torch.save(decoder.state_dict(), os.path.join(
+                    args.model_path, 'decoder-{}-{}.ckpt'.format(epoch+1, i+1)))
+                torch.save(encoder.state_dict(), os.path.join(
+                    args.model_path, 'encoder-{}-{}.ckpt'.format(epoch+1, i+1)))
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model_path', type=str, default='models/' , help='path for saving trained models')
+    parser.add_argument('--crop_size', type=int, default=224 , help='size for randomly cropping images')
+    parser.add_argument('--vocab_path', type=str, default='data/vocab.pkl', help='path for vocabulary wrapper')
+    parser.add_argument('--image_dir', type=str, default='data/resized2014', help='directory for resized images')
+    parser.add_argument('--caption_path', type=str, default='data/annotations/captions_train2014.json', help='path for train annotation json file')
+    parser.add_argument('--log_step', type=int , default=10, help='step size for prining log info')
+    parser.add_argument('--save_step', type=int , default=1000, help='step size for saving trained models')
+    
+    # Model parameters
+    parser.add_argument('--embed_size', type=int , default=256, help='dimension of word embedding vectors')
+    parser.add_argument('--hidden_size', type=int , default=512, help='dimension of lstm hidden states')
+    parser.add_argument('--num_layers', type=int , default=1, help='number of layers in lstm')
+    
+    parser.add_argument('--num_epochs', type=int, default=5)
+    parser.add_argument('--batch_size', type=int, default=128)
+    parser.add_argument('--num_workers', type=int, default=2)
+    parser.add_argument('--learning_rate', type=float, default=0.001)
+    args = parser.parse_args()
+    print(args)
+    main(args)

tutorials/03-advanced/neural_style_transfer/README.md

@@ -0,0 +1,33 @@
+# Neural Style Transfer
+
+[Neural style transfer](https://arxiv.org/abs/1508.06576) is an algorithm that combines the content of one image with the style of another image using CNN. Given a content image and a style image, the goal is to generate a target image that minimizes the content difference with the content image and the style difference with the style image. 
+
+<p align="center"><img width="100%" src="png/neural_style2.png" /></p>
+
+
+#### Content loss
+
+To minimize the content difference, we forward propagate the content image and the target image to pretrained [VGGNet](https://arxiv.org/abs/1409.1556) respectively, and extract feature maps from multiple convolutional layers. Then, the target image is updated to minimize the [mean-squared error](https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/03-advanced/neural_style_transfer/main.py#L92-L93) between the feature maps of the content image and its feature maps. 
+
+#### Style loss
+
+As in computing the content loss, we forward propagate the style image and the target image to the VGGNet and extract convolutional feature maps. To generate a texture that matches the style of the style image, we update the target image by minimizing the mean-squared error between the Gram matrix of the style image and the Gram matrix of the target image (feature correlation minimization). See [here](https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/03-advanced/neural_style_transfer/main.py#L95-L105) for how to compute the style loss.
+
+
+
+
+<br>
+
+## Usage 
+
+```bash
+$ pip install -r requirements.txt
+$ python main.py --content='png/content.png' --style='png/style.png'
+```
+
+<br>
+
+## Results
+The following is the result of applying variaous styles of artwork to Anne Hathaway's photograph.
+
+![alt text](png/neural_style.png)

tutorials/03-advanced/neural_style_transfer/main.py

@@ -0,0 +1,126 @@
+from __future__ import division
+from torchvision import models
+from torchvision import transforms
+from PIL import Image
+import argparse
+import torch
+import torchvision
+import torch.nn as nn
+import numpy as np
+
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+def load_image(image_path, transform=None, max_size=None, shape=None):
+    """Load an image and convert it to a torch tensor."""
+    image = Image.open(image_path)
+    
+    if max_size:
+        scale = max_size / max(image.size)
+        size = np.array(image.size) * scale
+        image = image.resize(size.astype(int), Image.ANTIALIAS)
+    
+    if shape:
+        image = image.resize(shape, Image.LANCZOS)
+    
+    if transform:
+        image = transform(image).unsqueeze(0)
+    
+    return image.to(device)
+
+
+class VGGNet(nn.Module):
+    def __init__(self):
+        """Select conv1_1 ~ conv5_1 activation maps."""
+        super(VGGNet, self).__init__()
+        self.select = ['0', '5', '10', '19', '28'] 
+        self.vgg = models.vgg19(pretrained=True).features
+        
+    def forward(self, x):
+        """Extract multiple convolutional feature maps."""
+        features = []
+        for name, layer in self.vgg._modules.items():
+            x = layer(x)
+            if name in self.select:
+                features.append(x)
+        return features
+
+
+def main(config):
+    
+    # Image preprocessing
+    # VGGNet was trained on ImageNet where images are normalized by mean=[0.485, 0.456, 0.406] and std=[0.229, 0.224, 0.225].
+    # We use the same normalization statistics here.
+    transform = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Normalize(mean=(0.485, 0.456, 0.406), 
+                             std=(0.229, 0.224, 0.225))])
+    
+    # Load content and style images
+    # Make the style image same size as the content image
+    content = load_image(config.content, transform, max_size=config.max_size)
+    style = load_image(config.style, transform, shape=[content.size(2), content.size(3)])
+    
+    # Initialize a target image with the content image
+    target = content.clone().requires_grad_(True)
+    
+    optimizer = torch.optim.Adam([target], lr=config.lr, betas=[0.5, 0.999])
+    vgg = VGGNet().to(device).eval()
+    
+    for step in range(config.total_step):
+        
+        # Extract multiple(5) conv feature vectors
+        target_features = vgg(target)
+        content_features = vgg(content)
+        style_features = vgg(style)
+
+        style_loss = 0
+        content_loss = 0
+        for f1, f2, f3 in zip(target_features, content_features, style_features):
+            # Compute content loss with target and content images
+            content_loss += torch.mean((f1 - f2)**2)
+
+            # Reshape convolutional feature maps
+            _, c, h, w = f1.size()
+            f1 = f1.view(c, h * w)
+            f3 = f3.view(c, h * w)
+
+            # Compute gram matrix
+            f1 = torch.mm(f1, f1.t())
+            f3 = torch.mm(f3, f3.t())
+
+            # Compute style loss with target and style images
+            style_loss += torch.mean((f1 - f3)**2) / (c * h * w) 
+        
+        # Compute total loss, backprop and optimize
+        loss = content_loss + config.style_weight * style_loss 
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        if (step+1) % config.log_step == 0:
+            print ('Step [{}/{}], Content Loss: {:.4f}, Style Loss: {:.4f}' 
+                   .format(step+1, config.total_step, content_loss.item(), style_loss.item()))
+
+        if (step+1) % config.sample_step == 0:
+            # Save the generated image
+            denorm = transforms.Normalize((-2.12, -2.04, -1.80), (4.37, 4.46, 4.44))
+            img = target.clone().squeeze()
+            img = denorm(img).clamp_(0, 1)
+            torchvision.utils.save_image(img, 'output-{}.png'.format(step+1))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--content', type=str, default='png/content.png')
+    parser.add_argument('--style', type=str, default='png/style.png')
+    parser.add_argument('--max_size', type=int, default=400)
+    parser.add_argument('--total_step', type=int, default=2000)
+    parser.add_argument('--log_step', type=int, default=10)
+    parser.add_argument('--sample_step', type=int, default=500)
+    parser.add_argument('--style_weight', type=float, default=100)
+    parser.add_argument('--lr', type=float, default=0.003)
+    config = parser.parse_args()
+    print(config)
+    main(config)

tutorials/03-advanced/neural_style_transfer/requirements.txt

@@ -0,0 +1,4 @@
+argparse
+torch
+torchvision
+Pillow

tutorials/03-advanced/variational_autoencoder/main.py

@@ -0,0 +1,101 @@
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision
+from torchvision import transforms
+from torchvision.utils import save_image
+
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# Create a directory if not exists
+sample_dir = 'samples'
+if not os.path.exists(sample_dir):
+    os.makedirs(sample_dir)
+
+# Hyper-parameters
+image_size = 784
+h_dim = 400
+z_dim = 20
+num_epochs = 15
+batch_size = 128
+learning_rate = 1e-3
+
+# MNIST dataset
+dataset = torchvision.datasets.MNIST(root='../../data',
+                                     train=True,
+                                     transform=transforms.ToTensor(),
+                                     download=True)
+
+# Data loader
+data_loader = torch.utils.data.DataLoader(dataset=dataset,
+                                          batch_size=batch_size, 
+                                          shuffle=True)
+
+
+# VAE model
+class VAE(nn.Module):
+    def __init__(self, image_size=784, h_dim=400, z_dim=20):
+        super(VAE, self).__init__()
+        self.fc1 = nn.Linear(image_size, h_dim)
+        self.fc2 = nn.Linear(h_dim, z_dim)
+        self.fc3 = nn.Linear(h_dim, z_dim)
+        self.fc4 = nn.Linear(z_dim, h_dim)
+        self.fc5 = nn.Linear(h_dim, image_size)
+        
+    def encode(self, x):
+        h = F.relu(self.fc1(x))
+        return self.fc2(h), self.fc3(h)
+    
+    def reparameterize(self, mu, log_var):
+        std = torch.exp(log_var/2)
+        eps = torch.randn_like(std)
+        return mu + eps * std
+
+    def decode(self, z):
+        h = F.relu(self.fc4(z))
+        return F.sigmoid(self.fc5(h))
+    
+    def forward(self, x):
+        mu, log_var = self.encode(x)
+        z = self.reparameterize(mu, log_var)
+        x_reconst = self.decode(z)
+        return x_reconst, mu, log_var
+
+model = VAE().to(device)
+optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
+
+# Start training
+for epoch in range(num_epochs):
+    for i, (x, _) in enumerate(data_loader):
+        # Forward pass
+        x = x.to(device).view(-1, image_size)
+        x_reconst, mu, log_var = model(x)
+        
+        # Compute reconstruction loss and kl divergence
+        # For KL divergence, see Appendix B in VAE paper or http://yunjey47.tistory.com/43
+        reconst_loss = F.binary_cross_entropy(x_reconst, x, size_average=False)
+        kl_div = - 0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())
+        
+        # Backprop and optimize
+        loss = reconst_loss + kl_div
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        
+        if (i+1) % 10 == 0:
+            print ("Epoch[{}/{}], Step [{}/{}], Reconst Loss: {:.4f}, KL Div: {:.4f}" 
+                   .format(epoch+1, num_epochs, i+1, len(data_loader), reconst_loss.item(), kl_div.item()))
+    
+    with torch.no_grad():
+        # Save the sampled images
+        z = torch.randn(batch_size, z_dim).to(device)
+        out = model.decode(z).view(-1, 1, 28, 28)
+        save_image(out, os.path.join(sample_dir, 'sampled-{}.png'.format(epoch+1)))
+
+        # Save the reconstructed images
+        out, _, _ = model(x)
+        x_concat = torch.cat([x.view(-1, 1, 28, 28), out.view(-1, 1, 28, 28)], dim=3)
+        save_image(x_concat, os.path.join(sample_dir, 'reconst-{}.png'.format(epoch+1)))

tutorials/04-utils/tensorboard/README.md

@@ -0,0 +1,25 @@
+# TensorBoard in PyTorch
+
+In this tutorial, we implement a MNIST classifier using a simple neural network and visualize the training process using [TensorBoard](https://www.tensorflow.org/get_started/summaries_and_tensorboard). In training phase, we plot the loss and accuracy functions through `scalar_summary` and visualize the training images through `image_summary`. In addition, we visualize the weight and gradient values of the parameters of the neural network using `histogram_summary`. PyTorch code for handling these summary functions can be found [here](https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/04-utils/tensorboard/main.py#L81-L97).
+
+![alt text](gif/tensorboard.gif)
+
+<br>
+
+## Usage 
+
+#### 1. Install the dependencies
+```bash
+$ pip install -r requirements.txt
+```
+
+#### 2. Train the model
+```bash
+$ python main.py
+```
+
+#### 3. Open the TensorBoard
+To run the TensorBoard, open a new terminal and run the command below. Then, open http://localhost:6006/ on your web browser.
+```bash
+$ tensorboard --logdir='./logs' --port=6006
+```

tutorials/04-utils/tensorboard/logger.py

@@ -0,0 +1,71 @@
+# Code referenced from https://gist.github.com/gyglim/1f8dfb1b5c82627ae3efcfbbadb9f514
+import tensorflow as tf
+import numpy as np
+import scipy.misc 
+try:
+    from StringIO import StringIO  # Python 2.7
+except ImportError:
+    from io import BytesIO         # Python 3.x
+
+
+class Logger(object):
+    
+    def __init__(self, log_dir):
+        """Create a summary writer logging to log_dir."""
+        self.writer = tf.summary.FileWriter(log_dir)
+
+    def scalar_summary(self, tag, value, step):
+        """Log a scalar variable."""
+        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=value)])
+        self.writer.add_summary(summary, step)
+
+    def image_summary(self, tag, images, step):
+        """Log a list of images."""
+
+        img_summaries = []
+        for i, img in enumerate(images):
+            # Write the image to a string
+            try:
+                s = StringIO()
+            except:
+                s = BytesIO()
+            scipy.misc.toimage(img).save(s, format="png")
+
+            # Create an Image object
+            img_sum = tf.Summary.Image(encoded_image_string=s.getvalue(),
+                                       height=img.shape[0],
+                                       width=img.shape[1])
+            # Create a Summary value
+            img_summaries.append(tf.Summary.Value(tag='%s/%d' % (tag, i), image=img_sum))
+
+        # Create and write Summary
+        summary = tf.Summary(value=img_summaries)
+        self.writer.add_summary(summary, step)
+        
+    def histo_summary(self, tag, values, step, bins=1000):
+        """Log a histogram of the tensor of values."""
+
+        # Create a histogram using numpy
+        counts, bin_edges = np.histogram(values, bins=bins)
+
+        # Fill the fields of the histogram proto
+        hist = tf.HistogramProto()
+        hist.min = float(np.min(values))
+        hist.max = float(np.max(values))
+        hist.num = int(np.prod(values.shape))
+        hist.sum = float(np.sum(values))
+        hist.sum_squares = float(np.sum(values**2))
+
+        # Drop the start of the first bin
+        bin_edges = bin_edges[1:]
+
+        # Add bin edges and counts
+        for edge in bin_edges:
+            hist.bucket_limit.append(edge)
+        for c in counts:
+            hist.bucket.append(c)
+
+        # Create and write Summary
+        summary = tf.Summary(value=[tf.Summary.Value(tag=tag, histo=hist)])
+        self.writer.add_summary(summary, step)
+        self.writer.flush()

tutorials/04-utils/tensorboard/main.py

@@ -0,0 +1,97 @@
+import torch
+import torch.nn as nn
+import torchvision
+from torchvision import transforms
+from logger import Logger
+
+
+# Device configuration
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+# MNIST dataset 
+dataset = torchvision.datasets.MNIST(root='../../data', 
+                                     train=True, 
+                                     transform=transforms.ToTensor(),  
+                                     download=True)
+
+# Data loader
+data_loader = torch.utils.data.DataLoader(dataset=dataset, 
+                                          batch_size=100, 
+                                          shuffle=True)
+
+
+# Fully connected neural network with one hidden layer
+class NeuralNet(nn.Module):
+    def __init__(self, input_size=784, hidden_size=500, num_classes=10):
+        super(NeuralNet, self).__init__()
+        self.fc1 = nn.Linear(input_size, hidden_size) 
+        self.relu = nn.ReLU()
+        self.fc2 = nn.Linear(hidden_size, num_classes)  
+    
+    def forward(self, x):
+        out = self.fc1(x)
+        out = self.relu(out)
+        out = self.fc2(out)
+        return out
+
+model = NeuralNet().to(device)
+
+logger = Logger('./logs')
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()  
+optimizer = torch.optim.Adam(model.parameters(), lr=0.00001)  
+
+data_iter = iter(data_loader)
+iter_per_epoch = len(data_loader)
+total_step = 50000
+
+# Start training
+for step in range(total_step):
+    
+    # Reset the data_iter
+    if (step+1) % iter_per_epoch == 0:
+        data_iter = iter(data_loader)
+
+    # Fetch images and labels
+    images, labels = next(data_iter)
+    images, labels = images.view(images.size(0), -1).to(device), labels.to(device)
+    
+    # Forward pass
+    outputs = model(images)
+    loss = criterion(outputs, labels)
+    
+    # Backward and optimize
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    # Compute accuracy
+    _, argmax = torch.max(outputs, 1)
+    accuracy = (labels == argmax.squeeze()).float().mean()
+
+    if (step+1) % 100 == 0:
+        print ('Step [{}/{}], Loss: {:.4f}, Acc: {:.2f}' 
+               .format(step+1, total_step, loss.item(), accuracy.item()))
+
+        # ================================================================== #
+        #                        Tensorboard Logging                         #
+        # ================================================================== #
+
+        # 1. Log scalar values (scalar summary)
+        info = { 'loss': loss.item(), 'accuracy': accuracy.item() }
+
+        for tag, value in info.items():
+            logger.scalar_summary(tag, value, step+1)
+
+        # 2. Log values and gradients of the parameters (histogram summary)
+        for tag, value in model.named_parameters():
+            tag = tag.replace('.', '/')
+            logger.histo_summary(tag, value.data.cpu().numpy(), step+1)
+            logger.histo_summary(tag+'/grad', value.grad.data.cpu().numpy(), step+1)
+
+        # 3. Log training images (image summary)
+        info = { 'images': images.view(-1, 28, 28)[:10].cpu().numpy() }
+
+        for tag, images in info.items():
+            logger.image_summary(tag, images, step+1)

tutorials/04-utils/tensorboard/requirements.txt

@@ -0,0 +1,5 @@
+tensorflow
+torch
+torchvision
+scipy
+numpy