Lab 3: Gesture Recognition

Lab 3: Gesture Recognition using Convolutional Neural Networks


Late Penalty: There is a penalty-free grace period of one hour past the deadline. Any work that is submitted between 1 hour and 24 hours past the deadline will receive a 20% grade deduction. No other late work is accepted. Quercus submission time will be used, not your local computer time. You can submit your labs as many times as you want before the deadline, so please submit often and early.

Grading TAs: Geoff Donoghue

This lab is based on an assignment developed by Prof. Lisa Zhang.

This lab will be completed in two parts. In Part A you will you will gain experience gathering your own data set (specifically images of hand gestures), and understand the challenges involved in the data cleaning process. In Part B you will train a convolutional neural network to make classifications on different hand gestures. By the end of the lab, you should be able to:

Generate and preprocess your own data
Load and split data for training, validation and testing
Train a Convolutional Neural Network
Apply transfer learning to improve your model
Note that for this lab we will not be providing you with any starter code. You should be able to take the code used in previous labs, tutorials and lectures and modify it accordingly to complete the tasks outlined below.

What to submit
Submission for Part A:
Submit a zip file containing your images. Three images each of American Sign Language gestures for letters A - I (total of 27 images). You will be required to clean the images before submitting them. Details are provided under Part A of the handout.

Individual image file names should follow the convention of student-number_Alphabet_file-number.jpg (e.g. 100343434_A_1.jpg).

Submission for Part B:
Submit a PDF file containing all your code, outputs, and write-up from parts 1-5. You can produce a PDF of your Google Colab file by going to File > Print and then save as PDF. The Colab instructions has more information. Make sure to review the PDF submission to ensure that your answers are easy to read. Make sure that your text is not cut off at the margins.

Do not submit any other files produced by your code.

Include a link to your colab file in your submission.

Please use Google Colab to complete this assignment. If you want to use Jupyter Notebook, please complete the assignment and upload your Jupyter Notebook file to Google Colab for submission.

Colab Link
Include a link to your colab file here

Colab Link: https://colab.research.google.com/drive/16cfA6vg7gJyE7Y7gBaYUwY8WF5Vkx4__?usp=sharing

Part A. Data Collection [10 pt]
So far, we have worked with data sets that have been collected, cleaned, and curated by machine learning researchers and practitioners. Datasets like MNIST and CIFAR are often used as toy examples, both by students and by researchers testing new machine learning models.

In the real world, getting a clean data set is never that easy. More than half the work in applying machine learning is finding, gathering, cleaning, and formatting your data set.

The purpose of this lab is to help you gain experience gathering your own data set, and understand the challenges involved in the data cleaning process.

American Sign Language
American Sign Language (ASL) is a complete, complex language that employs signs made by moving the hands combined with facial expressions and postures of the body. It is the primary language of many North Americans who are deaf and is one of several communication options used by people who are deaf or hard-of-hearing.

The hand gestures representing English alphabet are shown below. This lab focuses on classifying a subset of these hand gesture images using convolutional neural networks. Specifically, given an image of a hand showing one of the letters A-I, we want to detect which letter is being represented.

alt text

Generating Data
We will produce the images required for this lab by ourselves. Each student will collect, clean and submit three images each of Americal Sign Language gestures for letters A - I (total of 27 images) Steps involved in data collection

Familiarize yourself with American Sign Language gestures for letters from A - I (9 letters).
Take three pictures at slightly different orientation for each letter gesture using your mobile phone.
Ensure adequate lighting while you are capturing the images.
Use a white wall as your background.
Use your right hand to create gestures (for consistency).
Keep your right hand fairly apart from your body and any other obstructions.
Avoid having shadows on parts of your hand.
Transfer the images to your laptop for cleaning.
Cleaning Data
To simplify the machine learning the task, we will standardize the training images. We will make sure that all our images are of the same size (224 x 224 pixels RGB), and have the hand in the center of the cropped regions.

You may use the following applications to crop and resize your images:

Mac

Use Preview: – Holding down CMD + Shift will keep a square aspect ratio while selecting the hand area. – Resize to 224x224 pixels.
Windows 10

Use Photos app to edit and crop the image and keep the aspect ratio a square.
Use Paint to resize the image to the final image size of 224x224 pixels.
Linux

You can use GIMP, imagemagick, or other tools of your choosing. You may also use online tools such as http://picresize.com All the above steps are illustrative only. You need not follow these steps but following these will ensure that you produce a good quality dataset. You will be judged based on the quality of the images alone. Please do not edit your photos in any other way. You should not need to change the aspect ratio of your image. You also should not digitally remove the background or shadows—instead, take photos with a white background and minimal shadows.
Accepted Images
Images will be accepted and graded based on the criteria below

The final image should be size 224x224 pixels (RGB).
The file format should be a .jpg file.
The hand should be approximately centered on the frame.
The hand should not be obscured or cut off.
The photos follows the ASL gestures posted earlier.
The photos were not edited in any other way (e.g. no electronic removal of shadows or background).
Submission
Submit a zip file containing your images. There should be a total of 27 images (3 for each category)

Individual image file names should follow the convention of student-number_Alphabet_file-number.jpg (e.g. 100343434_A_1.jpg)
Zip all the images together and name it with the following convention: last-name_student-number.zip (e.g. last-name_100343434.zip).
Submit the zipped folder. We will be anonymizing and combining the images that everyone submits. We will announce when the combined data set will be available for download.
alt text

Part B. Building a CNN [50 pt]
For this lab, we are not going to give you any starter code. You will be writing a convolutional neural network from scratch. You are welcome to use any code from previous labs, lectures and tutorials. You should also write your own code.

You may use the PyTorch documentation freely. You might also find online tutorials helpful. However, all code that you submit must be your own.

Make sure that your code is vectorized, and does not contain obvious inefficiencies (for example, unecessary for loops, or unnecessary calls to unsqueeze()). Ensure enough comments are included in the code so that your TA can understand what you are doing. It is your responsibility to show that you understand what you write.

This is much more challenging and time-consuming than the previous labs. Make sure that you give yourself plenty of time by starting early.

1. Data Loading and Splitting [5 pt]
Download the anonymized data provided on Quercus. To allow you to get a heads start on this project we will provide you with sample data from previous years. Split the data into training, validation, and test sets.

Note: Data splitting is not as trivial in this lab. We want our test set to closely resemble the setting in which our model will be used. In particular, our test set should contain hands that are never seen in training!

Explain how you split the data, either by describing what you did, or by showing the code that you used. Justify your choice of splitting strategy. How many training, validation, and test images do you have?

For loading the data, you can use plt.imread as in Lab 1, or any other method that you choose. You may find torchvision.datasets.ImageFolder helpful. (see https://pytorch.org/docs/stable/torchvision/datasets.html?highlight=image%20folder#torchvision.datasets.ImageFolder )

Answer: I manually split the data into three folders which are called train, validation and test. Also, these three folders do not have the same image and have the same proportion of each class in each set. And I split them in ratio of approximately 80:10:10. The number of training, validation, and test images are 1904, 266 and 261 respectively.

# Since the function writes files to disk, you will need to mount
# your Google Drive. If you are working on the lab locally, you
# can comment out this code.

from google.colab import drive
drive.mount('/content/gdrive')
Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount("/content/gdrive", force_remount=True).
import numpy as np
import time
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
from torchvision import datasets, models, transforms
from torch.utils.data.sampler import SubsetRandomSampler
import matplotlib.pyplot as plt
rm -rf /root/datasets/Lab_3b_Gesture_Dataset
#data path on google drive
!unzip '/content/gdrive/My Drive/APS360/Lab_3b_Gesture_Dataset.zip' -d '/root/datasets'
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#define training and test data directories
data_dir= '/root/datasets/Lab_3b_Gesture_Dataset'
train_dir=os.path.join(data_dir, 'train/')
val_dir=os.path.join(data_dir, 'validation/')
test_dir=os.path.join(data_dir, 'test/')

#classes are folders in each directory with these names
classes = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I']
#load and transform data using ImageFolder

#resize all images to 224 x 224
data_transform = transforms.Compose([transforms.CenterCrop([224,224]), transforms.ToTensor()])

train_data=datasets.ImageFolder(train_dir, transform=data_transform)
val_data= datasets.ImageFolder(val_dir, transform=data_transform)
test_data= datasets.ImageFolder(test_dir, transform=data_transform)

#print out some data stats
print('Num training images: ', len(train_data))
print('Num validation images: ', len(val_data))
print('Num test images: ', len(test_data))
Num training images:  1904
Num validation images:  266
Num test images:  261
#define dataloader parameters
batch_size=256
num_workers=0

# prepare data loaders
train_loader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, 
                                           num_workers=num_workers, shuffle=True)
val_loader = torch.utils.data.DataLoader(val_data, batch_size=batch_size, 
                                          num_workers=num_workers, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_data, batch_size=batch_size, 
                                          num_workers=num_workers, shuffle=True)

# Visualize some sample data

# obtain one batch of training images
dataiter = iter(train_loader)
images, labels = dataiter.next()
images = images.numpy() # convert images to numpy for display

# plot the images in the batch, along with the corresponding labels
fig = plt.figure(figsize=(25, 4))
for idx in np.arange(20):
    ax = fig.add_subplot(2, 20/2, idx+1, xticks=[], yticks=[])
    plt.imshow(np.transpose(images[idx], (1, 2, 0)))
    ax.set_title(classes[labels[idx]])

2. Model Building and Sanity Checking [15 pt]
Part (a) Convolutional Network - 5 pt
Build a convolutional neural network model that takes the (224x224 RGB) image as input, and predicts the gesture letter. Your model should be a subclass of nn.Module. Explain your choice of neural network architecture: how many layers did you choose? What types of layers did you use? Were they fully-connected or convolutional? What about other decisions like pooling layers, activation functions, number of channels / hidden units?

Answer: I choose 2 convolutional layers for identifying the features, 2 maxpooling layers, 2 fully connected layers for classifying, and 1 output layers.

The input data for conv1 is 3x224x224 o=(i-k)+1=(224-5)+1=220, the size becomes 5x220x220.

Then using relu as activation function and maxpooling with kernel_size=2, stride=2 to reduce hidden units, the size becomes 5x110x110.

The input data for conv2 is 5x110x110, o=(110-5)+1=106, the size becomes 10x106x106

Finally use relu as activation function and maxpooling with kernel_size=2, stride=2 again to reduce hidden units, the size becomes 10x53x53.

class GestureCNN(nn.Module):
  def __init__(self):
    super(GestureCNN, self).__init__()
    self.name = "gesture"
    self.conv1 = nn.Conv2d(3, 5, 5)#in_channels=3 (RGB), out_chanels=5,  kernel_size=5
    self.pool = nn.MaxPool2d(2, 2)#kernel_size=2, stride=2 
    self.conv2 = nn.Conv2d(5, 10, 5)#in_channels=5 (RGB), out_chanels=10,  kernel_size=5
    self.fc1 = nn.Linear(10*53*53, 32)
    self.fc2 = nn.Linear(32, 9)

  def forward(self, x):
    x = self.pool(F.relu(self.conv1(x))) #activation functions
    x = self.pool(F.relu(self.conv2(x))) #activation functions
    x = x.view(-1, 10*53*53)
    x = F.relu(self.fc1(x)) #activation functions
    x = self.fc2(x)
    #x = x.squeeze(1)
    return x
Part (b) Training Code - 5 pt
Write code that trains your neural network given some training data. Your training code should make it easy to tweak the usual hyperparameters, like batch size, learning rate, and the model object itself. Make sure that you are checkpointing your models from time to time (the frequency is up to you). Explain your choice of loss function and optimizer.

Answer: I choose CrossEntropyLoss as my loss function, since it is better measure for classification than MSE and can better minimizes the difference between two probability distributions.

I choose Adam Optimizer as my optimizer, since it is much faster than SGD and can handle sparse gradients on noisy problems, also its default hyperparameters works well on most problems.

def get_accuracy(model, data_loader):
    correct = 0
    total = 0
    for imgs, labels in data_loader:
        
        #############################################
        #To Enable GPU Usage
        if use_cuda and torch.cuda.is_available():
          imgs = imgs.cuda()
          labels = labels.cuda()
        #############################################

        output = model(imgs)
        
        #select index with maximum prediction score
        pred = output.max(1, keepdim=True)[1]
        correct += pred.eq(labels.view_as(pred)).sum().item()
        total += imgs.shape[0]
    return correct / total
def train(model, train_dataset, val_dataset, batch_size=32, num_epochs=1, learn_rate = 0.001):

    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
    val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=True)

    torch.manual_seed(1000)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=learn_rate)

    
    iters, losses, train_acc, val_acc = [], [], [], []
    # training
    print ("Training Started...")
    start_time = time.time()
    n = 0 # the number of iterations
    for epoch in range(num_epochs):
        for imgs, labels in iter(train_loader):
            #############################################
            #To Enable GPU Usage
            if use_cuda and torch.cuda.is_available():
              imgs = imgs.cuda()
              labels = labels.cuda()
            #############################################

            out = model(imgs)             # forward pass
            loss = criterion(out, labels) # compute the total loss
            
            loss.backward()               # backward pass (compute parameter updates)
            optimizer.step()              # make the updates for each parameter
            optimizer.zero_grad()         # a clean up step for PyTorch
            n += 1
        # save the current training information
        iters.append(epoch)
        losses.append(float(loss)/batch_size) # compute *average* loss
        train_acc.append(get_accuracy(model, train_loader))
        val_acc.append(get_accuracy(model, val_loader))# compute validation accuracy
        print(("Epoch {}: Training Accuracy {} |"+"Validation Accuracy {}").format(epoch, train_acc[epoch], val_acc[epoch]))
        
    end_time = time.time()
    print("Total Training time:", end_time-start_time)
    print("Final Training Accuracy: {}".format(train_acc[-1]))
    print("Final Validation Accuracy: {}".format(val_acc[-1]))
    # plotting
    plt.title("Training Curve")
    plt.plot(iters, losses, label="Train")
    plt.xlabel("Iterations")
    plt.ylabel("Loss")
    plt.show()

    plt.title("Training Curve")
    plt.plot(iters, train_acc, label="Training")
    plt.plot(iters, val_acc, label="Validation")
    plt.xlabel("Epochs")
    plt.ylabel("Training Accuracy")
    plt.legend(loc='best')
    plt.show()
    return
net = GestureCNN()
use_cuda = True
if use_cuda and torch.cuda.is_available():
   net.cuda()
   print('CUDA is available!  Training on GPU ...')
else:
   print('CUDA is not available.  Training on CPU ...')

train(net, train_data, val_data, batch_size=32, num_epochs=15, learn_rate = 0.001)
CUDA is available!  Training on GPU ...
Training Started...
Epoch 0: Training Accuracy 0.6050420168067226 |Validation Accuracy 0.5488721804511278
Epoch 1: Training Accuracy 0.7001050420168067 |Validation Accuracy 0.650375939849624
Epoch 2: Training Accuracy 0.717436974789916 |Validation Accuracy 0.650375939849624
Epoch 3: Training Accuracy 0.7962184873949579 |Validation Accuracy 0.6842105263157895
Epoch 4: Training Accuracy 0.8340336134453782 |Validation Accuracy 0.7255639097744361
Epoch 5: Training Accuracy 0.8897058823529411 |Validation Accuracy 0.7105263157894737
Epoch 6: Training Accuracy 0.9275210084033614 |Validation Accuracy 0.7481203007518797
Epoch 7: Training Accuracy 0.9275210084033614 |Validation Accuracy 0.7142857142857143
Epoch 8: Training Accuracy 0.9112394957983193 |Validation Accuracy 0.6917293233082706
Epoch 9: Training Accuracy 0.9485294117647058 |Validation Accuracy 0.7180451127819549
Epoch 10: Training Accuracy 0.9810924369747899 |Validation Accuracy 0.7180451127819549
Epoch 11: Training Accuracy 0.9894957983193278 |Validation Accuracy 0.6992481203007519
Epoch 12: Training Accuracy 0.9926470588235294 |Validation Accuracy 0.6842105263157895
Epoch 13: Training Accuracy 0.9957983193277311 |Validation Accuracy 0.7406015037593985
Epoch 14: Training Accuracy 0.9984243697478992 |Validation Accuracy 0.7255639097744361
Total Training time: 127.46921634674072
Final Training Accuracy: 0.9984243697478992
Final Validation Accuracy: 0.7255639097744361


Part (c) “Overfit” to a Small Dataset - 5 pt
One way to sanity check our neural network model and training code is to check whether the model is capable of “overfitting” or “memorizing” a small dataset. A properly constructed CNN with correct training code should be able to memorize the answers to a small number of images quickly.

Construct a small dataset (e.g. just the images that you have collected). Then show that your model and training code is capable of memorizing the labels of this small data set.

With a large batch size (e.g. the entire small dataset) and learning rate that is not too high, You should be able to obtain a 100% training accuracy on that small dataset relatively quickly (within 200 iterations).

#data path on google drive
!unzip '/content/gdrive/My Drive/APS360/Lab_3b_Dataset_small.zip' -d '/root/datasets'
Archive:  /content/gdrive/My Drive/APS360/Lab_3b_Dataset_small.zip
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#define training and test data directories
small_data_dir= '/root/datasets/Lab_3b_Dataset_small'
small_train_dir=os.path.join(small_data_dir, 'train/')
small_val_dir=os.path.join(small_data_dir, 'validation/')
small_test_dir=os.path.join(small_data_dir, 'test/')

#classes are folders in each directory with these names
small_classes = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I']

#load and transform data using ImageFolder

#resize all images to 224 x 224
small_data_transform = transforms.Compose([transforms.CenterCrop([224,224]), transforms.ToTensor()])

small_train_data=datasets.ImageFolder(small_train_dir, transform=small_data_transform)
small_val_data= datasets.ImageFolder(small_val_dir, transform=small_data_transform)
small_test_data= datasets.ImageFolder(small_test_dir, transform=small_data_transform)

#print out some data stats
print('Num training images: ', len(small_train_data))
print('Num validation images: ', len(small_val_data))
print('Num test images: ', len(small_test_data))
Num training images:  27
Num validation images:  27
Num test images:  27
small_net = GestureCNN()
use_cuda = True
if use_cuda and torch.cuda.is_available():
  small_net.cuda()
  print('CUDA is available!  Training on GPU ...')
else:
  print('CUDA is not available.  Training on CPU ...')
 
train(small_net, small_train_data, small_val_data, batch_size=27, num_epochs=200, learn_rate = 0.001)
CUDA is available!  Training on GPU ...
Training Started...
Epoch 0: Training Accuracy 0.1111111111111111 |Validation Accuracy 0.1111111111111111
Epoch 1: Training Accuracy 0.14814814814814814 |Validation Accuracy 0.1111111111111111
Epoch 2: Training Accuracy 0.2222222222222222 |Validation Accuracy 0.14814814814814814
Epoch 3: Training Accuracy 0.14814814814814814 |Validation Accuracy 0.1111111111111111
Epoch 4: Training Accuracy 0.14814814814814814 |Validation Accuracy 0.14814814814814814
Epoch 5: Training Accuracy 0.14814814814814814 |Validation Accuracy 0.1111111111111111
Epoch 6: Training Accuracy 0.14814814814814814 |Validation Accuracy 0.1111111111111111
Epoch 7: Training Accuracy 0.18518518518518517 |Validation Accuracy 0.1111111111111111
Epoch 8: Training Accuracy 0.2962962962962963 |Validation Accuracy 0.1111111111111111
Epoch 9: Training Accuracy 0.25925925925925924 |Validation Accuracy 0.1111111111111111
Epoch 10: Training Accuracy 0.2962962962962963 |Validation Accuracy 0.1111111111111111
Epoch 11: Training Accuracy 0.3333333333333333 |Validation Accuracy 0.1111111111111111
Epoch 12: Training Accuracy 0.37037037037037035 |Validation Accuracy 0.14814814814814814
Epoch 13: Training Accuracy 0.4074074074074074 |Validation Accuracy 0.1111111111111111
Epoch 14: Training Accuracy 0.4444444444444444 |Validation Accuracy 0.14814814814814814
Epoch 15: Training Accuracy 0.5925925925925926 |Validation Accuracy 0.14814814814814814
Epoch 16: Training Accuracy 0.5925925925925926 |Validation Accuracy 0.14814814814814814
Epoch 17: Training Accuracy 0.5555555555555556 |Validation Accuracy 0.1111111111111111
Epoch 18: Training Accuracy 0.6296296296296297 |Validation Accuracy 0.14814814814814814
Epoch 19: Training Accuracy 0.6296296296296297 |Validation Accuracy 0.1111111111111111
Epoch 20: Training Accuracy 0.8148148148148148 |Validation Accuracy 0.2962962962962963
Epoch 21: Training Accuracy 0.8148148148148148 |Validation Accuracy 0.2962962962962963
Epoch 22: Training Accuracy 0.7777777777777778 |Validation Accuracy 0.2222222222222222
Epoch 23: Training Accuracy 0.8148148148148148 |Validation Accuracy 0.2222222222222222
Epoch 24: Training Accuracy 0.8888888888888888 |Validation Accuracy 0.25925925925925924
Epoch 25: Training Accuracy 0.9259259259259259 |Validation Accuracy 0.25925925925925924
Epoch 26: Training Accuracy 0.9629629629629629 |Validation Accuracy 0.3333333333333333
Epoch 27: Training Accuracy 0.9629629629629629 |Validation Accuracy 0.3333333333333333
Epoch 28: Training Accuracy 0.9629629629629629 |Validation Accuracy 0.2962962962962963
Epoch 29: Training Accuracy 0.9629629629629629 |Validation Accuracy 0.25925925925925924
Epoch 30: Training Accuracy 1.0 |Validation Accuracy 0.2962962962962963
Epoch 31: Training Accuracy 1.0 |Validation Accuracy 0.3333333333333333
Epoch 32: Training Accuracy 1.0 |Validation Accuracy 0.3333333333333333
Epoch 33: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 34: Training Accuracy 1.0 |Validation Accuracy 0.2962962962962963
Epoch 35: Training Accuracy 1.0 |Validation Accuracy 0.2962962962962963
Epoch 36: Training Accuracy 1.0 |Validation Accuracy 0.2962962962962963
Epoch 37: Training Accuracy 1.0 |Validation Accuracy 0.2962962962962963
Epoch 38: Training Accuracy 1.0 |Validation Accuracy 0.2962962962962963
Epoch 39: Training Accuracy 1.0 |Validation Accuracy 0.2962962962962963
Epoch 40: Training Accuracy 1.0 |Validation Accuracy 0.2962962962962963
Epoch 41: Training Accuracy 1.0 |Validation Accuracy 0.3333333333333333
Epoch 42: Training Accuracy 1.0 |Validation Accuracy 0.2962962962962963
Epoch 43: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 44: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 45: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 46: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 47: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 48: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 49: Training Accuracy 1.0 |Validation Accuracy 0.2962962962962963
Epoch 50: Training Accuracy 1.0 |Validation Accuracy 0.2962962962962963
Epoch 51: Training Accuracy 1.0 |Validation Accuracy 0.2962962962962963
Epoch 52: Training Accuracy 1.0 |Validation Accuracy 0.2962962962962963
Epoch 53: Training Accuracy 1.0 |Validation Accuracy 0.2962962962962963
Epoch 54: Training Accuracy 1.0 |Validation Accuracy 0.2962962962962963
Epoch 55: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 56: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 57: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 58: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 59: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 60: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 61: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 62: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 63: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 64: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 65: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 66: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 67: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 68: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 69: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 70: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 71: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 72: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 73: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 74: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 75: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 76: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 77: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 78: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 79: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 80: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 81: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 82: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 83: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 84: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 85: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 86: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 87: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 88: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 89: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 90: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 91: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 92: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 93: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 94: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 95: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 96: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 97: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 98: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 99: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 100: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 101: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 102: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 103: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 104: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 105: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 106: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 107: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 108: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 109: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 110: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 111: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 112: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 113: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 114: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 115: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 116: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 117: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 118: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 119: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 120: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 121: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 122: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 123: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 124: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 125: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 126: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 127: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 128: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 129: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 130: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 131: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 132: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 133: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 134: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 135: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 136: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 137: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 138: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 139: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 140: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 141: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 142: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 143: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 144: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 145: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 146: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 147: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 148: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 149: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 150: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 151: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 152: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 153: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 154: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 155: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 156: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 157: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 158: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 159: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 160: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 161: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 162: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 163: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 164: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 165: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 166: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 167: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 168: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 169: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 170: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 171: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 172: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 173: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 174: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 175: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 176: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 177: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 178: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 179: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 180: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 181: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 182: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 183: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 184: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 185: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 186: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 187: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 188: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 189: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 190: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 191: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 192: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 193: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 194: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 195: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 196: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 197: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 198: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Epoch 199: Training Accuracy 1.0 |Validation Accuracy 0.25925925925925924
Total Training time: 57.11477732658386
Final Training Accuracy: 1.0
Final Validation Accuracy: 0.25925925925925924


3. Hyperparameter Search [10 pt]
Part (a) - 1 pt
List 3 hyperparameters that you think are most worth tuning. Choose at least one hyperparameter related to the model architecture.

Answer: Learning rate, batch size, number of convolutional layers

Part (b) - 5 pt
Tune the hyperparameters you listed in Part (a), trying as many values as you need to until you feel satisfied that you are getting a good model. Plot the training curve of at least 4 different hyperparameter settings.

net_1 = GestureCNN()
if use_cuda and torch.cuda.is_available():
  net_1.cuda()
  print('CUDA is available!  Training on GPU ...')
else:
  print('CUDA is not available.  Training on CPU ...')
train(net_1, train_data, val_data, batch_size=32, num_epochs=10, learn_rate = 0.001)
CUDA is available!  Training on GPU ...
Training Started...
Epoch 0: Training Accuracy 0.31932773109243695 |Validation Accuracy 0.31954887218045114
Epoch 1: Training Accuracy 0.6622899159663865 |Validation Accuracy 0.650375939849624
Epoch 2: Training Accuracy 0.7153361344537815 |Validation Accuracy 0.6541353383458647
Epoch 3: Training Accuracy 0.8130252100840336 |Validation Accuracy 0.7105263157894737
Epoch 4: Training Accuracy 0.8487394957983193 |Validation Accuracy 0.6917293233082706
Epoch 5: Training Accuracy 0.9154411764705882 |Validation Accuracy 0.6879699248120301
Epoch 6: Training Accuracy 0.9327731092436975 |Validation Accuracy 0.7030075187969925
Epoch 7: Training Accuracy 0.9611344537815126 |Validation Accuracy 0.7142857142857143
Epoch 8: Training Accuracy 0.9837184873949579 |Validation Accuracy 0.7180451127819549
Epoch 9: Training Accuracy 0.9873949579831933 |Validation Accuracy 0.7142857142857143
Total Training time: 123.84412002563477
Final Training Accuracy: 0.9873949579831933
Final Validation Accuracy: 0.7142857142857143


net_1 = GestureCNN()
if use_cuda and torch.cuda.is_available():
  net_1.cuda()
  print('CUDA is available!  Training on GPU ...')
else:
  print('CUDA is not available.  Training on CPU ...')
train(net_1, train_data, val_data, batch_size=32, num_epochs=10, learn_rate = 0.001)
CUDA is available!  Training on GPU ...
Training Started...
Epoch 0: Training Accuracy 0.19065126050420167 |Validation Accuracy 0.19924812030075187
Epoch 1: Training Accuracy 0.5031512605042017 |Validation Accuracy 0.518796992481203
Epoch 2: Training Accuracy 0.6607142857142857 |Validation Accuracy 0.575187969924812
Epoch 3: Training Accuracy 0.7095588235294118 |Validation Accuracy 0.6127819548872181
Epoch 4: Training Accuracy 0.7342436974789915 |Validation Accuracy 0.631578947368421
Epoch 5: Training Accuracy 0.7715336134453782 |Validation Accuracy 0.6428571428571429
Epoch 6: Training Accuracy 0.7909663865546218 |Validation Accuracy 0.6917293233082706
Epoch 7: Training Accuracy 0.7899159663865546 |Validation Accuracy 0.6992481203007519
Epoch 8: Training Accuracy 0.8109243697478992 |Validation Accuracy 0.7105263157894737
Epoch 9: Training Accuracy 0.8272058823529411 |Validation Accuracy 0.7293233082706767
Total Training time: 79.43201518058777
Final Training Accuracy: 0.8272058823529411
Final Validation Accuracy: 0.7293233082706767


net_2 = GestureCNN()
if use_cuda and torch.cuda.is_available():
  net_2.cuda()
  print('CUDA is available!  Training on GPU ...')
else:
  print('CUDA is not available.  Training on CPU ...')
train(net_2, train_data, val_data, batch_size=128, num_epochs=10, learn_rate = 0.001)
CUDA is available!  Training on GPU ...
Training Started...
Epoch 0: Training Accuracy 0.3403361344537815 |Validation Accuracy 0.3533834586466165
Epoch 1: Training Accuracy 0.39915966386554624 |Validation Accuracy 0.37593984962406013
Epoch 2: Training Accuracy 0.5887605042016807 |Validation Accuracy 0.5375939849624061
Epoch 3: Training Accuracy 0.6360294117647058 |Validation Accuracy 0.5639097744360902
Epoch 4: Training Accuracy 0.6727941176470589 |Validation Accuracy 0.6127819548872181
Epoch 5: Training Accuracy 0.6964285714285714 |Validation Accuracy 0.6278195488721805
Epoch 6: Training Accuracy 0.740546218487395 |Validation Accuracy 0.6691729323308271
Epoch 7: Training Accuracy 0.7647058823529411 |Validation Accuracy 0.6691729323308271
Epoch 8: Training Accuracy 0.7605042016806722 |Validation Accuracy 0.6691729323308271
Epoch 9: Training Accuracy 0.7988445378151261 |Validation Accuracy 0.6879699248120301
Total Training time: 80.48492407798767
Final Training Accuracy: 0.7988445378151261
Final Validation Accuracy: 0.6879699248120301


net_3 = GestureCNN()
if use_cuda and torch.cuda.is_available():
  net_3.cuda()
  print('CUDA is available!  Training on GPU ...')
else:
  print('CUDA is not available.  Training on CPU ...')
train(net_3, train_data, val_data, batch_size=32, num_epochs=10, learn_rate = 0.005)
CUDA is available!  Training on GPU ...
Training Started...
Epoch 0: Training Accuracy 0.1055672268907563 |Validation Accuracy 0.10150375939849623
Epoch 1: Training Accuracy 0.11186974789915967 |Validation Accuracy 0.11278195488721804
Epoch 2: Training Accuracy 0.11186974789915967 |Validation Accuracy 0.11278195488721804
Epoch 3: Training Accuracy 0.11186974789915967 |Validation Accuracy 0.11278195488721804
Epoch 4: Training Accuracy 0.11186974789915967 |Validation Accuracy 0.11278195488721804
Epoch 5: Training Accuracy 0.11186974789915967 |Validation Accuracy 0.11278195488721804
Epoch 6: Training Accuracy 0.11186974789915967 |Validation Accuracy 0.11278195488721804
Epoch 7: Training Accuracy 0.11186974789915967 |Validation Accuracy 0.11278195488721804
Epoch 8: Training Accuracy 0.11186974789915967 |Validation Accuracy 0.11278195488721804
Epoch 9: Training Accuracy 0.11186974789915967 |Validation Accuracy 0.11278195488721804
Total Training time: 83.62594389915466
Final Training Accuracy: 0.11186974789915967
Final Validation Accuracy: 0.11278195488721804


net_4 = GestureCNN()
if use_cuda and torch.cuda.is_available():
  net_4.cuda()
  print('CUDA is available!  Training on GPU ...')
else:
  print('CUDA is not available.  Training on CPU ...')
train(net_4, train_data, val_data, batch_size=64, num_epochs=10, learn_rate = 0.001)
CUDA is available!  Training on GPU ...
Training Started...
Epoch 0: Training Accuracy 0.3398109243697479 |Validation Accuracy 0.34962406015037595
Epoch 1: Training Accuracy 0.5819327731092437 |Validation Accuracy 0.5902255639097744
Epoch 2: Training Accuracy 0.6890756302521008 |Validation Accuracy 0.6466165413533834
Epoch 3: Training Accuracy 0.7342436974789915 |Validation Accuracy 0.6353383458646616
Epoch 4: Training Accuracy 0.779936974789916 |Validation Accuracy 0.6428571428571429
Epoch 5: Training Accuracy 0.8072478991596639 |Validation Accuracy 0.6879699248120301
Epoch 6: Training Accuracy 0.803046218487395 |Validation Accuracy 0.6616541353383458
Epoch 7: Training Accuracy 0.8345588235294118 |Validation Accuracy 0.6428571428571429
Epoch 8: Training Accuracy 0.8665966386554622 |Validation Accuracy 0.6917293233082706
Epoch 9: Training Accuracy 0.8781512605042017 |Validation Accuracy 0.6616541353383458
Total Training time: 81.10305857658386
Final Training Accuracy: 0.8781512605042017
Final Validation Accuracy: 0.6616541353383458


Part (c) - 2 pt
Choose the best model out of all the ones that you have trained. Justify your choice.

Answer: The first model is the best model so far, with learning rate=0.001, batch size=32, num_epochs=10. The first model has the highest training and validation accuracy. The training accuracy is 0.987, which is near to 1, and the validation accuracy is 0.714. Also, the loss curve converges fastest among four loss plots.

net_1 = GestureCNN()
if use_cuda and torch.cuda.is_available():
  net_1.cuda()
  print('CUDA is available!  Training on GPU ...')
else:
  print('CUDA is not available.  Training on CPU ...')
train(net_1, train_data, val_data, batch_size=32, num_epochs=10, learn_rate = 0.001)
CUDA is available!  Training on GPU ...
Training Started...
Epoch 0: Training Accuracy 0.31932773109243695 |Validation Accuracy 0.31954887218045114
Epoch 1: Training Accuracy 0.6622899159663865 |Validation Accuracy 0.650375939849624
Epoch 2: Training Accuracy 0.7153361344537815 |Validation Accuracy 0.6541353383458647
Epoch 3: Training Accuracy 0.8130252100840336 |Validation Accuracy 0.7105263157894737
Epoch 4: Training Accuracy 0.8487394957983193 |Validation Accuracy 0.6917293233082706
Epoch 5: Training Accuracy 0.9154411764705882 |Validation Accuracy 0.6879699248120301
Epoch 6: Training Accuracy 0.9327731092436975 |Validation Accuracy 0.7030075187969925
Epoch 7: Training Accuracy 0.9611344537815126 |Validation Accuracy 0.7142857142857143
Epoch 8: Training Accuracy 0.9837184873949579 |Validation Accuracy 0.7180451127819549
Epoch 9: Training Accuracy 0.9873949579831933 |Validation Accuracy 0.7142857142857143
Total Training time: 123.84412002563477
Final Training Accuracy: 0.9873949579831933
Final Validation Accuracy: 0.7142857142857143


Part (d) - 2 pt
Report the test accuracy of your best model. You should only do this step once and prior to this step you should have only used the training and validation data.

test_loader = torch.utils.data.DataLoader(test_data, batch_size=32, 
                                          num_workers=num_workers, shuffle=True)
test_accuracy = get_accuracy(net_1, test_loader)

print(("Test accuracy is {}").format(test_accuracy))
Test accuracy is 0.6973180076628352
4. Transfer Learning [15 pt]
For many image classification tasks, it is generally not a good idea to train a very large deep neural network model from scratch due to the enormous compute requirements and lack of sufficient amounts of training data.

One of the better options is to try using an existing model that performs a similar task to the one you need to solve. This method of utilizing a pre-trained network for other similar tasks is broadly termed Transfer Learning. In this assignment, we will use Transfer Learning to extract features from the hand gesture images. Then, train a smaller network to use these features as input and classify the hand gestures.

As you have learned from the CNN lecture, convolution layers extract various features from the images which get utilized by the fully connected layers for correct classification. AlexNet architecture played a pivotal role in establishing Deep Neural Nets as a go-to tool for image classification problems and we will use an ImageNet pre-trained AlexNet model to extract features in this assignment.

Part (a) - 5 pt
Here is the code to load the AlexNet network, with pretrained weights. When you first run the code, PyTorch will download the pretrained weights from the internet.

import torchvision.models
alexnet = torchvision.models.alexnet(pretrained=True)
The alexnet model is split up into two components: alexnet.features and alexnet.classifier. The first neural network component, alexnet.features, is used to compute convolutional features, which are taken as input in alexnet.classifier.

The neural network alexnet.features expects an image tensor of shape Nx3x224x224 as input and it will output a tensor of shape Nx256x6x6 . (N = batch size).

Compute the AlexNet features for each of your training, validation, and test data. Here is an example code snippet showing how you can compute the AlexNet features for some images (your actual code might be different):

# img = ... a PyTorch tensor with shape [N,3,224,224] containing hand images ...

train_data_iter = iter(train_loader)
train_img, train_label = train_data_iter.next()
train_features = alexnet.features(train_img)

val_data_iter = iter(val_loader)
val_img, val_label = val_data_iter.next()
val_features = alexnet.features(val_img)

test_data_iter = iter(test_loader)
test_img, test_label = test_data_iter.next()
test_features = alexnet.features(test_img)

print("training:\n", train_features.shape)
print("validation:\n",val_features.shape)
print("test:\n",test_features.shape)
training:
 torch.Size([256, 256, 6, 6])
validation:
 torch.Size([256, 256, 6, 6])
test:
 torch.Size([256, 256, 6, 6])
Save the computed features. You will be using these features as input to your neural network in Part (b), and you do not want to re-compute the features every time. Instead, run alexnet.features once for each image, and save the result.

alexnet = torchvision.models.alexnet(pretrained=True)

batch_size = 1 # save 1 file at a time
num_workers = 0
train_loader_alexnet = torch.utils.data.DataLoader(train_data, batch_size=batch_size, 
                                           num_workers=num_workers, shuffle=True)
val_loader_alexnet = torch.utils.data.DataLoader(val_data, batch_size=batch_size, 
                                           num_workers=num_workers, shuffle=True)
test_loader_alexnet = torch.utils.data.DataLoader(test_data, batch_size=batch_size, 
                                           num_workers=num_workers, shuffle=True)

classes = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I']
train_path = '/content/gdrive/My Drive/APS360/lab3/Features/train'
val_path = '/content/gdrive/My Drive/APS360/lab3/Features/validation'
test_path = '/content/gdrive/My Drive/APS360/lab3/Features/test'
# save features to folder as tensors
n = 0
for img, label in train_loader_alexnet:
  features = alexnet.features(img)
  features_tensor = torch.from_numpy(features.detach().numpy())
  folder_name = train_path + '/' + str(classes[label])
  if not os.path.isdir(folder_name):
    os.mkdir(folder_name)
  torch.save(features_tensor.squeeze(0), folder_name + '/' + str(n) + '.tensor')
  n += 1

n = 0
for img, label in val_loader_alexnet:
  features = alexnet.features(img)
  features_tensor = torch.from_numpy(features.detach().numpy())
  folder_name = val_path + '/' + str(classes[label])
  if not os.path.isdir(folder_name):
    os.mkdir(folder_name)
  torch.save(features_tensor.squeeze(0), folder_name + '/' + str(n) + '.tensor')
  n += 1

n = 0
for img, label in test_loader_alexnet:
  features = alexnet.features(img)
  features_tensor = torch.from_numpy(features.detach().numpy())
  folder_name = test_path + '/' + str(classes[label])
  if not os.path.isdir(folder_name):
    os.mkdir(folder_name)
  torch.save(features_tensor.squeeze(0), folder_name + '/' + str(n) + '.tensor')
  n += 1
# Load features from folder
feature_train_data = torchvision.datasets.DatasetFolder(train_path, loader=torch.load, extensions=('.tensor'))
feature_val_data = torchvision.datasets.DatasetFolder(val_path, loader=torch.load, extensions=('.tensor'))
feature_test_data = torchvision.datasets.DatasetFolder(test_path, loader=torch.load, extensions=('.tensor'))

# Prepare Dataloader
batch_size = 32
num_workers = 0
feature_train_loader = torch.utils.data.DataLoader(feature_train_data, batch_size=batch_size, 
                                           num_workers=num_workers, shuffle=True)
feature_val_loader = torch.utils.data.DataLoader(feature_val_data, batch_size=batch_size, 
                                           num_workers=num_workers, shuffle=True)
feature_test_loader = torch.utils.data.DataLoader(feature_test_data, batch_size=batch_size, 
                                           num_workers=num_workers, shuffle=True)


train_dataiter = iter(feature_train_loader)
train_feature, train_label = train_dataiter.next()
print("training:")
print(train_feature.shape)
print(train_label.shape)

val_dataiter = iter(feature_val_loader)
val_feature, val_label = val_dataiter.next()
print("validation:")
print(val_feature.shape)
print(val_label.shape)

test_dataiter = iter(feature_test_loader)
test_feature, test_label = test_dataiter.next()
print("test:")
print(test_feature.shape)
print(test_label.shape)
training:
torch.Size([32, 256, 6, 6])
torch.Size([32])
validation:
torch.Size([32, 256, 6, 6])
torch.Size([32])
test:
torch.Size([32, 256, 6, 6])
torch.Size([32])
Part (b) - 3 pt
Build a convolutional neural network model that takes as input these AlexNet features, and makes a prediction. Your model should be a subclass of nn.Module.

Explain your choice of neural network architecture: how many layers did you choose? What types of layers did you use: fully-connected or convolutional? What about other decisions like pooling layers, activation functions, number of channels / hidden units in each layer?

Here is an example of how your model may be called:

Answer:

I choose 1 convolutional layers for identifying the features, no maxpooling layer since the output size is small(6x6), 2 fully connected layers, and 1 output layers. The output size for first nn.linear is 32, for second nn.linear is 9.

The input data for conv1 is 256x6x6, o=(i-k)+2p+1=(6-5)+2x2+1=1+4+1=6, the size then is 160x6x6. Then using relu as activation function.

class CNNClassifier(nn.Module):
    def __init__(self):
          self.name = "CNNClassifier"
          super(CNNClassifier, self).__init__()
          self.conv1 = nn.Conv2d(256, 160, 5, padding=2) #in_channels=256, out_chanels=160, kernel_size=5, padding=2
          self.fc1 = nn.Linear(160*6*6, 32)
          self.fc2 = nn.Linear(32, 9)

    def forward(self, x):
          x = F.relu(self.conv1(x)) #activation function
          x = x.view(-1, 160*6*6) 
          x = F.relu(self.fc1(x))#activation function
          x = self.fc2(x)
          return x
# features = ... load precomputed alexnet.features(img) ...
#output = model(features)
#prob = F.softmax(output)
Part (c) - 5 pt
Train your new network, including any hyperparameter tuning. Plot and submit the training curve of your best model only.

Note: Depending on how you are caching (saving) your AlexNet features, PyTorch might still be tracking updates to the AlexNet weights, which we are not tuning. One workaround is to convert your AlexNet feature tensor into a numpy array, and then back into a PyTorch tensor.

net_5 = CNNClassifier()
use_cuda = True
if use_cuda and torch.cuda.is_available():
  net_5.cuda()
  print('CUDA is available!  Training on GPU ...')
else:
  print('CUDA is not available.  Training on CPU ...')

train(net_5, feature_train_data, feature_val_data, batch_size=32, num_epochs=10, learn_rate = 0.001)
CUDA is available!  Training on GPU ...
Training Started...
Epoch 0: Training Accuracy 0.9944826150322715 |Validation Accuracy 0.9274395329441201
Epoch 1: Training Accuracy 0.9995835935873413 |Validation Accuracy 0.9457881567973311
Epoch 2: Training Accuracy 1.0 |Validation Accuracy 0.9674728940783986
Epoch 3: Training Accuracy 1.0 |Validation Accuracy 0.9649708090075062
Epoch 4: Training Accuracy 1.0 |Validation Accuracy 0.9649708090075062
Epoch 5: Training Accuracy 1.0 |Validation Accuracy 0.9649708090075062
Epoch 6: Training Accuracy 1.0 |Validation Accuracy 0.9649708090075062
Epoch 7: Training Accuracy 1.0 |Validation Accuracy 0.9649708090075062
Epoch 8: Training Accuracy 1.0 |Validation Accuracy 0.9649708090075062
Epoch 9: Training Accuracy 1.0 |Validation Accuracy 0.9649708090075062
Total Training time: 465.30307936668396
Final Training Accuracy: 1.0
Final Validation Accuracy: 0.9649708090075062


#tensor = torch.from_numpy(tensor.detach().numpy())
Part (d) - 2 pt
Report the test accuracy of your best model. How does the test accuracy compare to Part 3(d) without transfer learning?

Answer: The new test accuracy is 0.9217462932454695, which is much higher than the result in part3(d) that only 0.6973180076628352. Since AlexNet extracts the important features of the dataset and reduces size of the input data, its performance is much better.

feature_test_loader = torch.utils.data.DataLoader(feature_test_data, batch_size=32, 
                                           num_workers=num_workers, shuffle=True)
feature_test_accuracy = get_accuracy(net_5, feature_test_loader)
print(("Test accuracy is {}").format(feature_test_accuracy))
Test accuracy is 0.9217462932454695
5. Additional Testing [5 pt]
As a final step in testing we will be revisiting the sample images that you had collected and submitted at the start of this lab. These sample images should be untouched and will be used to demonstrate how well your model works at identifying your hand guestures.

Using the best transfer learning model developed in Part 4. Report the test accuracy on your sample images and how it compares to the test accuracy obtained in Part 4(d)? How well did your model do for the different hand guestures? Provide an explanation for why you think your model performed the way it did?

Answer: The test accuracy on sample images is 1, which higher than the test accuracy obtained in part4(d). This indicates my model can accurately classify every different hand guestures, all predicts are correct. The test accuracy reach 1 becasue among 27 images I used is similar to each other, each letter has three similar images. I could try to test more images.

#data path on google drive
!unzip '/content/gdrive/My Drive/APS360/sample.zip' -d '/root/datasets'
Archive:  /content/gdrive/My Drive/APS360/sample.zip
   creating: /root/datasets/sample/
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  inflating: /root/datasets/__MACOSX/sample/test/E/._1005092427_E_2.jpg  
  inflating: /root/datasets/sample/test/B/1005092427_B_3.jpg  
  inflating: /root/datasets/__MACOSX/sample/test/B/._1005092427_B_3.jpg  
  inflating: /root/datasets/sample/test/B/1005092427_B_2.jpg  
  inflating: /root/datasets/__MACOSX/sample/test/B/._1005092427_B_2.jpg  
  inflating: /root/datasets/sample/test/B/1005092427_B_1.jpg  
  inflating: /root/datasets/__MACOSX/sample/test/B/._1005092427_B_1.jpg  
#define training and test data directories
sample_dir= '/root/datasets/sample/test'
#sample_data_dir=os.path.join(sample_dir, 'zhao_1005092427/')


classes = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I']
#resize all images to 224 x 224
sample_data_transform = transforms.Compose([transforms.CenterCrop([224,224]), transforms.ToTensor()])
#sample_data= datasets.ImageFolder(sample_data_dir, transform=sample_data_transform)
sample_data =datasets.ImageFolder(sample_dir,transform=sample_data_transform)
print(sample_data)
Dataset ImageFolder
    Number of datapoints: 27
    Root location: /root/datasets/sample/test
    StandardTransform
Transform: Compose(
               CenterCrop(size=[224, 224])
               ToTensor()
           )
alexnet = torchvision.models.alexnet(pretrained=True)

batch_size = 1 # save 1 file at a time
num_workers = 0
classes = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I']
sample_loader_alexnet = torch.utils.data.DataLoader(sample_data, batch_size=batch_size, 
                                           num_workers=num_workers, shuffle=True)



sample_path = '/content/gdrive/My Drive/APS360/lab3/FeaturesSample/test'
# save features to folder as tensors
n = 0
for img, label in sample_loader_alexnet:
  features = alexnet.features(img)
  features_tensor = torch.from_numpy(features.detach().numpy())
  folder_name = sample_path + '/' + str(classes[label])
  if not os.path.isdir(folder_name):
    os.mkdir(folder_name)
  torch.save(features_tensor.squeeze(0), folder_name + '/' + str(n) + '.tensor')
  n += 1
# Load features from folder
feature_sample_data = torchvision.datasets.DatasetFolder(sample_path, loader=torch.load, extensions=('.tensor'))

# Prepare Dataloader
batch_size = 32
num_workers = 0

feature_sample_loader = torch.utils.data.DataLoader(feature_sample_data, batch_size=batch_size, 
                                           num_workers=num_workers, shuffle=True)

sample_dataiter = iter(feature_sample_loader)
sample_feature, sample_label = sample_dataiter.next()
print("test:")
print(sample_feature.shape)
print(sample_label.shape)
test:
torch.Size([27, 256, 6, 6])
torch.Size([27])
sample_data_accuracy = get_accuracy(net_5, feature_sample_loader)
print(("Test accuracy is {}").format(sample_data_accuracy))
Test accuracy is 1.0
#%%shell
#jupyter nbconvert --to html /Lab_3_Gesture_Recognition.ipynb