Assignment 5 Neural Networks for Recognition1

COMP5421 Computer Vision, 
Homework Assignment 5
Neural Networks for Recognition1
Total Points: 133
Instructions
1. Integrity and collaboration: Students are encouraged to work in groups but each student
must submit their own work. Include the names of your collaborators in your write up. Code
should NOT be shared or copied. Please DO NOT use external code unless permitted.
Plagiarism is prohibited and may lead to failure of this course.
2. Submission: You will be submitting both your writeup and code zip file to CASS. The zip
file, <ustlogin-id.zip> contains your code, write-up and any results files we ask you to save.
3. Do not submit anything from the data/ folder in your submission.
4. For your code submission, DO NOT use any libraries other than numpy, scipy, scikitimage,
matplotlib and (in the appropriate section) pytorch. Including other libraries (for example,
cv2, ipdb, etc.) may lead to loss of credit on the assignment.
5. To get the data, we have included some scripts in scripts.
1 Theory
Q1.1 Theory [2 points] Prove that softmax is invariant to translation, that is
softmax(x) = softmax(x + c)∀c ∈ R
Softmax is defined as below, for each index i in a vector x.
softmax(xi) = e
xi
P
j
e
xj
Often we use c = − max xi
. Why is that a good idea? (Tip: consider the range of values that
numerator will have with c = 0 and c = max xi)
Q1.2 Theory [2 points] Softmax can be written as a three step processes, with si = e
xi
, S =
Psi
and softmax(xi) = 1
S
si
.
• As x ∈ R
d
, what are the properties of softmax(x), namely what is the range of each element?
What is the sum over all elements?
• One could say that “softmax takes an arbitrary real valued vector x and turns it into a
”.
• Can you see the role of each step in the multi-step process now? Explain them.
1Credit to CMU Simon Lucey, Gaurav Mittal, Akshita Mittel, Sowmya Munukutla, Nathaniel Chodosh, MingFang Chang, Chengqian Che
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Q1.3 Theory [2 points] Show that multi-layer neural networks without a non-linear activation
function are equivalent to linear regression.
Q1.4 Theory [3 points] Given the sigmoid activation function σ(x) = 1
1+e−x , derive the gradient
of the sigmoid function and show that it can be written as a function of σ(x) (without having access
to x directly).
Q1.5 Theory [12 points] Given y = x
TW + b (or yj =
Pd
i=1 xiWij + bj ), and the gradient of
some loss J with respect y, show how to get ∂J
∂W ,
∂J
∂x ,
∂J
∂b . Be sure to do the derivatives with scalars
and re-form the matrix form afterwards. Here is some notatiional suggestions.
∂J
∂y = δ ∈ R
k×1 W ∈ R
d×k x ∈ R
d×1
b ∈ R
k×1
We won’t grade the derivative with respect to b but you should do it anyways, you will need it
later in the assignment.
Q1.6 Theory [4 points] When the neural network applies the elementwise activation function
(such as sigmoid), the gradient of the activation function scales the backpropogation update. This
is directly from the chain rule, d
dx f(g(x)) = f
0
(g(x))g
0
(x).
1. Consider the sigmoid activation function for deep neural networks. Why might it lead to a
“vanishing gradient” problem if it is used for many layers (consider plotting Q1.3)?
2. Often it is replaced with tanh(x) = 1−e−2x
1+e−2x . What are the output ranges of both tanh and
sigmoid? Why might we prefer tanh?
3. Why does tanh(x) have less of a vanishing gradient problem? (plotting the derivatives helps!
for reference: tanh0
(x) = 1 − tanh(x)
2
)
4. tanh is a scaled and shifted version of the sigmoid. Show how tanh(x) can be written in terms
of σ(x). (Hint: consider how to make it have the same range)
2 Implement a Fully Connected Network
All of these functions should be implemented in python/nn.py
2.1 Network Initialization
Q2.1.1 Theory [2 points] Why is it not a good idea to initialize a network with all zeros? If you
imagine that every layer has weights and biases, what can a zero-initialized network output after
training?
Q.2 Code [1 points] Implement a function to initialize neural network weights with Xavier
initialization [1], where Var[w] = 2
nin+nout
where n is the dimensionality of the vectors out and you
use a uniform distribution to sample random numbers (see eq 16 in the paper).
Q2.1.3 Theory [1 points] Why do we initialize with random numbers? Why do we scale the
initialization depending on layer size (see near Fig 6 in the paper)?
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2.2 Forward Propagation
The appendix (sec 8) has the math for forward propagation, we will implement it here.
Q2.2.1 Code [4 points] Implement sigmoid, along with forward propagation for a single layer
with an activation function, namely y = σ(XW +b), returning the output and intermediate results
for an N ×D dimension input X, with examples along the rows, data dimensions along the columns.
Q2.2.2 Code [3 points] Implement the softmax function. Be sure the use the numerical stability
trick you derived in Q1.1 softmax.
Q2.2.3 Code [3 points] Write a function to compute the accuracy of a set of labels, along with the
scalar loss across the data. The loss function generally used for classification is the cross-entropy
loss.
Lf (D) = X
(x,by)∈D
y · log(f(x))
Here D is the full training dataset of data samples x (N × 1 vectors, N =dimensionality of data)
and labels y (C × 1 one-hot vectors, C = number of classes).
2.3 Backwards Propagation
Q2.3.1 Code [7 points] Compute backpropogation for a single layer, given the original weights,
the appropriate intermediate results, and given gradient with respect to the loss. You should return
the gradient with respect to X so you can feed it into the next layer. As a size check, your gradients
should be the same dimensions as the original objects.
2.4 Training Loop
You will tend to see gradient descent in three forms: “normal”, “stochastic” and “batch”. “Normal” gradient descent aggregates the updates for the entire dataset before changing the weights.
Stochastic gradient descent applies updates after every single data example. Batch gradient descent is a compromise, where random subsets of the full dataset are evaluated before applying the
gradient update.
Q2.4.1 Code [5 points] Write a training loop that generates random batches, iterates over them
for many iterations, does forward and backward propagation, and applies a gradient update step.
2.5 Numerical Gradient Checker
Q2.5.1 [5 points] Implement a numerical gradient checker. Instead of using the analytical gradients computed from the chain rule, add  offset to each element in the weights, and compute
the numerical gradient of the loss with central differences. Central differences is just f(x+e)−f(x−e)
2
.
Remember, this needs to be done for each scalar dimension in all of your weights independently.
3 Training Models
First, be sure to run the script, from inside the scripts folder, get data.sh. This will use wget and
unzip to download
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https://cmu.box.com/shared/static/ebklocte1t2wl4dja61dg1ct285l65md.zip
https://cmu.box.com/shared/static/147jyd6uewh1fff96yfh6e11s98v11xm.zip
and extract them to data and image folders
Since our input images are 32 × 32 images, unrolled into one 1024 dimensional vector, that gets
multiplied by W(1), each row of W(1) can be seen as a weight image. Reshaping each row into a
32 × 32 image can give us an idea of what types of images each unit in the hidden layer has a high
response to.
We have provided you three data .mat files to use for this section. The training data in
nist36 train.mat contains samples for each of the 26 upper-case letters of the alphabet and the
10 digits. This is the set you should use for training your network. The cross-validation set in
nist36 valid.mat contains samples from each class, and should be used in the training loop to
see how the network is performing on data that it is not training on. This will help to spot over
fitting. Finally, the test data in nist36 test.mat contains testing data, and should be used for
the final evaluation on your best model to see how well it will generalize to new unseen data.
Q3.1.1 Code [3 points] Train a network from scratch. Use a single hidden layer with 64 hidden
units, and train for at least 30 epochs. Modify the script to plot generate two plots: one showing
the accuracy on both the training and validation set over the epochs, and the other showing the
cross-entropy loss averaged over the data. The x-axis should represent the epoch number, while
the y-axis represents the accuracy or loss. With these settings, you should see an accuracy on the
validation set of at least 75%.
Q3.1.2 Writeup [2 points] Use your modified training script to train three networks, one with
your best learning rate, one with 10 times that learning rate and one with one tenth that learning
rate. Include all 4 plots in your writeup. Comment on how the learning rates affect the training,
and report the final accuracy of the best network on the test set.
Q3.1.3 Writeup [3 points] Visualize the first layer weights that your network learned (using
reshape and ImageGrid). Compare these to the network weights immediately after initialization.
Include both visualizations in your writeup. Comment on the learned weights. Do you notice any
patterns?
Q3.1.4 Writeup [2 points] Visualize the confusion matrix for your best model. Comment on the
top few pairs of classes that are most commonly confused.
4 Extract Text from Images
Now that you have a network that can recognize handwritten letters with reasonable accuracy, you
can now use it to parse text in an image. Given an image with some text on it, our goal is to have
a function that returns the actual text in the image. However, since your neural network expects
a a binary image with a single character, you will need to process the input image to extract each
character. There are various approaches that can be done so feel free to use any strategy you like.
Here we outline one possible method, another is that given in a tutorial
https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_label.html.
1. Process the image (blur, threshold, opening morphology, etc. (perhaps in that order)) to
classify all pixels as being part of a character or background.
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Figure 1: Sample image with handwritten characters annotated with boxes around each character.
2. Find connected groups of character pixels (see skimage.measure.label). Place a bounding
box around each connected component.
3. Group the letters based on which line of the text they are a part of, and sort each group so
that the letters are in the order they appear on the page.
4. Take each bounding box one at a time and resize it to 32 × 32, classify it with your network,
and report the characters in order (inserting spaces when it makes sense).
Since the network you trained likely does not have perfect accuracy, you can expect there to
be some errors in your final text parsing. Whichever method you choose to implement for the
character detection, you should be able to place a box on most of there characters in the image.
We have provided you with 01 list.jpg, 02 letters.jpg, 03 haiku.jpg and 04 deep.jpg to test
your implementation on.
Q4.1 Theory [2 points] The method outlined above is pretty simplistic, and makes several
assumptions. What are two big assumptions that the sample method makes. In your writeup,
include two example images where you expect the character detection to fail (either miss valid
letters, or respond to non-letters).
Q4.2 Code [10 points] Find letters in the image. Given an RGB image, this function should
return bounding boxes for all of the located handwritten characters in the image, as well as a binary
black-and-white version of the image im. Each row of the matrix should contain [y1,x1,y2,x2]
the positions of the top-left and bottom-right corners of the box. The black and white image should
be floating point, 0 to 1, with the characters in black and background in white.
Q4.3 Writeup [3 points] Run findLetters(..) on all of the provided sample images in images/.
Plot all of the located boxes on top of the image to show the accuracy of your findLetters(..)
function. Include all the result images in your writeup.
Q4.4 Code/Writeup [8 points] Now you will load the image, find the character locations, classify
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each one with the network you trained in Q3.2.1, and return the text contained in the image. Be
sure you try to make your detected images look like the images from the training set. Visualize
them and act accordingly.
Run your run q4 on all of the provided sample images in images/. Include the extracted text
in your write-up.
5 Image Compression with Autoencoders
An autoencoder is a neural network that is trained to attempt to copy its input to its output,
but it usually allows copying only approximately. This is typically achieved by restricting the
number of hidden nodes inside the autoencoder; in other words, the autoencoder would be forced
to learn to represent data with this limited number of hidden nodes. This is a useful way of learning
compressed representations. In this section, we will continue using the NIST36 dataset you have
from the previous questions.
5.1 Building the Autoencoder
Q5.1.1 Code [5 points] Due to the difficulty in training auto-encoders, we have to move to the
relu(x) = max(x, 0) activation function. It is provided for you in util.py. Implement a 2 hidden
layer autoencoder where the layers are
• 1024 to 32 dimensions, followed by a ReLU
• 32 to 32 dimensions, followed by a ReLU
• 32 to 32 dimensions, followed by a ReLU
• 32 to 1024 dimensions, followed by a sigmoid (this normalizes the image output for us)
The loss function that you’re using is total squared error for the output image compared to the
input image (they should be the same!).
Q5.1.2 Code [3 points] To help even more with convergence speed, we will implement momentum.
Now, instead of updating W = W − α
∂J
∂W , we will use the update rules MW = 0.9MW − α
∂J
∂W and
W = W + MW . To implement this, populate the parameters dictionary with zero-initialized
momentum accumulators, one for each parameter. Then simply perform both update equations for
every batch.
5.2 Training the Autoencoder
Q5.2 Writeup/Code [2 points] Using the provided default settings, train the network for 100
epochs. What do you observe in the plotted training loss curve as it progresses?
5.3 Evaluating the Autoencoder
Q5.3.1 Writeup/Code [2 points] Now lets evaluate how well the autoencoder has been trained.
Select 5 classes from the total 36 classes in your dataset and for each selected class include in your
report 2 validation images and their reconstruction. What differences do you observe that exist in
the reconstructed validation images, compared to the original ones?
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Q5.3.2 Writeup [2 points] Let’s evaluate the reconstruction quality using Peak Signal-to-noise
Ratio (PSNR). PSNR is defined as
PSNR = 20 log10(MAXI ) − 10 log10(MSE)
where MAXI is the maximum possible pixel value of the image, and MSE (mean squared error) is
computed across all pixels. You may use skimage.measure.compare psnr for convenience. Report
the average PSNR you get from the autoencoder across all validation images.
6 Comparing against PCA
As a baseline for comparison, we will use one of the most popular methods for data dimensionality
reduction - Principle Component Analysis (PCA). PCA allows one to find the best low-rank approximation of the data by keeping only a specified number of principle components. To perform
PCA, we will use a factorization method called Singular Value Decomposition (SVD).
Run SVD on the training data. One of the matrices will be an orthonormal matrix that indicates
the components of your data, sorted by their importances. Extract the first 32 principle components
and form a projection matrix; you will need to figure out how to do these from the U,S,V matrices.
Q6.1 Writeup [2 points] What is the size of your projection matrix? What is its rank? This
projection matrix was “trained” from our training data. Now let’s “test” it on our test data.
Use the projection matrix on test data to obtain the reconstructed test images. Note that these
reconstructions can also be represented with only 32 numbers.
Q6.2 Writeup [2 points] Use the classes you selected in Q5.3.1, and for each of these 5 classes,
include in your report 2 test images and their reconstruction. You may use test labels to help you
find the corresponding classes. What differences do you observe that exist in the reconstructed test
images, compared to the original ones? How do they compare to the ones reconstructed from your
autoencoder?
Q6.3 Writeup [2 points] Report the average PSNR you get from PCA. Is it better than your
autoencoder? Why?
Q6.4 Writeup [2 points] Count the number of learned parameters for both your autoencoder
and the PCA model. How many parameters do the respective approaches learn? Why is there such
a difference in terms of performance?
7 PyTorch
While you were able to derive manual backpropogation rules for sigmoid and fully-connected layers,
wouldn’t it be nice if someone did that for lots of useful primatives and made it fast and easy to use
for general computation? Meet automatic differentiation. Since we have high-dimensional inputs
(images) and low-dimensional outputs (a scalar loss), it turns out forward mode AD is very
efficient. Popular autodiff packages include pytorch (Facebook), tensorflow (Google), autograd
(Boston-area academics). Autograd provides its own replacement for numpy operators and is a
drop-in replacement for numpy, except you can ask for gradients now. The other two are able
to act as shim layers for cuDNN, an implementation of auto-diff made by Nvidia for use on their
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GPUs. Since GPUs are able to perform large amounts of math much faster than CPUs, this makes
the former two packages very popular for researchers who train large networks. Tensorflow asks you
to build a computational graph using its API, and then is able to pass data through that graph.
PyTorch builds a dynamic graph and allows you to mix autograd functions with normal python
code much more smoothly, so it is currently more popular among HKUST students.
For extra credit, we will use PyTorch as a framework. Many computer vision projects use neural
networks as a basic building block, so familiarity with one of these frameworks is a good skill to
develop. Here, we basically replicate and slightly expand our handwritten character recognition
networks, but do it in PyTorch instead of doing it ourselves. Feel free to use any tutorial you like,
but we like the offical one or this tutorial (in a jupyter notebook) or these slides (starting from
number 35).
For this section, you’re free to implement these however you like. All of the tasks
required here are fairly small and don’t require a GPU if you use small networks.
Including 7.2.
7.1 Train a neural network in PyTorch
Q7.1.1 Code/Writeup [5 points] Re-write and re-train your fully-connected network on NIST36
in PyTorch. Plot training accuracy and loss over time.
Q7.1.2 Code/Writeup [2 points] Train a convolutional neural network with PyTorch on MNIST.
Plot training accuracy and loss over time.
Q7.1.3 Code/Writeup [3 points] Train a convolutional neural network with PyTorch on the
included NIST36 dataset.
Q7.1.4 Code/Writeup [10 points] Train a convolutional neural network with PyTorch on the
EMNIST Balanced dataset and evaluate it on the findLetters bounded boxes from the images folder.
7.2 Fine Tuning
When training from scratch, a lot of epochs and data are often needed to learn anything meaningful.
One way to avoid this is to instead initialize the weights more intelligently.
These days, it is most common to initialize a network with weights from another deep network
that was trained for a different purpose. This is because, whether we are doing image classification,
segmentation, recognition etc..., most real images share common properties. Simply copying the
weights from the other network to yours gives your network a head start, so your network does not
need to learn these common weights from scratch all over again. This is also referred to as fine
tuning.
Q7.2.1 Code/Writeup [5 points] Fine-tune a single layer classifier using pytorch on the flowers 17
(or flowers 102!) dataset using squeezenet1 1, as well as an architecture you’ve designed yourself
(3 conv layers, followed 2 fc layers, it’s standard slide 6) and trained from scratch. How do they
compare?
We include a script in scripts/ to fetch the flowers dataset and extract it in a way that PyTorch
ImageFolder can consume it, see an example from data/oxford-flowers17. You should look at
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how SqueezeNet is defined, and just replace the classifier layer. There exists a pretty good example
for fine-tuning in PyTorch.
References
1. Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward
neural networks. 2010. http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf
2. P. J. Grother. Nist special database 19 handprinted forms and characters database. https:
//www.nist.gov/srd/nist-special-database-19, 1995.
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8 Appendix: Neural Network Overview
Deep learning has quickly become one of the most applied machine learning techniques in computer vision. Convolutional neural networks have been applied to many different computer vision
problems such as image classification, recognition, and segmentation with great success. In this
assignment, you will first implement a fully connected feed forward neural network for hand written
character classification. Then in the second part, you will implement a system to locate characters
in an image, which you can then classify with your deep network. The end result will be a system
that, given an image of hand written text, will output the text contained in the image.
8.1 Basic Use
Here we will give a brief overview of the math for a single hidden layer feed forward network. For
a more detailed look at the math and derivation, please see the class slides.
A fully-connected network f, for classification, applies a series of linear and non-linear functions
to an input data vector x of size N × 1 to produce an output vector f(x) of size C × 1, where each
element i of the output vector represents the probability of x belonging to the class i. Since the
data samples are of dimensionality N, this means the input layer has N input units. To compute
the value of the output units, we must first compute the values of all the hidden layers. The first
hidden layer pre-activation a
(1)(x) is given by
a
(1)(x) = W(1)x + b
(1)
Then the post-activation values of the first hidden layer h
(1)(x) are computed by applying a nonlinear activation function g to the pre-activation values
h
(1)(x) = g(a
(1)(x)) = g(W(1)x + b
(1))
Subsequent hidden layer (1 < t ≤ T) pre- and post activations are given by:
a
(t)
(x) = W(t)h
(t−1) + b
(t)
h
(t)
(x) = g(a
(t)
(x))
The output layer pre-activations a
(T)
(x) are computed in a similar way
a
(T)
(x) = W(T)h
(T −1)(x) + b
(T)
and finally the post-activation values of the output layer are computed with
f(x) = o(a
(T)
(x)) = o(W(T)h
(T −1)(x) + b
(T)
)
where o is the output activation function. Please note the difference between g and o! For this
assignment, we will be using the sigmoid activation function for the hidden layer, so:
g(y) = 1
1 + exp(−y)
where when g is applied to a vector, it is applied element wise across the vector.
Since we are using this deep network for classification, a common output activation function to
use is the softmax function. This will allow us to turn the real value, possibly negative values of
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Figure 2: Samples from NIST Special 19 dataset [2]
a
(T)
(x) into a set of probabilities (vector of positive numbers that sum to 1). Letting xi denote the
i
th element of the vector x, the softmax function is defined as:
oi(y) = exp(yi)
P
j
exp(yj )
Gradient descent is an iterative optimisation algorithm, used to find the local optima. To find the
local minima, we start at a point on the function and move in the direction of negative gradient
(steepest descent) till some stopping criteria is met.
8.2 Backprop
The update equation for a general weight W
(t)
ij and bias b
(t)
i
is
W
(t)
ij = W
(t)
ij − α
∂L
∂W(t)
ij
(x) b
(t)
i = b
(t)
i − α
∂L
∂b(t)
i
(x)
α is the learning rate. Please refer to the backpropagation slides for more details on how to derive
the gradients. Note that here we are using softmax loss (which is different from the least square
loss in the slides).
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