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Project 3 Handwritten Digits Recognition solution

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Project Overview:
In this part, we will revisit the Handwritten Digits Recognition task in Part 1, using
a
convolutional neural network. The basic dataset is the same MNIST dataset from
Part I,
but you may choose to use only a subset for training and testing, if speed
performance with the entire dataset becomes a bottleneck. For example, you
may use only 6000 samples for training (each digit with 600 samples) and 1000
samples for testing (each digit with 100 samples).
The basic requirement of this part is to experiment with a convolutional neural
network with the following parameter settings:
(1) The input size is the size of the image (28×28).

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Project Overview:
In this part, we will revisit the Handwritten Digits Recognition task in Part 1, using
a
convolutional neural network. The basic dataset is the same MNIST dataset from
Part I,
but you may choose to use only a subset for training and testing, if speed
performance with the entire dataset becomes a bottleneck. For example, you
may use only 6000 samples for training (each digit with 600 samples) and 1000
samples for testing (each digit with 100 samples).
The basic requirement of this part is to experiment with a convolutional neural
network with the following parameter settings:
(1) The input size is the size of the image (28×28).
(2) The first hidden layer is a convolutional layer, with 6 feature maps. The
convolution
kernels are of 3×3 in size. Use stride 1 for convolution.
(3) The convolutional layer is followed by a max pooling layer. The pooling is 2×2
with stride 1.
(4) After max pooling, the layer is connected to the next convolutional layer, with
16 feature maps. The convolution kernels are of 3×3 in size. Use stride 1 for
convolution.
(5) The second convolutional layer is followed by a max pooling layer. The
pooling is 2×2 with stride 1.
(6) After max pooling, the layer is fully connected to the next hidden layer with
120 nodes and relu as the activation function.
(7) The fully connected layer is followed by another fully connected layer with 84
nodes and relu as the activation function, then connected to a softmax layer with
10 output nodes (corresponding to the 10 classes).
We will train such a network with the training set and then test it on the testing
set.
You are required to plot the training error and the testing error as a function of
the learning epochs. You are also required to change some of the hyperparameters (the kernel size, the number of feature maps, etc), and then repeat
the experiment and plot training and testing errors under the new setting.
These are the minimum requirements. Additional requirements may be added
(like
experimenting with different kernel sizes, number of feature maps, ways of doing
pooling, or even introducing drop-out in training, etc.).
Algorithm:
Convolutional Neural Network
Resources:
MNIST dataset, Google CoLab
Workspace:
Google CoLab (see file intro_to_colab.docx for more details)
Software:
Google CoLab
Language(s):
Python
Getting Started:
Read this document carefully, as well as additional files included
(intro_to_colab.docx and baseline.docx
Actions
). For more details about Colab, please go
to https://colab.research.google.com/notebooks/welcome.ipynb (Links to an external
site.)
Required Tasks:
1. Read intro_to_colab.docx
Actions
to get familiar with the platform.
2. Run the baseline code (baseline.docx
Actions
) and report the accuracy.
3. Change the kernel size to 5*5, redo the experiment, plot the learning errors
along with the epoch, and report the testing error and accuracy on the test set.
4. Change the number of the feature maps in the first and second convolutional
layers, redo the experiment, plot the learning errors along with the epoch, and
report the testing error and accuracy on the test set.
5. Submit a brief report summarizing the above results, along with your code.
What to Submit and Due Dates
1. Code: please add comment properly to explain what you do.
2. A report including the plots for the learning/testing errors and the final
classification accuracy.