The MNIST database of handwritten digits (from 0 to 9) has a training set of 55,000 examples,… 1 answer below »

Problem Description

The MNIST database of handwritten digits (from 0 to 9) has a training set of 55,000

examples, and a test set of 10,000 examples. The digits have been size-normalized and

centered in a fixed-size image (28×28 pixels) with values from 0 to 1. You can use the

following code with TensorFlow in Python to download the data.

from keras.datasets import mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()

Every MNIST data point has two parts: an image of a handwritten digit and a

corresponding label. We will call the images ? and the labels ?. Both the training set and

test set contain ? and ?.

Each image is 28 pixels by 28 pixels and can be flattened into a vector of 28×28 = 784

numbers.

As mentioned, the corresponding labels in the MNIST are numbers between 0 and 9,

describing which digit a given image is of. In this assignment, we regard the labels as

one-hot vectors, i.e. 0 in most dimensions, and 1 in a single dimension. In this case,

the ?-th digit will be represented as a vector which is 1 in the ? dimensions. For

example, 3 would be [0,0,0,1,0,0,0,0,0,0].

The assignment aims to build NNs for classifying handwritten digits in the MNIST

database, train it on the training set and test it on the test set.

Please read the following comments and requirements very carefully before starting the

assignment:

1. The assignment is based on the content of Labs.

2. In Lecture 1, we talked about the use of training set, validation set and test set

in machine learning. In the assignment, you are asked to train the NN on the

training set and test the NN on the test set, instead of doing the two steps on the

same data set as what was done in Lab 5. You do NOT need the validation set in

the assignment.

3. In the assignment, the performance of a NN is measured by the its prediction

accuracy in classifying images from the test set, i.e. number of the correctly

predicted images / number of the images in the test set.

4. You are asked to model THREE NNs by changing the architecture. For example,

you may change the number of layers, use different type of layers, and try

various activation layers.

5. You are encouraged to repeatedly train and test your NNs with different

parameter setting, e.g. learning rate.

6. Your report MUST at least contain the following content

a. Names and student numbers of all group members;

Attachments:

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