Project #2: Convolutional Neural Networks
Amir Sadovnik
COSC 424/525: Deep Learning (Spring 2023)
1 Overview
In this project you will be adding a few layer types to your artificial neural network li
ary.
More specifically, you are required to add the following layers:
1. ConvolutionalLaye
2. MaxPoolingLayer (COSC 525 only)
3. FlattenLaye
In addition you will get your first experience using tensorflow with the keras API. (tf.keras)
As in the previous project you are required to code this in the manner described below.
Make sure to use your own solution and your own code.
2 Problem Description
Your are tasked with writing the following functions:
1. Change your NeuralNetwork class.
(a) Instead of sending all the information about the layers of the network during
initialization, simply initialize the network with the input size, loss function and
learning rate.
(b) Add a method to the class called addLayer. This method should accept all the de-
tails needed to initialize the layer (this should include allowing for setting weights.
Note that the input size should not be set as a parameter, but simply set to the
cu
ent final layer.
2. Write a ConvolutionalLayer class:
1
(a) In this project we will restrict the layer to 2d convolutions.
(b) Should be initialized with the number of kernels in the layer, the size of the kernel
(assume it is square), the activation function for all the neurons in the layer, the
dimension of the inputs, the learning rate, and possibly a vector of weights (if
not set to random). For this project we will assume the stride is always 1 and
padding is set to valid (if you would like to add a stride parameter or padding it
is up to you).
(c) Each neuron (as opposed to each Kernel) should be represented as a Neuron
object. Since all neurons with the same kernel share the same weights, make sure
to initialize them with the same values.
(d) Should have a calculate method which given an input calculates the output of
all the neurons in the layer.
(e) Should have a calculatewdeltas which given the
∑
w × δ from the next layer,
goes through all the neurons in the layer to calculate their partial derivative (using
the calcpartialderivative method). Note that since there are shared weights
you will need to add the returned
∑
w×δ in the co
ect way. In addition, for each
filter you will need to add all the partial derivatives fom the different locations
to calculate the final update. Then simply updates all the weights (using the
updateweights method) and returns its own
∑
w × δ.
3. (COSC 525 only) Write a MaxPoolingLayer class:
(a) In this project we will restrict the layer to 2d max pooling.
(b) Should be initialized with the size of the kernel (assume it is square), and the
dimension of the inputs. For this project we will assume the stride is always the
same as the filter size and no padding is needed (if you would like to add a stride
parameter, that is up to you).
(c) Should have a calculate method which given an input calculates the output of
the layer (No need for actual neurons). In order to perform back propagation you
will need to store the locations of the max.
(d) Should have a calculatewdeltas which given the
∑
w × δ from the next layer,
eturns a new
∑
w× δ which is the size of the input and the values are set in the
co
ect locations (the max).
4. Write a FlattenLayer class.
(a) This class only needs to be initialized with the input size.
(b) Should have a calculate method which given an input simply resizes it to create
the output of the layer (No need for actual neurons).
(c) Should have a calculatewdeltas which given the
∑
w × δ from the next layer,
simply resizes it to the size of the input.
5. Your main method will compare three setups with results obtained from using keras (in
order to verify co
ectness). For each run one step of back propagation and compare
2
the weights you get using your method with the results from tensorflow (note that
tensorflow does not use the 0.5 in the calculation of the square e
or loss):
(a) If given example1, run a network with a 5x5 input, one 3x3 convolution laye
with a single kernel, a flatten layer, and single neuron for the output.
(b) If given example2, run a network with a 7x7 input, one 3x3 convolution layer with
two kernels, another 3x3 convolution layer with a single kernel, a flatten layer,
and single neuron for the output.
(c) (COSC 525 only) If given example3, run a network with a 8x8 input, one 3x3
convolution layer with two kernels, a 2x2 max pooling layer, a flatten layer, and
single neuron for the output.
3 Additional Information
You must do so under the following constraints:
1. You can use the same activations and loss functions as the previous project.
2. You must use Python3 with only the numpy, sys, matplotlib li
aries allowed.
3. Make sure to comment your code.
4. In order to compare the results with tensorflow/keras you will need to have tensorflow
2.0 installed. Let me know soon if you have any problems.
5. I have provided code for example2 in Tensorflow to show you how to produce the
numbers you need to compare to. You can use that as your base for the other two
examples.
4 Report
You should submit a short PDF report with the following (if you do not have anything to
add for a section, simply put the section title and then state there is nothing to add):
1. A short introduction to the problem.
2. Assumptions/choices you have made.
3. Problems/Issues you were not able to solve.
4. How to run you code (if there is any difference from what is stated)
5. Show screen shots of your weight comparison for the three examples.
3
5 Submission
You are required to submit 3 files:
1. pdf file for the report
2. pdf file for the code
3. A zip file with your code.
4
Overview
Problem Description
Additional Information
Report
Submission