1. cs638/838 - Spring 2017 (Shavlik©), Week 7
CS 540 Fall 2015 (Shavlik)
2/28/2019
Today’s Topics
How Many Weights?
2/28/17
cs638/838 - Spring 2017 (Shavlik©), Week 7

2. Back to Deep ANNs - Convolution & Max Pooling (Repeat)
CS 540 Fall 2015 (Shavlik)
2/28/2019
Back to Deep ANNs - Convolution & Max Pooling (Repeat)
C = Convolution, MP = Max Pooling
(ie, a CHANGE OF REP)
My implementation (no dropout yet) of the above topology takes about 1-2 mins per epoch on the provided TRAIN/TUNE/TEST set (I measure TRAIN, TUNE and TEST accuracy after each epoch)
2/28/17
cs638/838 - Spring 2017 (Shavlik©), Week 7

3. cs638/838 - Spring 2017 (Shavlik©), Week 7
How Many Weights?
Assume 32x32 images and using RGB+Gray
Assume 300 HUs connected to 6 Outputs
For DEEP, assume Slide 2’s Topology
2/28/17
cs638/838 - Spring 2017 (Shavlik©), Week 7

4. cs638/838 - Spring 2017 (Shavlik©), Week 7
How Many Weights?
Perceptrons
(4 x 32 x )  6 = 24,582
One Layer of HU’s
(4 x 32 x ) x ( ) x 6 =  1,230,906 
2/28/17
cs638/838 - Spring 2017 (Shavlik©), Week 7

5. cs638/838 - Spring 2017 (Shavlik©), Week 7
How Many Weights?
DEEP
20 plates x ( 4 x  (5 x 5) kernel + 1)  +
20 plates x (20 x (5 x 5) kernel + 1)  +    
20 plates x (20 x (3 x 3) kernel + 1)  +
20 x (3 x 3 + 1) x 300 HUs +  
( ) x 6 outputs
= 77,466
2/28/17
cs638/838 - Spring 2017 (Shavlik©), Week 7