In order to classify the CIFAR-10 dataset I made two convolutional neural network models, which are named Model-A and Model-B.

The architecture of Model-A is as follows:

1. Convolutional Layer 1 - 3× 3 ×12 - strides - 1× 1
2. Activated by Relu
3. Max pooling Layer - 2× 2
4. Convolutional Layer 2 - 3× 3 ×24 - strides - 1× 1
5. Activated by Relu
6. Max pooling Layer - 2× 2
7. Convolutional Layer 3 - 3× 3 ×48 - strides - 1× 1
8. Activated by Relu
9. Max pooling Layer - 2× 2
10. Convolutional Layer 4 - 3× 3 ×96 - strides - 1× 1
11. Activated by Relu
12. Max pooling Layer - 2× 2
13. Flatten layer
14. Fully connected layer - 512 neurons
15. Activated by Relu
16. Dropout - 40%
17. Fully connected layer - 512 neurons
18. Activated by Relu
19. Droptout - 40%
20. Output layer - 10 neurons
21. Activated by Softmax

The architecture of Model-B is as follows:

1. Convolutional Layer 1 - 3× 3 ×24 - strides - 1× 1
2. Activated by Relu
3. Max pooling Layer - 2× 2
4. Convolutional Layer 2 - 3× 3 ×24 - strides - 1× 1
5. Activated by Relu
6. Max pooling Layer - 2× 2
7. Flatten layer
8. Fully connected layer - 512 neurons
9. Activated by Relu
10. Dropout - 25%
11. Fully connected layer - 256 neurons
12. Activated by Relu
13. Droptout - 25%
14. Output layer - 10 neurons
15. Activated by Softmax

After training both the networks the clear observation was that size of the network is important when it comes to accuracy. Model-A with more convo- lutional layer had a better accuracy than the Model-B with half the convo-lutional layers of Model-A.

The computational graphs, cost vs epochs chart and accuracy vs epochs chart for both Model-A and Model-B is provided below. The x-axis of both the graphs is epochs and the y-axis is the accuracy orcost.

Computational Graph Model-A

Model-A Accuracy vs Epochs

Model-A Cost vs Epochs

Computational Graph Model-B

Model-B Accuracy vs Epochs

Model-B Cost vs Epochs