1. Image Classification
Using CNN and Tensorflow

2. Team members
➔ Noura Hussein Fekry
@noura_7ussein
➔ Mohamed Mahmoud Shehata
@Shehata

3. Project Description
Building a model to classify images into their appropriate
categories using the CIFAR-10 dataset.
Using CNN (python 3.6 and tensorflow)
● TensorFlow is an open-source software library for
dataflow programming across a range of tasks. It is a
symbolic math library, and is also used for machine
learning applications such as neural networks.

4. The CIFAR-10 Dataset
The CIFAR-10 dataset contains 60
thousand images of various objects like:
Airplanes, Cars, Birds, trucks, deers, dogs,
frogs, horses, cats and ships.
The Images are (32*32*3)
It contains 50 thousand images as a training
data and 10 thousand testing data.

5. Phases
Preprocess Data
Phase
CNN Phase
Getting the data
Preprocessing
functions
Building the network
Training the network
Model Testing

6. Preprocess functions
● Normalization
Transforming image X into a NumPy array.
● One Hot Encoded
Labeling the images into 0 or 1, by indexing the classes,
giving 1 to the class and 0 to the rest of the classes.
● Randomize the data
Making sure that the data is as random as possible, to insure maximum randomization

7. So, why did we use a CNN?
In machine learning, a convolutional neural network is a
class of deep, feed-forward artificial neural networks that
has successfully been applied to analyzing visual imagery.
CNNs use a variation of multilayer perceptrons designed to
require minimal preprocessing. They are also known as shift
invariant or space invariant artificial neural networks
(SIANN), based on their shared-weights architecture and
translation invariance characteristics.

8. CNN Building Blocks
● Convolutional Layer
● Pooling Layer
● ReLU Layer
● Fully Connected Layer
● Loss Layer

9. CNN’s design consists of Convolutional, pooling, fully
connected and weights.

10. Building the network
➔ Input
➔ Convolution and Max Pooling layer
➔ Flatten Layer
➔ Fully Connected Layer
➔ Output Layer

11. Training the neural
net
● Single Optimization
● Hyperparameters
● Training on a single CIFAR-10 Batch
● Fully Trained Model
Testing Model
● Accuracy: 71.44%
● Softmax Predictions

12. Test Samples

13. Resources:
● https://en.wikipedia.org/wiki/Convolu
tional_neural_network
● https://www.youtube.com/watch?v=F
Tr3n7uBIuE

14. Thank you.