Multi-class Image Classification (TensorFlow)

SIGNS Dataset

Approach:

One Hot Encodings, SoftMax Function, Xavier Initialization for weights and Zero Initialization for biases

Results:

Binary Image Classification

1) Planar data classification

The data looks like a "flower" with some red (label y=0) and some blue (y=1) points. The goal is to build a model to fit this data.

Approach:

2-class (binary) classification neural network with a single hidden layer

Results:

Logistic Regression:

Neural Network (5 hidden layer):

Learning outcomes:

• Accuracy of the neural network ( around 90%) is high compared to Logistic Regression (47%) (as the dataset is not linearly separable). So the model has learned the leaf patterns of the flower.
• The larger models (with more hidden units) are able to fit the training set better, until eventually the largest models overfit the data.
• The best hidden layer size seems to be around n_h = 5. Indeed, a value around here seems to fits the data well without also incurring noticeable overfitting.

Reference:

• http://scs.ryerson.ca/~aharley/neural-networks/
• http://cs231n.github.io/neural-networks-case-study/

2) Cat vs non-Cat Classification

Building a deep network, and applying it to cat vs non-cat classification (supervised learning)

Approach:

2-class (binary) classification neural network (2-layer and 4-layer)

Results:

2-layer (72% accuracy):

4-layer (80% accuracy):