Logistic Regression Classifier

This project will build a logistic regression classifier to recognize cats.

Workflow

• Build the general architecture of a learning algorithm, including:
  • Initializing parameters
  • Calculating the cost function and its gradient
  • Using an optimization algorithm (gradient descent)
• Gather all three functions above into a main model function, in the right order.

Packages

To run this project, import all the packages:

• numpy is the fundamental package for scientific computing with Python
• h5py is a common package to interact with a dataset that is stored on an H5 file.
• matplotlib is a famous library to plot graphs in Python.
• PIL and scipy are used here to test your model with your own picture at the end.

Notes

• Preprocessing the dataset is important
• Implement each function separately: initialize(), propagate(), optimize(). Then build a model()
• Tuning the learning rate (which is an example of a "hyperparameter") can make a big difference to the algorithm.

Results

Learning Rate at 0.005

Cost after iteration 0: 0.693147
Cost after iteration 100: 0.584508
Cost after iteration 200: 0.466949
Cost after iteration 300: 0.376007
Cost after iteration 400: 0.331463
Cost after iteration 500: 0.303273
Cost after iteration 600: 0.279880
Cost after iteration 700: 0.260042
Cost after iteration 800: 0.242941
Cost after iteration 900: 0.228004
Cost after iteration 1000: 0.214820
Cost after iteration 1100: 0.203078
Cost after iteration 1200: 0.192544
Cost after iteration 1300: 0.183033
Cost after iteration 1400: 0.174399
Cost after iteration 1500: 0.166521
Cost after iteration 1600: 0.159305
Cost after iteration 1700: 0.152667
Cost after iteration 1800: 0.146542
Cost after iteration 1900: 0.140872
train accuracy: 99.04306220095694 %
test accuracy: 70.0 %

Different Learning Rate Comparison

learning rate is: 0.01
train accuracy: 99.52153110047847 %
test accuracy: 68.0 %

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learning rate is: 0.001
train accuracy: 88.99521531100478 %
test accuracy: 64.0 %

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learning rate is: 0.0001
train accuracy: 68.42105263157895 %
test accuracy: 36.0 %

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Predict Real Images

y = 0.0, your algorithm predicts a "non-cat" picture.

y = 1.0, your algorithm predicts a "cat" picture.