Naive-Bayes-Number-Recognition

A Naive Bayes hand-written number classifier implemented in Python using only built-in libraries. (MNIST dataset)

Dataset

I used MNIST dataset to train the model. The files are too large. You may download them here.
You should REMOVE the first row to avoid errors.

• Training: 50000
• Validation: 10000
• Testing: 10000

Model

• Input Layer: Size 784 (28 * 28 representing each pixel in an image)
• Output Layer: Size 10 (representing 10 digits)

Features

• performs approximately 85% correct on test data.
• supports terminal "graphics" for user to view the image through ACSII arts.
• uses about 30 seconds to train

Sample Run

Testing, Graphics On:

NO.0
predict: 7
actual: 7
accumulative precision: 1.0







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press Enter to continue
NO.1
predict: 2
actual: 2
accumulative precision: 1.0



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press Enter to continue
NO.2
predict: 1
actual: 1
accumulative precision: 1.0




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press Enter to continue
NO.3
predict: 0
actual: 0
accumulative precision: 1.0




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press Enter to continue
NO.4
predict: 4
actual: 4
accumulative precision: 1.0





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press Enter to continue
NO.5
predict: 1
actual: 1
accumulative precision: 1.0





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press Enter to continue
NO.6
predict: 4
actual: 4
accumulative precision: 1.0





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press Enter to continue
NO.7
predict: 9
actual: 9
accumulative precision: 1.0






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press Enter to continue
NO.8
predict: 4
actual: 5
accumulative precision: 0.8888888888888888




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press Enter to continue
NO.9
predict: 9
actual: 9
accumulative precision: 0.9







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press Enter to continue

...

Testing, Graphics Off:

.
.
.

NO.9995
predict: 2
actual: 2
accumulative precision: 0.8414365746298519
NO.9996
predict: 3
actual: 3
accumulative precision: 0.8414524357307193
NO.9997
predict: 9
actual: 4
accumulative precision: 0.841368273654731
NO.9998
predict: 5
actual: 5
accumulative precision: 0.8413841384138414
NO.9999
predict: 6
actual: 6
accumulative precision: 0.8414

Run Locally

py naive_bayes_mnist.py