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This framework prepares, trains and validates an image classifier.

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Keras Machine Learning Framework

This machine learning framework prepares, trains and validates an image classifier.

In short

Trains the data path F:/data/path/to/data with the included classes and saves the model to F:/data/processed/model.h5.

(keras-gpu) C:\Users> ml train --environment-path=F:/data --data-path=path/to/data \
  --model-file=processed/model.h5 -m MobileNetV2 -v

See Train, build and save the model (ml train) for more details.

Install Keras with GPU Support (Preparation)

Why is it important to choose a GPU over a CPU?

If you intend to implement and optimize Deep Neuronal Networks (DNN), the calculations must take place on the GPU. It is also possible to run calculations on the CPU. Also the installation of Keras for CPU driven computations is much easier, because the installation of the GPU drivers is not necessary. The disadvantage of this, however, is that it takes much longer to train larger models. Good models for the classification of e.g. pictures are only achieved after several training units. Training units require a lot of computing power in the form of many matrix operations. A GPU is predestined for matrix operations[1].

Python is not really intended for production environments

..but perfect for machine learning experiments. ;)

Some background knowledge

Demos

To test this framework immediately without any data, there is a selection of demos which can be executed immediately. Good to e.g. test the installation or make a GPU vs CPU comparison on the fly.

Here is an overview of the demos:

First attempts to train an image classifier

Further attempts

A. Further Tutorials

B. Sources

C. Authors

D. License

This tutorial is licensed under the MIT License - see the LICENSE.md file for details

E. Closing words

Have fun! :)

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