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pythonmachine-learningkerasconv-neural-networktransfer-learning

Keras Transfer Learning

Let's say that I wanted to train a ConvNet to tell me if in a photo it is raining or not, how will the procedures be?

Given that I have two train variables, trainX and trainY, the trainX will be the photo and trainY will be the labels (e.g. rain or no-rain).

The goal of the network is to output the "right" answer. The question is: do I just need to run the model.predict() function and expect valid results?

Thank you for any help in advance.

Solution

  • 1) Build your CNN Model: Layers, Activation-Functions...

    2) Train it with your existing trainX and trainY-Dataset. (use Augmentation to get better results in the end)

    3) Validate with another Dataset, lets say they are called: testX and testY

    4) Modify the settings of your Model until your accuracy and loss are high enough for what you need them...

    5) enjoy your CNN

    This could help you on your way: Building a CNN with Keras