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Community Blog Handwriting Generation with a Reinforcement Learning Model

Handwriting Generation with a Reinforcement Learning Model

In this article, we will introduce how to generate a handwriting image using a reinforcement learning model.

Reinforcement Learning (RL) is a type of machine learning algorithm that trains algorithms based on a mechanism in which certain actions are associated with certain rewards.

Reinforcement Learning comes into play when there is no hard-coded method for performing a task, but rather there are some set of rules that need to be followed in order for a model to achieve its desired objectives. Reinforcement Learning as a machine learning algorithm models how humans learn and has been predicted as being pivotal in attaining Artificial General Intelligence in AI-based applications, like generating handwriting images.

With Anaconda initiated on your Alibaba Cloud ECS Console and Jupyter Notebook instance ready for Keras, you can build and train the reinforcement learning model.

  1. Import the needed libraries.
  2. Load the data and split into train and test sets.
  3. Normalize and reshape the data before ingestion into the model.
  4. Setting up the model architecture: 1) Define the input; 2) add the encoding layer where your activation fuction is RELU; 3) add the decoding layer and specify your activation function as SIGMOID; 4) define your auto-encoder model, compile it and fit to your train dataset; 5) use the model to rebuild the handwritten digits; 6) analyze the loss and accuracy for each epoch; 7) verify your results.

handwriting generation

As showed above, with the Auto-encoders Reinforcement Learning technique, you can generate an entirely new handwriting image close to the original one.

For step by step tutorial with codes, please go to Implementing Reinforcement Learning with Keras.

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