TensorFlow Variable-Length Sequence Labelling

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TensorFlow Variable-Length Sequence Labelling

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Last edited Jun 2, 2016

Created on Feb 27, 2018

This example demonstrates a TensorFlow recurrent neural network that labels variable-length sequences, using GRU cells to process input data and a softmax layer to classify each time step. It defines a `VariableSequenceLabelling` class with lazy properties for prediction, cost, and error, and relies on TensorFlow’s `dynamic_rnn`, `rnn_cell.GRUCell`, and Adam optimizer. The model is trained and evaluated on an OCR dataset from the `sets` library, reshaping flattened images and using one-hot encoded targets, with training and test splits provided by `sets.Split`.

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