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01_​Training

Generate Text Using a Many-To-One LSTM Network (Training)

The workflow builds, trains, and saves an RNN with an LSTM layer to generate new fictive fairy tales. The brown nodes define the network structure. The "Pre-Processing" metanode reads fairy tales and index-encodes them, and creates semi-overlapping sequences. The Keras Network Learner node trains the network using the index-encoded fairy tales. Finally, the trained network is converted into a TensorFlow model, and saved to a file.

Pre Processing and Encoding Train and Save Network Define Network Structure Generate Text Using a Many-To-One LSTM Network (Training)The workflow builds, trains, and saves an RNN with an LSTM layer to generate new fictive fairy tales. The brown nodesdefine the network structure. The "Pre-Processing" metanode reads fairy tales and index-encodes them. The KerasNetwork Learner node trains the network using index-encoded fairy tales. Finally, the trained network is converted into aTensorFlow model, and saved to a file. Apply dictionaryCreate overlappingsequencesfor regularization?, dictionary sizeCreate inputcollectionCreate targetcollection one column,each char in single rowoutput: sequence of hidden statesdictionary sizeRead Grimm'sfairy talesRead dictionary Cell Replacer Lag Column Keras Dropout Layer Keras Input Layer Keras NetworkLearner Create CollectionColumn Create CollectionColumn Keras to TensorFlowNetwork Converter Resort Columns Column Filter Reshape Text Keras NetworkWriter TensorFlowNetwork Writer Keras LSTM Layer Keras Dense Layer File Reader(Complex Format) Table Reader Pre Processing and Encoding Train and Save Network Define Network Structure Generate Text Using a Many-To-One LSTM Network (Training)The workflow builds, trains, and saves an RNN with an LSTM layer to generate new fictive fairy tales. The brown nodesdefine the network structure. The "Pre-Processing" metanode reads fairy tales and index-encodes them. The KerasNetwork Learner node trains the network using index-encoded fairy tales. Finally, the trained network is converted into aTensorFlow model, and saved to a file. Apply dictionaryCreate overlappingsequencesfor regularization?, dictionary sizeCreate inputcollectionCreate targetcollection one column,each char in single rowoutput: sequence of hidden statesdictionary sizeRead Grimm'sfairy talesRead dictionary Cell Replacer Lag Column Keras Dropout Layer Keras Input Layer Keras NetworkLearner Create CollectionColumn Create CollectionColumn Keras to TensorFlowNetwork Converter Resort Columns Column Filter Reshape Text Keras NetworkWriter TensorFlowNetwork Writer Keras LSTM Layer Keras Dense Layer File Reader(Complex Format) Table Reader

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