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01_​Training_​LSTM_​Exercise

01_Training_LSTM_Exercise
Pre Processing and Encoding Train the network and save it as a TensorFlow model- Train the network using the Keras Network Learner node Tips: - Use for the input and the target data the conversion: From Collection of Number (integer) to One-hot Tensor - Use Categorical-Cross Entropy as loss function - The trained model in the data folder was trained on half of the book for 50epochs. This took two days. You can stop the training earlier by clicking the Stoplearning button in the Monitor View and move on. In the deployment workflow you canuse the pretrained network.- Convert the network using the Keras to TensorFlow Network Converter node- Save the model uisng the TensorFlow Network Writer node Tip: Don't overwrite the trained model- Optional: Save the model as Keras model (.h5 file) using the Keras Network Writernode Define a network structure for a many-to-one architecture- Keras Input Layer node Tips: - The input shape should be ?, dictionary size - Check the dictionary table to get the dictionary size- Kears LSTM Layer node Tip: Change the number of units to 512- Keras Dropout Layer node- Keras Dense Layer node Tip: Use the activation softmax and the dictionary size as the number of units one column,each char in single rowApply dictionaryCreate overlappingsequencesOthelloWrite dictionaryonly 100 rows Reshape Text &Create Dictionary Cell Replacer Lag Column File Reader Table Writer Row Filter Resort Columns Pre Processing and Encoding Train the network and save it as a TensorFlow model- Train the network using the Keras Network Learner node Tips: - Use for the input and the target data the conversion: From Collection of Number (integer) to One-hot Tensor - Use Categorical-Cross Entropy as loss function - The trained model in the data folder was trained on half of the book for 50epochs. This took two days. You can stop the training earlier by clicking the Stoplearning button in the Monitor View and move on. In the deployment workflow you canuse the pretrained network.- Convert the network using the Keras to TensorFlow Network Converter node- Save the model uisng the TensorFlow Network Writer node Tip: Don't overwrite the trained model- Optional: Save the model as Keras model (.h5 file) using the Keras Network Writernode Define a network structure for a many-to-one architecture- Keras Input Layer node Tips: - The input shape should be ?, dictionary size - Check the dictionary table to get the dictionary size- Kears LSTM Layer node Tip: Change the number of units to 512- Keras Dropout Layer node- Keras Dense Layer node Tip: Use the activation softmax and the dictionary size as the number of units one column,each char in single rowApply dictionaryCreate overlappingsequencesOthelloWrite dictionaryonly 100 rows Reshape Text &Create Dictionary Cell Replacer Lag Column File Reader Table Writer Row Filter Resort Columns

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Created by: kathrinmelcher
Created at: 2018-11-23
On NodePit since: 2020-12-20
Last update: 2021-01-27
Created with KNIME version: v4.3.0
Tags: GIDSexerciseacademiaDeep LearningLSTMTheGuideBook

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