NMT-Keras
  • Installation
    • Requirements
  • Usage
    • Training
    • Decoding
    • Scoring
  • Configuration options
    • Naming and experiment setup
    • Input/output
    • Evaluation
    • Decoding
    • Search normalization
    • Sampling
    • Unknown words treatment
    • Word representation
    • Text representation
    • Input text
    • Output text
    • Optimization
      • Optimizer setup
      • Advanced parameters for optimizers
    • Learning rate schedule
    • Training options
    • Early stop
    • Model main hyperparameters
      • Source word embeddings
      • Target word embedding
      • Deepness
    • AttentionRNNEncoderDecoder model
      • Encoder configuration
      • Decoder configuration
    • Transformer model
    • Regularizers
      • Regularization functions
      • Dropout
      • Gaussian noise
      • Batch normalization
      • Additional normalization layers
    • Tensorboard
    • Storage and plotting
  • Resources
    • Theoretical NMT
    • NMT-Keras Step-by-step
    • NMT-Keras Output
    • Tensorboard integration
      • Loss curve
      • Model graphs
      • Embedding visualization
  • Tutorials
  • Modules
    • nmt_keras package
      • Submodules
      • model_zoo
      • training
      • apply_model
      • build_callbacks
      • Module contents
    • data_engine package
      • Submodules
      • prepare_data module
      • Module contents
    • utils package
      • Submodules
      • evaluate_from_file module
      • preprocess_binary_word_vectors module
      • preprocess_text_word_vectors module
      • utils module
      • Module contents
  • Contact
    • Acknowledgement
      • Related projects
NMT-Keras
  • Docs »
  • Python Module Index

Python Module Index

d | n | u
 
d
- data_engine
    data_engine.prepare_data
 
n
- nmt_keras
    nmt_keras.apply_model
    nmt_keras.build_callbacks
    nmt_keras.model_zoo
    nmt_keras.training
 
u
- utils
    utils.preprocess_binary_word_vectors
    utils.preprocess_text_word_vectors
    utils.utils

© Copyright 2017, Álvaro Peris Revision 865613dd.

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