logai.algorithms.nn_model package

Subpackages

• logai.algorithms.nn_model.forecast_nn package
  • Submodules
  • logai.algorithms.nn_model.forecast_nn.base_nn module
    • Embedder
      • Embedder.forward()
    • ForecastBasedNN
      • ForecastBasedNN.fit()
      • ForecastBasedNN.load_model()
      • ForecastBasedNN.predict()
      • ForecastBasedNN.save_model()
    • ForecastBasedNNParams
      • ForecastBasedNNParams.batch_size
      • ForecastBasedNNParams.embedding_dim
      • ForecastBasedNNParams.eval_type
      • ForecastBasedNNParams.feature_type
      • ForecastBasedNNParams.freeze
      • ForecastBasedNNParams.gpu
      • ForecastBasedNNParams.hidden_size
      • ForecastBasedNNParams.label_type
      • ForecastBasedNNParams.learning_rate
      • ForecastBasedNNParams.metadata_filepath
      • ForecastBasedNNParams.model_name
      • ForecastBasedNNParams.num_train_epochs
      • ForecastBasedNNParams.output_dir
      • ForecastBasedNNParams.patience
      • ForecastBasedNNParams.topk
  • logai.algorithms.nn_model.forecast_nn.cnn module
    • CNN
      • CNN.forward()
    • CNNParams
      • CNNParams.kernel_sizes
  • logai.algorithms.nn_model.forecast_nn.lstm module
    • Attention
      • Attention.forward()
      • Attention.glorot()
      • Attention.zeros()
    • LSTM
      • LSTM.forward()
    • LSTMParams
      • LSTMParams.max_token_len
      • LSTMParams.num_directions
      • LSTMParams.num_layers
      • LSTMParams.use_attention
  • logai.algorithms.nn_model.forecast_nn.transformer module
    • Transformer
      • Transformer.forward()
    • TransformerParams
      • TransformerParams.nhead
      • TransformerParams.num_layers
  • logai.algorithms.nn_model.forecast_nn.utils module
    • seed_everything()
    • set_device()
    • tensor2flatten_arr()
  • Module contents
• logai.algorithms.nn_model.logbert package
  • Submodules
  • logai.algorithms.nn_model.logbert.configs module
    • LogBERTConfig
      • LogBERTConfig.eval_accumulation_steps
      • LogBERTConfig.eval_steps
      • LogBERTConfig.evaluation_strategy
      • LogBERTConfig.learning_rate
      • LogBERTConfig.logging_steps
      • LogBERTConfig.mask_ngram
      • LogBERTConfig.max_token_len
      • LogBERTConfig.mlm_probability
      • LogBERTConfig.model_dirname
      • LogBERTConfig.model_name
      • LogBERTConfig.num_eval_shards
      • LogBERTConfig.num_train_epochs
      • LogBERTConfig.output_dir
      • LogBERTConfig.per_device_eval_batch_size
      • LogBERTConfig.per_device_train_batch_size
      • LogBERTConfig.pretrain_from_scratch
      • LogBERTConfig.resume_from_checkpoint
      • LogBERTConfig.save_steps
      • LogBERTConfig.tokenizer_dirpath
      • LogBERTConfig.weight_decay
  • logai.algorithms.nn_model.logbert.eval_metric_utils module
    • compute_metrics()
  • logai.algorithms.nn_model.logbert.predict module
    • LogBERTPredict
      • LogBERTPredict.load_model()
      • LogBERTPredict.predict()
  • logai.algorithms.nn_model.logbert.predict_utils module
    • PredictionLabelSmoother
      • PredictionLabelSmoother.epsilon
      • PredictionLabelSmoother.eval_metrics_per_instance
      • PredictionLabelSmoother.ignore_index
    • Predictor
      • Predictor.compute_loss()
      • Predictor.get_test_dataloader()
  • logai.algorithms.nn_model.logbert.tokenizer_utils module
    • get_mask_id()
    • get_special_token_ids()
    • get_special_tokens()
    • get_tokenizer()
    • get_tokenizer_vocab()
  • logai.algorithms.nn_model.logbert.train module
    • LogBERTTrain
      • LogBERTTrain.evaluate()
      • LogBERTTrain.fit()
      • LogBERTTrain.get_model_checkpoint()
  • Module contents

Submodules

logai.algorithms.nn_model.transformers module

Module contents