Python tensor2tensor.models.transformer.transformer_big() Examples

The following are 16 code examples of tensor2tensor.models.transformer.transformer_big(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensor2tensor.models.transformer , or try the search function .
Example #1
Source File: transformer_revnet.py    From fine-lm with MIT License 5 votes vote down vote up 
def transformer_revnet_base():
  """Base hparams for TransformerRevnet."""
  hparams = transformer.transformer_big()

  # Use settings from transformer_n_da
  hparams.layer_preprocess_sequence = "n"
  hparams.layer_postprocess_sequence = "da"
  hparams.learning_rate = 0.4

  return hparams
Example #2
Source File: universal_transformer.py    From fine-lm with MIT License 5 votes vote down vote up 
def universal_transformer_base():
  hparams = transformer.transformer_big()
  hparams = update_hparams_for_universal_transformer(hparams)
  return hparams
Example #3
Source File: universal_transformer.py    From fine-lm with MIT License 5 votes vote down vote up 
def universal_transformer_big():
  hparams = transformer.transformer_big()
  hparams = update_hparams_for_universal_transformer(hparams)
  hparams.hidden_size = 2048
  hparams.filter_size = 8192
  return hparams
Example #4
Source File: transformer_revnet.py    From tensor2tensor with Apache License 2.0 5 votes vote down vote up 
def transformer_revnet_base():
  """Base hparams for TransformerRevnet."""
  hparams = transformer.transformer_big()

  # Use settings from transformer_n_da
  hparams.layer_preprocess_sequence = "n"
  hparams.layer_postprocess_sequence = "da"
  hparams.learning_rate = 0.4

  return hparams
Example #5
Source File: transformer_parallel.py    From tensor2tensor with Apache License 2.0 5 votes vote down vote up 
def transformer_big_bs1():
  hparams = transformer.transformer_big()
  hparams.add_hparam("block_size", 1)
  return hparams
Example #6
Source File: evolved_transformer.py    From tensor2tensor with Apache License 2.0 5 votes vote down vote up 
def evolved_transformer_big():
  """Big parameters for Evolved Transformer model on WMT."""
  return add_evolved_transformer_hparams(transformer.transformer_big())
Example #7
Source File: evolved_transformer.py    From tensor2tensor with Apache License 2.0 5 votes vote down vote up 
def evolved_transformer_deep():
  """Deep parameters for Evolved Transformer model on WMT."""
  hparams = add_evolved_transformer_hparams(transformer.transformer_big())
  hparams.num_encoder_layers = 9
  hparams.num_decoder_layers = 10
  hparams.hidden_size = 640
  return hparams
Example #8
Source File: transformer_revnet.py    From BERT with Apache License 2.0 5 votes vote down vote up 
def transformer_revnet_base():
  """Base hparams for TransformerRevnet."""
  hparams = transformer.transformer_big()

  # Use settings from transformer_n_da
  hparams.layer_preprocess_sequence = "n"
  hparams.layer_postprocess_sequence = "da"
  hparams.learning_rate = 0.4

  return hparams
Example #9
Source File: transformer_parallel.py    From BERT with Apache License 2.0 5 votes vote down vote up 
def transformer_big_bs1():
  hparams = transformer.transformer_big()
  hparams.add_hparam("block_size", 1)
  return hparams
Example #10
Source File: evolved_transformer.py    From BERT with Apache License 2.0 5 votes vote down vote up 
def evolved_transformer_big():
  """Big parameters for Evolved Transformer model on WMT."""
  return add_evolved_transformer_hparams(transformer.transformer_big())
Example #11
Source File: evolved_transformer.py    From BERT with Apache License 2.0 5 votes vote down vote up 
def evolved_transformer_deep():
  """Deep parameters for Evolved Transformer model on WMT."""
  hparams = add_evolved_transformer_hparams(transformer.transformer_big())
  hparams.num_encoder_layers = 9
  hparams.num_decoder_layers = 10
  hparams.hidden_size = 640
  return hparams
Example #12
Source File: transformer_revnet.py    From training_results_v0.5 with Apache License 2.0 5 votes vote down vote up 
def transformer_revnet_base():
  """Base hparams for TransformerRevnet."""
  hparams = transformer.transformer_big()

  # Use settings from transformer_n_da
  hparams.layer_preprocess_sequence = "n"
  hparams.layer_postprocess_sequence = "da"
  hparams.learning_rate = 0.4

  return hparams
Example #13
Source File: universal_transformer.py    From training_results_v0.5 with Apache License 2.0 5 votes vote down vote up 
def universal_transformer_base():
  hparams = transformer.transformer_big()
  hparams = update_hparams_for_universal_transformer(hparams)
  return hparams
Example #14
Source File: universal_transformer.py    From training_results_v0.5 with Apache License 2.0 5 votes vote down vote up 
def universal_transformer_big():
  hparams = transformer.transformer_big()
  hparams = update_hparams_for_universal_transformer(hparams)
  hparams.hidden_size = 2048
  hparams.filter_size = 8192
  return hparams
Example #15
Source File: universal_transformer_modified.py    From Graph-Transformer with Apache License 2.0 5 votes vote down vote up 
def universal_transformer_base1():
  hparams = transformer.transformer_big()
  hparams = update_hparams_for_universal_transformer(hparams)
  return hparams
Example #16
Source File: universal_transformer_modified.py    From Graph-Transformer with Apache License 2.0 5 votes vote down vote up 
def universal_transformer_big1():
  hparams = transformer.transformer_big()
  hparams = update_hparams_for_universal_transformer(hparams)
  hparams.hidden_size = 2048
  hparams.filter_size = 8192
  return hparams