def __init__(self, cfg):
FormSelect.__init__(self)
TFModel.__init__(self,
scope_name='formselect-' +
cfg.get('scope_suffix', ''))
# load configuration
self._sample = cfg.get('form_sample', False)
self.randomize = cfg.get('randomize', True)
self.emb_size = cfg.get('emb_size', 50)
self.passes = cfg.get('passes', 200)
self.alpha = cfg.get('alpha', 1)
self.batch_size = cfg.get('batch_size', 1)
self.max_sent_len = cfg.get('max_sent_len', 32)
self.cell_type = cfg.get('cell_type', 'lstm')
self.max_grad_norm = cfg.get('max_grad_norm', 100)
self.optimizer_type = cfg.get('optimizer_type', 'adam')
self.max_cores = cfg.get('max_cores', 4)
self.alpha_decay = cfg.get('alpha_decay', 0.0)
self.vocab = {
'<VOID>': self.VOID,
'<GO>': self.GO,
'<STOP>': self.STOP,
'<UNK>': self.UNK
}
self.reverse_dict = {
self.VOID: '<VOID>',
self.GO: '<GO>',
self.STOP: '<STOP>',
self.UNK: '<UNK>'
}
self.vocab_size = None
np.random.seed(rnd.randint(0, 2**32 - 1))
def __init__(self, cfg):
"""Initialize the generator, fill in the configuration."""
Seq2SeqBase.__init__(self, cfg)
TFModel.__init__(self, scope_name='seq2seq_gen-' + cfg.get('scope_suffix', ''))
# extract the individual elements out of the configuration dict
self.emb_size = cfg.get('emb_size', 50)
self.batch_size = cfg.get('batch_size', 10)
self.dropout_keep_prob = cfg.get('dropout_prob', 1)
self.optimizer_type = cfg.get('optimizer_type', 'adam')
self.passes = cfg.get('passes', 5)
self.min_passes = cfg.get('min_passes', 1)
self.improve_interval = cfg.get('improve_interval', 10)
self.top_k = cfg.get('top_k', 5)
# self.checkpoint_dir = cfg.get('checkpoint_dir', '/tmp/') # TODO fix (not used now)
self.use_dec_cost = cfg.get('use_dec_cost', False)
self.alpha = cfg.get('alpha', 1e-3)
self.alpha_decay = cfg.get('alpha_decay', 0.0)
self.validation_size = cfg.get('validation_size', 0)
self.validation_freq = cfg.get('validation_freq', 10)
self.multiple_refs = cfg.get('multiple_refs', False) # multiple references for validation
self.ref_selectors = cfg.get('ref_selectors', None) # selectors of validation trees (if in separate file)
self.max_cores = cfg.get('max_cores')
self.use_tokens = cfg.get('use_tokens', False)
self.nn_type = cfg.get('nn_type', 'emb_seq2seq')
self.randomize = cfg.get('randomize', True)
self.cell_type = cfg.get('cell_type', 'lstm')
self.bleu_validation_weight = cfg.get('bleu_validation_weight', 0.0)
self.use_context = cfg.get('use_context', False)
def __init__(self, cfg):
"""Initialize the generator, fill in the configuration."""
Seq2SeqBase.__init__(self, cfg)
TFModel.__init__(self, scope_name='seq2seq_gen-' + cfg.get('scope_suffix', ''))
# extract the individual elements out of the configuration dict
self.emb_size = cfg.get('emb_size', 50)
self.batch_size = cfg.get('batch_size', 10)
self.dropout_keep_prob = cfg.get('dropout_prob', 1)
self.optimizer_type = cfg.get('optimizer_type', 'adam')
self.passes = cfg.get('passes', 5)
self.min_passes = cfg.get('min_passes', 1)
self.improve_interval = cfg.get('improve_interval', 10)
self.top_k = cfg.get('top_k', 5)
# self.checkpoint_dir = cfg.get('checkpoint_dir', '/tmp/') # TODO fix (not used now)
self.use_dec_cost = cfg.get('use_dec_cost', False)
self.alpha = cfg.get('alpha', 1e-3)
self.alpha_decay = cfg.get('alpha_decay', 0.0)
self.validation_size = cfg.get('validation_size', 0)
self.validation_freq = cfg.get('validation_freq', 10)
self.multiple_refs = cfg.get('multiple_refs', False) # multiple references for validation
self.ref_selectors = cfg.get('ref_selectors', None) # selectors of validation trees (if in separate file)
self.max_cores = cfg.get('max_cores')
self.mode = cfg.get('mode', 'tokens' if cfg.get('use_tokens') else 'trees')
self.nn_type = cfg.get('nn_type', 'emb_seq2seq')
self.randomize = cfg.get('randomize', True)
self.cell_type = cfg.get('cell_type', 'lstm')
self.bleu_validation_weight = cfg.get('bleu_validation_weight', 0.0)
self.use_context = cfg.get('use_context', False)
def __init__(self, cfg):
Reranker.__init__(self, cfg)
TFModel.__init__(self, scope_name='rerank-' + cfg.get('scope_suffix', ''))
self.tree_embs = cfg.get('nn', '').startswith('emb')
if self.tree_embs:
self.tree_embs = TreeEmbeddingClassifExtract(cfg)
self.emb_size = cfg.get('emb_size', 50)
self.nn_shape = cfg.get('nn_shape', 'ff')
self.num_hidden_units = cfg.get('num_hidden_units', 512)
self.passes = cfg.get('passes', 200)
self.min_passes = cfg.get('min_passes', 0)
self.alpha = cfg.get('alpha', 0.1)
self.randomize = cfg.get('randomize', True)
self.batch_size = cfg.get('batch_size', 1)
self.validation_freq = cfg.get('validation_freq', 10)
self.checkpoint_path = None
self.max_cores = cfg.get('max_cores')
# Train Summaries
self.train_summary_dir = cfg.get('tb_summary_dir', None)
if self.train_summary_dir:
self.loss_summary_reranker = None
self.train_summary_op = None
self.train_summary_writer = None
# backward compatibility flag -- will be 1 when loading older models
self.version = 2