def wt2_opt():
reader_opt = ChainMap(
{
'sentences': False,
# if sentences is False
'min_seq_len': 35,
'max_seq_len': 35,
# fi
'shuffle': False,
'batch_size': 64,
'vocab_path': '', # auto to data
'text_path': '' # auto to data
},
reader.default_reader_opt())
model_opt = ChainMap(
{
'emb_dim': 650,
'rnn_dim': 650,
'rnn_layers': 2,
'rnn_variational': True,
'rnn_input_keep_prob': 0.5,
'rnn_layer_keep_prob': 0.7,
'rnn_output_keep_prob': 0.5,
'rnn_state_keep_prob': 0.7,
'logit_weight_tying': True,
'vocab_size': -1 # auto to data
},
lm.default_rnnlm_opt())
train_opt = ChainMap(
{
'loss_key': 'mean_token_nll', # or sum_token_nll
'ngram_loss_coeff': 0.1,
'init_learning_rate': 0.003,
'decay_rate': 0.85,
'staircase': True,
'optim': 'tensorflow.train.AdamOptimizer',
# if adam
'optim_beta1': 0.0,
'optim_beta2': 0.999,
'optim_epsilon': 1e-8,
# fi
'clip_gradients': 5.0,
'max_epochs': 40,
'checkpoint_path': 'tmp', # auto to exp_dir
'decay_steps': -1 # if -1 auto to an epoch
},
lm.default_train_opt())
return reader_opt, model_opt, train_opt
'vocab_path': data_path('vocab.txt'),
'text_path': data_path('train.txt'),
'min_seq_len': 2,
'max_seq_len': 7,
})
# loading training data and validating data
train_iter_wrapper = reader.get_batch_iter_from_file(reader_opt)
reader_opt['text_path'] = data_path('valid.txt')
valid_iter_wrapper = reader.get_batch_iter_from_file(reader_opt,
train_iter_wrapper.vocab)
# creating models
with open(exp_path('model_opt.json')) as fp:
model_opt = json.load(fp)
model_opt = ChainMap(model_opt, lm.default_rnnlm_opt())
model_opt['vocab_size'] = train_iter_wrapper.vocab.vocab_size
# model_opt['rnn_get_all_states'] = True
with tf.variable_scope('model'):
eval_lm = lm.create_rnnlm(model_opt, with_dropout=False)
with tf.variable_scope('encoder'):
train_enc = lm.create_encoder(model_opt, eval_lm, with_dropout=True)
with tf.variable_scope('encoder', reuse=True):
eval_enc = lm.create_encoder(model_opt, eval_lm, with_dropout=False)
train_model = ChainMap(train_enc, eval_lm)
eval_model = ChainMap(eval_enc, eval_lm)
# creating optim
if train_opt['decay_steps'] == -1:
train_opt['decay_steps'] = train_iter_wrapper.num_batches
