def add_extra_summary_avg_bleu(vocab_i2s, decoder_output, ref_words, collections=None):
hypo_tokens = decoder.str_tokens(decoder_output, vocab_i2s)
hypo_len = decoder.seq_length(decoder_output)
ref_tokens = vocab_i2s.lookup(ref_words)
ref_len = length_pre_embedding(ref_words)
avg_bleu = get_avg_bleu_smmary(ref_tokens, hypo_tokens, ref_len, hypo_len)
tf.summary.scalar('bleu', avg_bleu, collections)
return avg_bleu
def add_extra_summary_avg_bleu(vocab_i2s,
decoder_output,
tgt_words,
collections=None):
pred_tokens = decoder.str_tokens(decoder_output, vocab_i2s)
pred_len = decoder.seq_length(decoder_output)
tgt_tokens = vocab_i2s.lookup(tgt_words)
tgt_len = length_pre_embedding(tgt_words)
avg_bleu = get_avg_bleu_smmary(tgt_tokens, pred_tokens, tgt_len, pred_len)
tf.summary.scalar('bleu', avg_bleu, collections)
return avg_bleu
def add_extra_summary_trace(vocab_i2s, decoder_output,
base_words, output_words,
src_words, tgt_words, inserted_words, deleted_words,
collections=None):
pred_tokens = decoder.str_tokens(decoder_output, vocab_i2s)
pred_len = decoder.seq_length(decoder_output)
tgt_tokens = vocab_i2s.lookup(tgt_words)
tgt_len = length_pre_embedding(tgt_words)
trace_summary = get_trace_summary(vocab_i2s, pred_tokens, tgt_tokens, src_words, inserted_words, deleted_words,
pred_len, tgt_len)
tf.summary.text('trace', trace_summary, collections)
return trace_summary
def model_fn(features, mode, config, embedding_matrix, vocab_tables):
if mode == tf.estimator.ModeKeys.PREDICT:
base_words, src_words, tgt_words, inserted_words, deleted_words = features
else:
src_words, tgt_words, inserted_words, deleted_words = features
base_words = src_words
if mode != tf.estimator.ModeKeys.TRAIN:
config.put('editor.enable_dropout', False)
config.put('editor.dropout_keep', 1.0)
vocab_s2i = vocab_tables[vocab.STR_TO_INT]
vocab_i2s = vocab_tables[vocab.INT_TO_STR]
vocab.init_embeddings(embedding_matrix)
train_decoder_output, infer_decoder_output, \
gold_dec_out, gold_dec_out_len = editor.editor_train(
base_words, src_words, tgt_words, inserted_words, deleted_words, embedding_matrix, vocab_s2i,
config.editor.hidden_dim, config.editor.agenda_dim, config.editor.edit_dim,
config.editor.encoder_layers, config.editor.decoder_layers, config.editor.attention_dim,
config.editor.beam_width,
config.editor.max_sent_length, config.editor.dropout_keep, config.editor.lamb_reg,
config.editor.norm_eps, config.editor.norm_max, config.editor.kill_edit,
config.editor.draw_edit, config.editor.use_swap_memory,
config.get('editor.use_beam_decoder', False), config.get('editor.enable_dropout', False),
config.get('editor.no_insert_delete_attn', False), config.get('editor.enable_vae', True)
)
loss = optimizer.loss(train_decoder_output, gold_dec_out, gold_dec_out_len)
train_op, gradients_norm = optimizer.train(
loss, config.optim.learning_rate, config.optim.max_norm_observe_steps)
if mode == tf.estimator.ModeKeys.TRAIN:
tf.summary.scalar('grad_norm', gradients_norm)
ops = add_extra_summary(config, vocab_i2s, train_decoder_output,
src_words, tgt_words, inserted_words,
deleted_words, ['extra'])
hooks = [
get_train_extra_summary_writer(config),
get_extra_summary_logger(ops, config),
]
if config.get('logger.enable_profiler', False):
hooks.append(get_profiler_hook(config))
return tf.estimator.EstimatorSpec(mode,
train_op=train_op,
loss=loss,
training_hooks=hooks)
elif mode == tf.estimator.ModeKeys.EVAL:
ops = add_extra_summary(config, vocab_i2s, train_decoder_output,
src_words, tgt_words, inserted_words,
deleted_words, ['extra'])
return tf.estimator.EstimatorSpec(
mode,
loss=loss,
evaluation_hooks=[get_extra_summary_logger(ops, config)],
eval_metric_ops={'bleu': tf_metrics.streaming_mean(ops[ES_BLEU])})
elif mode == tf.estimator.ModeKeys.PREDICT:
lengths = decoder.seq_length(infer_decoder_output)
tokens = decoder.str_tokens(infer_decoder_output, vocab_i2s)
preds = {
'str_tokens': tokens,
'sample_id': decoder.sample_id(infer_decoder_output),
'lengths': lengths,
'joined': metrics.join_tokens(tokens, lengths),
}
return tf.estimator.EstimatorSpec(mode, predictions=preds)
