def test_zero_one():
def generate_beam(min_val=10,
max_val=50,
useless_val=88,
min_len=5,
max_len=10,
beam_size=2):
seqs = []
lengths = []
for _ in range(beam_size):
seq_len = random.randint(min_len, max_len)
seq = [
bytes(str(random.randint(min_val, max_val)), encoding='utf8')
for _ in range(seq_len)
]
seq += [bytes(str(useless_val), encoding='utf8')
] * (max_len - seq_len)
seqs.append(seq)
lengths.append(seq_len)
seqs = list(zip(*seqs))
return seqs, lengths
batch_size = 3
ref = [
[b'41', b'12', b'13', b'14', b'15', b'<stop>'],
[b'21', b'22', b'<stop>', b'p', b'p', b'p'],
[b'<start>', b'31', b'62', b'33', b'<stop>', b'p'],
]
ref_lengths = [6, 3, 5]
pred = [
[[b'11', b'12', b'13', b'14', b'15', b'16'],
[b'11', b'12', b'13', b'14', b'15', b'16']],
[[b'<start>', b'21', b'22', b'p', b'p', b'p'],
[b'<start>', b'21', b'22', b'p', b'p', b'p']],
[[b'31', b'32', b'33', b'18', b'p', b'p'],
[b'31', b'32', b'33', b'18', b'p', b'p']],
]
pred_lengths = [[6, 6], [5, 5], [4, 4]]
ref = tf.constant(ref, dtype=tf.string)
pred = tf.constant(pred, dtype=tf.string)
pred = tf.transpose(pred, [0, 2, 1])
ref_lengths = tf.constant(ref_lengths)
pred_lengths = tf.constant(pred_lengths)
best_pred = pred_lengths[:, :, 0]
blues = create_bleu_metric_ops(ref, pred, ref_lengths, pred_lengths)
with tf.Session() as sess:
print()
print(sess.run(blues))
def get_avg_bleu_smmary(tgt_tokens, pred_tokens, tgt_len, pred_len):
if pred_tokens.shape.ndims > 2:
best_tokens = pred_tokens[:, :, 0]
best_len = pred_len[:, 0]
else:
best_tokens = pred_tokens
best_len = pred_len
bleu_score = metrics.create_bleu_metric_ops(tgt_tokens, best_tokens, tgt_len, best_len)
avg_bleu = tf.reduce_mean(bleu_score)
return avg_bleu