TensorFlow で CTC 実装がどのように機能するかを理解しようとしています。CTC 機能をテストするためだけに簡単な例を書きましたが、何らかの理由でinf、いくつかのターゲット/入力値を取得しています。
コード：
import tensorflow as tf
import numpy as np
# https://github.com/philipperemy/tensorflow-ctc-speech-recognition/blob/master/utils.py
def sparse_tuple_from(sequences, dtype=np.int32):
"""Create a sparse representention of x.
Args:
sequences: a list of lists of type dtype where each element is a sequence
Returns:
A tuple with (indices, values, shape)
"""
indices = []
values = []
for n, seq in enumerate(sequences):
indices.extend(zip([n] * len(seq), range(len(seq))))
values.extend(seq)
indices = np.asarray(indices, dtype=np.int64)
values = np.asarray(values, dtype=dtype)
shape = np.asarray([len(sequences), np.asarray(indices).max(0)[1] + 1], dtype=np.int64)
return indices, values, shape
batch_size = 1
seq_length = 2
n_labels = 2
seq_len = tf.placeholder(tf.int32, [None])
targets = tf.sparse_placeholder(tf.int32)
logits = tf.constant(np.random.random((batch_size, seq_length, n_labels+1)),dtype=tf.float32) # +1 for the blank label
loss = tf.reduce_mean(tf.nn.ctc_loss(targets, logits, seq_len, time_major = False))
with tf.Session() as sess:
for it in range(10):
rand_target = np.random.randint(n_labels, size=(seq_length))
sample_target = sparse_tuple_from([rand_target])
logitsval = sess.run(logits)
lossval = sess.run(loss, feed_dict={seq_len: [seq_length], targets: sample_target})
print('******* Iter: %d *******'%it)
print('logits:', logitsval)
print('rand_target:', rand_target)
print('rand_sparse_target:', sample_target)
print('loss:', lossval)
print()
サンプル出力:
******* Iter: 0 *******
logits: [[[ 0.10151503 0.88581538 0.56466645]
[ 0.76043415 0.52718711 0.01166286]]]
rand_target: [0 1]
rand_sparse_target: (array([[0, 0],
[0, 1]]), array([0, 1], dtype=int32), array([1, 2]))
loss: 2.61521
******* Iter: 1 *******
logits: [[[ 0.10151503 0.88581538 0.56466645]
[ 0.76043415 0.52718711 0.01166286]]]
rand_target: [1 1]
rand_sparse_target: (array([[0, 0],
[0, 1]]), array([1, 1], dtype=int32), array([1, 2]))
loss: inf
******* Iter: 2 *******
logits: [[[ 0.10151503 0.88581538 0.56466645]
[ 0.76043415 0.52718711 0.01166286]]]
rand_target: [0 1]
rand_sparse_target: (array([[0, 0],
[0, 1]]), array([0, 1], dtype=int32), array([1, 2]))
loss: 2.61521
******* Iter: 3 *******
logits: [[[ 0.10151503 0.88581538 0.56466645]
[ 0.76043415 0.52718711 0.01166286]]]
rand_target: [1 0]
rand_sparse_target: (array([[0, 0],
[0, 1]]), array([1, 0], dtype=int32), array([1, 2]))
loss: 1.59766
******* Iter: 4 *******
logits: [[[ 0.10151503 0.88581538 0.56466645]
[ 0.76043415 0.52718711 0.01166286]]]
rand_target: [0 0]
rand_sparse_target: (array([[0, 0],
[0, 1]]), array([0, 0], dtype=int32), array([1, 2]))
loss: inf
******* Iter: 5 *******
logits: [[[ 0.10151503 0.88581538 0.56466645]
[ 0.76043415 0.52718711 0.01166286]]]
rand_target: [0 1]
rand_sparse_target: (array([[0, 0],
[0, 1]]), array([0, 1], dtype=int32), array([1, 2]))
loss: 2.61521
******* Iter: 6 *******
logits: [[[ 0.10151503 0.88581538 0.56466645]
[ 0.76043415 0.52718711 0.01166286]]]
rand_target: [1 0]
rand_sparse_target: (array([[0, 0],
[0, 1]]), array([1, 0], dtype=int32), array([1, 2]))
loss: 1.59766
******* Iter: 7 *******
logits: [[[ 0.10151503 0.88581538 0.56466645]
[ 0.76043415 0.52718711 0.01166286]]]
rand_target: [1 1]
rand_sparse_target: (array([[0, 0],
[0, 1]]), array([1, 1], dtype=int32), array([1, 2]))
loss: inf
******* Iter: 8 *******
logits: [[[ 0.10151503 0.88581538 0.56466645]
[ 0.76043415 0.52718711 0.01166286]]]
rand_target: [0 1]
rand_sparse_target: (array([[0, 0],
[0, 1]]), array([0, 1], dtype=int32), array([1, 2]))
loss: 2.61521
******* Iter: 9 *******
logits: [[[ 0.10151503 0.88581538 0.56466645]
[ 0.76043415 0.52718711 0.01166286]]]
rand_target: [0 0]
rand_sparse_target: (array([[0, 0],
[0, 1]]), array([0, 0], dtype=int32), array([1, 2]))
loss: inf
私はそこに何が欠けているのですか!?
