这是 Keras测试test_ctc_decode_greedy。ctc_decode
这是我稍作修改的示例：
def test_ctc_decode_greedy():
def _remove_repeats(inds):
is_not_repeat = np.insert(np.diff(inds).astype(np.bool), 0, True)
return inds[is_not_repeat]
def _remove_blanks(inds, n_classes):
return inds[inds < (n_classes - 1)]
def ctc_decode_np(y_pred, input_length):
# Note:
# Last element in alphabet treated as blank character
# decoded_dense padded with -1
n_samples = y_pred.shape[0]
n_classes = y_pred.shape[-1]
log_prob = np.zeros((n_samples, 1))
decoded_dense = -np.ones_like(y_pred[..., 0])
decoded_length = np.zeros((n_samples,), dtype=np.int)
for i in range(n_samples):
print('-'*60)
# [n_time_steps, alphabet_size]
prob = y_pred[i]
length = input_length[i]
decoded = np.argmax(prob[:length], axis=-1)
print('decoded:', decoded)
log_prob[i] = -np.sum(np.log(prob[np.arange(length), decoded]))
decoded = _remove_repeats(decoded)
print('decoded remove_repeats:', decoded)
decoded = _remove_blanks(decoded, n_classes)
print('decoded remove_blanks:', decoded)
decoded_length[i] = len(decoded)
decoded_dense[i, :len(decoded)] = decoded
print('-' * 60)
return decoded_dense[:, :np.max(decoded_length)], log_prob
n_time_steps = 6
alphabet_size = 4
seq_len_0 = 4
input_prob_matrix_0 = np.asarray(
[[1.0, 0.0, 0.0, 0.0], # t=0
[0.0, 0.0, 0.4, 0.6], # t=1
[0.0, 0.0, 0.4, 0.6], # t=2
[0.0, 0.9, 0.1, 0.0], # t=3
[0.0, 0.0, 0.0, 0.0], # t=4 (ignored)
[0.0, 0.0, 0.0, 0.0]], # t=5 (ignored)
dtype=np.float32)
seq_len_1 = 5
input_prob_matrix_1 = np.asarray(
[[0.1, 0.9, 0.0, 0.0], # t=0
[0.0, 0.9, 0.1, 0.0], # t=1
[0.0, 0.0, 0.1, 0.9], # t=2
[0.0, 0.9, 0.1, 0.1], # t=3
[0.9, 0.1, 0.0, 0.0], # t=4
[0.0, 0.0, 0.0, 0.0]], # t=5 (ignored)
dtype=np.float32)
# [batch_size, max_chars_in_text, alphabet_size]
inputs = np.array([input_prob_matrix_0, input_prob_matrix_1])
print('inputs.shape', inputs.shape)
# [batch_size, ]
input_length = np.array([seq_len_0, seq_len_1], dtype=np.int32)
print('input_length.shape', input_length.shape)
decode_pred_np, log_prob_pred_np = ctc_decode_np(inputs, input_length)
# max_decoded_text_lenght depends on batch, other shorter samples in batch padded with -1
# [batch_size, max_decoded_text_lenght]
print('decode_pred_np.shape', decode_pred_np.shape)
print(decode_pred_np)
# [batch_size, 1]
print('log_prob_pred_np.shape', log_prob_pred_np.shape)
print(log_prob_pred_np)
inputs = K.variable(inputs)
input_length = K.variable(input_length)
decode_pred_tf, log_prob_pred_tf = K.ctc_decode(inputs, input_length, greedy=True)
assert len(decode_pred_tf) == 1
decode_pred = K.eval(decode_pred_tf[0])
log_prob_pred = K.eval(log_prob_pred_tf)
assert np.alltrue(decode_pred_np == decode_pred)
assert np.allclose(log_prob_pred_np, log_prob_pred)
输出：
------------------------------------------------------------
keras version: 2.1.6
tensorflow version: 1.14.0
------------------------------------------------------------
inputs.shape (2, 6, 4)
input_length.shape (2,)
------------------------------------------------------------
decoded: [0 3 3 1]
decoded remove_repeats: [0 3 1]
decoded remove_blanks: [0 1]
------------------------------------------------------------
decoded: [1 1 3 1 0]
decoded remove_repeats: [1 3 1 0]
decoded remove_blanks: [1 1 0]
------------------------------------------------------------
decode_pred_np.shape (2, 3)
[[ 0. 1. -1.]
[ 1. 1. 0.]]
log_prob_pred_np.shape (2, 1)
[[1.12701166]
[0.52680272]]
问题：
在推理时，我们如何获得预测的有效时间步数（例如，一些最后一行被忽略）？据我了解，我们有固定大小的矩阵作为网络的输出，我们是否需要另一个输出来预测有效时间步的数量？
据我了解
log_prob是负对数似然（值越小越好->网络对输出更有信心），但由于时间步数可能不同，log_prob不同时间步长的样本之间的值不可比较（因为我不看不到长度的任何归一化）？
更新：
为了在这里提供更多上下文，我使用真实字母和文本编码/解码的更具可读性的示例，在这里我使用input_length = n_time_steps：
def test_ctc_decode_greedy():
def labels_to_text(labels, alphabet):
chars = []
for i in labels:
chars.append(alphabet[i])
text = ''.join(chars)
return text
def _remove_repeats(inds):
is_not_repeat = np.insert(np.diff(inds).astype(np.bool), 0, True)
return inds[is_not_repeat]
def _remove_blanks(inds, n_classes):
return inds[inds < (n_classes - 1)]
def ctc_decode_np(y_pred, input_length):
# Note:
# Last element in alphabet treated as blank character
# decoded_dense padded with -1
n_samples = y_pred.shape[0]
n_classes = y_pred.shape[-1] # len(alphabet) + 1
log_prob = np.zeros((n_samples, 1))
decoded_dense = -np.ones_like(y_pred[..., 0])
print('n_samples', n_samples) #
print('n_classes', n_classes) #
print('log_prob.shape', log_prob.shape) #
print('decoded_dense.shape', decoded_dense.shape) #
for i in range(n_samples):
print('-'*60)
# [n_time_steps, alphabet_size]
prob = y_pred[i]
length = input_length[i]
decoded = np.argmax(prob[:length], axis=-1)
print('decoded:', decoded, labels_to_text(decoded, alphabet)) #
print('prob[np.arange(length), decoded] :', prob[np.arange(length), decoded]) #
print('np.log(prob[np.arange(length), decoded]) :', np.log(prob[np.arange(length), decoded])) #
log_prob[i] = -np.sum(np.log(prob[np.arange(length), decoded]))
print('log_prob[i]', log_prob[i]) #
decoded = _remove_repeats(decoded)
print('decoded: remove_repeats:', decoded, labels_to_text(decoded, alphabet)) #
decoded = _remove_blanks(decoded, n_classes)
print('decoded: remove_blanks:', decoded, labels_to_text(decoded, alphabet)) #
decoded_dense[i, :len(decoded)] = decoded
print('-' * 60)
return decoded_dense, log_prob
n_time_steps = 8
alphabet = ['h', 'e', 'l', 'o', '-']
alphabet_size = len(alphabet)
seq_len_0 = n_time_steps
# 'hhel-lo-' after decoding should be 'hello'
input_prob_matrix_0 = np.asarray(
[[1.0, 0.0, 0.0, 0.0, 0.0], # t=0 # 'h'
[1.0, 0.0, 0.0, 0.0, 0.0], # t=1 # 'h'
[0.0, 1.0, 0.0, 0.0, 0.0], # t=2 # 'e'
[0.0, 0.0, 1.0, 0.0, 0.0], # t=3 # 'l'
[0.0, 0.0, 0.0, 0.0, 1.0], # t=4 # '-'
[0.0, 0.0, 1.0, 0.0, 0.0], # t=5 # 'l'
[0.0, 0.0, 0.0, 1.0, 0.0], # t=6 # 'o'
[0.0, 0.0, 0.0, 0.0, 1.0]], # t=7 # '-'
dtype=np.float32)
# [batch_size, n_time_steps, alphabet_size]
inputs = np.array([input_prob_matrix_0])
print('inputs.shape', inputs.shape) #
# [batch_size, ]
input_length = np.array([seq_len_0], dtype=np.int32)
print('input_length.shape', input_length.shape) #
decode_pred_np, log_prob_pred_np = ctc_decode_np(inputs, input_length)
# [batch_size, n_time_steps]
print('decode_pred_np.shape', decode_pred_np.shape) #
print(decode_pred_np)
# [batch_size, 1]
print('log_prob_pred_np.shape', log_prob_pred_np.shape) #
print(log_prob_pred_np)
输出：
------------------------------------------------------------
inputs.shape (1, 8, 5)
input_length.shape (1,)
n_samples 1
n_classes 5
log_prob.shape (1, 1)
decoded_dense.shape (1, 8)
------------------------------------------------------------
decoded: [0 0 1 2 4 2 3 4] hhel-lo-
prob[np.arange(length), decoded] : [1. 1. 1. 1. 1. 1. 1. 1.]
np.log(prob[np.arange(length), decoded]) : [0. 0. 0. 0. 0. 0. 0. 0.]
log_prob[i] [-0.]
decoded: remove_repeats: [0 1 2 4 2 3 4] hel-lo-
decoded: remove_blanks: [0 1 2 2 3] hello
------------------------------------------------------------
decode_pred_np.shape (1, 8)
[[ 0. 1. 2. 2. 3. -1. -1. -1.]]
log_prob_pred_np.shape (1, 1)
[[-0.]]