def ctc_lambda_func(self, args):
y_pred, labels, input_length, label_length = args
return K.ctc_batch_cost(y_true=labels,
y_pred=y_pred,
input_length=input_length,
label_length=label_length)
def ctc_batch_loss(y_true, y_pred):
'''
CTC的loss函数
这里目前有bug
'''
loss = ctc_batch_cost(y_true, y_pred,
tf.Variable((748, 1283), dtype=tf.int64),
tf.Variable((64, 1283), dtype=tf.int64))
return tf.Variable(loss, dtype=tf.int64)
def test_ctc(self):
# simplified version of TensorFlow's test
label_lens = np.expand_dims(np.asarray([5, 4]), 1)
input_lens = np.expand_dims(np.asarray([5, 5]),
1) # number of timesteps
# the Theano and Tensorflow CTC code use different methods to ensure
# numerical stability. The Theano code subtracts out the max
# before the final log, so the results are different but scale
# identically and still train properly
loss_log_probs_tf = [3.34211, 5.42262]
loss_log_probs_th = [1.73308, 3.81351]
# dimensions are batch x time x categories
labels = np.asarray([[0, 1, 2, 1, 0], [0, 1, 1, 0, -1]])
inputs = np.asarray(
[[[0.633766, 0.221185, 0.0917319, 0.0129757, 0.0142857, 0.0260553],
[0.111121, 0.588392, 0.278779, 0.0055756, 0.00569609, 0.010436],
[
0.0357786, 0.633813, 0.321418, 0.00249248, 0.00272882,
0.0037688
],
[
0.0663296, 0.643849, 0.280111, 0.00283995, 0.0035545,
0.00331533
],
[
0.458235, 0.396634, 0.123377, 0.00648837, 0.00903441,
0.00623107
]],
[[0.30176, 0.28562, 0.0831517, 0.0862751, 0.0816851, 0.161508],
[0.24082, 0.397533, 0.0557226, 0.0546814, 0.0557528, 0.19549],
[0.230246, 0.450868, 0.0389607, 0.038309, 0.0391602, 0.202456],
[0.280884, 0.429522, 0.0326593, 0.0339046, 0.0326856, 0.190345],
[0.423286, 0.315517, 0.0338439, 0.0393744, 0.0339315, 0.154046]]
],
dtype=np.float32)
labels_tf = KTF.variable(labels, dtype="int32")
inputs_tf = KTF.variable(inputs, dtype="float32")
input_lens_tf = KTF.variable(input_lens, dtype="int32")
label_lens_tf = KTF.variable(label_lens, dtype="int32")
res = KTF.eval(
KTF.ctc_batch_cost(labels_tf, inputs_tf, input_lens_tf,
label_lens_tf))
assert_allclose(res[:, 0], loss_log_probs_tf, atol=1e-05)
labels_th = KTH.variable(labels, dtype="int32")
inputs_th = KTH.variable(inputs, dtype="float32")
input_lens_th = KTH.variable(input_lens, dtype="int32")
label_lens_th = KTH.variable(label_lens, dtype="int32")
res = KTH.eval(
KTH.ctc_batch_cost(labels_th, inputs_th, input_lens_th,
label_lens_th))
assert_allclose(res[0, :], loss_log_probs_th, atol=1e-05)
def test_ctc(self):
# simplified version of TensorFlow's test
label_lens = np.expand_dims(np.asarray([5, 4]), 1)
input_lens = np.expand_dims(np.asarray([5, 5]), 1) # number of timesteps
# the Theano and Tensorflow CTC code use different methods to ensure
# numerical stability. The Theano code subtracts out the max
# before the final log, so the results are different but scale
# identically and still train properly
loss_log_probs_tf = [3.34211, 5.42262]
loss_log_probs_th = [1.73308, 3.81351]
# dimensions are batch x time x categories
labels = np.asarray([[0, 1, 2, 1, 0], [0, 1, 1, 0, -1]])
inputs = np.asarray(
[
[
[0.633766, 0.221185, 0.0917319, 0.0129757, 0.0142857, 0.0260553],
[0.111121, 0.588392, 0.278779, 0.0055756, 0.00569609, 0.010436],
[0.0357786, 0.633813, 0.321418, 0.00249248, 0.00272882, 0.0037688],
[0.0663296, 0.643849, 0.280111, 0.00283995, 0.0035545, 0.00331533],
[0.458235, 0.396634, 0.123377, 0.00648837, 0.00903441, 0.00623107],
],
[
[0.30176, 0.28562, 0.0831517, 0.0862751, 0.0816851, 0.161508],
[0.24082, 0.397533, 0.0557226, 0.0546814, 0.0557528, 0.19549],
[0.230246, 0.450868, 0.0389607, 0.038309, 0.0391602, 0.202456],
[0.280884, 0.429522, 0.0326593, 0.0339046, 0.0326856, 0.190345],
[0.423286, 0.315517, 0.0338439, 0.0393744, 0.0339315, 0.154046],
],
],
dtype=np.float32,
)
labels_tf = KTF.variable(labels, dtype="int32")
inputs_tf = KTF.variable(inputs, dtype="float32")
input_lens_tf = KTF.variable(input_lens, dtype="int32")
label_lens_tf = KTF.variable(label_lens, dtype="int32")
res = KTF.eval(KTF.ctc_batch_cost(labels_tf, inputs_tf, input_lens_tf, label_lens_tf))
assert_allclose(res[:, 0], loss_log_probs_tf, atol=1e-05)
labels_th = KTH.variable(labels, dtype="int32")
inputs_th = KTH.variable(inputs, dtype="float32")
input_lens_th = KTH.variable(input_lens, dtype="int32")
label_lens_th = KTH.variable(label_lens, dtype="int32")
res = KTH.eval(KTH.ctc_batch_cost(labels_th, inputs_th, input_lens_th, label_lens_th))
assert_allclose(res[0, :], loss_log_probs_th, atol=1e-05)
def ctc_batch_loss(y_true, y_pred): ''' CTC的loss函数 这里目前有bug ''' a=list() b=list() for i in range(0,32): a.append(748) b.append(64) #print(a,b) y_true_length = tf.Variable([1],dtype=tf.int64) y_pred_length = tf.Variable([1],dtype=tf.int64) #y_pred = y_pred[:, 2:, :] loss = ctc_batch_cost(y_true, y_pred, y_true_length, y_pred_length) return tf.Variable(loss,dtype=tf.int64)
def ctc_lambda_func(args):
# https://www.tensorflow.org/api_docs/python/tf/keras/backend/ctc_batch_cost
y_true, y_pred, input_length, label_length = args
return K.ctc_batch_cost(y_true, y_pred, input_length, label_length)
def ctc_lambda_func(args):
y_pred, labels, input_length, label_length = args
return K.ctc_batch_cost(labels, y_pred, input_length, label_length)
