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)
