diff options
| -rw-r--r-- | ctc.py | 21 | ||||
| -rw-r--r-- | main.py | 2 |
2 files changed, 12 insertions, 11 deletions
@@ -1,3 +1,5 @@ +import numpy + from theano import tensor, scan from blocks.bricks import Brick @@ -31,7 +33,7 @@ class CTC(Brick): B = l.shape[1] # l_blk = l with interleaved blanks - l_blk = C * tensor.ones((S, B)) + l_blk = C * tensor.ones((S, B), dtype='int32') l_blk = tensor.set_subtensor(l_blk[1::2,:],l) l_blk = l_blk.T # now l_blk is B x S @@ -41,8 +43,8 @@ class CTC(Brick): # T x B # first value of alpha (size B x S) alpha0 = tensor.concatenate([ - probs[0, :, C], - probs[0][tensor.arange(B), l[0]], + probs[0, :, C][:,None], + probs[0][tensor.arange(B), l[0]][:,None], tensor.zeros((B, S-2)) ], axis=1) c0 = alpha0.sum(axis=1) @@ -50,18 +52,17 @@ class CTC(Brick): # recursion l_blk_2 = tensor.concatenate([-tensor.ones((B,2)), l_blk[:,:-2]], axis=1) - l_case2 = tensor.ne(l_blk, numpy.float32(C)) * tensor.ne(l_blk, l_blk_2) + l_case2 = tensor.neq(l_blk, C) * tensor.neq(l_blk, l_blk_2) # l_case2 is B x S def recursion(p, p_mask, prev_alpha, prev_c): - prev_alpha = prev_alpha[-1] # p is B x C+1 # prev_alpha is B x S prev_alpha_1 = tensor.concatenate([tensor.zeros((B,1)),prev_alpha[:,:-1]], axis=1) prev_alpha_2 = tensor.concatenate([tensor.zeros((B,2)),prev_alpha[:,:-2]], axis=1) - alphabar = prev_alpha + prev_alpha1 - alphabar = tensor.switch(l_case2, alphabar + prev_alpha2, alphabar) + alpha_bar = prev_alpha + prev_alpha_1 + alpha_bar = tensor.switch(l_case2, alpha_bar + prev_alpha_2, alpha_bar) next_alpha = alpha_bar * p[tensor.arange(B)[:,None].repeat(S,axis=1).flatten(), l_blk.flatten()].reshape((B,S)) next_alpha = tensor.switch(p_mask[:,None], next_alpha, prev_alpha) next_c = next_alpha.sum(axis=1) @@ -69,9 +70,9 @@ class CTC(Brick): return next_alpha / next_c[:, None], next_c # apply the recursion with scan - alpha, c = tensor.scan(fn=recursion, - sequences=[probs, probs_mask], - outputs_info=[alpha0, c0]) + [alpha, c], _ = scan(fn=recursion, + sequences=[probs, probs_mask], + outputs_info=[alpha0, c0]) # return the log probability of the labellings return tensor.log(c).sum(axis=0) @@ -92,7 +92,7 @@ y_hat.name = 'y_hat' y_hat_mask = x_mask # Cost -cost = CTC().apply(y, y_hat, y_mask, y_hat_mask) +cost = CTC().apply(y, y_hat, y_mask.sum(axis=1), y_hat_mask).mean() cost.name = 'CTC' # Initialization |