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| -rw-r--r-- | ctc.py | 73 |
1 files changed, 73 insertions, 0 deletions
@@ -0,0 +1,73 @@ +from theano import tensor, scan + +from blocks.bricks import Brick + +# T: INPUT_SEQUENCE_LENGTH +# B: BATCH_SIZE +# L: OUTPUT_SEQUENCE_LENGTH +# C: NUM_CLASSES +class CTC(Brick): + def apply(l, probs, l_mask=None, probs_mask=None): + """ + Numeration: + Characters 0 to C-1 are true characters + Character C is the blank character + Inputs: + l : L x B : the sequence labelling + probs : T x B x C+1 : the probabilities output by the RNN + l_mask : L x B + probs_mask : T x B + Output: the B probabilities of the labelling sequences + Steps: + - Calculate y' the labelling sequence with blanks + - Calculate the recurrence relationship for the alphas + - Calculate the sequence of the alphas + - Return the probability found at the end of that sequence + """ + T = probs.shape[0] + C = probs.shape[2]-1 + L = l.shape[0] + S = 2*L+1 + B = l.shape[1] + + # l_blk = l with interleaved blanks + l_blk = tensor.zeros((S, B)) + l_blk = tensor.set_subtensor(l_blk[1::2,:],l) + + # dimension of alpha : + # T x B x S + # dimension of c : + # T x B + # first value of alpha (size B x S) + alpha0 = tensor.concatenate([ + probs[0, :, C], + probs[0][tensor.arange(B), l[0]], + tensor.zeros((B, S-2)) + ], axis=1) + c0 = alpha0.sum(axis=2) + + # recursion + def recursion(p, p_mask, prev_alpha, prev_c): + # TODO + return prev_alpha[-1], prev_c[-1] + + # apply the recursion with scan + alpha, c = tensor.scan(fn=recursion, + sequences=[probs, probs_mask], + outputs_info=[alpha0, c0]) + + # return the probability of the labellings + + + + def best_path_decoding(y_hat, y_hat_mask=None): + # Easy one ! + pass + + def prefix_search(y_hat, y_hat_mask=None): + # Hard one... + pass + + + +# vim: set sts=4 ts=4 sw=4 sw=4 tw=0 et: |