From b543ac7f330fa7b9f1400d76d1720aba1e550d6c Mon Sep 17 00:00:00 2001
From: Thomas Mesnard <thomas.mesnard@ens.fr>
Date: Sun, 20 Dec 2015 12:10:24 +0100
Subject: Start our own implementation

---
 ctc.py | 73 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 73 insertions(+)
 create mode 100644 ctc.py

(limited to 'ctc.py')

diff --git a/ctc.py b/ctc.py
new file mode 100644
index 0000000..cf629c1
--- /dev/null
+++ b/ctc.py
@@ -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:
-- 
cgit v1.2.3