Move docstring from CTC class to ctc function and fix docstring of CTC class

theano/tensor/nnet/ctc.py

@@ -24,24 +24,12 @@ class ConnectionistTemporalClassification(gof.COp, gof.OpenMPOp):
 
     Parameters
     ----------
-    activations
-        Three-dimensional tensor, which has a shape of (t, m, p), where
-        t is the time index, m is the minibatch index, and p is the index
-        over the probabilities of each symbol in the alphabet. The memory
-        layout is assumed to be in C-order, which consists in the slowest
-        to the fastest changing dimension, from left to right. In this case,
-        p is the fastest changing dimension.
-    labels
-        A 1-D tensor of all the labels for the minibatch.
-    input_lengths
-        A 1-D tensor with the number of time steps for each sequence in
-        the minibatch.
-
+    compute_grad
+        If set to True, enables the computation of gradients of the CTC loss function.
     Returns
     -------
-    1-D tensor
-        Cost of each example in the minibatch. Tensor is of shape
-        (time index, minibatch index, probabilities).
+    Op
+        An instance of the CTC loss computation Op
     """
     __props__ = ('compute_grad',)
 
@@ -134,6 +122,36 @@ class ConnectionistTemporalClassification(gof.COp, gof.OpenMPOp):
 
 
 def ctc(activations, labels, input_lengths):
+    """
+    Compute CTC loss function.
+
+    Notes
+    -----
+    Using the wrapper requires that Baidu's warp-ctc library is installed and the
+    configuration variables `config.ctc.enabled` and `config.ctc.root` be properly
+    set.
+
+    Parameters
+    ----------
+    activations
+        Three-dimensional tensor, which has a shape of (t, m, p), where
+        t is the time index, m is the minibatch index, and p is the index
+        over the probabilities of each symbol in the alphabet. The memory
+        layout is assumed to be in C-order, which consists in the slowest
+        to the fastest changing dimension, from left to right. In this case,
+        p is the fastest changing dimension.
+    labels
+        A 1-D tensor of all the labels for the minibatch.
+    input_lengths
+        A 1-D tensor with the number of time steps for each sequence in
+        the minibatch.
+
+    Returns
+    -------
+    1-D tensor
+        Cost of each example in the minibatch. Tensor is of shape
+        (time index, minibatch index, probabilities).
+    """
     return ConnectionistTemporalClassification()(activations, labels, input_lengths)