reformat

tensorflow_addons/text/crf.py

@@ -23,15 +23,16 @@
 # TODO: Wrap functions in @tf.function once
 # https://github.com/tensorflow/tensorflow/issues/29075 is resolved
 
+
 def crf_sequence_score(inputs, tag_indices, sequence_lengths,
                        transition_params):
     """Computes the unnormalized score for a tag sequence.
 
     Args:
       inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials
           to use as input to the CRF layer.
-      tag_indices: A [batch_size, max_seq_len] matrix of tag indices for which we
-          compute the unnormalized score.
+      tag_indices: A [batch_size, max_seq_len] matrix of tag indices for which
+          we compute the unnormalized score.
       sequence_lengths: A [batch_size] vector of true sequence lengths.
       transition_params: A [num_tags, num_tags] transition matrix.
     Returns:
@@ -171,15 +172,16 @@ def crf_log_likelihood(inputs,
     Args:
       inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials
           to use as input to the CRF layer.
-      tag_indices: A [batch_size, max_seq_len] matrix of tag indices for which we
-          compute the log-likelihood.
+      tag_indices: A [batch_size, max_seq_len] matrix of tag indices for which
+          we compute the log-likelihood.
       sequence_lengths: A [batch_size] vector of true sequence lengths.
-      transition_params: A [num_tags, num_tags] transition matrix, if available.
+      transition_params: A [num_tags, num_tags] transition matrix,
+          if available.
     Returns:
       log_likelihood: A [batch_size] `Tensor` containing the log-likelihood of
         each example, given the sequence of tag indices.
-      transition_params: A [num_tags, num_tags] transition matrix. This is either
-          provided by the caller or created in this function.
+      transition_params: A [num_tags, num_tags] transition matrix. This is
+          either provided by the caller or created in this function.
     """
     # Get shape information.
     num_tags = inputs.shape[2]
@@ -252,7 +254,8 @@ def crf_binary_score(tag_indices, sequence_lengths, transition_params):
     end_tag_indices = tf.slice(tag_indices, [0, 1], [-1, num_transitions])
 
     # Encode the indices in a flattened representation.
-    flattened_transition_indices = start_tag_indices * num_tags + end_tag_indices
+    flattened_transition_indices = start_tag_indices * \
+        num_tags + end_tag_indices
     flattened_transition_params = tf.reshape(transition_params, [-1])
 
     # Get the binary scores based on the flattened representation.
@@ -281,7 +284,7 @@ def crf_forward(inputs, state, transition_params, sequence_lengths):
       sequence_lengths: A [batch_size] vector of true sequence lengths.
 
     Returns:
-      new_alphas: A [batch_size, num_tags] matrix containing the 
+      new_alphas: A [batch_size, num_tags] matrix containing the
           new alpha values.
     """