Compatible for tf.keras in crf layer.

keras_contrib/layers/crf.py

@@ -14,7 +14,7 @@
 from keras_contrib.losses import crf_loss
 from keras_contrib.metrics import crf_marginal_accuracy
 from keras_contrib.metrics import crf_viterbi_accuracy
-from keras_contrib.utils.test_utils import to_tuple
+from keras_contrib.utils.test_utils import to_tuple, is_tf_keras
 
 
 class CRF(Layer):
@@ -460,15 +460,22 @@ def step(self, input_energy_t, states, return_logZ=True):
             if K.backend() == 'theano':
                 m = states[3][:, t:(t + 2)]
             else:
-                m = K.slice(states[3], [0, t], [-1, 2])
+                if is_tf_keras:
+                    m = tf.slice(states[3], [0, t], [-1, 2])
+                else:
+                    m = K.slice(states[3], [0, t], [-1, 2])
             input_energy_t = input_energy_t * K.expand_dims(m[:, 0])
             # (1, F, F)*(B, 1, 1) -> (B, F, F)
             chain_energy = chain_energy * K.expand_dims(
                 K.expand_dims(m[:, 0] * m[:, 1]))
         if return_logZ:
             # shapes: (1, B, F) + (B, F, 1) -> (B, F, F)
             energy = chain_energy + K.expand_dims(input_energy_t - prev_target_val, 2)
-            new_target_val = K.logsumexp(-energy, 1)  # shapes: (B, F)
+            if is_tf_keras:
+                import tensorflow as tf
+                new_target_val = tf.reduce_logsumexp(-energy, 1)  # shapes: (B, F)
+            else:
+                new_target_val = K.logsumexp(-energy, 1)  # shapes: (B, F)
             return new_target_val, [new_target_val, i + 1]
         else:
             energy = chain_energy + K.expand_dims(input_energy_t + prev_target_val, 2)

keras_contrib/losses/crf_losses.py

@@ -29,8 +29,8 @@ def crf_nll(y_true, y_pred):
         raise TypeError('When learn_model="join", CRF must be the last layer.')
     if crf.sparse_target:
         y_true = K.one_hot(K.cast(y_true[:, :, 0], 'int32'), crf.units)
-    X = crf._inbound_nodes[idx].input_tensors[0]
-    mask = crf._inbound_nodes[idx].input_masks[0]
+    X = crf.get_input_at(idx)
+    mask = crf.get_input_mask_at(idx)
     nloglik = crf.get_negative_log_likelihood(y_true, X, mask)
     return nloglik
 

keras_contrib/metrics/crf_accuracies.py

@@ -19,8 +19,8 @@ def crf_viterbi_accuracy(y_true, y_pred):
     '''Use Viterbi algorithm to get best path, and compute its accuracy.
     `y_pred` must be an output from CRF.'''
     crf, idx = y_pred._keras_history[:2]
-    X = crf._inbound_nodes[idx].input_tensors[0]
-    mask = crf._inbound_nodes[idx].input_masks[0]
+    X = crf.get_input_at(idx)
+    mask = crf.get_input_mask_at(idx)
     y_pred = crf.viterbi_decoding(X, mask)
     return _get_accuracy(y_true, y_pred, mask, crf.sparse_target)
 
@@ -29,8 +29,8 @@ def crf_marginal_accuracy(y_true, y_pred):
     '''Use time-wise marginal argmax as prediction.
     `y_pred` must be an output from CRF with `learn_mode="marginal"`.'''
     crf, idx = y_pred._keras_history[:2]
-    X = crf._inbound_nodes[idx].input_tensors[0]
-    mask = crf._inbound_nodes[idx].input_masks[0]
+    X = crf.get_input_at(idx)
+    mask = crf.get_input_mask_at(idx)
     y_pred = crf.get_marginal_prob(X, mask)
     return _get_accuracy(y_true, y_pred, mask, crf.sparse_target)