fixed PEP8

Author: linxihui

examples/conll2000_chunking_crf.py

@@ -33,6 +33,7 @@
 vocab = ['<pad>', '<unk>'] + [w for w, f in word_counts.iteritems() if f >= 3]
 word2idx = dict((w, i) for i, w in enumerate(vocab))
 
+
 def process_data(data, maxlen=None, onehot=False):
     if maxlen is None:
         maxlen = max(len(s) for s in data)
@@ -46,6 +47,7 @@ def process_data(data, maxlen=None, onehot=False):
     else:
         return x, numpy.expand_dims(y, 2)
 
+
 train_x, train_y = process_data(train)
 test_x, test_y = process_data(test)
 
@@ -56,7 +58,7 @@ def process_data(data, maxlen=None, onehot=False):
 print('==== training CRF ====')
 
 model = Sequential()
-model.add(Embedding(len(vocab), 200, mask_zero=True)) # Random embedding
+model.add(Embedding(len(vocab), 200, mask_zero=True))  # Random embedding
 crf = CRF(len(class_labels), sparse_target=True)
 model.add(crf)
 model.summary()
@@ -77,7 +79,7 @@ def process_data(data, maxlen=None, onehot=False):
 print('==== training BiLSTM-CRF ====')
 
 model = Sequential()
-model.add(Embedding(len(vocab), 200, mask_zero=True)) # Random embedding
+model.add(Embedding(len(vocab), 200, mask_zero=True))  # Random embedding
 model.add(Bidirectional(LSTM(100, return_sequences=True)))
 crf = CRF(len(class_labels), sparse_target=True)
 model.add(crf)

keras/layers/crf.py

@@ -45,15 +45,15 @@ class CRF(Layer):
         model = Sequential()
         model.add(Embedding(3001, 300, mask_zero=True)(X)
 
-        # use learn_mode = 'join', test_mode = 'viterbi'
-        crf = CRF(10)
-        model.add(crf(Embed))
+        # use learn_mode = 'join', test_mode = 'viterbi', sparse_target = True (label indice output)
+        crf = CRF(10, sparse_target=True)
+        model.add(crf)
 
         # crf.accuracy is default to Viterbi acc if using join-mode (default).
         # One can add crf.marginal_acc if interested, but may slow down learning
         model.compile('adam', loss=crf.loss_function, metrics=[crf.accuracy])
 
-        # y can be either onehot representation or label indices (with shape 1 at dim 3)
+        # y must be label indices (with shape 1 at dim 3) here, since `sparse_target=True`
         model.fit(x, y)
 
         # prediction give onehot representation of Viterbi best path
@@ -331,16 +331,16 @@ def get_logZ(self, in_energy, mask):
     def get_energy(self, y_true, in_energy, mask):
         '''Energy = a1' y1 + u1' y1 + y1' U y2 + u2' y2 + y2' U y3 + u3' y3 + an' y3
         '''
-        in_energy = K.sum(in_energy * y_true, 2) # (B, T)
+        in_energy = K.sum(in_energy * y_true, 2)  # (B, T)
         chain_energy = K.sum(K.dot(y_true[:, :-1, :], self.U) * y_true[:, 1:, :], 2)  # (B, T-1)
         chain_energy = self.chain_activation(chain_energy)
 
         if mask is not None:
             mask = K.cast(mask, K.floatx())
-            chain_mask = mask[:, :-1] * mask[:, 1:] # (B, T-1), mask[:,:-1]*mask[:,1:] makes it work with any padding
+            chain_mask = mask[:, :-1] * mask[:, 1:]  # (B, T-1), mask[:,:-1]*mask[:,1:] makes it work with any padding
             in_energy = in_energy * mask
             chain_energy = chain_energy * chain_mask
-        total_energy = K.sum(in_energy, -1) + K.sum(chain_energy, -1) # (B, )
+        total_energy = K.sum(in_energy, -1) + K.sum(chain_energy, -1)  # (B, )
 
         return total_energy
 
@@ -368,19 +368,19 @@ def step(self, in_energy_t, states, return_logZ=True):
         t = K.cast(i[0, 0], dtype='int32')
         if len(states) > 3:
             if K._BACKEND == 'theano':
-                m = states[3][:, t:(t+2)]
+                m = states[3][:, t:(t + 2)]
             else:
                 m = tf.slice(states[3], [0, t], [-1, 2])
             in_energy_t = in_energy_t * K.expand_dims(m[:, 0])
             chain_energy = chain_energy * K.expand_dims(K.expand_dims(m[:, 0] * m[:, 1]))  # (1, F, F)*(B, 1, 1) -> (B, F, F)
         if return_logZ:
-            energy = chain_energy + K.expand_dims(in_energy_t - prev_target_val, 2) # shapes: (1, B, F) + (B, F, 1) -> (B, F, F)
-            new_target_val = self.log_sum_exp(-energy, 1) # shapes: (B, F)
+            energy = chain_energy + K.expand_dims(in_energy_t - prev_target_val, 2)  # shapes: (1, B, F) + (B, F, 1) -> (B, F, F)
+            new_target_val = self.log_sum_exp(-energy, 1)  # shapes: (B, F)
             return new_target_val, [new_target_val, i + 1]
         else:
             energy = chain_energy + K.expand_dims(in_energy_t + prev_target_val, 2)
             min_energy = K.min(energy, 1)
-            argmin_table = K.cast(K.argmin(energy, 1), K.floatx()) # cast for tf-version `K.rnn`
+            argmin_table = K.cast(K.argmin(energy, 1), K.floatx())  # cast for tf-version `K.rnn`
             return argmin_table, [min_energy, i + 1]
 
     def recursion(self, in_energy, mask=None, go_backwards=False, return_sequences=True, return_logZ=True):
@@ -403,8 +403,8 @@ def recursion(self, in_energy, mask=None, go_backwards=False, return_sequences=T
         If `return_logZ = False`, compute the Viterbi's best path lookup table.
         '''
         chain_energy = self.chain_activation(self.U)
-        chain_energy = K.expand_dims(chain_energy, 0) # shape=(1, F, F): F=num of output features. 1st F is for t-1, 2nd F for t
-        prev_target_val = K.zeros_like(in_energy[:, 0, :]) # shape=(B, F), dtype=float32
+        chain_energy = K.expand_dims(chain_energy, 0)  # shape=(1, F, F): F=num of output features. 1st F is for t-1, 2nd F for t
+        prev_target_val = K.zeros_like(in_energy[:, 0, :])  # shape=(B, F), dtype=float32
 
         if go_backwards:
             in_energy = K.reverse(in_energy, 1)
@@ -458,7 +458,7 @@ def viterbi_decoding(self, X, mask=None):
 
         # backward to find best path, `initial_best_idx` can be any, as all elements in the last argmin_table are the same
         argmin_tables = K.reverse(argmin_tables, 1)
-        initial_best_idx = [K.expand_dims(argmin_tables[:, 0, 0])] # matrix instead of vector is required by tf `K.rnn`
+        initial_best_idx = [K.expand_dims(argmin_tables[:, 0, 0])]  # matrix instead of vector is required by tf `K.rnn`
 
         def gather_each_row(params, indices):
             n = K.shape(indices)[0]

tests/keras/layers/test_crf.py

@@ -12,11 +12,11 @@
 def test_CRF():
     # data
     x = np.random.randint(1, embedding_num, nb_samples * timesteps).reshape((nb_samples, timesteps))
-    x[0, -4:] = 0 # right padding
-    x[1, :5] = 0 # left padding
+    x[0, -4:] = 0  # right padding
+    x[1, :5] = 0  # left padding
     y = np.random.randint(0, output_dim, nb_samples * timesteps).reshape((nb_samples, timesteps))
     y_onehot = np.eye(output_dim)[y]
-    y = np.expand_dims(y, 2) # .astype('float32')
+    y = np.expand_dims(y, 2)  # .astype('float32')
 
     # test with no masking, onehot, fix length
     model = Sequential()
@@ -37,8 +37,8 @@ def test_CRF():
 
     # check mask
     y_pred = model.predict(x).argmax(-1)
-    assert (y_pred[0, -4:] == 0).all() # right padding
-    assert (y_pred[1, :5] == 0).all() # left padding
+    assert (y_pred[0, -4:] == 0).all()  # right padding
+    assert (y_pred[1, :5] == 0).all()  # left padding
 
     # test `viterbi_acc
     _, v_acc, _ = model.evaluate(x, y)
@@ -48,7 +48,7 @@ def test_CRF():
     # test config
     model.get_config()
     model = model_from_json(model.to_json())
-    
+
     # test marginal learn mode, fix length, unroll
 
     model = Sequential()