Fixed issue with train_dyn_rnn.py training report nan (#808)

Fixed issue with train_dyn_rnn.py training report nan

06.understand_sentiment/train_dyn_rnn.py

@@ -44,35 +44,11 @@ def parse_args():
 def dynamic_rnn_lstm(data, input_dim, class_dim, emb_dim, lstm_size):
     emb = fluid.layers.embedding(
         input=data, size=[input_dim, emb_dim], is_sparse=True)
-    sentence = fluid.layers.fc(input=emb, size=lstm_size, act='tanh')
-
-    rnn = fluid.layers.DynamicRNN()
-    with rnn.block():
-        word = rnn.step_input(sentence)
-        prev_hidden = rnn.memory(value=0.0, shape=[lstm_size])
-        prev_cell = rnn.memory(value=0.0, shape=[lstm_size])
-
-        def gate_common(ipt, hidden, size):
-            gate0 = fluid.layers.fc(input=ipt, size=size, bias_attr=True)
-            gate1 = fluid.layers.fc(input=hidden, size=size, bias_attr=False)
-            return gate0 + gate1
-
-        forget_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden,
-                                                         lstm_size))
-        input_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden,
-                                                        lstm_size))
-        output_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden,
-                                                         lstm_size))
-        cell_gate = fluid.layers.sigmoid(x=gate_common(word, prev_hidden,
-                                                       lstm_size))
-
-        cell = forget_gate * prev_cell + input_gate * cell_gate
-        hidden = output_gate * fluid.layers.tanh(x=cell)
-        rnn.update_memory(prev_cell, cell)
-        rnn.update_memory(prev_hidden, hidden)
-        rnn.output(hidden)
-
-    last = fluid.layers.sequence_last_step(rnn())
+    sentence = fluid.layers.fc(input=emb, size=lstm_size * 4, act='tanh')
+
+    lstm, _ = fluid.layers.dynamic_lstm(sentence, size=lstm_size * 4)
+
+    last = fluid.layers.sequence_last_step(lstm)
     prediction = fluid.layers.fc(input=last, size=class_dim, act="softmax")
     return prediction