tf.reset_default_graph()

# define placeholders

sentences        = tf.placeholder(tf.int32, [None, None], name='sentences')

#embedding
self.word_embedding = tf.get_variable(name='word_embedding',
                                shape=[vocab_size,wdim],
                                 dtype=tf.float32,
                                 initializer=tf.constant_initializer(np.array(word_embeddi)),trainable=True)

#embedding_lookup
embedding_lookup   = tf.nn.embedding_lookup(self.word_embedding,sentences)


#actual sequence length after padding ex : padded sentence [ 1,3,4,0,0,0 ] then seq_len is 3 ( exclude 0's)
sequence_len       = tf.count_nonzero(sentences, axis=-1)


#bi-directional rnn

with tf.variable_scope('encoder') as scope:

    _ , (fsf, fsb) = tf.nn.bidirectional_dynamic_rnn(
            rnn.DropoutWrapper(tf.nn.rnn_cell.LSTMCell(hdim),dtype=tf.float32),
            rnn.DropoutWrapper(tf.nn.rnn_cell.LSTMCell(hdim),dtype=tf.float32),
            inputs=embedding_lookup,
            sequence_length=sequence_len,
            dtype=tf.float32)


#since it's bidirectional so concat the forward and backward rnn
state_output = tf.concat([fsf.c, fsb.c], axis=-1)