import tensorflow as tf

            logit_b = tf.get_variable('b', shape=[1], initializer=tf.constant_initializer(0.0), dtype=dtype)
        logits = tf.squeeze(tf.nn.bias_add(tf.matmul(dnn_output, logit_w), logit_b), squeeze_dims=[1])
        prediction = tf.nn.sigmoid(logits)
        prediction_inspect = tf.reshape(prediction, [batch_size, rnn_nunroll])
        prediction_final = tf.squeeze(tf.slice(prediction_inspect, [0, rnn_nunroll - 1], [-1, 1]), squeeze_dims=[1])
        print('logit: {}'.format(logits.get_shape()))

        # Compute loss
