import tensorflow as tf

                 ksize=[1, sequence_length - filter_size + 1, 1, 1],
                 strides=[1, 1, 1, 1],
                 padding='VALID',
                 name="pool")
        
        pooled_outputs.append(pooled)

with tf.name_scope("preFc"):
    # combine all pooled outputs
    total_filters = num_filter * len(filter_list)
    # concat all the pooled weights
    H_pool = tf.concat(pooled_outputs, 3)
    #flatten it for fully connected layer
    H_pool_flat = tf.reshape(H_pool, [-1, total_filters])
