import tensorflow as tf

                 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])

with tf.name_scope("dropout"):
    H_drop = tf.nn.dropout(H_pool_flat, keep_prob = keep_prob)

# Final (unnormalized) layer
with tf.name_scope("output"):
    W = tf.get_variable("W",
        shape=[total_filters, nb_classes],
        initializer=tf.contrib.layers.xavier_initializer())
    # add final layer bias
    b = tf.Variable(tf.constant(0.1, shape=[nb_classes]), name="b")
    # calc l2 losses
    l2_loss += tf.nn.l2_loss(W)
    l2_loss += tf.nn.l2_loss(b)
    
