more parametrization

        l1_sym8    = NN_LAYOUT1[l1] // 8

        l1_non_sum = NN_LAYOUT1[l1] % 8

        TILES_PER_LINE1 = 2

        TILES_PER_LINE2 = 4

        ZERO_SPAN1 = 0.0002

        ZERO_SPAN2 = 0.0002

        tile_side1 = TILE_SIDE

        tile_side2 = int(math.sqrt(NN_LAYOUT2[-1]))                

        cluster_side = CLUSTER_RADIUS*2+1

        cluster_size = cluster_side*cluster_side 

        l1_w = (tile_side1+1)*TILE_LAYERS*TILES_PER_LINE1

        l1_h = (tile_side1+1)*8//TILES_PER_LINE1

        if l1_non_sum==0:

            wimg1_placeholder = tf.placeholder(tf.float32, [1,40,80,3])

            wimg1_placeholder = tf.placeholder(tf.float32, [1,l1_h,l1_w,3])

            wimg1 = tf.summary.image('weights/sub_'+str(l1), wimg1_placeholder)

        l2 = NN_LAYOUT2.index(next(filter(lambda x: x!=0, NN_LAYOUT2)))

        wimg2_placeholder = tf.placeholder(tf.float32, [1,250,100,3])

        l2_w = (tile_side2+1)*cluster_side*TILES_PER_LINE2

        l2_h = (tile_side2+1)*cluster_side*NN_LAYOUT2[l2]//TILES_PER_LINE2

        wimg2_placeholder = tf.placeholder(tf.float32, [1,l2_h,l2_w,3])

        wimg2 = tf.summary.image('weights/inter_'+str(l2), wimg2_placeholder)

    # display weights, part 1 end

                with tf.variable_scope('g_fc_sub'+str(l1),reuse=tf.AUTO_REUSE):

                    w = tf.get_variable('weights',shape=[325,l1_sym8])

                    w = tf.transpose(w,(1,0))            

                    img1 = npw.tiles(npw.coldmap(w.eval(),zero_span=0.0002),(1,4,9,9),tiles_per_line=2,borders=True)

                    img1 = npw.tiles(npw.coldmap(w.eval(),zero_span=ZERO_SPAN1),(1,TILE_LAYERS,tile_side1,tile_side1),tiles_per_line=TILES_PER_LINE1,borders=True)

                    img1 = img1[np.newaxis,...]

                    train_writer.add_summary(wimg1.eval(feed_dict={wimg1_placeholder: img1}), epoch)

            #l2 = NN_LAYOUT2.index(next(filter(lambda x: x!=0, NN_LAYOUT2)))

            with tf.variable_scope('g_fc_inter'+str(l2),reuse=tf.AUTO_REUSE):

                w = tf.get_variable('weights',shape=[400,NN_LAYOUT2[l2]])

                w = tf.get_variable('weights',shape=[cluster_size*NN_LAYOUT2[-1],NN_LAYOUT2[l2]])

                w = tf.transpose(w,(1,0))

                img2 = npw.tiles(npw.coldmap(w.eval(),zero_span=0.0002),(5,5,4,4),tiles_per_line=4,borders=True)

                img2 = npw.tiles(npw.coldmap(w.eval(),zero_span=ZERO_SPAN2),(cluster_side,cluster_side,tile_side2,tile_side2),tiles_per_line=TILES_PER_LINE2,borders=True)

                img2 = img2[np.newaxis,...]

                train_writer.add_summary(wimg2.eval(feed_dict={wimg2_placeholder: img2}), epoch)