import tensorflow as tf

            bottom = out # l (not l + 1) because from previous layer
            W = weight_variable([1, FLAGS.conv_kernel, FLAGS.conv_kernel, FLAGS.feats_per_layer, FLAGS.feats_per_layer])

        b = bias_variable([FLAGS.feats_per_layer])
        Wx_b = tf.nn.conv3d(bottom, W, strides=[1,1,1,1,1], padding='VALID') + b
        out = tf.nn.relu(Wx_b)
        shape = out.get_shape()
        print('conv{}'.format(l+1))
        print('\t{} --> {}'.format(bottom.name, out.name))
        print('\t{} --> {}'.format(bottom.get_shape(), out.get_shape()))
