import tensorflow as tf


        return l2_out


    def __forward_pass(self, x, reuse):
        fc_size1 = 384
        fc_size2 = 192

            # convolutional layers
        with tf.variable_scope('conv1'):
            layer1, weights1 = new_conv_layer(x, name="conv1", num_input_channels=3, num_filters=64, filter_size=5, ac_fun=tf.nn.relu,
                                    pool_ksize=[1, 3, 3, 1])
        with tf.variable_scope('conv2'):
            layer2, weights2 = new_conv_layer(input=layer1, name="conv2", num_input_channels=64, num_filters=64, filter_size=5,
                                    ac_fun=tf.nn.relu, pool_ksize=[1, 3, 3, 1])
        with tf.name_scope('flatten'):
            layer3, num_features = flatten_layer(layer2)
        # fully connected layers
