import tensorflow as tf

            units=10,
            activation=None,
            use_bias=False,
            name='out'
        )

        return out, img_summary


def main(_):
    tf.reset_default_graph()

    # Import data
    cifar = cf.cifar10(batchSize=FLAGS.batch_size, downloadDir=FLAGS.data_dir)

    with tf.variable_scope('inputs'):
        # Create the model
        x = tf.placeholder(tf.float32, [None, FLAGS.img_width * FLAGS.img_height * FLAGS.img_channels])
        # Define loss and optimizer
        y_ = tf.placeholder(tf.float32, [None, FLAGS.num_classes])
        # Whether model is training
        train = tf.placeholder(tf.bool, [])

    # Build the graph for the deep net
    y_conv, img_summary = deepnn(x, train)

    # Define your loss function - softmax_cross_entropy
    with tf.variable_scope('x_entropy'):
        cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
    
