import tensorflow as tf

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)
