import tensorflow as tf

    # 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))
    
    # Define your AdamOptimiser, using FLAGS.learning_rate to minimixe the loss function
