def evaluate(): """Eval CNN for a number of steps.""" with tf.Graph().as_default(): # Get images and labels for ADNI. eval_data = FLAGS.eval_data == 'test' images, labels = DeepLearningAD.inputs(eval_data=eval_data) # Build a Graph that computes the logits predictions from the # inference model. logits = DeepLearningAD.inference(images) # Calculate predictions. top_k_op = tf.nn.in_top_k(logits, labels, 1) # Restore the moving average version of the learned variables for eval. variable_averages = tf.train.ExponentialMovingAverage( DeepLearningAD.MOVING_AVERAGE_DECAY) variables_to_restore = variable_averages.variables_to_restore() saver = tf.train.Saver(variables_to_restore) # Build the summary operation based on the TF collection of Summaries. summary_op = tf.merge_all_summaries() graph_def = tf.get_default_graph().as_graph_def() summary_writer = tf.train.SummaryWriter(FLAGS.eval_dir, graph_def=graph_def) while True: eval_once(saver, summary_writer, top_k_op, summary_op) if FLAGS.run_once: break time.sleep(FLAGS.eval_interval_secs)
def evaluate(): """Eval CNN for a number of steps.""" with tf.Graph().as_default(): # Get images and labels for ADNI. eval_data = FLAGS.eval_data == "test" images, labels = DeepLearningAD.inputs(eval_data=eval_data) # Build a Graph that computes the logits predictions from the # inference model. logits = DeepLearningAD.inference(images) # Calculate predictions. top_k_op = tf.nn.in_top_k(logits, labels, 1) # Restore the moving average version of the learned variables for eval. variable_averages = tf.train.ExponentialMovingAverage(DeepLearningAD.MOVING_AVERAGE_DECAY) variables_to_restore = variable_averages.variables_to_restore() saver = tf.train.Saver(variables_to_restore) # Build the summary operation based on the TF collection of Summaries. summary_op = tf.merge_all_summaries() graph_def = tf.get_default_graph().as_graph_def() summary_writer = tf.train.SummaryWriter(FLAGS.eval_dir, graph_def=graph_def) while True: eval_once(saver, summary_writer, top_k_op, summary_op) if FLAGS.run_once: break time.sleep(FLAGS.eval_interval_secs)
def train(): """Train the CNN for a number of steps.""" with tf.Graph().as_default(): global_step = tf.Variable(0, trainable=False) # Get images and labels for ADNI. images, labels = DeepLearningAD.inputs(False) # Build a Graph that computes the logits predictions from the # inference model. logits = DeepLearningAD.inference(images) # Calculate loss. loss = DeepLearningAD.loss(logits, labels) # Build a Graph that trains the model with one batch of examples and # updates the model parameters. train_op = DeepLearningAD.train(loss, global_step) # Create a saver. saver = tf.train.Saver(tf.all_variables()) # Build the summary operation based on the TF collection of Summaries. summary_op = tf.merge_all_summaries() # Build an initialization operation to run below. init = tf.initialize_all_variables() # Start running operations on the Graph. sess = tf.Session(config=tf.ConfigProto( log_device_placement=FLAGS.log_device_placement)) sess.run(init) # Start the queue runners. tf.train.start_queue_runners(sess=sess) summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, graph_def=sess.graph_def) for step in xrange(FLAGS.max_steps): # Save the model checkpoint periodically. if step % 1000 == 0 or (step + 1) == FLAGS.max_steps: checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt') saver.save(sess, checkpoint_path, global_step=step)