def evaluate(): """Eval tellMeGoal for a number of steps.""" with tf.Graph().as_default() as g: # Get images and labels for tellMeGoal. eval_data = FLAGS.eval_data == 'test' images, labels = tellMeGoal.inputs(eval_data=eval_data) # Build a Graph that computes the logits predictions from the # inference model. logits = tellMeGoal.inference(images) # Calculate predictions. top_k_op = tf.nn.in_top_k(logits, labels, 1) amount_ones = tf.reduce_sum(labels, tf.constant([0])) a = tf.Print(amount_ones, [amount_ones], message="This is the sum: ", summarize=10) # Restore the moving average version of the learned variables for eval. variable_averages = tf.train.ExponentialMovingAverage( tellMeGoal.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() summary_writer = tf.train.SummaryWriter(FLAGS.eval_dir, g) while True: eval_once(saver, summary_writer, top_k_op, summary_op, a, amount_ones) if FLAGS.run_once: break time.sleep(FLAGS.eval_interval_secs)
def train(): """Train tellMeGoal for a number of steps.""" with tf.Graph().as_default(): global_step = tf.Variable(0, trainable=False) # Get images and labels for tellMeGoal. images, labels = tellMeGoal.distorted_inputs() # Build a Graph that computes the logits predictions from the # inference model. logits = tellMeGoal.inference(images) # Calculate loss. loss = tellMeGoal.loss(logits, labels) # Build a Graph that trains the model with one batch of examples and # updates the model parameters. train_op = tellMeGoal.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, sess.graph) for step in xrange(FLAGS.max_steps): start_time = time.time() _, loss_value = sess.run([train_op, loss]) duration = time.time() - start_time assert not np.isnan(loss_value), 'Model diverged with loss = NaN' if step % 10 == 0: num_examples_per_step = FLAGS.batch_size examples_per_sec = num_examples_per_step / duration sec_per_batch = float(duration) format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f ' 'sec/batch)') print (format_str % (datetime.now(), step, loss_value, examples_per_sec, sec_per_batch)) if step % 1000 == 0: summary_str = sess.run(summary_op) summary_writer.add_summary(summary_str, step) # 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)