def evaluate(): """Eval CIFAR-10 for a number of steps.""" with tf.Graph().as_default(): # Get images and labels for CIFAR-10. eval_data = FLAGS.eval_data == 'test' images, labels = cifar10.inputs(eval_data=eval_data) # Build a Graph that computes the logits predictions from the # inference model. logits = cifar10.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( cifar10.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(): ''' 训练 CIFAR-10 ''' with tf.Graph().as_default(): global_step = tf.Variable(0, trainable=False) # Get images and labels for CIFAR-10 images, labels = cifar10.distorted_inputs() # 构建inference图 logits = cifar10.inference(images) loss = cifar10.loss(logits, labels) # 构建训练图 train_op = cifar10.train(loss, global_step) # saver saver = tf.train.Saver(tf.all_variables()) # 构建总结操作 summary_op = tf.merge_all_summaries() # 初始化操作 init = tf.initialize_all_variables() sess = tf.Session(config=tf.ConfigProto(log_device_placement=FLAGS.log_device_placement)) sess.run(init) tf.train.start_queue_runners(sess=sess) summary_writer = tf.train.SummaryWriter(FLAGS.train_dir, graph_def=sess.graph_def) for step in range(FLAGS.max_steps): start_time = time.time() _, loss_value = sess.run([train_op, loss]) duration = time.time() - start_time # 模型收敛 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 % 100 == 0: summary_str = sess.run(summary_op) summary_writer.add_summary(summary_str, step) 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)