a2a5ca90ab475392100abe1cb3530f4edbf117e7,models/experimental/resnet50_keras/resnet50_tf2.py,,main,#Any#,168

Before Change

  logging.info("Training model using data in directory "%s".", FLAGS.data)

  with tf.device("/job:worker"):
    with strategy.scope():
      logging.info("Building Keras ResNet-50 model")
      model = resnet_model.ResNet50(num_classes=NUM_CLASSES)

      logging.info("Compiling model.")
      metrics = ["sparse_categorical_accuracy"]

      if FLAGS.eval_top_5_accuracy:
        metrics.append(sparse_top_k_categorical_accuracy)

      model.compile(
          optimizer=tf.keras.optimizers.SGD(
              learning_rate=BASE_LEARNING_RATE, momentum=0.9, nesterov=True),
          loss="sparse_categorical_crossentropy",
          metrics=metrics)

    imagenet_train = imagenet_input.ImageNetInput(
        is_training=True, data_dir=FLAGS.data, batch_size=batch_size,
        use_bfloat16=USE_BFLOAT16)
    imagenet_eval = imagenet_input.ImageNetInput(

After Change

  logging.info("Enable top 5 accuracy: %s.", FLAGS.eval_top_5_accuracy)
  logging.info("Training model using data in directory "%s".", FLAGS.data)

  with strategy.scope():
    logging.info("Building Keras ResNet-50 model")
    model = resnet_model.ResNet50(num_classes=NUM_CLASSES)

Instances