Yardım Kaggle üzerinde TensorFlow ile Büyük Bariyer Resifi korumak Meydan Üyelik

Bir dağıtım stratejisi kullanarak bir modeli kaydedin ve yükleyin

TensorFlow.org'da görüntüleyin Google Colab'da çalıştırın Kaynağı GitHub'da görüntüleyin Not defterini indir

genel bakış

Eğitim sırasında bir modeli kaydetmek ve yüklemek yaygındır. Bir keras modelini kaydetmek ve yüklemek için iki API grubu vardır: yüksek seviyeli API ve düşük seviyeli API. Bu öğretici, tf.distribute.Strategy kullanırken tf.distribute.Strategy API'lerini nasıl kullanabileceğinizi gösterir. SavedModel ve genel olarak serileştirme hakkında bilgi edinmek için lütfen kayıtlı model kılavuzunu ve Keras model serileştirme kılavuzunu okuyun. Basit bir örnekle başlayalım:

Bağımlılıkları içe aktar:

import tensorflow_datasets as tfds

import tensorflow as tf

Verileri ve modeli tf.distribute.Strategy kullanarak hazırlayın:

mirrored_strategy = tf.distribute.MirroredStrategy()

def get_data():
  datasets, ds_info = tfds.load(name='mnist', with_info=True, as_supervised=True)
  mnist_train, mnist_test = datasets['train'], datasets['test']

  BUFFER_SIZE = 10000

  BATCH_SIZE_PER_REPLICA = 64
  BATCH_SIZE = BATCH_SIZE_PER_REPLICA * mirrored_strategy.num_replicas_in_sync

  def scale(image, label):
    image = tf.cast(image, tf.float32)
    image /= 255

    return image, label

  train_dataset = mnist_train.map(scale).cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE)
  eval_dataset = mnist_test.map(scale).batch(BATCH_SIZE)

  return train_dataset, eval_dataset

def get_model():
  with mirrored_strategy.scope():
    model = tf.keras.Sequential([
        tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(28, 28, 1)),
        tf.keras.layers.MaxPooling2D(),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(64, activation='relu'),
        tf.keras.layers.Dense(10)
    ])

    model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                  optimizer=tf.keras.optimizers.Adam(),
                  metrics=[tf.metrics.SparseCategoricalAccuracy()])
    return model
tutucu2 l10n-yer
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)

Modeli eğitin:

model = get_model()
train_dataset, eval_dataset = get_data()
model.fit(train_dataset, epochs=2)
tutucu4 l10n-yer
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
2021-10-26 01:26:36.109959: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:461] The `assert_cardinality` transformation is currently not handled by the auto-shard rewrite and will be removed.
Epoch 1/2
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).
938/938 [==============================] - 13s 3ms/step - loss: 0.2015 - sparse_categorical_accuracy: 0.9410
Epoch 2/2
938/938 [==============================] - 3s 3ms/step - loss: 0.0663 - sparse_categorical_accuracy: 0.9807
<keras.callbacks.History at 0x7fa92037bc90>

Modeli kaydedin ve yükleyin

Artık çalışacak basit bir modeliniz olduğuna göre, API'leri kaydetme/yükleme işlemlerine bir göz atalım. Kullanılabilir iki API grubu vardır:

Keras API'leri

Keras API'leri ile bir modeli kaydetme ve yükleme örneği:

keras_model_path = "/tmp/keras_save"
model.save(keras_model_path)
tutucu6 l10n-yer
2021-10-26 01:26:52.520058: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.
INFO:tensorflow:Assets written to: /tmp/keras_save/assets
INFO:tensorflow:Assets written to: /tmp/keras_save/assets

Modeli tf.distribute.Strategy olmadan geri yükleyin:

restored_keras_model = tf.keras.models.load_model(keras_model_path)
restored_keras_model.fit(train_dataset, epochs=2)
tutucu8 l10n-yer
Epoch 1/2
938/938 [==============================] - 2s 2ms/step - loss: 0.0491 - sparse_categorical_accuracy: 0.9851
Epoch 2/2
938/938 [==============================] - 2s 2ms/step - loss: 0.0356 - sparse_categorical_accuracy: 0.9890
<keras.callbacks.History at 0x7fa8dc6d6690>

Modeli geri yükledikten sonra, kaydetmeden önce derlenmiş olduğundan, tekrar compile() öğesini çağırmanıza gerek kalmadan üzerinde eğitime devam edebilirsiniz. Model, TensorFlow'un standart SavedModel proto biçiminde kaydedilir. Daha fazla bilgi için lütfen saved_model formatı kılavuzuna bakın.

Şimdi modeli yüklemek ve bir tf.distribute.Strategy kullanarak eğitmek için:

another_strategy = tf.distribute.OneDeviceStrategy("/cpu:0")
with another_strategy.scope():
  restored_keras_model_ds = tf.keras.models.load_model(keras_model_path)
  restored_keras_model_ds.fit(train_dataset, epochs=2)
tutucu10 l10n-yer
2021-10-26 01:26:57.965185: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:461] The `assert_cardinality` transformation is currently not handled by the auto-shard rewrite and will be removed.
2021-10-26 01:26:58.004038: W tensorflow/core/framework/dataset.cc:679] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.
Epoch 1/2
938/938 [==============================] - 9s 9ms/step - loss: 0.0493 - sparse_categorical_accuracy: 0.9846
Epoch 2/2
938/938 [==============================] - 8s 9ms/step - loss: 0.0345 - sparse_categorical_accuracy: 0.9898

Gördüğünüz gibi, yükleme tf.distribute.Strategy ile beklendiği gibi çalışıyor. Burada kullanılan strateji, kaydetmeden önce kullanılan stratejiyle aynı olmak zorunda değildir.

tf.saved_model API'leri

Şimdi alt seviye API'lere bir göz atalım. Modeli kaydetmek, keras API'sine benzer:

model = get_model()  # get a fresh model
saved_model_path = "/tmp/tf_save"
tf.saved_model.save(model, saved_model_path)
tutucu12 l10n-yer
INFO:tensorflow:Assets written to: /tmp/tf_save/assets
INFO:tensorflow:Assets written to: /tmp/tf_save/assets

tf.saved_model.load() ile yükleme yapılabilir. Ancak, daha düşük seviyede bir API olduğundan (ve dolayısıyla daha geniş bir kullanım durumu yelpazesine sahiptir), bir Keras modeli döndürmez. Bunun yerine, çıkarım yapmak için kullanılabilecek işlevleri içeren bir nesne döndürür. Örneğin:

DEFAULT_FUNCTION_KEY = "serving_default"
loaded = tf.saved_model.load(saved_model_path)
inference_func = loaded.signatures[DEFAULT_FUNCTION_KEY]

Yüklenen nesne, her biri bir anahtarla ilişkilendirilmiş birden çok işlev içerebilir. "serving_default" , kaydedilmiş bir Keras modeliyle çıkarsama işlevi için varsayılan anahtardır. Bu fonksiyonla bir çıkarım yapmak için:

predict_dataset = eval_dataset.map(lambda image, label: image)
for batch in predict_dataset.take(1):
  print(inference_func(batch))
tutucu15 l10n-yer
{'dense_3': <tf.Tensor: shape=(64, 10), dtype=float32, numpy=
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       [ 0.06917666, -0.07515088, -0.15344585,  0.08451273,  0.16555418,
        -0.00663652, -0.03506049, -0.19360425, -0.01485892, -0.1411201 ],
       [ 0.08957651, -0.0336723 , -0.16066113,  0.09386282,  0.21388392,
        -0.01653587, -0.02893457, -0.04395334, -0.03723653,  0.07710503]],
      dtype=float32)>}
2021-10-26 01:27:16.715879: W tensorflow/core/kernels/data/cache_dataset_ops.cc:768] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset  will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

Ayrıca dağıtılmış bir şekilde yükleyebilir ve çıkarım yapabilirsiniz:

another_strategy = tf.distribute.MirroredStrategy()
with another_strategy.scope():
  loaded = tf.saved_model.load(saved_model_path)
  inference_func = loaded.signatures[DEFAULT_FUNCTION_KEY]

  dist_predict_dataset = another_strategy.experimental_distribute_dataset(
      predict_dataset)

  # Calling the function in a distributed manner
  for batch in dist_predict_dataset:
    another_strategy.run(inference_func,args=(batch,))
tutucu17 l10n-yer
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
2021-10-26 01:27:16.888897: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:461] The `assert_cardinality` transformation is currently not handled by the auto-shard rewrite and will be removed.
WARNING:tensorflow:Using MirroredStrategy eagerly has significant overhead currently. We will be working on improving this in the future, but for now please wrap `call_for_each_replica` or `experimental_run` or `run` inside a tf.function to get the best performance.
WARNING:tensorflow:Using MirroredStrategy eagerly has significant overhead currently. We will be working on improving this in the future, but for now please wrap `call_for_each_replica` or `experimental_run` or `run` inside a tf.function to get the best performance.
WARNING:tensorflow:Using MirroredStrategy eagerly has significant overhead currently. We will be working on improving this in the future, but for now please wrap `call_for_each_replica` or `experimental_run` or `run` inside a tf.function to get the best performance.
WARNING:tensorflow:Using MirroredStrategy eagerly has significant overhead currently. We will be working on improving this in the future, but for now please wrap `call_for_each_replica` or `experimental_run` or `run` inside a tf.function to get the best performance.
WARNING:tensorflow:Using MirroredStrategy eagerly has significant overhead currently. We will be working on improving this in the future, but for now please wrap `call_for_each_replica` or `experimental_run` or `run` inside a tf.function to get the best performance.
WARNING:tensorflow:Using MirroredStrategy eagerly has significant overhead currently. We will be working on improving this in the future, but for now please wrap `call_for_each_replica` or `experimental_run` or `run` inside a tf.function to get the best performance.
WARNING:tensorflow:Using MirroredStrategy eagerly has significant overhead currently. We will be working on improving this in the future, but for now please wrap `call_for_each_replica` or `experimental_run` or `run` inside a tf.function to get the best performance.
WARNING:tensorflow:Using MirroredStrategy eagerly has significant overhead currently. We will be working on improving this in the future, but for now please wrap `call_for_each_replica` or `experimental_run` or `run` inside a tf.function to get the best performance.
WARNING:tensorflow:Using MirroredStrategy eagerly has significant overhead currently. We will be working on improving this in the future, but for now please wrap `call_for_each_replica` or `experimental_run` or `run` inside a tf.function to get the best performance.
WARNING:tensorflow:Using MirroredStrategy eagerly has significant overhead currently. We will be working on improving this in the future, but for now please wrap `call_for_each_replica` or `experimental_run` or `run` inside a tf.function to get the best performance.

Geri yüklenen işlevin çağrılması, kaydedilen modelde (tahmin) yalnızca ileriye doğru bir geçiştir. Yüklenen işlevi eğitmeye devam etmek isterseniz ne olur? Veya yüklenen işlevi daha büyük bir modele gömmek mi? Yaygın bir uygulama, bunu başarmak için bu yüklenen nesneyi bir Keras katmanına sarmaktır. Neyse ki, TF Hub'ın bu amaç için hub.KerasLayer'ı vardır, burada gösterilmiştir:

import tensorflow_hub as hub

def build_model(loaded):
  x = tf.keras.layers.Input(shape=(28, 28, 1), name='input_x')
  # Wrap what's loaded to a KerasLayer
  keras_layer = hub.KerasLayer(loaded, trainable=True)(x)
  model = tf.keras.Model(x, keras_layer)
  return model

another_strategy = tf.distribute.MirroredStrategy()
with another_strategy.scope():
  loaded = tf.saved_model.load(saved_model_path)
  model = build_model(loaded)

  model.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                optimizer=tf.keras.optimizers.Adam(),
                metrics=[tf.metrics.SparseCategoricalAccuracy()])
  model.fit(train_dataset, epochs=2)
tutucu19 l10n-yer
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
2021-10-26 01:27:18.637232: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:461] The `assert_cardinality` transformation is currently not handled by the auto-shard rewrite and will be removed.
Epoch 1/2
938/938 [==============================] - 5s 3ms/step - loss: 0.2057 - sparse_categorical_accuracy: 0.9392
Epoch 2/2
938/938 [==============================] - 3s 3ms/step - loss: 0.0688 - sparse_categorical_accuracy: 0.9802

Gördüğünüz gibi, hub.KerasLayer , tf.saved_model.load() 'dan yüklenen sonucu başka bir model oluşturmak için kullanılabilecek bir Keras katmanına sarar. Bu, transfer öğrenimi için çok yararlıdır.

Hangi API'yi kullanmalıyım?

Tasarruf için, bir keras modeliyle çalışıyorsanız, hemen hemen her zaman model.save() API'sini kullanmanız önerilir. Kaydettiğiniz şey bir Keras modeli değilse, tek seçeneğiniz daha düşük seviyeli API'dir.

Yükleme için hangi API'yi kullanacağınız, yükleme API'sinden ne almak istediğinize bağlıdır. Bir Keras modeli alamıyorsanız (veya istemiyorsanız), tf.saved_model.load() kullanın. Aksi takdirde, tf.keras.models.load_model() kullanın. Bir Keras modelini ancak bir Keras modelini kaydettiyseniz geri alabileceğinizi unutmayın.

API'leri karıştırmak ve eşleştirmek mümkündür. model.save ile bir Keras modelini kaydedebilir ve düşük seviyeli API, tf.saved_model.load ile model.save olmayan bir modeli yükleyebilirsiniz.

model = get_model()

# Saving the model using Keras's save() API
model.save(keras_model_path) 

another_strategy = tf.distribute.MirroredStrategy()
# Loading the model using lower level API
with another_strategy.scope():
  loaded = tf.saved_model.load(keras_model_path)
tutucu21 l10n-yer
INFO:tensorflow:Assets written to: /tmp/keras_save/assets
INFO:tensorflow:Assets written to: /tmp/keras_save/assets
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)

Yerel cihazdan kaydetme/yükleme

Uzaktan çalışırken, örneğin bir bulut TPU kullanarak yerel bir io cihazından kaydederken ve yüklerken, io cihazını localhost'a ayarlamak için experimental_io_device seçeneği kullanılmalıdır.

model = get_model()

# Saving the model to a path on localhost.
saved_model_path = "/tmp/tf_save"
save_options = tf.saved_model.SaveOptions(experimental_io_device='/job:localhost')
model.save(saved_model_path, options=save_options)

# Loading the model from a path on localhost.
another_strategy = tf.distribute.MirroredStrategy()
with another_strategy.scope():
  load_options = tf.saved_model.LoadOptions(experimental_io_device='/job:localhost')
  loaded = tf.keras.models.load_model(saved_model_path, options=load_options)
tutucu23 l10n-yer
INFO:tensorflow:Assets written to: /tmp/tf_save/assets
INFO:tensorflow:Assets written to: /tmp/tf_save/assets
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)

uyarılar

Özel bir durum, iyi tanımlanmış girdilere sahip olmayan bir Keras modeliniz olduğunda ortaya çıkar. Örneğin, herhangi bir girdi şekli olmadan Sıralı bir model oluşturulabilir ( Sequential([Dense(3), ...] ) Alt sınıflı modeller de başlatmadan sonra iyi tanımlanmış girdilere sahip değildir. Bu durumda, hem kaydetme hem de yüklemede daha düşük seviyeli API'ler, aksi takdirde bir hata alırsınız.

Modelinizin iyi tanımlanmış girdileri olup olmadığını kontrol etmek için model.inputs öğesinin None olup olmadığını kontrol edin. None değilse, hepiniz iyisiniz. Model .fit , .evaluate , .predict içinde kullanıldığında veya modeli çağırırken ( model(inputs) ) girdi şekilleri otomatik olarak tanımlanır.

İşte bir örnek:

class SubclassedModel(tf.keras.Model):

  output_name = 'output_layer'

  def __init__(self):
    super(SubclassedModel, self).__init__()
    self._dense_layer = tf.keras.layers.Dense(
        5, dtype=tf.dtypes.float32, name=self.output_name)

  def call(self, inputs):
    return self._dense_layer(inputs)

my_model = SubclassedModel()
# my_model.save(keras_model_path)  # ERROR! 
tf.saved_model.save(my_model, saved_model_path)
tutucu25 l10n-yer
WARNING:tensorflow:Skipping full serialization of Keras layer <__main__.SubclassedModel object at 0x7fa4f68ee5d0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <__main__.SubclassedModel object at 0x7fa4f68ee5d0>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.Dense object at 0x7fa4f68ee490>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.Dense object at 0x7fa4f68ee490>, because it is not built.
INFO:tensorflow:Assets written to: /tmp/tf_save/assets
INFO:tensorflow:Assets written to: /tmp/tf_save/assets