حفظ وتحميل نموذج باستخدام استراتيجية التوزيع

تنظيم صفحاتك في مجموعات يمكنك حفظ المحتوى وتصنيفه حسب إعداداتك المفضّلة.

عرض على TensorFlow.org تشغيل في Google Colab عرض المصدر على جيثب تحميل دفتر

ملخص

من الشائع حفظ النموذج وتحميله أثناء التدريب. هناك مجموعتان من واجهات برمجة التطبيقات لحفظ وتحميل نموذج keras: واجهة برمجة تطبيقات عالية المستوى وواجهة برمجة تطبيقات منخفضة المستوى. يوضح هذا البرنامج التعليمي كيف يمكنك استخدام SavedModel APIs عند استخدام tf.distribute.Strategy . للتعرف على SavedModel والتسلسل بشكل عام ، يرجى قراءة دليل النموذج المحفوظ ودليل تسلسل طراز Keras . لنبدأ بمثال بسيط:

تبعيات الاستيراد:

import tensorflow_datasets as tfds

import tensorflow as tf

قم بإعداد البيانات والنموذج باستخدام tf.distribute.Strategy . الإستراتيجية:

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
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)

تدريب النموذج:

model = get_model()
train_dataset, eval_dataset = get_data()
model.fit(train_dataset, epochs=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',).
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',).
Epoch 1/2
2022-01-26 05:41:11.916000: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:547] The `assert_cardinality` transformation is currently not handled by the auto-shard rewrite and will be removed.
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 [==============================] - 11s 5ms/step - loss: 0.1873 - sparse_categorical_accuracy: 0.9451
Epoch 2/2
938/938 [==============================] - 3s 3ms/step - loss: 0.0641 - sparse_categorical_accuracy: 0.9807
<keras.callbacks.History at 0x7f3b900396d0>

حفظ وتحميل النموذج

الآن بعد أن أصبح لديك نموذج بسيط للعمل معه ، دعنا نلقي نظرة على واجهات برمجة تطبيقات الحفظ / التحميل. هناك مجموعتان من واجهات برمجة التطبيقات المتاحة:

واجهات برمجة تطبيقات Keras

فيما يلي مثال على حفظ نموذج وتحميله باستخدام واجهات برمجة تطبيقات Keras:

keras_model_path = "/tmp/keras_save"
model.save(keras_model_path)
2022-01-26 05:41:26.593570: W tensorflow/python/util/util.cc:368] 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

استعادة النموذج بدون tf.distribute.Strategy :

restored_keras_model = tf.keras.models.load_model(keras_model_path)
restored_keras_model.fit(train_dataset, epochs=2)
Epoch 1/2
938/938 [==============================] - 3s 3ms/step - loss: 0.0476 - sparse_categorical_accuracy: 0.9859
Epoch 2/2
938/938 [==============================] - 3s 3ms/step - loss: 0.0334 - sparse_categorical_accuracy: 0.9895
<keras.callbacks.History at 0x7f3b187b7150>

بعد استعادة النموذج ، يمكنك متابعة التدريب عليه ، حتى بدون الحاجة إلى استدعاء compile() مرة أخرى ، لأنه تم تجميعه بالفعل قبل الحفظ. يتم حفظ النموذج بتنسيق أولي SavedModel . لمزيد من المعلومات ، يرجى الرجوع إلى دليل تنسيق saved_model .

الآن لتحميل النموذج وتدريبه باستخدام tf.distribute.Strategy .

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)
Epoch 1/2
2022-01-26 05:41:33.036733: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:547] The `assert_cardinality` transformation is currently not handled by the auto-shard rewrite and will be removed.
2022-01-26 05:41:33.083001: W tensorflow/core/framework/dataset.cc:768] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.
938/938 [==============================] - 10s 10ms/step - loss: 0.0474 - sparse_categorical_accuracy: 0.9860
Epoch 2/2
938/938 [==============================] - 10s 10ms/step - loss: 0.0327 - sparse_categorical_accuracy: 0.9903

كما ترى ، يعمل التحميل كما هو متوقع باستخدام tf.distribute.Strategy . لا يجب أن تكون الإستراتيجية المستخدمة هنا هي نفس الإستراتيجية المستخدمة قبل الحفظ.

واجهات برمجة التطبيقات tf.saved_model

الآن دعونا نلقي نظرة على واجهات برمجة التطبيقات ذات المستوى الأدنى. حفظ النموذج مشابه لـ keras API:

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

يمكن أن يتم التحميل باستخدام tf.saved_model.load() . ومع ذلك ، نظرًا لأنها واجهة برمجة تطبيقات موجودة في المستوى الأدنى (وبالتالي لديها نطاق أوسع من حالات الاستخدام) ، فإنها لا تُرجع نموذج Keras. بدلاً من ذلك ، تقوم بإرجاع كائن يحتوي على وظائف يمكن استخدامها لإجراء الاستدلال. فمثلا:

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

قد يحتوي الكائن الذي تم تحميله على وظائف متعددة ، يرتبط كل منها بمفتاح. "serving_default" هو المفتاح الافتراضي لوظيفة الاستدلال بنموذج Keras المحفوظ. للقيام بالاستدلال بهذه الوظيفة:

predict_dataset = eval_dataset.map(lambda image, label: image)
for batch in predict_dataset.take(1):
  print(inference_func(batch))
{'dense_3': <tf.Tensor: shape=(64, 10), dtype=float32, numpy=
array([[-1.18789300e-01, -1.78404614e-01,  4.92432676e-02,
        -9.37875658e-02,  1.14302970e-01, -8.99422392e-02,
         9.47709680e-02, -7.75382966e-02,  4.04430032e-02,
         2.41404288e-02],
       [-2.35370561e-01, -3.39397341e-02,  2.73427293e-02,
        -1.08200148e-01,  5.10682352e-02,  1.36142194e-01,
         9.28785652e-02, -5.35808355e-02,  2.56292164e-01,
         1.05301209e-01],
       [-1.91031799e-01, -7.72745535e-02, -7.23153427e-02,
        -1.99329913e-01, -7.45072216e-02,  2.42738128e-02,
         2.07733169e-01, -3.15396488e-03,  4.95976806e-02,
         2.14848563e-01],
       [-9.82482210e-02, -6.13910556e-02,  1.00815810e-01,
        -1.87558904e-01,  1.14685424e-01,  1.53835595e-01,
         1.85714245e-01, -8.74890238e-02,  1.07493028e-01,
         1.57510787e-02],
       [-8.56257528e-02,  3.23683321e-02, -3.66768315e-02,
        -1.47201523e-01, -5.31517603e-02,  1.52744055e-02,
         1.69184029e-01, -5.42814359e-02,  1.11524366e-01,
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       [-1.50604844e-01, -7.87255913e-03,  1.26651973e-01,
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        -8.25636461e-03, -8.25546682e-03,  2.41342247e-01,
         8.24805871e-02],
       [-1.42795354e-01,  6.54597580e-02,  2.05058958e-02,
        -1.28471941e-01,  1.10977650e-01,  4.51317504e-02,
         2.44124904e-01,  1.90523565e-02,  3.11958641e-02,
         6.49511665e-02],
       [-1.33037239e-01, -2.72594951e-02,  8.09026062e-02,
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       [-1.43770471e-01, -2.53150351e-02,  4.18904647e-02,
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       [-1.90395206e-01,  2.93233991e-03,  1.48900077e-02,
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       [-2.60747373e-01, -1.45188004e-01,  7.10044056e-04,
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       [-1.62942797e-01, -3.63466889e-02, -1.33987352e-01,
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         1.53577864e-01, -6.73157908e-03,  9.31103677e-02,
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       [-1.29348308e-01, -3.03804129e-03,  2.82487050e-02,
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       [-1.20486066e-01,  3.77080180e-02,  1.14158325e-01,
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         2.76539754e-02, -1.94141679e-02,  8.87098238e-02,
         6.60644472e-02],
       [-2.20715180e-01, -7.20694065e-02, -6.08972833e-02,
        -4.82957587e-02,  1.28858402e-01,  1.30042464e-01,
         1.32807568e-01, -7.52742141e-02,  9.51702446e-02,
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       [-1.09407350e-01, -5.27948700e-03,  1.29588693e-03,
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         4.13139611e-02],
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       [-1.78249478e-01, -7.55607188e-02,  7.75147527e-02,
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       [-1.22261971e-01, -6.94630146e-02, -7.97796808e-03,
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       [-6.78652674e-02, -1.08500615e-01,  5.66991530e-02,
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       [-2.02808753e-01, -3.39423120e-02,  1.82233751e-03,
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       [-9.52449068e-02, -3.35062481e-02, -1.00178778e-01,
        -9.72513855e-02, -3.58061343e-02,  3.04423086e-02,
         5.70362583e-02, -4.03833576e-02, -4.28436548e-02,
         9.73245874e-02],
       [-2.06081957e-01, -1.71493232e-01,  2.52560824e-02,
        -1.55212343e-01, -4.33478206e-02,  2.34177694e-01,
         8.46128762e-02,  1.75322518e-02,  2.04347119e-01,
         1.54971585e-01],
       [-1.95310384e-01,  1.30968075e-02, -9.68117267e-03,
        -7.31432810e-02,  1.02618083e-01,  1.59629256e-01,
         1.66028887e-01, -7.12903216e-03,  1.78021699e-01,
        -2.17130631e-02],
       [-1.59163624e-01, -1.77137554e-05,  1.75410658e-02,
        -9.08103511e-02,  7.25786015e-02,  9.21041369e-02,
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         1.03661232e-01],
       [-1.93366870e-01, -4.36344892e-02,  1.37750164e-01,
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         2.15812266e-01,  5.38492575e-02,  1.36685073e-01,
         2.22119391e-01],
       [-1.65946245e-01,  7.89588690e-03, -1.65037125e-01,
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         1.67541012e-01, -6.63827211e-02,  2.98694819e-02,
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       [-1.56264767e-01, -1.72245800e-02, -4.98924702e-02,
        -2.98387632e-02,  2.80477256e-02,  4.94132042e-02,
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       [-1.46449029e-01, -1.12528354e-01, -4.66653258e-02,
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       [-1.92323536e-01, -1.43002480e-01,  5.29062748e-03,
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       [-1.70977920e-01,  1.04207098e-02,  1.18544906e-01,
        -4.29532528e-02, -3.53983864e-02,  1.80302024e-01,
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       [-2.13301033e-01, -6.96119964e-02,  2.32847631e-02,
        -7.73920864e-02,  1.10387571e-01,  1.13307782e-01,
         1.41805351e-01, -5.19381016e-02,  1.15313083e-01,
         1.40049949e-01],
       [-1.71651557e-01, -5.98860830e-02, -3.92800570e-03,
        -1.04376137e-01,  7.78115019e-02,  6.84583709e-02,
         2.51923770e-01, -1.05199262e-01,  1.64517179e-01,
         2.18875334e-01],
       [-2.60777414e-01, -8.93031508e-02,  1.27723843e-01,
        -1.97950065e-01,  1.19145498e-01,  7.30907321e-02,
         2.23771721e-01, -6.83849230e-02,  3.68930906e-01,
         1.86811388e-01],
       [-2.38028213e-01,  1.11199915e-03,  2.25015372e-01,
         8.22724327e-02, -1.14511400e-01,  1.57513067e-01,
         5.22858277e-02,  2.13724375e-03,  3.15639377e-02,
         2.08704025e-01],
       [-1.46687120e-01, -1.10313833e-01, -1.16352811e-02,
        -1.44550815e-01,  2.09794566e-02,  1.47883072e-02,
         3.96856442e-02, -2.15019658e-03, -4.90810722e-02,
         1.34708211e-01],
       [-2.02591017e-01, -2.29728431e-01,  6.73423260e-02,
        -1.24901496e-01, -1.38434023e-02,  8.64367038e-02,
         1.22342721e-01,  1.67826824e-02,  1.65354639e-01,
         1.83434993e-01],
       [-2.25799978e-01, -1.02682747e-01,  9.48531851e-02,
        -9.38871950e-02,  1.03806734e-01,  2.04695478e-01,
         8.09893832e-02, -1.45416632e-02,  1.33486420e-01,
        -6.27665371e-02],
       [-1.19375348e-01,  2.23235339e-02,  1.04302749e-01,
        -1.11149743e-01,  6.12434298e-02,  6.89433664e-02,
         2.08741099e-01, -3.81497070e-02, -1.42122135e-02,
         7.65201449e-03]], dtype=float32)>}
2022-01-26 05:41:53.590742: 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.

يمكنك أيضًا التحميل والاستدلال بطريقة موزعة:

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,))
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',)
2022-01-26 05:41:53.931428: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:547] 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.

استدعاء الوظيفة المستعادة هو مجرد مرور أمامي على النموذج المحفوظ (توقع). ماذا لو كنت ترغب في مواصلة تدريب الوظيفة المحملة؟ أو تضمين الوظيفة المحملة في نموذج أكبر؟ من الممارسات الشائعة لف هذا الكائن المحمل بطبقة Keras لتحقيق ذلك. لحسن الحظ ، يحتوي TF Hub على hub.KerasLayer لهذا الغرض ، موضح هنا:

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)
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',)
Epoch 1/2
2022-01-26 05:41:55.594317: W tensorflow/core/grappler/optimizers/data/auto_shard.cc:547] The `assert_cardinality` transformation is currently not handled by the auto-shard rewrite and will be removed.
938/938 [==============================] - 6s 3ms/step - loss: 0.1910 - sparse_categorical_accuracy: 0.9442
Epoch 2/2
938/938 [==============================] - 3s 4ms/step - loss: 0.0633 - sparse_categorical_accuracy: 0.9813

كما ترى ، يلف hub.KerasLayer النتيجة التي تم تحميلها مرة أخرى من tf.saved_model.load() إلى طبقة Keras التي يمكن استخدامها لبناء نموذج آخر. هذا مفيد جدا لنقل التعلم.

ما هي واجهة برمجة التطبيقات التي يجب علي استخدامها؟

للحفظ ، إذا كنت تعمل باستخدام نموذج keras ، فمن المستحسن دائمًا استخدام نموذج Keras's model.save() API. إذا لم يكن ما تقوم بحفظه من طراز Keras ، فإن واجهة برمجة التطبيقات ذات المستوى الأدنى هي خيارك الوحيد.

للتحميل ، تعتمد واجهة برمجة التطبيقات التي تستخدمها على ما تريد الحصول عليه من واجهة برمجة التطبيقات للتحميل. إذا لم تتمكن (أو لا تريد) الحصول على نموذج Keras ، فاستخدم tf.saved_model.load() . وإلا فاستخدم tf.keras.models.load_model() . لاحظ أنه لا يمكنك استعادة طراز Keras إلا إذا قمت بحفظ نموذج Keras.

من الممكن خلط ومطابقة واجهات برمجة التطبيقات. يمكنك حفظ نموذج Keras باستخدام model.save ، وتحميل نموذج ليس من طراز Keras باستخدام واجهة برمجة التطبيقات منخفضة المستوى ، tf.saved_model.load .

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)
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',)

حفظ / تحميل من الجهاز المحلي

عند الحفظ والتحميل من جهاز io محلي أثناء التشغيل عن بُعد ، على سبيل المثال باستخدام Cloud TPU ، يجب استخدام الخيار experimental_io_device io_device لضبط جهاز io على المضيف المحلي.

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)
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',)

تحفظات

هناك حالة خاصة عندما يكون لديك نموذج Keras لا يحتوي على مدخلات محددة جيدًا. على سبيل المثال ، يمكن إنشاء نموذج تسلسلي بدون أي أشكال إدخال ( Sequential([Dense(3), ...] ). لا تحتوي النماذج المصنفة أيضًا على مدخلات محددة جيدًا بعد التهيئة. في هذه الحالة ، يجب أن تلتزم بـ واجهات برمجة التطبيقات ذات المستوى الأقل عند الحفظ والتحميل ، وإلا ستحصل على خطأ.

للتحقق مما إذا كان النموذج الخاص بك يحتوي على مدخلات محددة جيدًا ، ما عليك سوى التحقق مما إذا كان model.inputs None . إذا لم يكن لا None ، فأنتم كلكم بخير. يتم تحديد أشكال الإدخال تلقائيًا عند استخدام النموذج في. .fit ،. .evaluate ،. .predict ، أو عند استدعاء النموذج ( model(inputs) ).

هنا مثال:

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)
WARNING:tensorflow:Skipping full serialization of Keras layer <__main__.SubclassedModel object at 0x7f3ad00f3510>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <__main__.SubclassedModel object at 0x7f3ad00f3510>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.dense.Dense object at 0x7f3ad00f3e90>, because it is not built.
WARNING:tensorflow:Skipping full serialization of Keras layer <keras.layers.core.dense.Dense object at 0x7f3ad00f3e90>, because it is not built.
INFO:tensorflow:Assets written to: /tmp/tf_save/assets
INFO:tensorflow:Assets written to: /tmp/tf_save/assets