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概要概要
トレーニング中にモデルを保存してロードするのが一般的です。 kerasモデルを保存およびロードするためのAPIには、高レベルAPIと低レベルAPIの2つのセットがあります。このチュートリアルでは、使用しているときにSavedModel APIを使用する方法を示しtf.distribute.Strategy 。 SavedModelとシリアル化全般については、保存済みモデルガイドとKerasモデルシリアル化ガイドをお読みください。簡単な例から始めましょう：
依存関係のインポート：
import tensorflow_datasets as tfds
import tensorflow as tf
tf.distribute.Strategyを使用してデータとモデルを準備します。
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',)
モデルをトレーニングします。
model = get_model()
train_dataset, eval_dataset = get_data()
model.fit(train_dataset, epochs=2)
Epoch 1/2
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py:601: get_next_as_optional (from tensorflow.python.data.ops.iterator_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Iterator.get_next_as_optional()` instead.
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py:601: get_next_as_optional (from tensorflow.python.data.ops.iterator_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Iterator.get_next_as_optional()` instead.
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',).
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',).
938/938 [==============================] - 4s 4ms/step - loss: 0.2095 - sparse_categorical_accuracy: 0.9386
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 2/2
938/938 [==============================] - 2s 3ms/step - loss: 0.0730 - sparse_categorical_accuracy: 0.9787
<tensorflow.python.keras.callbacks.History at 0x7f7470042b38>
モデルを保存してロードします
使用する簡単なモデルができたので、APIの保存/読み込みを見てみましょう。利用可能なAPIのセットは2つあります。
- 高レベルの
model.saveおよびtf.keras.models.load_model - 低レベルの
tf.saved_model.saveおよびtf.saved_model.load
Keras API
KerasAPIを使用してモデルを保存およびロードする例を次に示します。
keras_model_path = "/tmp/keras_save"
model.save(keras_model_path)
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version. Instructions for updating: This property should not be used in TensorFlow 2.0, as updates are applied automatically. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/tracking/tracking.py:111: Model.state_updates (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version. Instructions for updating: This property should not be used in TensorFlow 2.0, as updates are applied automatically. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version. Instructions for updating: This property should not be used in TensorFlow 2.0, as updates are applied automatically. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/tracking/tracking.py:111: Layer.updates (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version. Instructions for updating: This property should not be used in TensorFlow 2.0, as updates are applied automatically. INFO:tensorflow:Assets written to: /tmp/keras_save/assets INFO:tensorflow:Assets written to: /tmp/keras_save/assets
tf.distribute.Strategyなしでモデルを復元し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 [==============================] - 2s 2ms/step - loss: 0.0539 - sparse_categorical_accuracy: 0.9838 Epoch 2/2 938/938 [==============================] - 2s 2ms/step - loss: 0.0381 - sparse_categorical_accuracy: 0.9884 <tensorflow.python.keras.callbacks.History at 0x7f74d333f780>
モデルを復元した後は、保存する前にすでにコンパイルされているため、 compile()再度呼び出す必要がなくても、モデルのトレーニングを続行できます。モデルは、TensorFlowの標準のSavedModelプロト形式で保存されます。詳細については、 saved_model形式のガイドを参照してください。
次に、モデルをロードし、 tf.distribute.Strategyを使用してtf.distribute.Strategyます。
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 1/2 938/938 [==============================] - 9s 10ms/step - loss: 0.0530 - sparse_categorical_accuracy: 0.9844 Epoch 2/2 938/938 [==============================] - 9s 9ms/step - loss: 0.0388 - sparse_categorical_accuracy: 0.9882
ご覧のとおり、ロードはtf.distribute.Strategy期待どおりにtf.distribute.Strategyます。ここで使用される戦略は、保存する前に使用される戦略と同じである必要はありません。
tf.saved_model API
それでは、低レベルのAPIを見てみましょう。モデルの保存は、kerasAPIに似ています。
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
ロードはtf.saved_model.load()でtf.saved_model.load()できます。ただし、これは下位レベルのAPIであるため（したがって、使用例の範囲が広くなります）、Kerasモデルは返されません。代わりに、推論を行うために使用できる関数を含むオブジェクトを返します。例えば：
DEFAULT_FUNCTION_KEY = "serving_default"
loaded = tf.saved_model.load(saved_model_path)
inference_func = loaded.signatures[DEFAULT_FUNCTION_KEY]
ロードされたオブジェクトには、それぞれがキーに関連付けられた複数の関数が含まれる場合があります。 "serving_default"は、保存されたKerasモデルを使用した推論関数のデフォルトキーです。この関数で推論を行うには：
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=
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[-2.12152809e-01, -1.05831539e-02, 1.12541884e-01,
3.79703306e-02, -4.97136004e-02, -8.26531351e-02,
4.28089425e-02, 2.72401571e-01, -9.41082910e-02,
-8.25358368e-03],
[-2.12490350e-01, 5.10787666e-02, -4.91231680e-03,
1.71558380e-01, 8.33496898e-02, 8.03120583e-02,
5.97136915e-02, 2.78716445e-01, -5.66011816e-02,
-7.99765587e-02],
[-2.45497763e-01, -5.21367639e-02, 1.77163050e-01,
8.67958441e-02, -1.33168459e-01, 9.83412005e-03,
-1.34591311e-01, 1.48744047e-01, -6.65533617e-02,
-1.07505932e-01],
[-1.36525869e-01, -5.12802340e-02, 2.54329219e-02,
8.01228657e-02, -3.24120894e-02, -6.36913255e-03,
-7.75915161e-02, 1.81387305e-01, 6.72850609e-02,
-1.06104709e-01],
[-8.19087848e-02, -6.67821616e-02, 1.09396182e-01,
-8.99944529e-02, -1.08385280e-01, 6.29347712e-02,
7.26154894e-02, 1.68957621e-01, 1.90485001e-01,
-2.60798335e-01],
[-1.76897705e-01, 4.90825251e-02, 2.94402167e-02,
-2.41212249e-02, 3.94896790e-02, 1.18754521e-01,
1.69773921e-02, 1.10196158e-01, 7.08303824e-02,
-6.86142594e-02],
[-1.29656106e-01, -8.14089552e-02, 1.14682741e-01,
-1.32834181e-01, -1.49253279e-01, -2.83164792e-02,
3.45680863e-04, 2.52322882e-01, 2.89388448e-02,
-2.79281288e-01],
[-1.10502213e-01, 1.07094124e-01, 3.24486196e-02,
7.70951509e-02, -6.27939776e-02, 1.68845624e-01,
-1.44310594e-01, 1.45337492e-01, 2.03377791e-02,
-5.04231378e-02],
[-2.66523331e-01, -7.49082193e-02, 1.91363335e-01,
-6.39847219e-02, -1.04055285e-01, 8.31385702e-02,
8.82939398e-02, 1.99207246e-01, 5.35239354e-02,
-2.60884434e-01],
[-1.35722771e-01, 3.94147262e-02, -6.39424995e-02,
1.39283150e-01, 5.37211001e-02, -6.34303223e-03,
-1.70467123e-01, 2.55692095e-01, -7.66103566e-02,
-6.90388680e-02],
[-1.07885860e-01, 2.30858717e-02, 8.21547359e-02,
-3.12240291e-02, -9.89983678e-02, 7.22398609e-02,
-4.08478230e-02, 8.69123414e-02, 4.48577479e-02,
-6.41947538e-02],
[-2.28321850e-02, -3.88411283e-02, 1.47033811e-01,
-2.35385150e-01, -9.87000838e-02, 6.44287840e-02,
-1.87633559e-02, 1.17905587e-01, 9.70625877e-02,
-2.46781930e-01],
[-8.77917856e-02, -1.64044406e-02, 7.53755122e-02,
-8.24043527e-04, -7.77238905e-02, 1.16269790e-01,
-1.00877963e-01, 8.79124254e-02, 3.39440927e-02,
-5.94997481e-02],
[-1.41677827e-01, -1.40151009e-02, 8.84927809e-04,
1.03166051e-01, -1.66242346e-02, 2.62837298e-02,
-1.33589238e-01, 1.65735006e-01, 3.65820900e-02,
-1.46895535e-02],
[-1.61557034e-01, 5.66626638e-02, -1.61597617e-02,
2.58595943e-02, 3.39905620e-02, 1.01104185e-01,
-3.71510983e-02, 1.20341092e-01, 3.26242894e-02,
-4.07250933e-02],
[-2.17516154e-01, 7.85727724e-02, 9.79433060e-02,
6.97179586e-02, 4.95264679e-02, 1.92503840e-01,
-4.96265218e-02, 1.99431688e-01, -5.32730669e-03,
-2.50038877e-02],
[-1.35356426e-01, -6.96291253e-02, 3.92658785e-02,
-9.86322537e-02, -4.20986377e-02, 9.87840891e-02,
9.67663303e-02, 1.76262826e-01, 9.44406465e-02,
-2.23472387e-01],
[-1.25066608e-01, 7.71146417e-02, 4.02672291e-02,
-2.05352344e-02, 3.11498251e-02, 9.64582711e-02,
-5.39951548e-02, 2.29750067e-01, 1.61451437e-02,
-5.41997403e-02],
[-1.93750665e-01, -3.56721133e-03, -1.50568932e-02,
1.78796798e-02, 8.33508372e-03, -1.18013099e-02,
-5.35021350e-02, 2.02244624e-01, 3.02494057e-02,
-1.20312274e-01],
[-2.62067527e-01, 2.36408859e-02, 5.58489896e-02,
1.75756812e-01, -2.75299139e-02, 3.48872915e-02,
5.41301072e-03, 3.15880209e-01, -5.74782193e-02,
7.00992346e-03],
[-2.76674211e-01, -2.08131559e-02, -1.26259401e-02,
7.77718723e-02, -1.54706314e-01, 1.31996438e-01,
2.20355690e-02, 5.61908968e-02, 3.73308063e-02,
-1.17717944e-01],
[-1.59806639e-01, 1.20503023e-01, -4.36934829e-03,
1.16428092e-01, 5.47975339e-02, 1.25162587e-01,
4.78192419e-02, 1.28253624e-01, 7.34245628e-02,
-1.80039048e-01],
[-2.67963678e-01, 6.00077920e-02, 1.13472804e-01,
7.52071738e-02, -6.40357211e-02, 1.03171021e-01,
1.48901194e-01, 1.97019696e-01, 3.76104042e-02,
-1.68720663e-01],
[-2.01240778e-01, 2.47026011e-02, 3.10055390e-02,
-8.58910009e-03, -8.49897265e-02, -7.54948407e-02,
-9.39515531e-02, 1.34306327e-01, -1.71037674e-01,
-5.76597378e-02],
[-5.20152375e-02, 6.59879148e-02, -3.30656916e-02,
9.97125208e-02, 3.56362388e-02, 1.26982957e-01,
-2.69417539e-02, 1.59046397e-01, 1.10872082e-01,
-1.84650719e-01]], dtype=float32)>}
分散してロードして推論を行うこともできます。
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',)
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 `experimental_run_v2` 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 `experimental_run_v2` 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 `experimental_run_v2` 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 `experimental_run_v2` 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 `experimental_run_v2` 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 `experimental_run_v2` 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 `experimental_run_v2` 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 `experimental_run_v2` 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 `experimental_run_v2` 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 `experimental_run_v2` inside a tf.function to get the best performance.
復元された関数の呼び出しは、保存されたモデル（予測）のフォワードパスにすぎません。ロードされた関数のトレーニングを続行したい場合はどうなりますか？または、ロードされた関数をより大きなモデルに埋め込みますか？一般的な方法は、このロードされたオブジェクトをKerasレイヤーにラップしてこれを実現することです。幸いなことに、 TFハブにはこの目的のためのhub.KerasLayerがあります。
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
938/938 [==============================] - 2s 3ms/step - loss: 0.1981 - sparse_categorical_accuracy: 0.9412
Epoch 2/2
938/938 [==============================] - 2s 3ms/step - loss: 0.0655 - sparse_categorical_accuracy: 0.9804
ご覧のとおり、 hub.KerasLayerは、 hub.KerasLayer tf.saved_model.load()からロードされた結果を、別のモデルの構築に使用できるhub.KerasLayerラップします。これは、転移学習に非常に役立ちます。
どのAPIを使用する必要がありますか？
保存のために、kerasモデルを使用している場合は、ほとんどの場合、 model.save()のmodel.save() APIを使用することをお勧めします。保存しているものがKerasモデルでない場合は、低レベルのAPIが唯一の選択肢です。
ロードの場合、使用するAPIは、ロードAPIから取得するものによって異なります。 tf.saved_model.load()モデルを取得できない（または取得したくない）場合は、 tf.saved_model.load()使用します。それ以外の場合は、 tf.keras.models.load_model()使用します。 Kerasモデルを保存した場合にのみ、Kerasモデルを取り戻すことができることに注意してください。
APIを組み合わせて使用することができます。 model.saveしてmodel.saveモデルを保存し、低レベルtf.saved_model.load使用して非model.saveモデルをロードtf.saved_model.loadます。
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',)
ローカルデバイスからの保存/読み込み
たとえばクラウドTPUを使用して、リモートで実行しているときにローカルioデバイスから保存およびロードする場合、オプションexperimental_io_device使用してioデバイスをlocalhostに設定する必要があります。
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',)
警告
特別な場合は、明確に定義された入力がないKerasモデルがある場合です。たとえば、Sequentialモデルは、入力形状なしで作成できます（ Sequential([Dense(3), ...] ）。サブクラス化されたモデルにも、初期化後に明確に定義された入力がありません。この場合、保存と読み込みの両方で低レベルのAPIを使用しないと、エラーが発生します。
モデルに明確に定義された入力があるかどうかを確認するには、 model.inputsがNoneかどうかを確認します。 Noneでない場合は、すべて問題ありませNone 。入力形状は、モデルが.fit 、 .evaluate 、 .predictで使用されるとき、またはモデルを呼び出すときに自動的に定義されます（ model(inputs) ）。
次に例を示します。
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 0x7f74d29fffd0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <__main__.SubclassedModel object at 0x7f74d29fffd0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dense object at 0x7f74d2b37cc0>, because it is not built. WARNING:tensorflow:Skipping full serialization of Keras layer <tensorflow.python.keras.layers.core.Dense object at 0x7f74d2b37cc0>, because it is not built. INFO:tensorflow:Assets written to: /tmp/tf_save/assets INFO:tensorflow:Assets written to: /tmp/tf_save/assets