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Descripción general
Es común guardar y cargar un modelo durante el entrenamiento. Hay dos conjuntos de API para guardar y cargar un modelo de Keras: una API de alto nivel y una API de bajo nivel. Este tutorial demuestra cómo puede usar las API de modelo guardado al usar tf.distribute.Strategy . Para obtener más información sobre el modelo guardado y la serialización en general, lea la guía del modelo guardado y la guía de serialización del modelo Keras . Comencemos con un ejemplo simple:
Dependencias de importación:
import tensorflow_datasets as tfds
import tensorflow as tf
Prepare los datos y el modelo usando tf.distribute.Strategy :
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',)
Entrena el modelo:
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>
Guardar y cargar el modelo
Ahora que tiene un modelo simple con el que trabajar, echemos un vistazo a las API de guardado/carga. Hay dos conjuntos de API disponibles:
- Keras de alto nivel
model.saveytf.keras.models.load_model - Bajo nivel
tf.saved_model.saveytf.saved_model.load
Las API de Keras
Este es un ejemplo de cómo guardar y cargar un modelo con las API de 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
Restaure el modelo sin 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>
Después de restaurar el modelo, puede continuar entrenando en él, incluso sin necesidad de volver a llamar a compile() , ya que ya está compilado antes de guardar. El modelo se guarda en el formato de prototipo de modelo guardado estándar de SavedModel . Para obtener más información, consulte la guía de formato de saved_model .
Ahora, para cargar el modelo y entrenarlo usando 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
Como puede ver, la carga funciona como se esperaba con tf.distribute.Strategy . La estrategia utilizada aquí no tiene que ser la misma estrategia utilizada antes de guardar.
Las API tf.saved_model
Ahora echemos un vistazo a las API de nivel inferior. Guardar el modelo es similar a la API de Keras:
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
La carga se puede hacer con tf.saved_model.load() . Sin embargo, dado que es una API que se encuentra en el nivel inferior (y, por lo tanto, tiene una gama más amplia de casos de uso), no devuelve un modelo de Keras. En su lugar, devuelve un objeto que contiene funciones que se pueden usar para hacer inferencias. Por ejemplo:
DEFAULT_FUNCTION_KEY = "serving_default"
loaded = tf.saved_model.load(saved_model_path)
inference_func = loaded.signatures[DEFAULT_FUNCTION_KEY]
El objeto cargado puede contener múltiples funciones, cada una asociada con una tecla. El "serving_default" es la clave predeterminada para la función de inferencia con un modelo de Keras guardado. Para hacer una inferencia con esta función:
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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[-1.59163624e-01, -1.77137554e-05, 1.75410658e-02,
-9.08103511e-02, 7.25786015e-02, 9.21041369e-02,
1.24915361e-01, -6.55939505e-02, -1.13440230e-02,
1.03661232e-01],
[-1.93366870e-01, -4.36344892e-02, 1.37750164e-01,
-1.91939399e-01, -1.50268525e-03, 8.03942382e-02,
2.15812266e-01, 5.38492575e-02, 1.36685073e-01,
2.22119391e-01],
[-1.65946245e-01, 7.89588690e-03, -1.65037125e-01,
-1.23690292e-01, -8.57629776e-02, -2.55736727e-02,
1.67541012e-01, -6.63827211e-02, 2.98694819e-02,
1.71927184e-01],
[-1.56264767e-01, -1.72245800e-02, -4.98924702e-02,
-2.98387632e-02, 2.80477256e-02, 4.94132042e-02,
4.89805043e-02, 1.96998678e-02, -4.14144360e-02,
-5.05549274e-02],
[-1.46449029e-01, -1.12528354e-01, -4.66653258e-02,
-3.78398523e-02, 7.60737807e-03, -2.70657167e-02,
1.11277811e-01, 6.37479573e-02, -2.39458829e-02,
1.22067556e-01],
[-1.92323536e-01, -1.43002480e-01, 5.29062748e-03,
-1.70663983e-01, 8.39572400e-03, 6.37906119e-02,
1.24084033e-01, 6.02792688e-02, 7.18353763e-02,
5.03963791e-03],
[-1.70977920e-01, 1.04207098e-02, 1.18544906e-01,
-4.29532528e-02, -3.53983864e-02, 1.80302024e-01,
8.08775946e-02, 3.19045782e-02, 2.52931342e-02,
1.29424319e-01],
[-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.
También puede cargar y hacer inferencias de forma distribuida:
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.
Llamar a la función restaurada es solo un pase hacia adelante en el modelo guardado (predecir). ¿Qué sucede si desea continuar entrenando la función cargada? ¿O incrustar la función cargada en un modelo más grande? Una práctica común es envolver este objeto cargado en una capa de Keras para lograr esto. Afortunadamente, TF Hub tiene hub.KerasLayer para este propósito, que se muestra aquí:
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
Como puede ver, hub.KerasLayer envuelve el resultado cargado desde tf.saved_model.load() en una capa de Keras que se puede usar para construir otro modelo. Esto es muy útil para el aprendizaje por transferencia.
¿Qué API debo usar?
Para guardar, si está trabajando con un modelo de Keras, casi siempre se recomienda usar la API model.save() de Keras. Si lo que está guardando no es un modelo de Keras, entonces la API de nivel inferior es su única opción.
Para la carga, la API que utilice depende de lo que desee obtener de la API de carga. Si no puede (o no quiere) obtener un modelo de Keras, use tf.saved_model.load() . De lo contrario, use tf.keras.models.load_model() . Tenga en cuenta que puede recuperar un modelo de Keras solo si guardó un modelo de Keras.
Es posible mezclar y combinar las API. Puede guardar un modelo de Keras con model.save y cargar un modelo que no sea de Keras con la API de bajo nivel, 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',)
Guardar/Cargar desde dispositivo local
Al guardar y cargar desde un dispositivo io local mientras se ejecuta de forma remota, por ejemplo, usando una TPU en la nube, se debe usar la opción experimental_io_device para configurar el dispositivo io en localhost.
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',)
Advertencias
Un caso especial es cuando tienes un modelo de Keras que no tiene entradas bien definidas. Por ejemplo, se puede crear un modelo Sequential sin ninguna forma de entrada ( Sequential([Dense(3), ...] ). Los modelos subclasificados tampoco tienen entradas bien definidas después de la inicialización. En este caso, debe seguir con el API de nivel inferior tanto al guardar como al cargar; de lo contrario, obtendrá un error.
Para verificar si su modelo tiene entradas bien definidas, simplemente verifique si model.inputs es None . Si no es None , está todo bien. Las formas de entrada se definen automáticamente cuando el modelo se usa en .fit , .evaluate , .predict o cuando se llama al modelo ( model(inputs) ).
Aquí hay un ejemplo:
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