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В этом уроке мы будем готовить ту же поисковую модель , как мы это делали в основных поисковых обучающей программе , но со стратегией распределения.
Собирались:
- Получите наши данные и разделите их на обучающий и тестовый набор.
- Настройте два виртуальных графических процессора и TensorFlow MirroredStrategy.
- Реализуйте модель извлечения с помощью MirroredStrategy.
- Совместите его с MirrorredStrategy и оцените его.
Импорт
Давайте сначала уберем наш импорт.
pip install -q tensorflow-recommenderspip install -q --upgrade tensorflow-datasets
import os
import pprint
import tempfile
from typing import Dict, Text
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_recommenders as tfrs
Подготовка набора данных
Мы готовим набор данные точно так же, как мы делаем в основных поисковых обучающей программе .
# Ratings data.
ratings = tfds.load("movielens/100k-ratings", split="train")
# Features of all the available movies.
movies = tfds.load("movielens/100k-movies", split="train")
for x in ratings.take(1).as_numpy_iterator():
pprint.pprint(x)
for x in movies.take(1).as_numpy_iterator():
pprint.pprint(x)
ratings = ratings.map(lambda x: {
"movie_title": x["movie_title"],
"user_id": x["user_id"],
})
movies = movies.map(lambda x: x["movie_title"])
tf.random.set_seed(42)
shuffled = ratings.shuffle(100_000, seed=42, reshuffle_each_iteration=False)
train = shuffled.take(80_000)
test = shuffled.skip(80_000).take(20_000)
movie_titles = movies.batch(1_000)
user_ids = ratings.batch(1_000_000).map(lambda x: x["user_id"])
unique_movie_titles = np.unique(np.concatenate(list(movie_titles)))
unique_user_ids = np.unique(np.concatenate(list(user_ids)))
unique_movie_titles[:10]
{'bucketized_user_age': 45.0,
'movie_genres': array([7]),
'movie_id': b'357',
'movie_title': b"One Flew Over the Cuckoo's Nest (1975)",
'raw_user_age': 46.0,
'timestamp': 879024327,
'user_gender': True,
'user_id': b'138',
'user_occupation_label': 4,
'user_occupation_text': b'doctor',
'user_rating': 4.0,
'user_zip_code': b'53211'}
2021-10-14 11:16:44.748468: 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.
{'movie_genres': array([4]),
'movie_id': b'1681',
'movie_title': b'You So Crazy (1994)'}
2021-10-14 11:16:45.396856: 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.
array([b"'Til There Was You (1997)", b'1-900 (1994)',
b'101 Dalmatians (1996)', b'12 Angry Men (1957)', b'187 (1997)',
b'2 Days in the Valley (1996)',
b'20,000 Leagues Under the Sea (1954)',
b'2001: A Space Odyssey (1968)',
b'3 Ninjas: High Noon At Mega Mountain (1998)',
b'39 Steps, The (1935)'], dtype=object)
Настройте два виртуальных графических процессора
Если вы не добавляли ускорители графического процессора в свой Colab, отключите среду выполнения Colab и сделайте это сейчас. Нам нужен графический процессор для запуска кода ниже:
gpus = tf.config.list_physical_devices("GPU")
if gpus:
# Create 2 virtual GPUs with 1GB memory each
try:
tf.config.set_logical_device_configuration(
gpus[0],
[tf.config.LogicalDeviceConfiguration(memory_limit=1024),
tf.config.LogicalDeviceConfiguration(memory_limit=1024)])
logical_gpus = tf.config.list_logical_devices("GPU")
print(len(gpus), "Physical GPU,", len(logical_gpus), "Logical GPUs")
except RuntimeError as e:
# Virtual devices must be set before GPUs have been initialized
print(e)
strategy = tf.distribute.MirroredStrategy()
Virtual devices cannot be modified after being initialized
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',)
Реализация модели
Мы реализуем user_model, movie_model, метрику и задачи таким же образом , как мы делаем в основном поисковом учебнике, но мы завернуть их в рамках стратегии распределения:
embedding_dimension = 32
with strategy.scope():
user_model = tf.keras.Sequential([
tf.keras.layers.StringLookup(
vocabulary=unique_user_ids, mask_token=None),
# We add an additional embedding to account for unknown tokens.
tf.keras.layers.Embedding(len(unique_user_ids) + 1, embedding_dimension)
])
movie_model = tf.keras.Sequential([
tf.keras.layers.StringLookup(
vocabulary=unique_movie_titles, mask_token=None),
tf.keras.layers.Embedding(len(unique_movie_titles) + 1, embedding_dimension)
])
metrics = tfrs.metrics.FactorizedTopK(
candidates=movies.batch(128).map(movie_model)
)
task = tfrs.tasks.Retrieval(
metrics=metrics
)
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',).
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',).
Теперь мы можем собрать все это вместе в модель. Это точно так же , как и в основных поисковых обучающей программе .
class MovielensModel(tfrs.Model):
def __init__(self, user_model, movie_model):
super().__init__()
self.movie_model: tf.keras.Model = movie_model
self.user_model: tf.keras.Model = user_model
self.task: tf.keras.layers.Layer = task
def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:
# We pick out the user features and pass them into the user model.
user_embeddings = self.user_model(features["user_id"])
# And pick out the movie features and pass them into the movie model,
# getting embeddings back.
positive_movie_embeddings = self.movie_model(features["movie_title"])
# The task computes the loss and the metrics.
return self.task(user_embeddings, positive_movie_embeddings)
Установка и оценка
Теперь мы создаем экземпляр и компилируем модель в рамках стратегии распространения.
Обратите внимание , что мы используем Адам оптимизатор здесь вместо Adagrad как в основных поисковых обучающей программе, так как Adagrad не поддерживаются здесь.
with strategy.scope():
model = MovielensModel(user_model, movie_model)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.1))
Затем перемешайте, пакетируйте и кэшируйте данные обучения и оценки.
cached_train = train.shuffle(100_000).batch(8192).cache()
cached_test = test.batch(4096).cache()
Затем обучите модель:
model.fit(cached_train, epochs=3)
2021-10-14 11:16:50.692190: 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 1/3 10/10 [==============================] - 8s 328ms/step - factorized_top_k/top_1_categorical_accuracy: 5.0000e-05 - factorized_top_k/top_5_categorical_accuracy: 8.2500e-04 - factorized_top_k/top_10_categorical_accuracy: 0.0025 - factorized_top_k/top_50_categorical_accuracy: 0.0220 - factorized_top_k/top_100_categorical_accuracy: 0.0537 - loss: 70189.8047 - regularization_loss: 0.0000e+00 - total_loss: 70189.8047 Epoch 2/3 10/10 [==============================] - 3s 329ms/step - factorized_top_k/top_1_categorical_accuracy: 3.3750e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0113 - factorized_top_k/top_10_categorical_accuracy: 0.0251 - factorized_top_k/top_50_categorical_accuracy: 0.1268 - factorized_top_k/top_100_categorical_accuracy: 0.2325 - loss: 66736.4560 - regularization_loss: 0.0000e+00 - total_loss: 66736.4560 Epoch 3/3 10/10 [==============================] - 3s 332ms/step - factorized_top_k/top_1_categorical_accuracy: 0.0012 - factorized_top_k/top_5_categorical_accuracy: 0.0198 - factorized_top_k/top_10_categorical_accuracy: 0.0417 - factorized_top_k/top_50_categorical_accuracy: 0.1834 - factorized_top_k/top_100_categorical_accuracy: 0.3138 - loss: 64871.2997 - regularization_loss: 0.0000e+00 - total_loss: 64871.2997 <keras.callbacks.History at 0x7fb74c479190>
Из журнала обучения видно, что TFRS использует оба виртуальных графических процессора.
Наконец, мы можем оценить нашу модель на тестовом наборе:
model.evaluate(cached_test, return_dict=True)
2021-10-14 11:17:05.371963: 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.
5/5 [==============================] - 4s 193ms/step - factorized_top_k/top_1_categorical_accuracy: 5.0000e-05 - factorized_top_k/top_5_categorical_accuracy: 0.0013 - factorized_top_k/top_10_categorical_accuracy: 0.0043 - factorized_top_k/top_50_categorical_accuracy: 0.0639 - factorized_top_k/top_100_categorical_accuracy: 0.1531 - loss: 32404.8092 - regularization_loss: 0.0000e+00 - total_loss: 32404.8092
{'factorized_top_k/top_1_categorical_accuracy': 4.999999873689376e-05,
'factorized_top_k/top_5_categorical_accuracy': 0.0013000000035390258,
'factorized_top_k/top_10_categorical_accuracy': 0.00430000014603138,
'factorized_top_k/top_50_categorical_accuracy': 0.06385000050067902,
'factorized_top_k/top_100_categorical_accuracy': 0.1530500054359436,
'loss': 29363.98046875,
'regularization_loss': 0,
'total_loss': 29363.98046875}
На этом мы завершаем извлечение с учебником по стратегии распространения.