영화 추천: 배급 전략으로 검색

이 튜토리얼에서, 우리는 우리가에서했던 것과 같은 검색 모델을 훈련하는거야 기본 검색 자습서,하지만 유통 전략.

우리는 할 것입니다:

1. 데이터를 가져와 훈련 및 테스트 세트로 나눕니다.
2. 두 개의 가상 GPU와 TensorFlow MirroredStrategy를 설정합니다.
3. MirroredStrategy를 사용하여 검색 모델을 구현합니다.
4. MirrorredStrategy로 맞추고 평가하세요.

수입품

먼저 수입품을 제거합시다.

pip install -q tensorflow-recommenders
pip 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

데이터세트 준비

우리는에서와 같이 우리는 정확히 같은 방법으로 데이터 세트를 준비 기본 검색 자습서.

# 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)

두 개의 가상 GPU 설정

Colab에 GPU 가속기를 추가하지 않은 경우 Colab 런타임 연결을 끊고 지금 추가하세요. 아래 코드를 실행하려면 GPU가 필요합니다.

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',)

모델 구현

우리는에서와 우리는 같은 방법으로 user_model, movie_model, 측정 및 작업을 구현하는 기본 검색 자습서,하지만 우리는 유통 전략의 범위를 포장 :

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',).

이제 모든 것을 하나의 모델로 통합할 수 있습니다. 이 정확히에서와 동일 기본 검색 자습서.

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)

피팅 및 평가

이제 배포 전략 범위 내에서 모델을 인스턴스화하고 컴파일합니다.

우리가 같이 Adagrad 대신 여기에 아담 최적화를 사용하고 있는지 참고 기본 검색 Adagrad 이후 튜토리얼 것은 여기에 지원되지 않습니다.

with strategy.scope():
  model = MovielensModel(user_model, movie_model)
  model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.1))

그런 다음 훈련 및 평가 데이터를 섞고 일괄 처리하고 캐시합니다.

cached_train = train.shuffle(100_000).batch(8192).cache()
cached_test = test.batch(4096).cache()

그런 다음 모델을 학습시킵니다.

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>

훈련 로그에서 TFRS가 두 가상 GPU를 모두 사용하고 있음을 알 수 있습니다.

마지막으로 테스트 세트에서 모델을 평가할 수 있습니다.

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}

이것으로 배포 전략 튜토리얼을 통한 검색을 마칩니다.