 TensorFlow.org पर देखें |  Google Colab में चलाएं |  GitHub पर स्रोत देखें |  नोटबुक डाउनलोड करें |
इस ट्यूटोरियल में, हम एक ही पुनर्प्राप्ति मॉडल को प्रशिक्षित करने के लिए जा रहे हैं के रूप में हम में किया था बुनियादी पुनर्प्राप्ति ट्यूटोरियल है, लेकिन वितरण रणनीति के साथ।
करने के लिए जा रहे थे:
- हमारा डेटा प्राप्त करें और इसे प्रशिक्षण और परीक्षण सेट में विभाजित करें।
- दो वर्चुअल GPU और TensorFlow MirroredStrategy सेट करें।
- मिररडस्ट्रेटी का उपयोग करके एक पुनर्प्राप्ति मॉडल लागू करें।
- इसे मिरररेड स्ट्रैटेजी के साथ फिट करें और इसका मूल्यांकन करें।
आयात
आइए पहले अपने आयात को रास्ते से हटा दें।
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
डेटासेट तैयार करना
हम बिल्कुल उसी तरह डाटासेट तैयार के रूप में हम में क्या बुनियादी पुनर्प्राप्ति ट्यूटोरियल।
# 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)
दो वर्चुअल GPU सेट करें
यदि आपने अपने Colab में GPU त्वरक नहीं जोड़े हैं, तो कृपया Colab रनटाइम को डिस्कनेक्ट करें और इसे अभी करें। हमें नीचे दिए गए कोड को चलाने के लिए GPU की आवश्यकता है:
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',)
एक मॉडल लागू करना
हम एक ही तरह से user_model, movie_model, मैट्रिक्स और कार्य को लागू के रूप में हम में क्या बुनियादी पुनर्प्राप्ति ट्यूटोरियल, लेकिन हम उन्हें वितरण रणनीति दायरे में लपेट:
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',).
अब हम इन सबको मिलाकर एक मॉडल बना सकते हैं। यह ठीक में रूप में ही है बुनियादी पुनर्प्राप्ति ट्यूटोरियल।
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)
फिटिंग और मूल्यांकन
अब हम वितरण रणनीति के दायरे में मॉडल को तत्काल और संकलित करते हैं।
ध्यान दें कि हम में के रूप में एडम अनुकूलक का उपयोग कर रहे यहां Adagrad के बजाय बुनियादी पुनर्प्राप्ति Adagrad के बाद से ट्यूटोरियल यहाँ समर्थित नहीं है।
with strategy.scope():
model = MovielensModel(user_model, movie_model)
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.1))
फिर प्रशिक्षण और मूल्यांकन डेटा को फेरबदल, बैच और कैश करें।
cached_train = train.shuffle(100_000).batch(8192).cache()
cached_test = test.batch(4096).cache()
फिर मॉडल को प्रशिक्षित करें:
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>
आप प्रशिक्षण लॉग से देख सकते हैं कि TFRS दोनों वर्चुअल GPU का उपयोग कर रहा है।
अंत में, हम परीक्षण सेट पर अपने मॉडल का मूल्यांकन कर सकते हैं:
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}
यह वितरण रणनीति ट्यूटोरियल के साथ पुनर्प्राप्ति का समापन करता है।