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इस ट्यूटोरियल में, हम एक सरल मैट्रिक्स गुणन का उपयोग कर मॉडल बनाने MovieLens 100K डाटासेट TFRS साथ। हम किसी दिए गए उपयोगकर्ता के लिए फिल्मों की सिफारिश करने के लिए इस मॉडल का उपयोग कर सकते हैं।
आयात टीएफआरएस
सबसे पहले, TFRS स्थापित और आयात करें:
pip install -q tensorflow-recommenderspip install -q --upgrade tensorflow-datasets
from typing import Dict, Text
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_recommenders as tfrs
डेटा पढ़ें
# Ratings data.
ratings = tfds.load('movielens/100k-ratings', split="train")
# Features of all the available movies.
movies = tfds.load('movielens/100k-movies', split="train")
# Select the basic features.
ratings = ratings.map(lambda x: {
"movie_title": x["movie_title"],
"user_id": x["user_id"]
})
movies = movies.map(lambda x: x["movie_title"])
2021-10-02 12:07:32.719766: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
परतों को एम्बेड करने के लिए उपयोगकर्ता आईडी और मूवी शीर्षक को पूर्णांक सूचकांकों में बदलने के लिए शब्दसंग्रह बनाएं:
user_ids_vocabulary = tf.keras.layers.StringLookup(mask_token=None)
user_ids_vocabulary.adapt(ratings.map(lambda x: x["user_id"]))
movie_titles_vocabulary = tf.keras.layers.StringLookup(mask_token=None)
movie_titles_vocabulary.adapt(movies)
एक मॉडल को परिभाषित करें
हम से इनहेरिट द्वारा एक TFRS मॉडल को परिभाषित कर सकते tfrs.Model और लागू करने compute_loss विधि:
class MovieLensModel(tfrs.Model):
# We derive from a custom base class to help reduce boilerplate. Under the hood,
# these are still plain Keras Models.
def __init__(
self,
user_model: tf.keras.Model,
movie_model: tf.keras.Model,
task: tfrs.tasks.Retrieval):
super().__init__()
# Set up user and movie representations.
self.user_model = user_model
self.movie_model = movie_model
# Set up a retrieval task.
self.task = task
def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:
# Define how the loss is computed.
user_embeddings = self.user_model(features["user_id"])
movie_embeddings = self.movie_model(features["movie_title"])
return self.task(user_embeddings, movie_embeddings)
दो मॉडलों और पुनर्प्राप्ति कार्य को परिभाषित करें।
# Define user and movie models.
user_model = tf.keras.Sequential([
user_ids_vocabulary,
tf.keras.layers.Embedding(user_ids_vocabulary.vocab_size(), 64)
])
movie_model = tf.keras.Sequential([
movie_titles_vocabulary,
tf.keras.layers.Embedding(movie_titles_vocabulary.vocab_size(), 64)
])
# Define your objectives.
task = tfrs.tasks.Retrieval(metrics=tfrs.metrics.FactorizedTopK(
movies.batch(128).map(movie_model)
)
)
WARNING:tensorflow:vocab_size is deprecated, please use vocabulary_size. WARNING:tensorflow:vocab_size is deprecated, please use vocabulary_size. WARNING:tensorflow:vocab_size is deprecated, please use vocabulary_size. WARNING:tensorflow:vocab_size is deprecated, please use vocabulary_size.
फिट करें और उसका मूल्यांकन करें।
मॉडल बनाएं, उसे प्रशिक्षित करें और भविष्यवाणियां करें:
# Create a retrieval model.
model = MovieLensModel(user_model, movie_model, task)
model.compile(optimizer=tf.keras.optimizers.Adagrad(0.5))
# Train for 3 epochs.
model.fit(ratings.batch(4096), epochs=3)
# Use brute-force search to set up retrieval using the trained representations.
index = tfrs.layers.factorized_top_k.BruteForce(model.user_model)
index.index_from_dataset(
movies.batch(100).map(lambda title: (title, model.movie_model(title))))
# Get some recommendations.
_, titles = index(np.array(["42"]))
print(f"Top 3 recommendations for user 42: {titles[0, :3]}")
Epoch 1/3 25/25 [==============================] - 6s 194ms/step - factorized_top_k/top_1_categorical_accuracy: 3.0000e-05 - factorized_top_k/top_5_categorical_accuracy: 0.0016 - factorized_top_k/top_10_categorical_accuracy: 0.0052 - factorized_top_k/top_50_categorical_accuracy: 0.0442 - factorized_top_k/top_100_categorical_accuracy: 0.1010 - loss: 33092.9163 - regularization_loss: 0.0000e+00 - total_loss: 33092.9163 Epoch 2/3 25/25 [==============================] - 5s 194ms/step - factorized_top_k/top_1_categorical_accuracy: 1.7000e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0052 - factorized_top_k/top_10_categorical_accuracy: 0.0148 - factorized_top_k/top_50_categorical_accuracy: 0.1054 - factorized_top_k/top_100_categorical_accuracy: 0.2114 - loss: 31008.8447 - regularization_loss: 0.0000e+00 - total_loss: 31008.8447 Epoch 3/3 25/25 [==============================] - 5s 193ms/step - factorized_top_k/top_1_categorical_accuracy: 3.4000e-04 - factorized_top_k/top_5_categorical_accuracy: 0.0086 - factorized_top_k/top_10_categorical_accuracy: 0.0222 - factorized_top_k/top_50_categorical_accuracy: 0.1438 - factorized_top_k/top_100_categorical_accuracy: 0.2694 - loss: 30417.8776 - regularization_loss: 0.0000e+00 - total_loss: 30417.8776 Top 3 recommendations for user 42: [b'Rent-a-Kid (1995)' b'Just Cause (1995)' b'Land Before Time III: The Time of the Great Giving (1995) (V)']