TensorFlow|TensorFlow Recommenders: Quickstart TensorFlowRecommenders:Quickstart

In this tutorial, we build a simple matrix factorization model using the MovieLens 100K dataset with TFRS. We can use this model to recommend movies for a given user.
Import TFRS

from typing import Dict, Textimport numpy as np import tensorflow as tfimport tensorflow_datasets as tfds import tensorflow_recommenders as tfrs

Read the data

# 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"])

【TensorFlow|TensorFlow Recommenders: Quickstart】Build vocabularies to convert user ids and movie titles into integer indices for embedding layers:

user_ids_vocabulary = tf.keras.layers.experimental.preprocessing.StringLookup(mask_token=None) user_ids_vocabulary.adapt(ratings.map(lambda x: x["user_id"]))movie_titles_vocabulary = tf.keras.layers.experimental.preprocessing.StringLookup(mask_token=None) movie_titles_vocabulary.adapt(movies)

Define a model
We can define a TFRS model by inheriting from tfrs.Model and implementing the compute_loss method:

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 = taskdef 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)

Define the two models and the retrieval task.

# 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) ) )

Fit and evaluate it.
Create the model, train it, and generate predictions:

# 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(movies.batch(100).map(model.movie_model), movies)# Get some recommendations. _, titles = index(np.array(["42"])) print(f"Top 3 recommendations for user 42: {titles[0, :3]}")

代码地址: https://codechina.csdn.net/cs...