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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Working with TensorFlow Recommenders
Step1:  <strong>Note
Step2:  Read the data
Step3:  Build vocabularies to convert user ids and movie titles into integer indices for embedding layers
Step4:  Define a model
We can define a TFRS model by inheriting from tfrs.Model and implementing the compute_loss method
Step5:  Define the two models and the retrieval task.
Step6:  Fit and evaluate it.
Create the model, train it, and generate predictions
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Python Code:
# Install required packages.
!pip install -q tensorflow-recommenders
!pip install -q --upgrade tensorflow-datasets
Explanation: Working with TensorFlow Recommenders: Quickstart
Learning Objectives
Read the data and build vocabularies.
Define a model.
Create and train a model.
Introduction
In this tutorial, you build a simple matrix factorization model using the MovieLens 100K dataset with TFRS. You can use this model to recommend movies for a given user.
Each learning objective will correspond to a #TODO in this student lab notebook -- try to complete this notebook first and then review the solution notebook.
Import TFRS
First, install and import TFRS:
End of explanation
# Import necessary libraries.
from typing import Dict, Text
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_recommenders as tfrs
Explanation: <strong>Note:</strong> Please ignore any incompatibility warnings and errors and re-run the above cell before proceeding.
End of explanation
# TODO 1
# 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.
# TODO - Your code goes here
Explanation: Read the data
End of explanation
# Build the vocabularies
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)
Explanation: Build vocabularies to convert user ids and movie titles into integer indices for embedding layers:
End of explanation
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)
Explanation: Define a model
We can define a TFRS model by inheriting from tfrs.Model and implementing the compute_loss method:
End of explanation
# TODO 2
# Define user and movie models.
user_model = tf.keras.Sequential([
    user_ids_vocabulary,
    tf.keras.layers.Embedding(user_ids_vocabulary.vocabulary_size(), 64)
])
movie_model = tf.keras.Sequential([
    movie_titles_vocabulary,
    tf.keras.layers.Embedding(movie_titles_vocabulary.vocabulary_size(), 64)
])
# Define your objectives.
task = # TODO - Your code goes here
Explanation: Define the two models and the retrieval task.
End of explanation
# TODO 3
# Create a retrieval model.
model = MovieLensModel(user_model, movie_model, task)
model.compile(optimizer=tf.keras.optimizers.Adagrad(0.5))
# Train for 3 epochs.
# TODO - Your code goes here
# Use brute-force search to set up retrieval using the trained representations.
index = # TODO - Your code goes here
# Get some recommendations.
_, titles = index(np.array(["42"]))
print(f"Top 3 recommendations for user 42: {titles[0, :3]}")
Explanation: Fit and evaluate it.
Create the model, train it, and generate predictions:
End of explanation
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