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HenryAI
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KerasCodeExamples.txt

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4.99k
123/123 [==============================] - 1s 6ms/step - loss: 0.3088 - sparse_categorical_accuracy: 0.8559
Epoch 9/10
123/123 [==============================] - 1s 6ms/step - loss: 0.3066 - sparse_categorical_accuracy: 0.8573
Epoch 10/10
123/123 [==============================] - 1s 6ms/step - loss: 0.3048 - sparse_categorical_accuracy: 0.8573
Model training finished
Evaluating the model on the test data...
62/62 [==============================] - 2s 5ms/step - loss: 0.3140 - sparse_categorical_accuracy: 0.8533
Test accuracy: 85.33%
Recommending movies using a model trained on Movielens dataset.
Introduction
This example demonstrates Collaborative filtering using the Movielens dataset to recommend movies to users. The MovieLens ratings dataset lists the ratings given by a set of users to a set of movies. Our goal is to be able to predict ratings for movies a user has not yet watched. The movies with the highest predicted ratings can then be recommended to the user.
The steps in the model are as follows:
Map user ID to a \"user vector\" via an embedding matrix
Map movie ID to a \"movie vector\" via an embedding matrix
Compute the dot product between the user vector and movie vector, to obtain the a match score between the user and the movie (predicted rating).
Train the embeddings via gradient descent using all known user-movie pairs.
References:
Collaborative Filtering
Neural Collaborative Filtering
import pandas as pd
import numpy as np
from zipfile import ZipFile
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from pathlib import Path
import matplotlib.pyplot as plt
First, load the data and apply preprocessing
# Download the actual data from http://files.grouplens.org/datasets/movielens/ml-latest-small.zip\"
# Use the ratings.csv file
movielens_data_file_url = (
\"http://files.grouplens.org/datasets/movielens/ml-latest-small.zip\"
)
movielens_zipped_file = keras.utils.get_file(
\"ml-latest-small.zip\", movielens_data_file_url, extract=False
)
keras_datasets_path = Path(movielens_zipped_file).parents[0]
movielens_dir = keras_datasets_path / \"ml-latest-small\"
# Only extract the data the first time the script is run.
if not movielens_dir.exists():
with ZipFile(movielens_zipped_file, \"r\") as zip:
# Extract files
print(\"Extracting all the files now...\")
zip.extractall(path=keras_datasets_path)
print(\"Done!\")
ratings_file = movielens_dir / \"ratings.csv\"
df = pd.read_csv(ratings_file)
First, need to perform some preprocessing to encode users and movies as integer indices.
user_ids = df[\"userId\"].unique().tolist()
user2user_encoded = {x: i for i, x in enumerate(user_ids)}
userencoded2user = {i: x for i, x in enumerate(user_ids)}
movie_ids = df[\"movieId\"].unique().tolist()
movie2movie_encoded = {x: i for i, x in enumerate(movie_ids)}
movie_encoded2movie = {i: x for i, x in enumerate(movie_ids)}
df[\"user\"] = df[\"userId\"].map(user2user_encoded)
df[\"movie\"] = df[\"movieId\"].map(movie2movie_encoded)
num_users = len(user2user_encoded)
num_movies = len(movie_encoded2movie)
df[\"rating\"] = df[\"rating\"].values.astype(np.float32)
# min and max ratings will be used to normalize the ratings later
min_rating = min(df[\"rating\"])
max_rating = max(df[\"rating\"])
print(
\"Number of users: {}, Number of Movies: {}, Min rating: {}, Max rating: {}\".format(
num_users, num_movies, min_rating, max_rating
)
)
Number of users: 610, Number of Movies: 9724, Min rating: 0.5, Max rating: 5.0
Prepare training and validation data
df = df.sample(frac=1, random_state=42)
x = df[[\"user\", \"movie\"]].values
# Normalize the targets between 0 and 1. Makes it easy to train.
y = df[\"rating\"].apply(lambda x: (x - min_rating) / (max_rating - min_rating)).values
# Assuming training on 90% of the data and validating on 10%.
train_indices = int(0.9 * df.shape[0])
x_train, x_val, y_train, y_val = (
x[:train_indices],
x[train_indices:],
y[:train_indices],
y[train_indices:],
)
Create the model
We embed both users and movies in to 50-dimensional vectors.
The model computes a match score between user and movie embeddings via a dot product, and adds a per-movie and per-user bias. The match score is scaled to the [0, 1] interval via a sigmoid (since our ratings are normalized to this range).
EMBEDDING_SIZE = 50