Datasets:
HenryAI
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KerasCodeExamples.txt

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class RecommenderNet(keras.Model):
def __init__(self, num_users, num_movies, embedding_size, **kwargs):
super(RecommenderNet, self).__init__(**kwargs)
self.num_users = num_users
self.num_movies = num_movies
self.embedding_size = embedding_size
self.user_embedding = layers.Embedding(
num_users,
embedding_size,
embeddings_initializer=\"he_normal\",
embeddings_regularizer=keras.regularizers.l2(1e-6),
)
self.user_bias = layers.Embedding(num_users, 1)
self.movie_embedding = layers.Embedding(
num_movies,
embedding_size,
embeddings_initializer=\"he_normal\",
embeddings_regularizer=keras.regularizers.l2(1e-6),
)
self.movie_bias = layers.Embedding(num_movies, 1)
def call(self, inputs):
user_vector = self.user_embedding(inputs[:, 0])
user_bias = self.user_bias(inputs[:, 0])
movie_vector = self.movie_embedding(inputs[:, 1])
movie_bias = self.movie_bias(inputs[:, 1])
dot_user_movie = tf.tensordot(user_vector, movie_vector, 2)
# Add all the components (including bias)
x = dot_user_movie + user_bias + movie_bias
# The sigmoid activation forces the rating to between 0 and 1
return tf.nn.sigmoid(x)
model = RecommenderNet(num_users, num_movies, EMBEDDING_SIZE)
model.compile(
loss=tf.keras.losses.BinaryCrossentropy(), optimizer=keras.optimizers.Adam(lr=0.001)
)
Train the model based on the data split
history = model.fit(
x=x_train,
y=y_train,
batch_size=64,
epochs=5,
verbose=1,
validation_data=(x_val, y_val),
)
Epoch 1/5
1418/1418 [==============================] - 6s 4ms/step - loss: 0.6368 - val_loss: 0.6206
Epoch 2/5
1418/1418 [==============================] - 7s 5ms/step - loss: 0.6131 - val_loss: 0.6176
Epoch 3/5
1418/1418 [==============================] - 6s 4ms/step - loss: 0.6083 - val_loss: 0.6146
Epoch 4/5
1418/1418 [==============================] - 6s 4ms/step - loss: 0.6072 - val_loss: 0.6131
Epoch 5/5
1418/1418 [==============================] - 6s 4ms/step - loss: 0.6075 - val_loss: 0.6150
Plot training and validation loss
plt.plot(history.history[\"loss\"])
plt.plot(history.history[\"val_loss\"])
plt.title(\"model loss\")
plt.ylabel(\"loss\")
plt.xlabel(\"epoch\")
plt.legend([\"train\", \"test\"], loc=\"upper left\")
plt.show()
png
Show top 10 movie recommendations to a user
movie_df = pd.read_csv(movielens_dir / \"movies.csv\")
# Let us get a user and see the top recommendations.
user_id = df.userId.sample(1).iloc[0]
movies_watched_by_user = df[df.userId == user_id]
movies_not_watched = movie_df[
~movie_df[\"movieId\"].isin(movies_watched_by_user.movieId.values)
][\"movieId\"]
movies_not_watched = list(
set(movies_not_watched).intersection(set(movie2movie_encoded.keys()))
)
movies_not_watched = [[movie2movie_encoded.get(x)] for x in movies_not_watched]
user_encoder = user2user_encoded.get(user_id)
user_movie_array = np.hstack(
([[user_encoder]] * len(movies_not_watched), movies_not_watched)
)
ratings = model.predict(user_movie_array).flatten()
top_ratings_indices = ratings.argsort()[-10:][::-1]
recommended_movie_ids = [
movie_encoded2movie.get(movies_not_watched[x][0]) for x in top_ratings_indices
]
print(\"Showing recommendations for user: {}\".format(user_id))
print(\"====\" * 9)
print(\"Movies with high ratings from user\")
print(\"----\" * 8)
top_movies_user = (
movies_watched_by_user.sort_values(by=\"rating\", ascending=False)
.head(5)
.movieId.values
)
movie_df_rows = movie_df[movie_df[\"movieId\"].isin(top_movies_user)]
for row in movie_df_rows.itertuples():