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activation=\"relu\", |
name=\"process_movie_embedding_with_genres\", |
) |
## Define a function to encode a given movie id. |
def encode_movie(movie_id): |
# Convert the string input values into integer indices. |
movie_idx = movie_index_lookup(movie_id) |
movie_embedding = movie_embedding_encoder(movie_idx) |
encoded_movie = movie_embedding |
if include_movie_features: |
movie_genres_vector = movie_genres_lookup(movie_idx) |
encoded_movie = movie_embedding_processor( |
layers.concatenate([movie_embedding, movie_genres_vector]) |
) |
return encoded_movie |
## Encoding target_movie_id |
target_movie_id = inputs[\"target_movie_id\"] |
encoded_target_movie = encode_movie(target_movie_id) |
## Encoding sequence movie_ids. |
sequence_movies_ids = inputs[\"sequence_movie_ids\"] |
encoded_sequence_movies = encode_movie(sequence_movies_ids) |
# Create positional embedding. |
position_embedding_encoder = layers.Embedding( |
input_dim=sequence_length, |
output_dim=movie_embedding_dims, |
name=\"position_embedding\", |
) |
positions = tf.range(start=0, limit=sequence_length - 1, delta=1) |
encodded_positions = position_embedding_encoder(positions) |
# Retrieve sequence ratings to incorporate them into the encoding of the movie. |
sequence_ratings = tf.expand_dims(inputs[\"sequence_ratings\"], -1) |
# Add the positional encoding to the movie encodings and multiply them by rating. |
encoded_sequence_movies_with_poistion_and_rating = layers.Multiply()( |
[(encoded_sequence_movies + encodded_positions), sequence_ratings] |
) |
# Construct the transformer inputs. |
for encoded_movie in tf.unstack( |
encoded_sequence_movies_with_poistion_and_rating, axis=1 |
): |
encoded_transformer_features.append(tf.expand_dims(encoded_movie, 1)) |
encoded_transformer_features.append(encoded_target_movie) |
encoded_transformer_features = layers.concatenate( |
encoded_transformer_features, axis=1 |
) |
return encoded_transformer_features, encoded_other_features |
Create a BST model |
include_user_id = False |
include_user_features = False |
include_movie_features = False |
hidden_units = [256, 128] |
dropout_rate = 0.1 |
num_heads = 3 |
def create_model(): |
inputs = create_model_inputs() |
transformer_features, other_features = encode_input_features( |
inputs, include_user_id, include_user_features, include_movie_features |
) |
# Create a multi-headed attention layer. |
attention_output = layers.MultiHeadAttention( |
num_heads=num_heads, key_dim=transformer_features.shape[2], dropout=dropout_rate |
)(transformer_features, transformer_features) |
# Transformer block. |
attention_output = layers.Dropout(dropout_rate)(attention_output) |
x1 = layers.Add()([transformer_features, attention_output]) |
x1 = layers.LayerNormalization()(x1) |
x2 = layers.LeakyReLU()(x1) |
x2 = layers.Dense(units=x2.shape[-1])(x2) |
x2 = layers.Dropout(dropout_rate)(x2) |
transformer_features = layers.Add()([x1, x2]) |
transformer_features = layers.LayerNormalization()(transformer_features) |
features = layers.Flatten()(transformer_features) |
# Included the other features. |
if other_features is not None: |
features = layers.concatenate( |
[features, layers.Reshape([other_features.shape[-1]])(other_features)] |
) |
# Fully-connected layers. |
for num_units in hidden_units: |
features = layers.Dense(num_units)(features) |
features = layers.BatchNormalization()(features) |
features = layers.LeakyReLU()(features) |
features = layers.Dropout(dropout_rate)(features) |
outputs = layers.Dense(units=1)(features) |
model = keras.Model(inputs=inputs, outputs=outputs) |
return model |
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