Created
July 28, 2020 15:00
-
-
Save uni-3/eb5a09c92f5e9c64bc486df83a10f326 to your computer and use it in GitHub Desktop.
neural mf with tensorflow
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
from dataclasses import dataclass | |
import tensorflow as tf | |
# https://www.tensorflow.org/tutorials/quickstart/advanced | |
@dataclass | |
class NeuMF: | |
n_user: int | |
n_item: int | |
params: dict | |
def __post_init__(self): | |
self.model = self.construct_model() | |
def _construct_layers(self, user_input, item_input) -> tf.keras.Model: | |
n_user = self.n_user | |
n_item = self.n_item | |
params = self.params | |
model_layers = params["model_layers"] | |
mf_regularization = params["mf_regularization"] | |
mlp_reg_layers = params["mlp_reg_layers"] | |
# matrix factorization | |
mf_dim = params["mf_dim"] | |
if model_layers[0] % 2 != 0: | |
raise ValueError("The first layer size should be multiple of 2!") | |
embedding_initializer = "glorot_uniform" | |
# Embedding layer | |
mf_embedding_user = tf.keras.layers.Embedding( | |
input_dim=n_user, output_dim=mf_dim, name='mf_embedding_user', | |
embeddings_initializer=embedding_initializer, | |
embeddings_regularizer=tf.keras.regularizers.l2(mf_regularization), | |
input_length=1)(user_input) | |
mf_embedding_item = tf.keras.layers.Embedding( | |
input_dim=n_item, output_dim=mf_dim, name='mf_embedding_item', | |
embeddings_initializer=embedding_initializer, | |
embeddings_regularizer=tf.keras.regularizers.l2(mf_regularization), | |
input_length=1)(item_input) | |
mlp_embedding_user = tf.keras.layers.Embedding( | |
input_dim=n_user, output_dim=model_layers[0] // 2, name="mlp_embedding_user", | |
embeddings_initializer=embedding_initializer, | |
embeddings_regularizer=tf.keras.regularizers.l2(mf_regularization), | |
input_length=1)(user_input) | |
mlp_embedding_item = tf.keras.layers.Embedding( | |
input_dim=n_item, output_dim=model_layers[0] // 2, name='mlp_embedding_item', | |
embeddings_initializer=embedding_initializer, | |
embeddings_regularizer=tf.keras.regularizers.l2(mf_regularization), | |
input_length=1)(item_input) | |
# MF | |
mf_vector = tf.keras.layers.multiply([mf_embedding_user, mf_embedding_item]) | |
# MLP | |
mlp_vector = tf.keras.layers.concatenate([mlp_embedding_user, mlp_embedding_item]) | |
num_layer = len(model_layers) # Number of layers in the MLP | |
for layer in range(1, num_layer): | |
model_layer = tf.keras.layers.Dense( | |
model_layers[layer], | |
kernel_regularizer=tf.keras.regularizers.l2(mlp_reg_layers[layer]), | |
activation="relu") | |
mlp_vector = model_layer(mlp_vector) | |
# concat MF and MLP | |
predict_vector = tf.keras.layers.concatenate([mf_vector, mlp_vector]) | |
# prediction layer | |
logits = tf.keras.layers.Dense( | |
1, activation='sigmoid', | |
kernel_initializer="lecun_uniform", | |
name="rating")(predict_vector) | |
return tf.keras.Model(inputs=[user_input, item_input], | |
outputs=logits) | |
def construct_model(self) -> tf.keras.Model: | |
"""Constructs and returns the model.""" | |
user_input = tf.keras.layers.Input( | |
#shape=(1,), | |
shape=(), | |
name="user_id", | |
dtype=tf.int32 | |
) | |
item_input = tf.keras.layers.Input( | |
#shape=(1,), | |
shape=(), | |
name="item_id", | |
dtype=tf.int32 | |
) | |
base_model = self._construct_layers(user_input, item_input) | |
#logits = base_model.output | |
model = tf.keras.Model( | |
inputs={ | |
"item_id": item_input, | |
"user_id": user_input | |
}, | |
outputs=base_model.output) | |
return model | |
def load_saved_model(self, path='./models'): | |
self.model = tf.keras.models.load_model(path) | |
#return self.model |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment