class NeuMF(BaseModel):
def __init__(self, args, num_users, num_items):
BaseModel.__init__(self, args, num_users, num_items)
self.layers = eval(args.layers)
self.lambda_layers = eval(args.reg_layers)
self.num_factors = args.num_factors
self.model_GMF = GMF(args, num_users, num_items)
self.model_MLP = MLP(args, num_users, num_items)
def build_core_model(self, user_indices, item_indices):
vector_GMF, len_GMF, params_GMF = self.model_GMF.build_core_model(
user_indices, item_indices)
vector_MLP, len_MLP, params_MLP = self.model_MLP.build_core_model(
user_indices, item_indices)
model_vector = tf.concat([vector_GMF, vector_MLP], 1)
model_len = len_GMF + len_MLP
model_params = []
model_params.extend(params_GMF)
model_params.extend(params_MLP)
return model_vector, model_len, model_params
def build_model(self, user_indices=None, item_indices=None):
if not user_indices:
user_indices = tf.placeholder(tf.int32, [None])
self.user_indices = user_indices
if not item_indices:
item_indices = tf.placeholder(tf.int32, [None])
self.item_indices = item_indices
self.ratings = tf.placeholder(tf.float32, [None])
model_vector, model_len, model_params = self.build_core_model(
user_indices, item_indices)
self.output, self.loss, self.error, self.raw_error, self.train_step = self.build_train_model(
model_vector, model_len, self.ratings, model_params)
