def _get_embed_ncf(self):
user_max = self._config["user_max"]
ncf = NeuralCF(user_count=user_max,
item_count=self._config["movie_max"],
class_num=self._config["rate_dim"],
hidden_layers=[20, 10],
include_mf=False)
loaded = ncf.load_model(self._config["ncf_model_path"])
user_embed = loaded.get_weights()[0]
item_embed = loaded.get_weights()[1]
user_dict = {}
for i in range(1, self._config["user_max"] + 1):
user_dict[i] = user_embed[i][:]
item_dict = {}
for i in range(1, self._config["movie_max"] + 1):
item_dict[i] = item_embed[i][:]
return (user_dict, item_dict)
ncf = NeuralCF(user_count=max_user_id,
item_count=max_movie_id,
class_num=5,
hidden_layers=[20, 10],
include_mf = False)
ncf.compile(optimizer= "adam",
loss= "sparse_categorical_crossentropy",
metrics=['accuracy'])
ncf.fit(train_rdd,
nb_epoch= 10,
batch_size= 8000,
validation_data=val_rdd)
ncf.save_model("../save_model/movie_ncf.zoomodel", over_write=True)
#
weights = ncf.get_weights()
# print(weights)
print(len(weights))
for i, weight in enumerate(weights):
print(i)
print(weight.shape)
#
loaded = ncf.load_model("../save_model/movie_ncf.zoomodel")
user_embed = loaded.get_weights()[0]
print(user_embed.shape)
#
item_embed = loaded.get_weights()[1]
print(item_embed.shape)