def train(self):
self.train_dataset, \
self.test_dataset = self.data_process.generate_data(batch_size=self.batch_size,
epoch=self.epoch)
fm = FM(user_num=self.user_num,
movie_num=self.movie_num,
year_num=self.year_num,
genre_num=self.genre_num,
embedding_size=self.embedding_size,
is_sigmoid=True)
fm_model = fm.get_fm_model()
fm_model.summary()
fm_model.compile(optimizer="adam",
loss=keras.losses.binary_crossentropy,
metrics=["accuracy"])
fm_model.fit(self.train_dataset,
epochs=self.epoch,
steps_per_epoch=1270010 // self.batch_size + 1,
callbacks=[
get_callback(self.test_dataset, self.model_path,
self.version)
])
class DeepFM(object):
def __init__(self,
user_num=4691,
movie_num=2514,
year_num=76,
genre_num=9,
embedding_size=16):
self.user_num = user_num
self.movie_num = movie_num
self.year_num = year_num
self.genre_num = genre_num
self.embedding_size = embedding_size
self.fm = FM(user_num=self.user_num,
movie_num=self.movie_num,
year_num=self.year_num,
genre_num=self.genre_num,
embedding_size=self.embedding_size)
def get_deep_logit(self, sparse_embedding):
x = Concatenate(axis=2)(sparse_embedding)
x = Flatten()(x)
#x = Lambda(lambda x: tf.squeeze(x,axis=1))(x)
x = Dense(128, activation="relu")(x)
x = Dense(128, activation="relu")(x)
logit = Dense(1, use_bias=False)(x)
return logit
def get_deepfm_model(self):
sparse_embedding = self.fm.get_sparse_embedding()
fm_model = self.fm.get_fm_model(sparse_embedding)
deep_logit = self.get_deep_logit(sparse_embedding)
x = Add()([fm_model.outputs[0], deep_logit])
outputs = Dense(1, activation="sigmoid")(x)
model = Model(inputs=fm_model.inputs, outputs=outputs)
return model