def test_compile_fit(self):
def gen_rand_user_item_feature(user_num, item_num, class_num):
user_id = random.randint(1, user_num)
item_id = random.randint(1, item_num)
rating = random.randint(1, class_num)
sample = Sample.from_ndarray(np.array([user_id, item_id]),
np.array([rating]))
return UserItemFeature(user_id, item_id, sample)
model = NeuralCF(200, 80, 5)
model.summary()
data = self.sc.parallelize(range(0, 50)) \
.map(lambda i: gen_rand_user_item_feature(200, 80, 5)) \
.map(lambda pair: pair.sample)
model.compile(
optimizer="adam",
loss=SparseCategoricalCrossEntropy(zero_based_label=False),
metrics=['accuracy'])
tmp_log_dir = create_tmp_path()
model.set_tensorboard(tmp_log_dir, "training_test")
model.fit(data, nb_epoch=1, batch_size=32, validation_data=data)
train_loss = model.get_train_summary("Loss")
val_loss = model.get_validation_summary("Loss")
print(np.array(train_loss))
print(np.array(val_loss))
.map(lambda x: build_sample(x[0], x[1], x[2]-1))
pairFeatureRdds.take(3)
trainPairFeatureRdds, valPairFeatureRdds = pairFeatureRdds.randomSplit([0.8, 0.2], seed= 1)
valPairFeatureRdds.cache()
train_rdd= trainPairFeatureRdds.map(lambda pair_feature: pair_feature.sample)
val_rdd= valPairFeatureRdds.map(lambda pair_feature: pair_feature.sample)
val_rdd.persist()
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)