data_info,
embed_size=16,
n_epochs=2,
lr=1e-4,
lr_decay=False,
reg=None,
batch_size=2048,
num_neg=1,
use_bn=False,
dropout_rate=None,
hidden_units="128,64,32",
tf_sess_config=None)
deepfm.fit(train_data,
verbose=2,
shuffle=True,
eval_data=eval_data,
metrics=[
"loss", "balanced_accuracy", "roc_auc", "pr_auc",
"precision", "recall", "map", "ndcg"
])
print("prediction: ", deepfm.predict(user=1, item=2333))
print("recommendation: ", deepfm.recommend_user(user=1, n_rec=7))
reset_state("AutoInt")
autoint = AutoInt("ranking",
data_info,
embed_size=16,
n_epochs=2,
att_embed_size=(8, 8, 8),
num_heads=4,
use_residual=False,
lr=1e-3,
n_epochs=2,
lr=1e-4,
lr_decay=False,
reg=None,
batch_size=2048,
num_neg=1,
use_bn=False,
dropout_rate=None,
hidden_units="128,64,32",
tf_sess_config=None)
deepfm.fit(train_data,
verbose=2,
shuffle=True,
eval_data=test_data,
metrics=[
"loss", "balanced_accuracy", "roc_auc", "pr_auc",
"precision", "recall", "map", "ndcg"
],
eval_batch_size=8192,
k=10,
sample_user_num=2048)
data_info.save(path="model_path")
deepfm.save(path="model_path",
model_name="deepfm_model",
manual=True,
inference_only=True)
# =========================== load model ==============================
print("\n", "=" * 50, " after load model ", "=" * 50)
tf.reset_default_graph()
deepfm = DeepFM("rating",
data_info,
embed_size=16,
n_epochs=2,
lr=1e-4,
lr_decay=False,
reg=None,
batch_size=2048,
num_neg=1,
use_bn=False,
dropout_rate=None,
hidden_units="128,64,32",
tf_sess_config=None)
deepfm.fit(train_data,
verbose=2,
shuffle=True,
eval_data=eval_data,
metrics=["rmse", "mae", "r2"])
print("prediction: ", deepfm.predict(user=1, item=2333))
print("recommendation: ", deepfm.recommend_user(user=1, n_rec=7))
reset_state("AutoInt")
autoint = AutoInt("rating",
data_info,
embed_size=16,
n_epochs=2,
att_embed_size=(8, 8, 8),
num_heads=4,
use_residual=False,
lr=1e-3,
lr_decay=False,