59 lines
2.3 KiB
Python
59 lines
2.3 KiB
Python
from random import seed
|
|
import sys
|
|
sys.path.append("..")
|
|
import os
|
|
import time
|
|
from sklearn.preprocessing import LabelEncoder
|
|
from features import DenseFeat, SparseFeat, VarLenSparseFeat
|
|
from models import PNN
|
|
import tensorflow as tf
|
|
from tensorflow.keras.callbacks import TensorBoard
|
|
from preprocess import process_data
|
|
|
|
os.environ["CUDA_VISIBLE_DEVICES"] = "2"
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# feature_max_index_dict, train_input_dict, train_label = process_data(sample_num=5000000)
|
|
feature_max_index_dict, train_input_dict, train_label = process_data(sample_num=1000)
|
|
|
|
embed_dim = 4
|
|
|
|
# linear_feature_names = ['C1', 'banner_pos', 'site_category', 'app_category',
|
|
# 'device_type', 'device_conn_type', 'C15','C16','C18','C19','C21',
|
|
# 'is_weekend','weekday','hour_v2']
|
|
|
|
linear_feature_names = ['is_weekend','weekday','hour_v2']
|
|
|
|
dnn_feature_names = ['id', 'C1', 'banner_pos', 'site_id', 'site_domain',
|
|
'site_category', 'app_id', 'app_domain', 'app_category', 'device_id',
|
|
'device_ip', 'device_model', 'device_type','device_conn_type', 'C14',
|
|
'C15', 'C16', 'C17', 'C18', 'C19', 'C20', 'C21', 'is_weekend', 'weekday',
|
|
'hour_v2']
|
|
|
|
linear_feature_columns = [SparseFeat(name, vocabulary_size=feature_max_index_dict[name],
|
|
embedding_dim=embed_dim, embedding_name=name) for name in linear_feature_names]
|
|
|
|
dnn_feature_columns = [SparseFeat(name, vocabulary_size=feature_max_index_dict[name],
|
|
embedding_dim=embed_dim, embedding_name=name, group_name='pnn') for name in dnn_feature_names]
|
|
|
|
model = PNN(linear_feature_columns, dnn_feature_columns)
|
|
model.summary()
|
|
|
|
model.compile(
|
|
optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
|
|
loss=tf.keras.losses.BinaryCrossentropy(),
|
|
metrics=[tf.keras.metrics.AUC()])
|
|
|
|
model_name = "pnn-{}".format(int(time.time()))
|
|
tensorboard = TensorBoard(
|
|
log_dir='logs/{}'.format(model_name),
|
|
update_freq='batch')
|
|
|
|
model.fit(train_input_dict, train_label,
|
|
batch_size=1024, epochs=2, verbose=1,
|
|
validation_split=0.2, callbacks=[tensorboard])
|
|
|
|
# evaluation = model.evaluate(test_input_dict, test_label, batch_size=2048,
|
|
# verbose=1, return_dict=True, callbacks=[tensorboard])
|
|
# print(evaluation) |