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train_base.py
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train_base.py
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# coding: utf-8
import os, sys
import pandas as pd
import tensorflow as tf
from sklearn.metrics import log_loss, roc_auc_score
from tensorflow.python.keras import backend as K
from deepctr.utils import SingleFeat
from utils import *
from models import Base, Base_All_Fields
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
tfconfig = tf.ConfigProto()
tfconfig.gpu_options.allow_growth = True
K.set_session(tf.Session(config=tfconfig))
if __name__ == "__main__":
FRAC = FRAC
SESS_MAX_LEN = DIN_SESS_MAX_LEN
fd = pd.read_pickle(ROOT_DATA+'model_input/din_fd_' +
str(FRAC) + '_' + str(SESS_MAX_LEN) + '.pkl')
model_input = pd.read_pickle(
ROOT_DATA+'model_input/din_input_' + str(FRAC) + '_' + str(SESS_MAX_LEN) + '.pkl')
label = pd.read_pickle(ROOT_DATA+'model_input/din_label_' +
str(FRAC) + '_' + str(SESS_MAX_LEN) + '.pkl')
sample_sub = pd.read_pickle(
ROOT_DATA+'sampled_data/raw_sample_' + str(FRAC) + '.pkl')
sample_sub['idx'] = list(range(sample_sub.shape[0]))
train_idx = sample_sub.loc[sample_sub.time_stamp <
1494633600, 'idx'].values
test_idx = sample_sub.loc[sample_sub.time_stamp >=
1494633600, 'idx'].values
train_input = [i[train_idx] for i in model_input]
test_input = [i[test_idx] for i in model_input]
train_label = label[train_idx]
test_label = label[test_idx]
sess_len_max = SESS_MAX_LEN
BATCH_SIZE = 4096
sess_feature = ['cate_id', 'brand']
TEST_BATCH_SIZE = 2 ** 14
print('train len: %d\ttest_len: %d' % (train_label.shape[0], test_label.shape[0]))
model_type = sys.argv[1]
for i in range(5):
if model_type == 'Base':
print('Start training Base: ' + str(i))
log_path = ROOT_DATA + 'log/Base_log_' + str(i) + '.txt'
best_model_path = ROOT_DATA + 'best_model/base.h5'
model = Base(fd, sess_feature, embedding_size=64, hist_len_max=sess_len_max, dnn_hidden_units=(200, 80))
elif model_type=='Base_All_Fields_Add':
print('Start training Base_All_Fields_Add: ' + str(i))
log_path = ROOT_DATA + 'log/Base_All_Fields_Add_log_' + str(i) + '.txt'
best_model_path = ROOT_DATA + 'best_model/base_all_fields_add.h5'
model = Base_All_Fields(fd, sess_feature, embedding_size=64, hist_len_max=sess_len_max,
dnn_hidden_units=(200, 80), flag='add')
elif model_type=='Base_All_Fields_Concat':
print('Start training Base_All_Fields_Concat: ' + str(i))
log_path = ROOT_DATA + 'log/Base_All_Fields_Concat_log_' + str(i) + '.txt'
best_model_path = ROOT_DATA + 'best_model/base_all_fields_concat.h5'
model = Base_All_Fields(fd, sess_feature, embedding_size=64, hist_len_max=sess_len_max,
dnn_hidden_units=(200, 80), flag='concat')
model.compile('adagrad', 'binary_crossentropy')
hist_ = model.fit(train_input[:], train_label,
batch_size=BATCH_SIZE, epochs=10, initial_epoch=0, verbose=0,
callbacks=[LossHistory(log_path),
auc_callback(training_data=[train_input, train_label],
test_data=[test_input, test_label],
best_model_path=best_model_path)])
K.clear_session()
del model