def _eval(sess, model):
y_scores = []
y_true = []
for _, uij in data_uploder(test_set, feature_config_dict, test_batch_size):
y_scores += list(model._eval(sess, uij))
y_true += list(uij[-1])
y_scores = np.array(y_scores).flatten()
y_true = np.array(y_true).flatten()
test_gauc = roc_auc_score(y_true, y_scores)
all_result.append(test_gauc)
print("Now auc is ", test_gauc)
global best_auc
if best_auc < test_gauc:
best_auc = test_gauc
model.save(sess, 'save_ffm_path/ckpt')
pickle.dump(all_result, open("auc_ffm_all_result.pkl", "wb"))
return None
feature_config_dict = pickle.load(f)
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
model = Ffm(feature_config_dict)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer()) #
lr = 0.001
start_time = time.time()
for _ in range(50):
random.shuffle(train_set)
epoch_size = round(len(train_set) / train_batch_size)
for _, uij in data_uploder(train_set, feature_config_dict,
train_batch_size):
loss = model.train(sess, uij, lr)
if model.global_step.eval() % 1000 == 0:
_eval(sess, model)
if model.global_step.eval() % 336000 == 0:
lr *= 0.9
print('Epoch %d DONE\tCost time: %.2f' %
(model.global_epoch_step.eval(), time.time() - start_time))
sys.stdout.flush()
model.global_epoch_step_op.eval()
