tf.logging.set_verbosity(tf.logging.INFO)
lr_params = lr_default_params()
config = estimator_default_config()
ds_obj = lr_params['ds_obj']
model_dir = lr_params['model_dir']
num_epochs = 20
batch_size = 32
model_params = lr_params['model_params']
print(model_params)
if os.path.exists(model_dir): shutil.rmtree(model_dir) # clean model_dir
model_fn = make_model_fn(lr_arch_fn)
LR = tf.estimator.Estimator(model_fn=model_fn,
model_dir=model_dir,
params=model_params,
config=config)
LR.train(lambda: input_fn(ds_obj.file_tr, batch_size, num_epochs, True))
print('eval in tr dataset')
LR.evaluate(lambda: input_fn(ds_obj.file_tr, batch_size, 1))
print('eval in va dataset')
LR.evaluate(lambda: input_fn(ds_obj.file_va, batch_size, 1))
"""
eval in tr dataset
INFO:tensorflow:Saving dict for global step 56227: accuracy = 0.78388655, auc = 0.7319471, global_step = 56227, loss = 0.4900751
eval in va dataset
INFO:tensorflow:Saving dict for global step 56227: accuracy = 0.7866109, auc = 0.7285157, global_step = 56227, loss = 0.48628032
"""
params = deepFM_default_params()
config = estimator_default_config()
ds_obj = params['ds_obj']
model_dir = params['model_dir']
num_epochs = 20
batch_size = 32
model_params = params['model_params']
print(model_params)
if os.path.exists(model_dir): shutil.rmtree(model_dir) # clean model_dir
model_fn = make_model_fn(deepFM_arch_fn)
deepFM = tf.estimator.Estimator(model_fn=model_fn,
model_dir=model_dir,
params=model_params,
config=config)
deepFM.train(
lambda: input_fn(ds_obj.file_tr, batch_size, num_epochs, True))
print('eval in tr dataset')
deepFM.evaluate(lambda: input_fn(ds_obj.file_tr, batch_size, 1))
print('eval in va dataset')
deepFM.evaluate(lambda: input_fn(ds_obj.file_va, batch_size, 1))
"""
eval in tr dataset
INFO:tensorflow:Saving dict for global step 56227: accuracy = 0.7947467, auc = 0.7710146, global_step = 56227, loss = 0.46117908
eval in va dataset
INFO:tensorflow:Saving dict for global step 56227: accuracy = 0.79159194, auc = 0.7528646, global_step = 56227, loss = 0.47020566
"""