def main(_):
model_dir = os.path.join(os.path.dirname(__file__), 'logdata_lr')
print(model_dir)
# Build 2 hidden layer DNN with 10, 10 units respectively.
if False:
ws = tf.estimator.WarmStartSettings(
ckpt_to_initialize_from=
"/home/lucius/Projects/notebook/homework/ctr_predict/models/v1",
vars_to_warm_start=".*input_layer.*")
else:
ws = None
classifier = tf.estimator.Estimator(
model_fn=my_model,
params={'feature_columns': build_model_columns()},
model_dir=model_dir,
warm_start_from=ws)
for i in range(15):
print('perform {}s epoch...'.format(i))
# Train the Model.
classifier.train(input_fn=get_input_fn(
os.path.join(os.path.dirname(__file__),
'../../FE/FE1/train_split.csv'), 256, 1, 5000))
# Evaluate the model.
eval_result = classifier.evaluate(input_fn=get_input_fn(
os.path.join(os.path.dirname(__file__),
'../../FE/FE1/valid_split.csv'), 256, 1, 5000))
print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))
def main(_):
model_dir = os.path.join(os.path.dirname(__file__), 'logdata_fm_noreg')
restore_map = {
'input_layer/' + feature_name + '_embed_lr': feature_name + '_embed_lr'
for feature_name in COLUMN_NAMES[2:]
}
restore_map.update({
'input_layer/' + feature_name + '_embed_fm': feature_name + '_embed_lr'
for feature_name in COLUMN_NAMES[2:]
})
# Build 2 hidden layer DNN with 10, 10 units respectively.
if True:
ws = tf.estimator.WarmStartSettings(
ckpt_to_initialize_from=
"/home/lucius/Projects/notebook/homework/ctr_predict/models/v2/logdata_3_4",
vars_to_warm_start=".*input_layer.*")
else:
ws = None
classifier = tf.estimator.Estimator(model_fn=my_model,
params={
'feature_columns':
build_model_columns_fm(),
'optimizer':
'sgd'
},
model_dir=model_dir,
warm_start_from=ws)
for i in range(15):
print('perform {}s epoch...'.format(i))
# Train the Model.
classifier.train(input_fn=get_input_fn(os.path.join(
os.path.dirname(__file__), '../../FE/FE1/train_split.tfrecord'),
256,
1,
5000,
use_tfrecord=True))
# Evaluate the model.
eval_result = classifier.evaluate(input_fn=get_input_fn(
os.path.join(os.path.dirname(__file__),
'../../FE/FE1/valid_split.tfrecord'),
256,
1,
5000,
use_tfrecord=True))
print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))
def main(_):
# Build 2 hidden layer DNN with 10, 10 units respectively.
if False:
ws = tf.estimator.WarmStartSettings(
ckpt_to_initialize_from="/home/lucius/Projects/notebook/homework/ctr_predict/models/v1/logdata_fm_10",
vars_to_warm_start=".*input_layer.*")
else:
ws = None
for i in range(5):
fm_dim = i*5 + 3
for j in range(5):
model_reg = (10**(-j)) * 1.0
model_dir = os.path.join(os.path.dirname(__file__), 'logdata_' + str(i) + '_' + str(j))
for k in range(2):
classifier = tf.estimator.Estimator(
model_fn=my_model,
params={
'feature_columns': build_model_columns_fm(),
'optimizer': 'sgd' if k != 0 else None,
'learning_rate': 0.001,
'dim': fm_dim,
'regularizer': model_reg
},
model_dir=model_dir,
warm_start_from=ws)
print('perform {}s epoch...'.format(i))
# Train the Model.
classifier.train(
input_fn=get_input_fn(os.path.join(os.path.dirname(__file__), '../../FE/FE1/train_split.tfrecord'), 256, 1, 5000, use_tfrecord=True))
# Evaluate the model.
eval_result = classifier.evaluate(
input_fn=get_input_fn(os.path.join(os.path.dirname(__file__), '../../FE/FE1/valid_split.tfrecord'), 256, 1, 5000, use_tfrecord=True))
print('\nTest set accuracy: {accuracy:0.3f}\n'.format(**eval_result))