def build_and_test_estimator(self, model_type):
"""Ensure that model trains and minimizes loss."""
model = wide_deep.build_estimator(self.temp_dir, model_type)
# Train for 1 step to initialize model and evaluate initial loss
def get_input_fn(num_epochs, shuffle, batch_size):
def input_fn():
return wide_deep.input_fn(TEST_CSV,
num_epochs=num_epochs,
shuffle=shuffle,
batch_size=batch_size)
return input_fn
model.train(input_fn=get_input_fn(1, True, 1), steps=1)
initial_results = model.evaluate(input_fn=get_input_fn(1, False, 1))
# Train for 100 epochs at batch size 3 and evaluate final loss
model.train(input_fn=get_input_fn(100, True, 3))
final_results = model.evaluate(input_fn=get_input_fn(1, False, 1))
print('%s initial results:' % model_type, initial_results)
print('%s final results:' % model_type, final_results)
# Ensure loss has decreased, while accuracy and both AUCs have increased.
self.assertLess(final_results['loss'], initial_results['loss'])
self.assertGreater(final_results['auc'], initial_results['auc'])
self.assertGreater(final_results['auc_precision_recall'],
initial_results['auc_precision_recall'])
self.assertGreater(final_results['accuracy'],
initial_results['accuracy'])
def build_and_test_estimator(self, model_type):
"""Ensure that model trains and minimizes loss."""
model = wide_deep.build_estimator(self.temp_dir, model_type)
# Train for 1 step to initialize model and evaluate initial loss
def get_input_fn(num_epochs, shuffle, batch_size):
def input_fn():
return wide_deep.input_fn(
TEST_CSV, num_epochs=num_epochs, shuffle=shuffle,
batch_size=batch_size)
return input_fn
model.train(input_fn=get_input_fn(1, True, 1), steps=1)
initial_results = model.evaluate(input_fn=get_input_fn(1, False, 1))
# Train for 100 epochs at batch size 3 and evaluate final loss
model.train(input_fn=get_input_fn(100, True, 3))
final_results = model.evaluate(input_fn=get_input_fn(1, False, 1))
print('%s initial results:' % model_type, initial_results)
print('%s final results:' % model_type, final_results)
# Ensure loss has decreased, while accuracy and both AUCs have increased.
self.assertLess(final_results['loss'], initial_results['loss'])
self.assertGreater(final_results['auc'], initial_results['auc'])
self.assertGreater(final_results['auc_precision_recall'],
initial_results['auc_precision_recall'])
self.assertGreater(final_results['accuracy'], initial_results['accuracy'])