def test_train_with_validation_assert_data_is_passed(self):
trainer = SgdTrainer()
trainable = MagicMock()
trainable.update_params.return_value = (0.09, 100)
trainable.evaluate_validation_set.return_value = (0.10, 60.0)
trainer.train(
trainable,
np.array([0, 9, 8, 7, 6]),
np.array([1, 2, 3, 4, 5]),
np.array([1, 2, 3]),
np.array([0, 0.5, 0.7]),
None,
epochs=1,
validation_gap=1,
minibatch_size=5,
seed=0,
)
self.assertEqual(trainable.update_params.call_count, 1)
self.assertEqual(
trainable.evaluate_validation_set.call_count,
1,
)
((validation_dataset, validation_labels), _) = \
trainable.evaluate_validation_set.call_args
self.assertTrue(np.array_equal(
validation_dataset,
np.array([1, 2, 3]),
))
self.assertTrue(np.array_equal(
validation_labels,
np.array([0, 0.5, 0.7]),
))
def test_train_with_validation_on_odd_gap_check_recorded(self):
trainer = SgdTrainer()
trainable = MagicMock()
trainable.update_params.return_value = (0.66, 80.3)
trainable.evaluate_validation_set.return_value = (1.25, 50.1)
train_watcher = _TrainWatcherRecorder()
(epochs, validation_epochs) = trainer.train(
trainable,
np.array([1, 2, 3]),
np.array([0.1, 0.2, 0.3]),
np.array([1, 2, 3]),
np.array([0.1, 0.2, 0.3]),
train_watcher,
epochs=7,
validation_gap=5,
)
self.assertEqual(epochs, 7)
self.assertEqual(validation_epochs, 2)
self.assertEqual(train_watcher.epochs, list(range(7)))
self.assertEqual(train_watcher.costs, [0.66] * 7)
self.assertEqual(train_watcher.accuracies, [80.3] * 7)
self.assertEqual(train_watcher.validation_epochs, [0, 5])
self.assertEqual(train_watcher.validation_costs, [1.25] * 2)
self.assertEqual(train_watcher.validation_accuracies, [50.1] * 2)
def test_train_multiple_epoch_with_minibatches_check_recorded(self):
trainer = SgdTrainer()
trainable = MagicMock()
trainable.update_params.return_value = (1.2, 70.5)
train_watcher = _TrainWatcherRecorder()
(epochs, validation_epochs) = trainer.train(
trainable,
np.array([1, 2, 3]),
np.array([0.1, 0.2, 0.3]),
None,
None,
train_watcher,
epochs=5,
minibatch_size=1,
)
self.assertEqual(epochs, 5)
self.assertEqual(validation_epochs, 0)
self.assertEqual(train_watcher.epochs, list(range(5)))
self.assertEqual(train_watcher.costs, [1.2] * 5)
self.assertEqual(train_watcher.accuracies, [70.5] * 5)
self.assertEqual(train_watcher.validation_epochs, [])
self.assertEqual(train_watcher.validation_costs, [])
self.assertEqual(train_watcher.validation_accuracies, [])
def test_train_single_epoch_check_trainable(self):
trainer = SgdTrainer()
trainable = MagicMock()
trainable.update_params.return_value = (1.0, 0.5)
trainer.train(
trainable,
np.array([1, 2, 3]),
np.array([0.1, 0.2, 0.3]),
None,
None,
None,
epochs=1,
)
self.assertEqual(trainable.update_params.call_count, 1)
self.assertEqual(trainable.evaluate_validation_set.call_count, 0)
def test_train_with_multiple_minibatches_check_trainable(self):
trainer = SgdTrainer()
trainable = MagicMock()
trainable.update_params.return_value = (1.2, 70.5)
trainer.train(
trainable,
np.array([1, 2, 3]),
np.array([0.1, 0.2, 0.3]),
None,
None,
None,
epochs=5,
minibatch_size=1,
)
self.assertEqual(trainable.update_params.call_count, 15)
self.assertEqual(trainable.evaluate_validation_set.call_count, 0)
def test_train_with_validation_check_trainable(self):
trainer = SgdTrainer()
trainable = MagicMock()
trainable.update_params.return_value = (0.09, 100)
trainable.evaluate_validation_set.return_value = (0.10, 60.0)
trainer.train(
trainable,
np.array([1, 2, 3]),
np.array([0.1, 0.2, 0.3]),
np.array([1, 2, 3]),
np.array([0.1, 0.2, 0.3]),
None,
epochs=11,
validation_gap=5,
)
self.assertEqual(trainable.update_params.call_count, 11)
self.assertEqual(trainable.evaluate_validation_set.call_count, 3)
def test_train_assert_watcher_records_are_empty(self):
trainer = SgdTrainer()
trainable = MagicMock()
train_watcher = _TrainWatcherRecorder()
trainer.train(
trainable,
np.array([None]),
np.array([None]),
None,
None,
train_watcher,
)
self.assertEqual(train_watcher.epochs, [])
self.assertEqual(train_watcher.costs, [])
self.assertEqual(train_watcher.accuracies, [])
self.assertEqual(train_watcher.validation_epochs, [])
self.assertEqual(train_watcher.validation_costs, [])
self.assertEqual(train_watcher.validation_accuracies, [])
def test_train_assert_data_is_passed_and_shuffled(self):
trainer = SgdTrainer()
trainable = MagicMock()
trainable.update_params.return_value = (0.09, 100)
trainer.train(
trainable,
np.array([[1, 2], [2, 3], [3, 4]]),
np.array([0, 2, 4]),
None,
None,
None,
epochs=1,
minibatch_size=2,
seed=0,
)
call_args_list = trainable.update_params.call_args_list
(first_call, _), (second_call, _) = call_args_list
self.assertEqual(trainable.update_params.call_count, 2)
self.assertTrue(np.array_equal(
first_call[0],
np.array([[3, 4], [2, 3]]),
))
self.assertTrue(np.array_equal(
first_call[1],
np.array([4, 2]),
))
self.assertTrue(np.array_equal(
second_call[0],
np.array([[1, 2]]),
))
self.assertTrue(np.array_equal(
second_call[1],
np.array([0]),
))
def test_train_assert_returned_epochs_is_zero(self):
trainer = SgdTrainer()
trainable = MagicMock()
epochs, validation_epochs = trainer.train(
trainable,
np.array([None]),
np.array([None]),
None,
None,
None,
)
self.assertEqual(epochs, 0)
self.assertEqual(validation_epochs, 0)
def test_train_single_epoch_check_recorded(self):
trainer = SgdTrainer()
trainable = MagicMock()
trainable.update_params.return_value = (0.1, 90.5)
train_watcher = _TrainWatcherRecorder()
(epochs, validation_epochs) = trainer.train(
trainable,
np.array([1, 2, 3]),
np.array([0.1, 0.2, 0.3]),
None,
None,
train_watcher,
epochs=1,
)
self.assertEqual(epochs, 1)
self.assertEqual(validation_epochs, 0)
self.assertEqual(train_watcher.epochs, [0])
self.assertEqual(train_watcher.costs, [0.1])
self.assertEqual(train_watcher.accuracies, [90.5])
self.assertEqual(train_watcher.validation_epochs, [])
self.assertEqual(train_watcher.validation_costs, [])
self.assertEqual(train_watcher.validation_accuracies, [])