class TestTrainer(unittest.TestCase):
def setUp(self):
model = TwoLayerNet(input_size=2, hidden_size=10, output_size=3)
optimizer = SGD(lr=1.0)
self.trainer = Trainer(model, optimizer)
self.x, self.t = load_data()
self.data_size = len(self.x)
def test_shuffle_data(self):
xx, tt = self.trainer._shuffle_data(self.data_size, self.x, self.t)
self.assertEqual((300, 2), np.array(xx).shape)
self.assertEqual((300, 3), np.array(tt).shape)
def test_calculate_loss(self):
batch_size = 32
xx, tt = self.trainer._shuffle_data(self.data_size, self.x, self.t)
batch_x = xx[1 * batch_size:(1 + 1) * batch_size]
batch_t = tt[1 * batch_size:(1 + 1) * batch_size]
loss = self.trainer._calculate_loss(batch_x, batch_t)
self.assertEqual(1.1, round(loss, 1))
def test_remove_duplicate(self):
batch_size = 32
xx, tt = self.trainer._shuffle_data(self.data_size, self.x, self.t)
batch_x = xx[1 * batch_size:(1 + 1) * batch_size]
batch_t = tt[1 * batch_size:(1 + 1) * batch_size]
loss = self.trainer._calculate_loss(batch_x, batch_t)
params, grads = self.trainer._remove_duplicate()
param_1, param_2, param_3, param_4 = params
grad_1, grad_2, grad_3, grad_4 = grads
self.assertEqual((2, 10), param_1.shape)
self.assertEqual((10, ), param_2.shape)
self.assertEqual((10, 3), param_3.shape)
self.assertEqual((3, ), param_4.shape)
self.assertEqual((2, 10), grad_1.shape)
self.assertEqual((10, ), grad_2.shape)
self.assertEqual((10, 3), grad_3.shape)
self.assertEqual((3, ), grad_4.shape)
def test_evaluate(self):
batch_size = 32
xx, tt = self.trainer._shuffle_data(self.data_size, self.x, self.t)
batch_x = xx[1 * batch_size:(1 + 1) * batch_size]
batch_t = tt[1 * batch_size:(1 + 1) * batch_size]
total_loss = 0
loss_count = 0
loss = self.trainer._calculate_loss(batch_x, batch_t)
total_loss += loss
loss_count += 1
params, grads = self.trainer._remove_duplicate()
start_time = time.time()
current_epoch = 0
max_iters = self.data_size // batch_size
avarage_loss, training_status = self.trainer._evaluate(
total_loss, loss_count, start_time, current_epoch, 1, max_iters)
self.assertEqual(1.0982153338384055, avarage_loss)
self.assertEqual('| epoch 1 | iter 2 / 9 | time 0[s] | loss 1.10',
training_status)
def test_fit(self):
training_process = self.trainer.fit(self.x, self.t)
assert_array_equal(
np.array([
1.095670688853155, 1.166632183444487, 1.1176750111873825,
1.1790605366611793, 1.1308071491715788, 1.1796679902482832,
1.1401573329955508, 1.1263507954471639, 1.1260232915246664,
1.1018855740540143
]), np.array(self.trainer.loss_list))
self.assertEqual([
'| epoch 1 | iter 1 / 9 | time 0[s] | loss 1.10',
'| epoch 2 | iter 1 / 9 | time 0[s] | loss 1.17',
'| epoch 3 | iter 1 / 9 | time 0[s] | loss 1.12',
'| epoch 4 | iter 1 / 9 | time 0[s] | loss 1.18',
'| epoch 5 | iter 1 / 9 | time 0[s] | loss 1.13',
'| epoch 6 | iter 1 / 9 | time 0[s] | loss 1.18',
'| epoch 7 | iter 1 / 9 | time 0[s] | loss 1.14',
'| epoch 8 | iter 1 / 9 | time 0[s] | loss 1.13',
'| epoch 9 | iter 1 / 9 | time 0[s] | loss 1.13',
'| epoch 10 | iter 1 / 9 | time 0[s] | loss 1.10'
], training_process)
def test_save_plot_image(self):
self.trainer.fit(self.x, self.t, max_epoch=300, batch_size=30)
file_path = '../img/training_plot.png'
self.trainer.save_plot_image(file_path)
self.assertEqual(True, path.exists(file_path))
