def test_polynomial_decay(): lr1 = dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power) assert len(lr1) == total_step lr2 = dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power, True) assert len(lr2) == total_step
def test_learning_rate_value(self): lr = -1.0 with pytest.raises(ValueError): dr.exponential_decay_lr(lr, decay_rate, total_step, step_per_epoch, decay_epoch) with pytest.raises(ValueError): dr.polynomial_decay_lr(lr, end_learning_rate, total_step, step_per_epoch, decay_epoch, power)
def test_total_step1(self): total_step1 = 2.0 with pytest.raises(ValueError): dr.exponential_decay_lr(learning_rate, decay_rate, total_step1, step_per_epoch, decay_epoch) with pytest.raises(ValueError): dr.cosine_decay_lr(min_lr, max_lr, total_step1, step_per_epoch, decay_epoch) with pytest.raises(ValueError): dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step1, step_per_epoch, decay_epoch, power)
def test_decay_epoch1(self): decay_epoch1 = 'm' with pytest.raises(TypeError): dr.exponential_decay_lr(learning_rate, decay_rate, total_step, step_per_epoch, decay_epoch1) with pytest.raises(TypeError): dr.cosine_decay_lr(min_lr, max_lr, total_step, step_per_epoch, decay_epoch1) with pytest.raises(TypeError): dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch1, power)
def test_adam_group1(): """ test_adam_group_lr_and_weight_decay """ inputs = Tensor(np.ones([1, 64]).astype(np.float32)) label = Tensor(np.zeros([1, 10]).astype(np.float32)) net = Net() net.set_train() loss = nn.SoftmaxCrossEntropyWithLogits() net_with_loss = WithLossCell(net, loss) all_params = net.trainable_params() poly_decay_lr = polynomial_decay_lr(0.01, 0.0001, total_step=10, step_per_epoch=1, decay_epoch=3, power=1.0) group_params = [{ 'params': [all_params[0]], 'lr': poly_decay_lr, 'weight_decay': 0.9 }, { 'params': [all_params[1]] }] optimizer = nn.Adam(group_params, learning_rate=0.1) train_network = TrainOneStepCell(net_with_loss, optimizer) _executor.compile(train_network, inputs, label)
def test_end_learning_rate_type(self): lr = True with pytest.raises(TypeError): dr.polynomial_decay_lr(learning_rate, lr, total_step, step_per_epoch, decay_epoch, power)
def test_update_decay_epoch(self): update_decay_epoch = 1 with pytest.raises(TypeError): dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power, update_decay_epoch)
def test_power(self): power1 = True with pytest.raises(TypeError): dr.polynomial_decay_lr(learning_rate, end_learning_rate, total_step, step_per_epoch, decay_epoch, power1)