def test_big_linear_relu_forward():
np.random.seed(11785)
mytorch_mlp = Sequential(Linear(10, 20), ReLU(), Linear(20, 30), ReLU())
test_forward(mytorch_mlp)
return True
def test_linear_momentum():
np.random.seed(11785)
mytorch_mlp = Sequential(Linear(10, 20), ReLU())
mytorch_optimizer = SGD(mytorch_mlp.parameters(), momentum=0.9)
test_step(mytorch_mlp, mytorch_optimizer, 5, 0)
return True
def test_linear_backward():
np.random.seed(11785)
mytorch_mlp = Sequential(Linear(10, 20))
test_forward_backward(mytorch_mlp)
return True
def test_big_linear_batchnorm_relu_train_eval():
np.random.seed(11785)
mytorch_mlp = Sequential(Linear(10, 20), BatchNorm1d(20), ReLU())
mytorch_optimizer = SGD(mytorch_mlp.parameters())
test_step(mytorch_mlp, mytorch_optimizer, 5, 5)
return True
def test_linear_batchnorm_relu_backward_train():
np.random.seed(11785)
mytorch_mlp = Sequential(Linear(10, 20), BatchNorm1d(20), ReLU())
test_forward_backward(mytorch_mlp)
return True
def test_big_linear_relu_step():
np.random.seed(11785)
mytorch_mlp = Sequential(Linear(10, 20), ReLU(), Linear(20, 30), ReLU())
mytorch_optimizer = SGD(mytorch_mlp.parameters())
test_step(mytorch_mlp, mytorch_optimizer, 5, 5)
return True
def test_linear_forward():
np.random.seed(11785)
mytorch_mlp = Sequential(Linear(10, 20))
test_forward(mytorch_mlp)
check_model_param_settings(mytorch_mlp)
return True
def test_big_model_step():
np.random.seed(11785)
# run a big model
model = Sequential(Linear(10, 15), ReLU(), Dropout(p=0.2), Linear(15, 20),
ReLU(), Dropout(p=0.1))
x, y = generate_dataset_for_mytorch_model(model, 4)
x, y = Tensor(x), Tensor(y)
criterion = CrossEntropyLoss()
optimizer = Adam(model.parameters(),
lr=1e-3,
betas=(0.9, 0.999),
eps=1e-08)
# check output correct
out = model(x)
test_out = load_numpy_array(
'autograder/hw1_bonus_autograder/outputs/big_output.npy')
if not assertions_all(out.data, test_out, "test_big_model_step_out", 1e-5,
1e-6):
return False
# run backward
loss = criterion(out, y)
loss.backward()
# check params are correct (sorry this is ugly)
assert model[0].weight.grad is not None, "Linear layer must have gradient."
assert model[
0].weight.grad.grad is None, "Final gradient tensor must not have its own gradient"
assert model[
0].weight.grad.grad_fn is None, "Final gradient tensor must not have its own grad function"
assert model[
0].weight.requires_grad, "Weight tensor must have requires_grad==True"
assert model[
0].weight.is_parameter, "Weight tensor must be marked as a parameter tensor"
assert model[3].weight.grad is not None, "Linear layer must have gradient."
assert model[
3].weight.grad.grad is None, "Final gradient tensor must not have its own gradient"
assert model[
3].weight.grad.grad_fn is None, "Final gradient tensor must not have its own grad function"
assert model[
3].weight.requires_grad, "Weight tensor must have requires_grad==True"
assert model[
3].weight.is_parameter, "Weight tensor must be marked as a parameter tensor"
# check gradient for linear layer at idx 0 is correct
test_grad = load_numpy_array(
'autograder/hw1_bonus_autograder/outputs/big_grad.npy')
if not assertions_all(model[0].weight.grad.data, test_grad,
"test_big_model_grad_0", 1e-5, 1e-6):
return False
# check gradient for linear layer at idx 3 is correct
test_grad = load_numpy_array(
'autograder/hw1_bonus_autograder/outputs/big_grad_3.npy')
if not assertions_all(model[3].weight.grad.data, test_grad,
"test_big_model_grad_3", 1e-5, 1e-6):
return False
# weight update with adam
optimizer.step()
# check updated weight values
assert model[
0].weight.requires_grad, "Weight tensor must have requires_grad==True"
assert model[
0].weight.is_parameter, "Weight tensor must be marked as a parameter tensor"
test_weights_3 = load_numpy_array(
'autograder/hw1_bonus_autograder/outputs/big_weight_update_3.npy')
test_weights_0 = load_numpy_array(
'autograder/hw1_bonus_autograder/outputs/big_weight_update_0.npy')
return assertions_all(model[0].weight.data, test_weights_0, "test_big_weight_update_0", 1e-5, 1e-6) and \
assertions_all(model[3].weight.data, test_weights_3, "test_big_weight_update_3", 1e-5, 1e-6)
