def test_sum_ops():
t1 = Tensor([1, 3, 5], requires_grad=True)
t2 = Tensor([5, -2, -9], requires_grad=True)
t3 = (t1 + t2).sum()
assert t3.values == 3
t3.backward(2)
assert t1.grad.tolist() == [2, 2, 2]
assert t2.grad.tolist() == [2, 2, 2]
def test_div_ops():
t1 = Tensor([1, 2, 5], requires_grad=True)
t2 = Tensor([8, -2, -10], requires_grad=True)
t3 = t1 / t2
assert t3.values.tolist() == [0.125, -1, -0.5]
t3.backward([1, 1, 1])
assert t1.grad.tolist() == [0.125, -0.5, -0.1]
assert t2.grad.tolist() == [-0.015625, -0.5, -0.05]
def test_mul_ops():
t1 = Tensor([1, 3, 5], requires_grad=True)
t2 = Tensor([5, -2, -9], requires_grad=True)
t3 = t1 * t2
assert t3.values.tolist() == [5, -6, -45]
t3.backward([2, 2, 2])
assert t1.grad.tolist() == [2 * 5, 2 * (-2), 2 * (-9)]
assert t2.grad.tolist() == [2 * 1, 2 * 3, 2 * 5]
def test_minimum_ops():
t1 = Tensor([1, 3, 5], requires_grad=True)
t2 = Tensor([5, -2, 9], requires_grad=True)
t3 = ops.minimum_(t1, t2)
assert t3.values.tolist() == [1, -2, 5]
t3.backward([1, 2, 1])
assert t1.grad.tolist() == [1, 0, 1]
assert t2.grad.tolist() == [0, 2, 0]
def test_dot_ops():
t1 = Tensor([[1, 3, 5], [5, -2, 9]], requires_grad=True)
t2 = Tensor([[9, 8, 9, 7], [4, 0, 3, 0], [0, 8, 2, 7]], requires_grad=True)
t3 = t1 @ t2
assert t3.values.tolist() == [[21, 48, 28, 42], [37, 112, 57, 98]]
t3.backward([[1, 2, 3, 4], [4, 3, 2, 1]])
assert t1.grad.tolist() == [[80, 13, 50], [85, 22, 35]]
assert t2.grad.tolist() == [[21, 17, 13, 9], [-5, 0, 5, 10],
[41, 37, 33, 29]]
def test_max_ops():
t1 = Tensor([[1, 3, 5], [3, 7, -2]], requires_grad=True)
t2 = ops.max(t1, axis=None)
t3 = ops.max(t1, axis=0)
assert t2.values == 7
assert t3.values.tolist() == [3, 7, 5]
t2.backward()
assert t1.grad.tolist() == [[0, 0, 0], [0, 1, 0]]
t1.zero_grad()
t3.backward([1, 1, 1])
assert t1.grad.tolist() == [[0, 0, 1], [1, 1, 0]]
def main(args):
if args.seed >= 0:
random_seed(args.seed)
train_set, valid_set, test_set = prepare_dataset(args.data_dir)
train_x, train_y = train_set
test_x, test_y = test_set
train_y = get_one_hot(train_y, 10)
train_x = Tensor(train_x)
train_y = Tensor(train_y)
test_x = Tensor(test_x)
test_y = Tensor(test_y)
net = Net([
Dense(200),
ReLU(),
Dense(100),
ReLU(),
Dense(70),
ReLU(),
Dense(30),
ReLU(),
Dense(10)
])
model = Model(net=net,
loss=SoftmaxCrossEntropyLoss(),
optimizer=Adam(lr=args.lr))
loss_layer = SoftmaxCrossEntropyLoss()
iterator = BatchIterator(batch_size=args.batch_size)
evaluator = AccEvaluator()
loss_list = list()
for epoch in range(args.num_ep):
t_start = time.time()
for batch in iterator(train_x, train_y):
model.zero_grad()
pred = model.forward(batch.inputs)
loss = loss_layer.loss(pred, batch.targets)
loss.backward()
model.step()
loss_list.append(loss.values)
print("Epoch %d tim cost: %.4f" % (epoch, time.time() - t_start))
# evaluate
model.set_phase("TEST")
test_pred = model.forward(test_x)
test_pred_idx = np.argmax(test_pred, axis=1)
test_y_idx = test_y.values
res = evaluator.evaluate(test_pred_idx, test_y_idx)
print(res)
model.set_phase("TRAIN")
def test_transpose_ops():
shape = [2, 4, 6]
data = np.random.randn(*shape)
t1 = Tensor(data, requires_grad=True)
t2 = t1.T
assert list(t2.shape) == shape[::-1]
t2.backward(np.ones_like(t2.values))
assert list(t1.grad.shape) == shape
t2 = t1.transpose((2, 0, 1))
assert list(t2.shape) == [6, 2, 4]
t2.backward(np.ones_like(t2.values))
assert list(t1.grad.shape) == shape
def test_reshape_ops():
t1 = Tensor([[1, 2, 3], [4, 5, 6]], requires_grad=True)
t2 = ops.reshape(t1, (6, ))
assert t2.values.tolist() == [1, 2, 3, 4, 5, 6]
t2.backward(np.ones(6))
assert t1.grad.tolist() == [[1, 1, 1], [1, 1, 1]]
def test_neg_ops():
t1 = Tensor([1, 3, 5], requires_grad=True)
t2 = -t1
assert t2.values.tolist() == [-1, -3, -5]
t2.backward([1, 2, 3])
assert t1.grad.tolist() == [-1, -2, -3]
def test_clip_ops():
t1 = Tensor([1, -3, 5], requires_grad=True)
t2 = ops.clip(t1, 0)
assert t2.values.tolist() == [1, 0, 5]
grad = np.array([1, 2, 3])
t2.backward(grad)
assert t1.grad.tolist() == [1, 0, 3]
def test_flatten_ops():
t1 = Tensor([[1, 2, 3], [4, 5, 6]], requires_grad=True)
t2 = ops.flatten(t1)
assert t2.values.tolist() == [1, 2, 3, 4, 5, 6]
t2.backward(np.ones_like(t2.values))
assert t1.grad.shape == t1.shape
assert t1.grad.tolist() == [[1, 1, 1], [1, 1, 1]]
def test_log_ops():
t1 = Tensor([1, 3, 5], requires_grad=True)
t2 = ops.log(t1)
assert t2.values.tolist() == np.log(t1.values).tolist()
grad = np.array([1, 2, 3])
t2.backward(grad)
assert t1.grad.tolist() == (grad / np.array([1, 3, 5])).tolist()
def test_epx_ops():
t1 = Tensor([1, 3, 5], requires_grad=True)
t2 = ops.exp(t1)
assert t2.values.tolist() == np.exp(t1.values).tolist()
t2.backward([1, 2, 3])
assert t1.grad.tolist() == (np.exp(t1.values) *
np.array([1, 2, 3])).tolist()
def test_pad_ops():
t1 = Tensor([[1, 2, 3], [4, 5, 6]], requires_grad=True)
pad_width = [(1, 0), (1, 0)]
t2 = ops.pad(t1, pad_width)
assert t2.values.tolist() == [[0, 0, 0, 0], [0, 1, 2, 3], [0, 4, 5, 6]]
t2.backward(np.ones_like(t2.values))
assert t1.grad.shape == t1.shape
assert t1.grad.tolist() == [[1, 1, 1], [1, 1, 1]]
def test_add_op():
t1 = Tensor([1, 3, 5], requires_grad=True)
t2 = Tensor([5, -2, -9], requires_grad=True)
t3 = t1 + t2
assert t3.values.tolist() == [6, 1, -4]
t3.backward([2, 2, 2])
assert t1.grad.tolist() == [2, 2, 2]
assert t2.grad.tolist() == [2, 2, 2]
# broadcast (2, 3) + (3,) -> (2, 3)
t1 = Tensor([[1, 3, 5], [2, 3, 0]], requires_grad=True)
t2 = Tensor([5, -2, -9], requires_grad=True)
t3 = t1 + t2
assert t3.values.tolist() == [[6, 1, -4], [7, 1, -9]]
t3.backward([[1, 1, 1], [2, 2, 2]])
assert t1.grad.tolist() == [[1, 1, 1], [2, 2, 2]]
assert t2.grad.tolist() == [3, 3, 3]
# broadcast (2, 3) + (1, 3) -> (2, 3)
t1 = Tensor([[1, 3, 5], [2, 3, 0]], requires_grad=True)
t2 = Tensor([[5, -2, -9]], requires_grad=True)
t3 = t1 + t2
assert t3.values.tolist() == [[6, 1, -4], [7, 1, -9]]
t3.backward([[1, 1, 1], [2, 2, 2]])
assert t1.grad.tolist() == [[1, 1, 1], [2, 2, 2]]
assert t2.grad.tolist() == [[3, 3, 3]]
def test_minimal_nn():
x = Tensor(np.random.normal(0, 1.0, (100, 3)))
y = x * 3.14 + 30
w1 = Tensor(np.random.normal(0, 1.0, (3, 3)), requires_grad=True)
b1 = Tensor(np.random.normal(0, 1.0, 3), requires_grad=True)
previous_loss = 1e10
for _ in range(100):
w1.zero_grad()
b1.zero_grad()
predicted = x @ w1 + b1
err = predicted - y
loss = (err**2).sum()
loss.backward()
w1 -= 0.001 * w1.grad
b1 -= 0.001 * b1.grad
assert loss.values < previous_loss
previous_loss = loss.values
def __call__(self, shape):
values = self.init(shape)
return Tensor(values, requires_grad=True, dtype=np.float32)
def test_pow_ops():
t1 = Tensor([1, -3, 5], requires_grad=True)
t2 = t1**3
assert t2.values.tolist() == [1, -27, 125]
t2.backward([2, 2, 2])
assert t1.grad.tolist() == [2 * 3 * 1**2, 2 * 3 * (-3)**2, 2 * 3 * 5**2]
