import unittest
import importlib
import torch


def _dynamic(test, cond, ok, fail):
    if cond:
        test._testMethodName = ok
        test.assertTrue(True)
    else:
        test._testMethodName = fail
        test.fail(fail)


class TestUserCode(unittest.TestCase):

    # -------- manual seed --------
    def test_seed_set(self):
        with open("user_code.py") as f:
            src = f.read()

        cond = "torch.manual_seed" in src

        _dynamic(
            self,
            cond,
            "The manual seed is set correctly.",
            "You must set a manual seed using `torch.manual_seed()` before creating any tensors."
        )

    # -------- weights_layer1 --------
    def test_weights1_exists(self):
        import user_code
        cond = hasattr(user_code, "weights_layer1")
        _dynamic(
            self,
            cond,
            "`weights_layer1` is successfully defined.",
            "`weights_layer1` is missing. Assign your 3x4 uniform-random tensor to this variable."
        )

    def test_weights1_valid(self):
        import user_code
        try:
            t = user_code.weights_layer1
            cond = (
                isinstance(t, torch.Tensor)
                and t.shape == (3, 4)
                and torch.all(t >= 0)
                and torch.all(t < 1)
            )
        except:
            cond = False

        _dynamic(
            self,
            cond,
            "`weights_layer1` is a valid 3x4 tensor with values uniformly sampled from [0, 1).",
            "`weights_layer1` must be created with `torch.rand(3, 4)` so that all values lie between 0 and 1."
        )

    # -------- biases_layer1 --------
    def test_biases1_exists(self):
        import user_code
        cond = hasattr(user_code, "biases_layer1")
        _dynamic(
            self,
            cond,
            "`biases_layer1` is successfully defined.",
            "`biases_layer1` is missing. You must assign a 1x4 zero tensor to this variable."
        )

    def test_biases1_valid(self):
        import user_code
        try:
            t = user_code.biases_layer1
            cond = (
                isinstance(t, torch.Tensor)
                and t.shape == (1, 4)
                and torch.all(t == 0)
            )
        except:
            cond = False

        _dynamic(
            self,
            cond,
            "`biases_layer1` is correctly created as a 1x4 tensor of zeros.",
            "`biases_layer1` must be a 1x4 zero tensor created using `torch.zeros(1, 4)`."
        )

    # -------- weights_layer2 --------
    def test_weights2_exists(self):
        import user_code
        cond = hasattr(user_code, "weights_layer2")
        _dynamic(
            self,
            cond,
            "`weights_layer2` is successfully defined.",
            "`weights_layer2` is missing. Assign a 4x2 integer tensor created with `torch.randint()`."
        )

    def test_weights2_valid(self):
        import user_code
        try:
            t = user_code.weights_layer2
            cond = (
                isinstance(t, torch.Tensor)
                and t.shape == (4, 2)
                and torch.all(t >= -5)
                and torch.all(t <= 5)
                and torch.equal(t, t.int())
            )
        except:
            cond = False

        _dynamic(
            self,
            cond,
            "`weights_layer2` is a valid 4x2 integer tensor with values in the range [-5, 5].",
            "`weights_layer2` must be created using `torch.randint(-5, 5, (4, 2))` to generate integer weights in the specified range."
        )

    # -------- printing output --------
    def test_printed_output(self):
        import user_code

        import io, sys
        buf = io.StringIO()
        sys.stdout = buf

        importlib.reload(user_code)
        sys.stdout = sys.__stdout__

        cond = len(buf.getvalue().strip()) > 0

        _dynamic(
            self,
            cond,
            "All tensors are printed as required.",
            "No output detected. Ensure that you print all three tensors exactly as instructed."
        )


if __name__ == "__main__":
    unittest.main()


test_code.py

Learn the fundamental and advanced concepts needed to work with PyTorch efficiently. Gain a solid understanding tensors, including creation, operations, and reshaping. Explore the essentials of gradients, backpropagation, and linear regression before moving on to handling datasets. Master the skill needed build, train, and evaluate neural networks.

Explore the fundamentals of PyTorch, focusing on tensors—the core data structure used for computations. You will learn about tensor creation, random initialization, mathematical operations, and shape manipulation.

Explore key concepts for training models in PyTorch, including computing gradients and performing multi-step backpropagation. Master linear regression as a foundational machine learning model and introduces handling datasets efficiently.

Discover how to build, train, and evaluate neural networks using PyTorch. You will learn how to define a simple feedforward neural network, optimize its parameters through training, and assess its performance.

Challenge: Initializing Model Weights and Biases

解答