Initializing weights with **random integers** is not recommended in practice; this approach is used here solely for the purpose of this task.

Note

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

Lär dig de grundläggande och avancerade koncepten som krävs för att arbeta effektivt med PyTorch. Få en solid förståelse för tensorer, inklusive skapande, operationer och omformning. Utforska grunderna i gradienter, backpropagation och linjär regression innan du går vidare till hantering av dataset. Bemästra färdigheterna som krävs för att bygga, träna och utvärdera neurala nätverk.

Utforska grunderna i PyTorch med fokus på tensorer—den centrala datastrukturen för beräkningar. Du kommer att lära dig om skapande av tensorer, slumpmässig initialisering, matematiska operationer och formmanipulation.

Utforska centrala koncept för att träna modeller i PyTorch, inklusive beräkning av gradienter och utförande av flerstegs-backpropagation. Behärska linjär regression som en grundläggande maskininlärningsmodell och introducerar effektiv hantering av dataset.

Utforska hur man konstruerar, tränar och utvärderar neurala nätverk med PyTorch. Du kommer att lära dig att definiera ett enkelt feedforward-neuralt nätverk, optimera dess parametrar genom träning och bedöma dess prestanda.

Challenge: Initializing Model Weights and Biases

Lösning