L'initialisation des poids avec des **entiers aléatoires** n'est pas recommandée en pratique ; cette méthode est utilisée ici uniquement dans le cadre de cet exercice.

Remarque

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

Passez des bibliothèques de haut niveau à la flexibilité de PyTorch. Maîtrisez les éléments fondamentaux de l'apprentissage profond en travaillant avec les tenseurs, la structure de données centrale de PyTorch. Explorez la différenciation automatique avec les gradients, implémentez la rétropropagation et construisez des réseaux de neurones complets à partir de zéro. Apprenez à manipuler des ensembles de données réels et à gérer l'ensemble du cycle d'entraînement – de l'initialisation des poids à l'évaluation du modèle.

Défi : Initialisation des Poids et Biais du Modèle

Solution