2021 Version

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+*.\#*

README.md

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+# MiniTorch Module 0
+
+<img src="https://minitorch.github.io/_images/match.png" width="100px">
+
+* Docs: https://minitorch.github.io/
+
+* Overview: https://minitorch.github.io/module0.html

minitorch/__init__.py

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+from .module import *  # noqa: F401,F403
+from .testing import *  # noqa: F401,F403
+from .datasets import *  # noqa: F401,F403

minitorch/datasets.py

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+from dataclasses import dataclass
+import random
+
+
+def make_pts(N):
+    X = []
+    for i in range(N):
+        x_1 = random.random()
+        x_2 = random.random()
+        X.append((x_1, x_2))
+    return X
+
+
+@dataclass
+class Graph:
+    N: int
+    X: list
+    y: list
+
+
+def simple(N):
+    X = make_pts(N)
+    y = []
+    for x_1, x_2 in X:
+        y1 = 1 if x_1 < 0.5 else 0
+        y.append(y1)
+    return Graph(N, X, y)
+
+
+def diag(N):
+    X = make_pts(N)
+    y = []
+    for x_1, x_2 in X:
+        y1 = 1 if x_1 + x_2 < 0.5 else 0
+        y.append(y1)
+    return Graph(N, X, y)
+
+
+def split(N):
+    X = make_pts(N)
+    y = []
+    for x_1, x_2 in X:
+        y1 = 1 if x_1 < 0.2 or x_1 > 0.8 else 0
+        y.append(y1)
+    return Graph(N, X, y)
+
+
+def xor(N):
+    X = make_pts(N)
+    y = []
+    for x_1, x_2 in X:
+        y1 = 1 if ((x_1 < 0.5 and x_2 > 0.5) or (x_1 > 0.5 and x_2 < 0.5)) else 0
+        y.append(y1)
+    return Graph(N, X, y)
+
+
+datasets = {"Simple": simple, "Diag": diag, "Split": split, "Xor": xor}

minitorch/module.py

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+class Module:
+    """
+    Modules form a tree that store parameters and other
+    submodules. They make up the basis of neural network stacks.
+
+    Attributes:
+        _modules (dict of name x :class:`Module`): Storage of the child modules
+        _parameters (dict of name x :class:`Parameter`): Storage of the module's parameters
+        training (bool): Whether the module is in training mode or evaluation mode
+
+    """
+
+    def __init__(self):
+        self._modules = {}
+        self._parameters = {}
+        self.training = True
+
+    def modules(self):
+        "Return the direct child modules of this module."
+        return self.__dict__["_modules"].values()
+
+    def train(self):
+        "Set the mode of this module and all descendent modules to `train`."
+        # TODO: Implement for Task 0.4.
+        raise NotImplementedError('Need to implement for Task 0.4')
+
+    def eval(self):
+        "Set the mode of this module and all descendent modules to `eval`."
+        # TODO: Implement for Task 0.4.
+        raise NotImplementedError('Need to implement for Task 0.4')
+
+    def named_parameters(self):
+        """
+        Collect all the parameters of this module and its descendents.
+
+
+        Returns:
+            list of pairs: Contains the name and :class:`Parameter` of each ancestor parameter.
+        """
+        # TODO: Implement for Task 0.4.
+        raise NotImplementedError('Need to implement for Task 0.4')
+
+    def parameters(self):
+        "Enumerate over all the parameters of this module and its descendents."
+        # TODO: Implement for Task 0.4.
+        raise NotImplementedError('Need to implement for Task 0.4')
+
+    def add_parameter(self, k, v):
+        """
+        Manually add a parameter. Useful helper for scalar parameters.
+
+        Args:
+            k (str): Local name of the parameter.
+            v (value): Value for the parameter.
+
+        Returns:
+            Parameter: Newly created parameter.
+        """
+        val = Parameter(v, k)
+        self.__dict__["_parameters"][k] = val
+        return val
+
+    def __setattr__(self, key, val):
+        if isinstance(val, Parameter):
+            self.__dict__["_parameters"][key] = val
+        elif isinstance(val, Module):
+            self.__dict__["_modules"][key] = val
+        else:
+            super().__setattr__(key, val)
+
+    def __getattr__(self, key):
+        if key in self.__dict__["_parameters"]:
+            return self.__dict__["_parameters"][key]
+
+        if key in self.__dict__["_modules"]:
+            return self.__dict__["_modules"][key]
+
+    def __call__(self, *args, **kwargs):
+        return self.forward(*args, **kwargs)
+
+    def forward(self):
+        assert False, "Not Implemented"
+
+    def __repr__(self):
+        def _addindent(s_, numSpaces):
+            s = s_.split("\n")
+            if len(s) == 1:
+                return s_
+            first = s.pop(0)
+            s = [(numSpaces * " ") + line for line in s]
+            s = "\n".join(s)
+            s = first + "\n" + s
+            return s
+
+        child_lines = []
+
+        for key, module in self._modules.items():
+            mod_str = repr(module)
+            mod_str = _addindent(mod_str, 2)
+            child_lines.append("(" + key + "): " + mod_str)
+        lines = child_lines
+
+        main_str = self.__class__.__name__ + "("
+        if lines:
+            # simple one-liner info, which most builtin Modules will use
+            main_str += "\n  " + "\n  ".join(lines) + "\n"
+
+        main_str += ")"
+        return main_str
+
+
+class Parameter:
+    """
+    A Parameter is a special container stored in a :class:`Module`.
+
+    It is designed to hold a :class:`Variable`, but we allow it to hold
+    any value for testing.
+    """
+
+    def __init__(self, x=None, name=None):
+        self.value = x
+        self.name = name
+        if hasattr(x, "requires_grad_"):
+            self.value.requires_grad_(True)
+            if self.name:
+                self.value.name = self.name
+
+    def update(self, x):
+        "Update the parameter value."
+        self.value = x
+        if hasattr(x, "requires_grad_"):
+            self.value.requires_grad_(True)
+            if self.name:
+                self.value.name = self.name
+
+    def __repr__(self):
+        return repr(self.value)
+
+    def __str__(self):
+        return str(self.value)