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micrograd_ai.html
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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8" />
<link rel="icon" type="image/x-icon" href="./favicon.png">
<title>micrograd</title>
<link rel="stylesheet" href="https://pyscript.net/alpha/pyscript.css" />
<script defer src="https://pyscript.net/alpha/pyscript.js"></script>
<py-env>
- micrograd
- numpy
- matplotlib
</py-env>
<link href="https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
</head>
<body style="padding-top: 20px; padding-right: 20px; padding-bottom: 20px; padding-left: 20px">
<h1>Micrograd - A tiny Autograd engine (with a bite! :))</h1><br>
<div>
<p>
<a href="https://github.com/karpathy/micrograd">Micrograd</a> is a tiny Autograd engine created
by <a href="https://twitter.com/karpathy">Andrej Karpathy</a>. This app recreates the
<a href="https://github.com/karpathy/micrograd/blob/master/demo.ipynb">demo</a>
he prepared for this package using pyscript to train a basic model, written in Python, natively in
the browser. <br>
</p>
</div>
<div>
<p>
You may run each Python REPL cell interactively by pressing (Shift + Enter) or (Ctrl + Enter).
You can also modify the code directly as you wish. If you want to run all the code at once,
not each cell individually, you may instead click the 'Run All' button. Training the model
takes between 1-2 min if you decide to 'Run All' at once. 'Run All' is your only option if
you are running this on a mobile device where you cannot press (Shift + Enter). After the
model is trained, a plot image should be displayed depicting the model's ability to
classify the data. <br>
</p>
<p>
Currently the <code>></code> symbol is being imported incorrectly as <code>&gt;</code> into the REPL's.
In this app the <code>></code> symbol has been replaced with <code>().__gt__()</code> so you can run the code
without issue. Ex: instead of <code>a > b</code>, you will see <code>(a).__gt__(b)</code> instead. <br>
</p>
<p>
<py-script>import js; js.document.getElementById('python-status').innerHTML = 'Python is now ready. You may proceed.'</py-script>
<div id="python-status">Python is currently starting. Please wait...</div>
</p>
<p>
<button id="run-all-button" class="btn btn-primary" type="submit" py-onClick="run_all_micrograd_demo">Run All</button><br>
<py-script src="/micrograd_ai.py"></py-script>
<div id="micrograd-run-all-print-div"></div><br>
<div id="micrograd-run-all-fig1-div"></div>
<div id="micrograd-run-all-fig2-div"></div><br>
</p>
</div>
<py-repl auto-generate="false">
import random
import numpy as np
import matplotlib.pyplot as plt
</py-repl><br>
<py-repl auto-generate="false">
from micrograd.engine import Value
from micrograd.nn import Neuron, Layer, MLP
</py-repl><br>
<py-repl auto-generate="true">
np.random.seed(1337)
random.seed(1337)
</py-repl><br>
<py-repl auto-generate="true">
#An adaptation of sklearn's make_moons function https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html
def make_moons(n_samples=100, noise=None):
n_samples_out, n_samples_in = n_samples, n_samples
outer_circ_x = np.cos(np.linspace(0, np.pi, n_samples_out))
outer_circ_y = np.sin(np.linspace(0, np.pi, n_samples_out))
inner_circ_x = 1 - np.cos(np.linspace(0, np.pi, n_samples_in))
inner_circ_y = 1 - np.sin(np.linspace(0, np.pi, n_samples_in)) - 0.5
X = np.vstack([np.append(outer_circ_x, inner_circ_x), np.append(outer_circ_y, inner_circ_y)]).T
y = np.hstack([np.zeros(n_samples_out, dtype=np.intp), np.ones(n_samples_in, dtype=np.intp)])
if noise is not None: X += np.random.normal(loc=0.0, scale=noise, size=X.shape)
return X, y
X, y = make_moons(n_samples=100, noise=0.1)
</py-repl><br>
<py-repl auto-generate="true">
y = y*2 - 1 # make y be -1 or 1
# visualize in 2D
plt.figure(figsize=(5,5))
plt.scatter(X[:,0], X[:,1], c=y, s=20, cmap='jet')
plt
</py-repl><br>
<py-repl auto-generate="true">
model = MLP(2, [16, 16, 1]) # 2-layer neural network
print(model)
print("number of parameters", len(model.parameters()))
</py-repl><br>
<div>
Line 24 has been changed from: <br>
<code>accuracy = [(yi > 0) == (scorei.data > 0) for yi, scorei in zip(yb, scores)]</code><br>
to: <br>
<code>accuracy = [((yi).__gt__(0)) == ((scorei.data).__gt__(0)) for yi, scorei in zip(yb, scores)]</code><br>
</div>
<py-repl auto-generate="true">
# loss function
def loss(batch_size=None):
# inline DataLoader :)
if batch_size is None:
Xb, yb = X, y
else:
ri = np.random.permutation(X.shape[0])[:batch_size]
Xb, yb = X[ri], y[ri]
inputs = [list(map(Value, xrow)) for xrow in Xb]
# forward the model to get scores
scores = list(map(model, inputs))
# svm "max-margin" loss
losses = [(1 + -yi*scorei).relu() for yi, scorei in zip(yb, scores)]
data_loss = sum(losses) * (1.0 / len(losses))
# L2 regularization
alpha = 1e-4
reg_loss = alpha * sum((p*p for p in model.parameters()))
total_loss = data_loss + reg_loss
# also get accuracy
accuracy = [((yi).__gt__(0)) == ((scorei.data).__gt__(0)) for yi, scorei in zip(yb, scores)]
return total_loss, sum(accuracy) / len(accuracy)
total_loss, acc = loss()
print(total_loss, acc)
</py-repl><br>
<py-repl auto-generate="true">
# optimization
for k in range(20): #was 100. Accuracy can be further improved w/ more epochs (to 100%).
# forward
total_loss, acc = loss()
# backward
model.zero_grad()
total_loss.backward()
# update (sgd)
learning_rate = 1.0 - 0.9*k/100
for p in model.parameters():
p.data -= learning_rate * p.grad
if k % 1 == 0:
print(f"step {k} loss {total_loss.data}, accuracy {acc*100}%")
</py-repl><br>
<div>
<p>
Please wait for the training loop above to complete. It will not print out stats until it
has completely finished. This typically takes 1-2 min. <br><br>
Line 9 has been changed from: <br>
<code>Z = np.array([s.data > 0 for s in scores])</code><br>
to: <br>
<code>Z = np.array([(s.data).__gt__(0) for s in scores])</code><br>
</p>
</div>
<py-repl auto-generate="true">
h = 0.25
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))
Xmesh = np.c_[xx.ravel(), yy.ravel()]
inputs = [list(map(Value, xrow)) for xrow in Xmesh]
scores = list(map(model, inputs))
Z = np.array([(s.data).__gt__(0) for s in scores])
Z = Z.reshape(xx.shape)
fig = plt.figure()
plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral, alpha=0.8)
plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.Spectral)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt
</py-repl><br>
<py-repl auto-generate="true">
1+1
</py-repl><br>
</body>
</html>
<!-- Adapted by Mat Miller -->