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Apply prettier to css, html, js, md, ts, and yml (#1249)

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* Apply prettier to css, js, html, md, ts, and yml

As a followup I will add prettier to the .pre-commit config.
This patch is 100% generated by prettier.
I used a forked version of prettier that understands the
py-script tag.
See https://github.com/hoodmane/pyscript-prettier-precommit
for more info.

* Apply old pre-commit

* Revert some problems

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Revert some changes

* More changes

* Fix pre-commit

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Hood Chatham
2023-03-06 15:20:21 +01:00
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commit 08f34f748b
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examples/micrograd_ai.html
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@@ -1,191 +1,186 @@
<!DOCTYPE html>
<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8" />
<link rel="icon" type="image/x-icon" href="./favicon.png">
<head>
<meta charset="utf-8" />
<link rel="icon" type="image/x-icon" href="./favicon.png" />
<title>micrograd</title>
<title>micrograd</title>
<link rel="stylesheet" href="https://pyscript.net/latest/pyscript.css" />
<script defer src="https://pyscript.net/latest/pyscript.js"></script>
<link rel="stylesheet" href="https://pyscript.net/latest/pyscript.css" />
<script defer src="https://pyscript.net/latest/pyscript.js"></script>
<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
</head>
<link
href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/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>
<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 />
<py-config>
packages = [
"micrograd",
"numpy",
"matplotlib"
]
</py-config>
<py-config>
packages = [
"micrograd",
"numpy",
"matplotlib"
]
</py-config>
<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>&gt;</code> symbol is being imported incorrectly as <code>&amp;gt;</code> into the REPL's.
In this app the <code>&gt;</code> symbol has been replaced with <code>().__gt__()</code> so you can run the code
without issue. Ex: instead of <code>a &gt; b</code>, you will see <code>(a).__gt__(b)</code> instead. <br>
</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>
<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>
</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
<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>&gt;</code> symbol is being imported incorrectly as
<code>&amp;gt;</code> into the REPL's. In this app the
<code>&gt;</code> symbol has been replaced with
<code>().__gt__()</code> so you can run the code without issue. Ex:
instead of <code>a &gt; b</code>, you will see
<code>(a).__gt__(b)</code> instead. <br />
</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>
<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 />
</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 />
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
<div>
Line 24 has been changed from: <br />
<code
>accuracy = [(yi &gt; 0) == (scorei.data &gt; 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>
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>
<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 />
<div>
Line 24 has been changed from: <br>
<code>accuracy = [(yi &gt; 0) == (scorei.data &gt; 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 &gt; 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>
Line 9 has been changed from: <br />
<code>Z = np.array([s.data &gt; 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 -->