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Code Issues Projects Releases Wiki Activity

Add additional pre-commit hooks (#245)

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* Add and run end-of-file-fixer
* Add and run trailing-whitespace
* Add and run check-yaml
* Add and run check-json
* Add and run pretty-format-yaml
* Fix comment indentation
This commit is contained in:
Matt Kramer
2022-05-06 14:27:11 -05:00
committed by GitHub
parent 3347532f5e
commit cc6d6247a8
30 changed files with 172 additions and 158 deletions
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42
pyscriptjs/examples/micrograd_ai.html
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@@ -8,13 +8,13 @@
<link rel="stylesheet" href="../build/pyscript.css" />
<script defer src="../build/pyscript.js"></script>
<py-env>
- micrograd
- numpy
- matplotlib
</py-env>
<link href="https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
</head>
@@ -22,25 +22,25 @@
<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
<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 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
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
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>&ampgt;</code> into the REPL's.
Currently the <code>&gt;</code> symbol is being imported incorrectly as <code>&ampgt;</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: intead of <code>a &gt; b</code>, you will see <code>(a).__gt__(b)</code> instead. <br>
</p>
@@ -99,7 +99,7 @@ print("number of parameters", len(model.parameters()))
</py-repl><br>
<div>
Line 24 has been changed from: <br>
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>
@@ -108,7 +108,7 @@ print("number of parameters", len(model.parameters()))
<py-repl auto-generate="true">
# loss function
def loss(batch_size=None):
# inline DataLoader :)
if batch_size is None:
Xb, yb = X, y
@@ -116,10 +116,10 @@ def loss(batch_size=None):
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))
@@ -127,7 +127,7 @@ def loss(batch_size=None):
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)
@@ -138,25 +138,25 @@ print(total_loss, acc)
<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
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>