diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5c7785bb..f0784a62 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -9,3 +9,4 @@ repos:
hooks:
- id: isort
name: isort (python)
+ args: [--profile, black]
diff --git a/pyscriptjs/examples/micrograd_ai.py b/pyscriptjs/examples/micrograd_ai.py
index 05a0af3e..37cfc881 100644
--- a/pyscriptjs/examples/micrograd_ai.py
+++ b/pyscriptjs/examples/micrograd_ai.py
@@ -1,45 +1,49 @@
-#Credit: https://github.com/karpathy/micrograd/blob/master/demo.ipynb
-#cell
-import random
-import numpy as np
-import matplotlib.pyplot as plt
+# Credit: https://github.com/karpathy/micrograd/blob/master/demo.ipynb
+# cell
import datetime
+import random
-#cell
+import matplotlib.pyplot as plt
+import numpy as np
+
+# cell
from micrograd.engine import Value
-from micrograd.nn import Neuron, Layer, MLP
+from micrograd.nn import MLP, Layer, Neuron
print_statements = []
-def run_all_micrograd_demo(*args,**kwargs):
+
+def run_all_micrograd_demo(*args, **kwargs):
result = micrograd_demo()
- pyscript.write('micrograd-run-all-fig2-div', result)
+ pyscript.write("micrograd-run-all-fig2-div", result)
+
def print_div(o):
o = str(o)
- print_statements.append(o + ' \n
')
- pyscript.write('micrograd-run-all-print-div', ''.join(print_statements))
+ print_statements.append(o + " \n
")
+ pyscript.write("micrograd-run-all-print-div", "".join(print_statements))
-#All code is wrapped in this run_all function so it optionally executed (called)
-#from pyscript when a button is pressed.
-def micrograd_demo(*args,**kwargs):
+
+# All code is wrapped in this run_all function so it optionally executed (called)
+# from pyscript when a button is pressed.
+def micrograd_demo(*args, **kwargs):
"""
Runs the micrograd demo.
*args and **kwargs do nothing and are only there to capture any parameters passed
from pyscript when this function is called when a button is clicked.
"""
-
- #cell
- start = datetime.datetime.now()
- print_div('Starting...')
- #cell
+ # cell
+ start = datetime.datetime.now()
+ print_div("Starting...")
+
+ # cell
np.random.seed(1337)
random.seed(1337)
-#cell
-#An adaptation of sklearn's make_moons function https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html
+ # cell
+ # 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
@@ -48,26 +52,38 @@ def micrograd_demo(*args,**kwargs):
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)
+ 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)
- #cell
- y = y*2 - 1 # make y be -1 or 1
+ # cell
+ 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.figure(figsize=(5, 5))
+ plt.scatter(X[:, 0], X[:, 1], c=y, s=20, cmap="jet")
plt
- pyscript.write('micrograd-run-all-fig1-div', plt)
+ pyscript.write("micrograd-run-all-fig1-div", plt)
- #cell
- model = MLP(2, [16, 16, 1]) # 2-layer neural network
+ # cell
+ model = MLP(2, [16, 16, 1]) # 2-layer neural network
print_div(model)
print_div(("number of parameters", len(model.parameters())))
- #cell
+ # cell
# loss function
def loss(batch_size=None):
# inline DataLoader :)
@@ -77,51 +93,53 @@ def micrograd_demo(*args,**kwargs):
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)]
+ 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()))
+ 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)]
+ 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))
- #cell
+ # cell
# optimization
- for k in range(20): #was 100
-
+ for k in range(20): # was 100
+
# forward
total_loss, acc = loss()
-
+
# backward
model.zero_grad()
total_loss.backward()
-
+
# update (sgd)
- learning_rate = 1.0 - 0.9*k/100
+ 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}%")
print_div(f"step {k} loss {total_loss.data}, accuracy {acc*100}%")
- #cell
+ # cell
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))
+ 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))