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Commit88a0dd3

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Use black profile in isort to prevent conflicts (pyscript#244)
* Use black profile in isort to prevent conflicts
1 parentb5b150a commit88a0dd3

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2 files changed

+65
-46
lines changed

2 files changed

+65
-46
lines changed

‎.pre-commit-config.yaml

Lines changed: 1 addition & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -9,3 +9,4 @@ repos:
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hooks:
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-id:isort
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name:isort (python)
12+
args:[--profile, black]

‎pyscriptjs/examples/micrograd_ai.py

Lines changed: 64 additions & 46 deletions
Original file line numberDiff line numberDiff line change
@@ -1,45 +1,49 @@
1-
#Credit: https://github.com/karpathy/micrograd/blob/master/demo.ipynb
2-
#cell
1+
# Credit: https://github.com/karpathy/micrograd/blob/master/demo.ipynb
2+
# cell
3+
importdatetime
34
importrandom
4-
importnumpyasnp
5+
56
importmatplotlib.pyplotasplt
6-
importdatetime
7+
importnumpyasnp
78

8-
#cell
9+
#cell
910
frommicrograd.engineimportValue
10-
frommicrograd.nnimportNeuron,Layer,MLP
11+
frommicrograd.nnimportMLP,Layer,Neuron
1112

1213
print_statements= []
1314

14-
defrun_all_micrograd_demo(*args,**kwargs):
15+
16+
defrun_all_micrograd_demo(*args,**kwargs):
1517
result=micrograd_demo()
16-
pyscript.write('micrograd-run-all-fig2-div',result)
18+
pyscript.write("micrograd-run-all-fig2-div",result)
19+
1720

1821
defprint_div(o):
1922
o=str(o)
20-
print_statements.append(o+'\n<br>')
21-
pyscript.write('micrograd-run-all-print-div',''.join(print_statements))
23+
print_statements.append(o+"\n<br>")
24+
pyscript.write("micrograd-run-all-print-div","".join(print_statements))
2225

23-
#All code is wrapped in this run_all function so it optionally executed (called)
24-
#from pyscript when a button is pressed.
25-
defmicrograd_demo(*args,**kwargs):
26+
27+
# All code is wrapped in this run_all function so it optionally executed (called)
28+
# from pyscript when a button is pressed.
29+
defmicrograd_demo(*args,**kwargs):
2630
"""
2731
Runs the micrograd demo.
2832
2933
*args and **kwargs do nothing and are only there to capture any parameters passed
3034
from pyscript when this function is called when a button is clicked.
3135
"""
32-
33-
#cell
36+
37+
#cell
3438
start=datetime.datetime.now()
35-
print_div('Starting...')
39+
print_div("Starting...")
3640

37-
#cell
41+
#cell
3842
np.random.seed(1337)
3943
random.seed(1337)
4044

41-
#cell
42-
#An adaptation of sklearn's make_moons function https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html
45+
#cell
46+
#An adaptation of sklearn's make_moons function https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_moons.html
4347
defmake_moons(n_samples=100,noise=None):
4448
n_samples_out,n_samples_in=n_samples,n_samples
4549

@@ -48,26 +52,38 @@ def make_moons(n_samples=100, noise=None):
4852
inner_circ_x=1-np.cos(np.linspace(0,np.pi,n_samples_in))
4953
inner_circ_y=1-np.sin(np.linspace(0,np.pi,n_samples_in))-0.5
5054

51-
X=np.vstack([np.append(outer_circ_x,inner_circ_x),np.append(outer_circ_y,inner_circ_y)]).T
52-
y=np.hstack([np.zeros(n_samples_out,dtype=np.intp),np.ones(n_samples_in,dtype=np.intp)])
53-
ifnoiseisnotNone:X+=np.random.normal(loc=0.0,scale=noise,size=X.shape)
55+
X=np.vstack(
56+
[
57+
np.append(outer_circ_x,inner_circ_x),
58+
np.append(outer_circ_y,inner_circ_y),
59+
]
60+
).T
61+
y=np.hstack(
62+
[
63+
np.zeros(n_samples_out,dtype=np.intp),
64+
np.ones(n_samples_in,dtype=np.intp),
65+
]
66+
)
67+
ifnoiseisnotNone:
68+
X+=np.random.normal(loc=0.0,scale=noise,size=X.shape)
5469
returnX,y
70+
5571
X,y=make_moons(n_samples=100,noise=0.1)
5672

57-
#cell
58-
y=y*2-1# make y be -1 or 1
73+
#cell
74+
y=y*2-1# make y be -1 or 1
5975
# visualize in 2D
60-
plt.figure(figsize=(5,5))
61-
plt.scatter(X[:,0],X[:,1],c=y,s=20,cmap='jet')
76+
plt.figure(figsize=(5,5))
77+
plt.scatter(X[:,0],X[:,1],c=y,s=20,cmap="jet")
6278
plt
63-
pyscript.write('micrograd-run-all-fig1-div',plt)
79+
pyscript.write("micrograd-run-all-fig1-div",plt)
6480

65-
#cell
66-
model=MLP(2, [16,16,1])# 2-layer neural network
81+
#cell
82+
model=MLP(2, [16,16,1])# 2-layer neural network
6783
print_div(model)
6884
print_div(("number of parameters",len(model.parameters())))
6985

70-
#cell
86+
#cell
7187
# loss function
7288
defloss(batch_size=None):
7389
# inline DataLoader :)
@@ -77,51 +93,53 @@ def loss(batch_size=None):
7793
ri=np.random.permutation(X.shape[0])[:batch_size]
7894
Xb,yb=X[ri],y[ri]
7995
inputs= [list(map(Value,xrow))forxrowinXb]
80-
96+
8197
# forward the model to get scores
8298
scores=list(map(model,inputs))
83-
99+
84100
# svm "max-margin" loss
85-
losses= [(1+-yi*scorei).relu()foryi,scoreiinzip(yb,scores)]
101+
losses= [(1+-yi*scorei).relu()foryi,scoreiinzip(yb,scores)]
86102
data_loss=sum(losses)* (1.0/len(losses))
87103
# L2 regularization
88104
alpha=1e-4
89-
reg_loss=alpha*sum((p*pforpinmodel.parameters()))
105+
reg_loss=alpha*sum((p*pforpinmodel.parameters()))
90106
total_loss=data_loss+reg_loss
91-
107+
92108
# also get accuracy
93-
accuracy= [((yi).__gt__(0))== ((scorei.data).__gt__(0))foryi,scoreiinzip(yb,scores)]
109+
accuracy= [
110+
((yi).__gt__(0))== ((scorei.data).__gt__(0))
111+
foryi,scoreiinzip(yb,scores)
112+
]
94113
returntotal_loss,sum(accuracy)/len(accuracy)
95114

96115
total_loss,acc=loss()
97116
print((total_loss,acc))
98117

99-
#cell
118+
#cell
100119
# optimization
101-
forkinrange(20):#was 100
102-
120+
forkinrange(20):#was 100
121+
103122
# forward
104123
total_loss,acc=loss()
105-
124+
106125
# backward
107126
model.zero_grad()
108127
total_loss.backward()
109-
128+
110129
# update (sgd)
111-
learning_rate=1.0-0.9*k/100
130+
learning_rate=1.0-0.9*k/100
112131
forpinmodel.parameters():
113132
p.data-=learning_rate*p.grad
114-
133+
115134
ifk%1==0:
116135
# print(f"step {k} loss {total_loss.data}, accuracy {acc*100}%")
117136
print_div(f"step{k} loss{total_loss.data}, accuracy{acc*100}%")
118137

119-
#cell
138+
#cell
120139
h=0.25
121140
x_min,x_max=X[:,0].min()-1,X[:,0].max()+1
122141
y_min,y_max=X[:,1].min()-1,X[:,1].max()+1
123-
xx,yy=np.meshgrid(np.arange(x_min,x_max,h),
124-
np.arange(y_min,y_max,h))
142+
xx,yy=np.meshgrid(np.arange(x_min,x_max,h),np.arange(y_min,y_max,h))
125143
Xmesh=np.c_[xx.ravel(),yy.ravel()]
126144
inputs= [list(map(Value,xrow))forxrowinXmesh]
127145
scores=list(map(model,inputs))

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