Update

1 files changed, 0 insertions, 66 deletions

diff --git a/example.py b/example.py
deleted file mode 100755
index 66df486..0000000
--- a/example.py
+++ /dev/null
@@ -1,66 +0,0 @@
-#!/usr/bin/env python
-
-from src.scalar import Scalar
-from src.graph import Graph
-
-# Manual Backpropagation
-
-# a = Scalar(2, label='a')
-# b = Scalar(-3, label='b')
-# c = Scalar(10, label='c')
-# f = Scalar(-2, label='f')
-#
-# d = a * b; d.label = 'd'
-# e = d + c; e.label = 'e'
-# L = e * f; L.label = 'L'
-#
-# print(f'L before gradient descent: {L.data}')
-#
-# L.backward()
-#
-# g = Graph(L)
-#
-# for x in [a, b, c, f]:
-#     x.data += 0.01 * x.grad
-#
-# d = a * b
-# e = d + c
-# L = e * f
-#
-# print(f'L after gradient descent: {L.data}')
-# g.show()
-
-# Neuron
-
-x1 = Scalar(2, label='x1')
-x2 = Scalar(0, label='x2')
-
-w1 = Scalar(-3, label='w1')
-w2 = Scalar(1, label='w2')
-
-b = Scalar(6.7, label='b')
-
-x1w1 = x1 * w1; x1w1.label = 'x1w1'
-x2w2 = x2 * w2; x2w2.label = 'x2w2'
-
-x1w1x2w2 = x1w1 + x2w2; x1w1x2w2.label = 'x1w1 + x2w2'
-
-L = x1w1x2w2 + b; L.label = 'L'
-
-o = L.tanh(); o.label = 'o'
-
-o.zero_grad()
-o.backward()
-
-Graph(o).show()
-
-e = 2 * L
-f = e.exp()
-a = f - 1
-b = f + 1
-o = a / b
-
-o.zero_grad()
-o.backward()
-
-Graph(o).show()