Create a gist now

Instantly share code, notes, and snippets.

@masaponto /mlp.py
Last active Apr 26, 2016

What would you like to do?
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import random
class MLP:
def __init__(self, mid_num, out_num, epochs, r = 0.5, a = 1):
"""mlp using sigmoid
mid_num: 中間層ノード数
out_num: 出力層ノード数
epochs: 学習回数
r: 学習率
a: シグモイド関数の定数
"""
self.mid_num = mid_num
self.out_num = out_num
self.epochs = epochs
self.r = r
self.a = a
def sigmoid(self, x):
return 1 / (1 + np.exp(-self.a * x))
def sigmoid_(self, x):
"""シグモイド関数微分
"""
return self.a * x * (1.0 - x)
def calc_out(self, w_vs, x_v):
return self.sigmoid(np.dot(w_vs, x_v))
def out_error(self, d_v, out_v):
"""出力層の誤差
"""
return (out_v - d_v) * self.sigmoid_(out_v)
def mid_error(self, mid_v, eo_v):
"""中間層の誤差
"""
return np.dot(self.wo_vs.T, eo_v) * self.sigmoid_(mid_v)
def w_update(self, w_vs, e_v, i_v):
"""重み更新
"""
e_v = np.atleast_2d(e_v)
i_v = np.atleast_2d(i_v)
return w_vs - self.r * np.dot(e_v.T, i_v)
def add_bias(self, x_v):
"""バイアス項追加
"""
return np.append(x_v, 1)
def fit(self, X, y):
"""学習
"""
x_vs = X
d_vs = y
x_vs = [self.add_bias(x) for x in x_vs]
x_vd = len(x_vs[0])
# 重み
self.wm_vs = np.random.uniform(-1, 1., (self.mid_num, x_vd))
self.wo_vs = np.random.uniform(-1., 1., (self.out_num, self.mid_num))
for n in range(self.epochs):
for d_v, x_v in zip(d_vs, x_vs):
# forward phase
# 中間層の結果
mid_v = self.calc_out(self.wm_vs, x_v)
mid_v[-1] = -1
# 出力層の結果
out_v = self.calc_out(self.wo_vs, mid_v)
# backward phase
# 出力層の誤差
eo_v = self.out_error(d_v, out_v)
# 中間層
em_v = self.mid_error(mid_v, eo_v)
# weight update
# 中間層
self.wm_vs = self.w_update(self.wm_vs, em_v, x_v)
# 出力層
self.wo_vs = self.w_update(self.wo_vs, eo_v, mid_v)
def predict(self, x_v):
x_v = self.add_bias(x_v)
mid_v = self.calc_out(self.wm_vs, x_v)
out_v = self.calc_out(self.wo_vs, mid_v)
return out_v
if __name__ == "__main__":
# data
X_train = [[0, 0], [1, 0], [0, 1], [1, 1]]
y_train = [0, 1, 1, 0]
mid_num = 5
out_num = 1
epochs = 10000
mlp = MLP(mid_num, out_num, epochs)
mlp.fit(X_train, y_train)
result = [mlp.predict(x) for x in X_train]
[print(r, ":", y) for r, y in zip(result, y_train)]
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment