81 lines
2.4 KiB
Python
81 lines
2.4 KiB
Python
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#!/usr/bin/env python3
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# coding: utf-8
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import numpy as np
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# 计算tanh的值，激活函数
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def tanh(x):
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return np.tanh(x)
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# 求tanh的导数
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def tanh_deriv(x):
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return 1.0 - np.tanh(x)*np.tanh(x)
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# 逻辑函数
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def logistic(x):
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return 1.0/(1.0 + np.exp(-x))
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# 逻辑函数求导
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def logistic_deriv(x):
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return logistic(x)*(1.0 - logistic(x))
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class NeuralNetwork:
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def __init__(self, layers, activation='tanh'):
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"""
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:param layers: A list
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"""
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if activation == 'logistic':
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self.activation = logistic
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self.activation_deriv = logistic_deriv
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elif activation == 'tanh':
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self.activation = tanh
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self.activation_deriv = tanh_deriv
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self.weights = []
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for i in range(1, len(layers) - 1):
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# 初始化 权值范围 [-0.25,0.25)
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# [0,1) * 2 - 1 => [-1,1) => * 0.25 => [-0.25,0.25)
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self.weights.append((2*np.random.random((layers[i - 1] + 1, layers[i] + 1))-1)*0.25)
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self.weights.append((2*np.random.random((layers[i] + 1, layers[i+1]))-1)*0.25)
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def fit(self, x, y, learning_rate=0.2, epochs=10000):
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x = np.atleast_2d(x)
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temp = np.ones([x.shape[0], x.shape[1] + 1])
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temp[:, 0:-1] = x
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x = temp
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y = np.array(y)
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for k in range(epochs):
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i = np.random.randint(x.shape[0])
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a = [x[i]]
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# 正向计算
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for l in range(len(self.weights)):
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a.append(self.activation(np.dot(a[l], self.weights[l])))
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# 反向传播
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error = y[i] - a[-1]
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deltas = [error*self.activation_deriv(a[-1])]
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# 开始反向计算,从倒数第二层开始计算
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for j in range(len(a)-2, 0, -1):
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deltas.append(deltas[-1].dot(self.weights[j].T)*self.activation_deriv(a[j]))
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deltas.reverse()
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# 更新权值
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for i in range(len(self.weights)):
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layer = np.atleast_2d(a[i])
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delta = np.atleast_2d(deltas[i])
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self.weights[i] += learning_rate * layer.T.dot(delta)
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def predit(self, x):
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x = np.array(x)
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temp = np.ones(x.shape[0] + 1)
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temp[0:-1] = x
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a = temp
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for l in range(0, len(self.weights)):
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a = self.activation(np.dot(a, self.weights[l]))
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return a
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