kerasを使わないでsinを回帰

概要

kerasを使わないでsinを回帰

写真

サンプルコード

import numpy as np
import matplotlib.pyplot as plt

def tanh(x):
	return np.tanh(x)

def dtanh(x):
	return 1.0 - x ** 2

class TLP:
	def __init__(self, Input, Hidden, Output):
		self.Input = Input + 1
		self.Hidden = Hidden + 1
		self.Output = Output
		self.W1 = np.random.uniform(-1.0, 1.0, (self.Hidden, self.Input))
		self.W2 = np.random.uniform(-1.0, 1.0, (self.Output, self.Hidden))
	def fit(self, X, t, learning_rate = 0.1, epochs = 10000):
		X = np.hstack([np.ones([X.shape[0], 1]), X])
		t = np.array(t)
		for k in range(epochs):
			i = np.random.randint(X.shape[0])
			x = X[i]
			z = tanh(np.dot(self.W1, x))
			y = tanh(np.dot(self.W2, z))
			bias2 = y - t[i]
			if k % 1000 == 0:
				print (k, bias2)
			bias1 = dtanh(z) * np.dot(self.W2.T, bias2)
			x = np.atleast_2d(x)
			bias1 = np.atleast_2d(bias1)
			self.W1 -= learning_rate * np.dot(bias1.T, x)
			z = np.atleast_2d(z)
			bias2 = np.atleast_2d(bias2)
			self.W2 -= learning_rate * np.dot(bias2.T, z)
	def predict(self, x):
		x = np.array(x)
		x = np.insert(x, 0, 1)
		z = tanh(np.dot(self.W1, x))
		y = tanh(np.dot(self.W2, z))
		return y

if __name__ == "__main__":
	X = np.arange(-3, 3, 0.1)
	X = np.reshape(X, (60, 1))
	y = np.sin(X)
	tlp = TLP(1, 8, 1)
	tlp.fit(X, y, learning_rate = 0.1, epochs = 12001)
	data = np.arange(-3, 3, 0.1)
	p = []
	for x in data:
		pred = tlp.predict(x)
		p.append(pred)
	plt.plot(data, y, 'b', data, p, 'r--')
	plt.savefig("sin11.png")
	plt.show()

以上。