TensorFlow 使用多项式进行sin函数的拟合操作

定义一个主函数,对sin函数进行显示：

if __name__ == "__main__":
	samples = 200
	xs = np.random.uniform(-np.pi, np.pi, [samples])
	xs = sorted(xs)
	ys = sorted(ys)
	# 使用matplotlib进行图像的显示
	plt.plot(xs, ys)
	plt.show()

在主方法中调用SinApp类

app = SinApp(Config())
with app:
	xs_train, ys_train = app.train()
	xs_predict, ys_predict = app.predict()
plt.plot(xs_train,ys_train)
plt.plot(xs_predict, ys_predict)
plt.show()

将用到的参数放在Config的类中

class Config:
	def __init__(self):
		self.save_path = './model_sin/sin'
		self.lr = 0.001
		self.epoches = 2000
		self.batch_size = 200
		# 定义隐藏层的数量
		self.hidden_units = 200
		

定义一个张量类：Tensors

class Tensors:
	def __init__(slef, config):
		self.x = tf.placeholder(tf.float32, [None], 'x')
		self.y = tf.placeholder(tf.float32, [None], 'y')

		x = tf.reshape(slef.x, [-1, 1])
		x = tf.layer.dense(x, config.hidden_units, tf.nn.relu)

		y = tf.layer.dense(x, 1)

		self.y_predict = tf.reshape(y, [-1])
		self.loss = tf.reduce_mean(tf.square(tf.y_predict - self.y))  # 使用方差损失

		self.lr = tf.placeholder(tf.float32, [], 'lr')  # 定义学习步长（可以定义成动态的）

		opt = tf.train.AdaOptimizer(self.lr)  # 定义优化器
		self.train_op = opt.minimize(self.loss)

		self.loss = tf.sqrt(self.loss)  # 取sinx的平方根（求平方差）打印的会更合理的，减少误差

定义一个样本类：Sample（实际上大部分工作都是在处理样本的）

class Sample:
	def __init__(self, samples):
		self.xs = np.random.uniform(-np.pi, np.pi, [samples])  # 可以自己定义samples
		self.xs = sorted(self.xs)
		self.ys = np.sin(self.xs)
	
	@property
	def num_examples(self):
		return len(self.xs)

定义SinApp类

class SinApp:
	def __init__(self, config):
		self.ts = Tensors(config)
		self.session = tf.Session()
		self.saver = tf.train.Saver()

		try:
			self.saver.restore(self.session, config.save_path)
		except:
			self.session.run(tf.global_variables_initializer())


	def train(self):
		sample = Sample(self.config.samples)
		cfg = self.config
		ts = self.ts
		for _ in range(cfg.epoches):
			_ , loss = self.session.run([ts.train_op, ts.loss], {ts.x: sample.xs, ts.y:sample.ys, ts.lr:cfg.lr})
			self.save()
		return samples.xs, samples.ys  # 通过训练得到的xs和ys
	def	save(self):
		self.saver.save(self.session, self.config.save_path)
		print('save model into', self.save_path)
		
	def predict(self):
		sample = Sample(400)  # 样本数量400个，不是很重要就不写在config中了
		ys = self.session.run(self.ts.y_predict, {self.ts.x: samples.xs})  # 预测的值弄出来，按照顺序算出ys的值了。
		return sample.xs, ys  # 返回xs是400个样本点，ys是对应的正弦值

	def close(self):
		self.session.close()
	def __enter__(self):
		return self
	def __exit__(self):
		self.close()

运行结果为：

局部放大图：