Tensorflow学习笔记：多输入线性回归神经网络

版权声明：本文为博主原创文章，未经博主允许不得转载。 https://blog.csdn.net/WilliamCode/article/details/85240861

def myregression():
	"""
	实现多维输入的线性回归神经网络
	假设输入为x = [a,b,c,d,e,f,g],正确答案为:y_true
	则y_true = x1 * a + x2 * b + x3 * c + x4 * d + x5 * e + x6 * f + x7 * g + 1
		其中x取值为[0.1,0.2,0.3,0.4,0.5,0.6,0.7]
	"""
	#1、生成输入与标准答案数据集,x是一个[100,7]矩阵，应该乘以[7,1]的矩阵得到[100,1]的答案矩阵
	x = tf.random_normal([100,7],mean = 1.75,stddev = 0.5,name = 'input_data_x')
	y_true = tf.matmul(x,[[0.1],[0.2],[0.3],[0.4],[0.5],[0.6],[0.7]]) + 1

	#2、生成线性回归参数，计算预测结果
	w = tf.Variable(tf.random_normal([7,1],mean = 2,stddev=0.2,))
	b = tf.Variable(0.0)

	y_predict = tf.matmul(x,w) + b

	#3、计算loss
	loss = tf.reduce_mean(tf.square(y_predict - y_true))

	#4、反向传播优化参数
	train_op = tf.train.GradientDescentOptimizer(0.04).minimize(loss)

	init_op = tf.global_variables_initializer()

	with tf.Session() as sess:
		sess.run(init_op)
		for i in range(100000):
			sess.run(train_op)
			print(i,end=' ')
			tmp = w.eval()
			for j in tmp:
				print(j,end=' ')
			print(b.eval())

if __name__ == "__main__":
	import tensorflow as tf
	myregression()

新手代码，如有不当，请多指正

运算结果：在10000次训练之后，参数很好的向正确值收敛