代码如下
import tensorflow as tf
import numpy as np
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data*0.1 + 0.3
# print (x_data )
Weights = tf.Variable(tf.random_uniform([1],-1.0,1.0))
biases = tf.Variable(tf.zeros([1]))
y = Weights*x_data + biases
loss = tf.reduce_mean(tf.square(y-y_data)
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
init = tf.inininlize_all_variables()
sess = tf.Session()
sess.run(init)
for step in range(201):
sess.run(train)
if step%20 == 0:
print(step,sess.run(Weights),sess.run(biases))
是optimizer与 train的定义方式不对吗?但是我看视频上就是这么写的,他就能运行。
有更安全的表达方式吗,请各位高手指点一下!