Tensorflow 搭建自己的神经网络(一)

import tensorflow as tf
import numpy as np

#creat da
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 0.1 + 0.3

#create tensorflow structure start
Weights = tf.Variable(tf.random_uniform([1], -1, 0, 1))
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.initialize_all_variables()
#create tensorflow structure end

sess = tf.Session()
sess.run(init) #Very inmportant
step in range(201):
    sess.run(train)
    if(step % 20 == 0):
        print(step,sess.run(Weights), sess.run(biases))

import numpy as np
import tensorflow as tf

state = tf.Variable(0,name = "counter")
one = tf.constant(1)

new_value = tf.add(state, )
update = tf.assign(state, new_value)
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for _ in range(3):
        sess.run(update)
        print(sess.run(state))