import tensorflow as tf
import numpy as np

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

Weights = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
biases = tf.Variable(tf.zeros([1]))

y = Weights*x_data + biases

# 计算误差
loss = tf.reduce_mean(tf.square(y-y_data))

optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

# init = tf.initialize_all_variables() # tf 马上就要废弃这种写法
# 初始化变量
init = tf.global_variables_initializer()  # 替换成这样就好

sess = tf.Session()
writer = tf.summary.FileWriter("C:\\Users\\nashaofu\\Documents\\Github\\tensorflow-learning\\logs",sess.graph)
sess.run(init)          # Very important


for step in range(201):
    sess.run(train)
    if step % 20 == 0:
        print(step, sess.run(Weights), sess.run(biases))
import tensorflow as tf
import numpy as np

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

Weights = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
biases = tf.Variable(tf.zeros([1]))

y = Weights*x_data + biases

# 计算误差
loss = tf.reduce_mean(tf.square(y-y_data))

optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

# init = tf.initialize_all_variables() # tf 马上就要废弃这种写法
# 初始化变量
init = tf.global_variables_initializer()  # 替换成这样就好

sess = tf.Session()
writer = tf.summary.FileWriter("C:\\Users\\nashaofu\\Documents\\Github\\tensorflow-learning\\logs",sess.graph)
sess.run(init)          # Very important


for step in range(201):
    sess.run(train)
    if step % 20 == 0:
        print(step, sess.run(Weights), sess.run(biases))
# create data # 计算误差 # init = tf.initialize_all_variables() # tf 马上就要废弃这种写法 # 初始化变量 # 替换成这样就好 # Very important