tensorflow2

# step1 加载包import tensorflow as tf
import numpy as np
# step2 输入：随机产生数据
# Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3
x_data = np.random.rand(100).astype(np.float32)
y_data = x_data * 0.1 + 0.3
#step 3: 参数：定义参数并初始化
# Try to find values for W and b that compute y_data = W * x_data + b
# (We know that W should be 0.1 and b 0.3, but TensorFlow will
# figure that out for us.)
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = W * x_data + b
#steo 4:预测的值y，损失函数,求解器
# Minimize the mean squared errors.
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)
# step 5:初始化
# Before starting, initialize the variables.  We will ‘run‘ this first.
init = tf.initialize_all_variables()
# step 6: 创建会话并运行初始化
# Launch the graph.
sess = tf.Session()
sess.run(init)

# step 7: 迭代求解
# Fit the line.
for step in range(201):
    sess.run(train)
    if step % 20 == 0:
        print(step, sess.run(W), sess.run(b))

# Learns best fit is W: [0.1], b: [0.3]