单变量线性回归tensorflow版

TensorFlow1:

%matplotlib inline

import matplotlib.pyplot as plt

import numpy as np

import tensorflow as tf

np.random.seed(5)

x_data = np.linspace(-1,1,100)

#help(np.random.randn)

#print(np.random.randn(*x_data.shape))

#np.random.randn(*x_data.shape) *0.4

#y_data = 2*x_data+1+np.random.randn(*x_data.shape) *0.4

#plt.scatter(x_data,y_data)

#plt.plot(x_data,2*x_data+1,color='red',linewidth=3)

x = tf.compat.v1.placeholder(tf.float32,name='x')

y = tf.compat.v1.placeholder(tf.float32,name='y')

def model(x,w,b):

return tf.multiply(x,w)+b

w = tf.Variable(1.0,name='w0')

b = tf.Variable(0.0,name='b0')

pred = model(x,w,b)

train_epochs = 10

learning_rate = 0.05

loss_function = tf.reduce_mean(tf.square(y-pred))

optimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate).minimize(loss_function)

sess = tf.compat.v1.Session()

init = tf.compat.v1.global_variables_initializer()

sess.run(init)

for epoch in range(3):

for xs,ys in zip(x_data,y_data):

_,loss = sess.run([optimizer,loss_function],feed_dict={x:xs,y:ys})

b0temp = b.eval(session=sess)

w0temp = w.eval(session=sess)

plt.plot(x_data,w0temp*x_data+b0temp)

print("w:",sess.run(w))

print("b:",sess.run(b))

TensorFlow2:

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

%matplotlib inline

print(tf.__version__)

x_data = np.linspace(-1,1,100)

y_data = 2 * x_data + 1 +np.random.randn(*x_data.shape) * 0.4

w = tf.Variable(1.0,tf.float32)

b = tf.Variable(0.0,tf.float32)

def model(x,w,b):

return tf.multiply(w,x) + b

def loss(x,w,b,y):

pred = model(x,w,b)

square = tf.square(pred-y)

return tf.reduce_mean(square)

def grad(x,w,b,y):

with tf.GradientTape() as tape:

loss_ = loss(x,w,b,y)

return tape.gradient(loss_,[w,b])

step = 0

loss_list = []

disply_step = 10

training_epochs = 10

learning_rate = 0.05

#BGD

for epochs in range(training_epochs):

loss_ = loss(x_data,w,b,y_data)

loss_list.append(loss_)

print(loss_.numpy())

delta_w,delta_b = grad(xs,w,b,ys)

change_w = delta_w * learning_rate

change_b = delta_b * learning_rate

w.assign_sub(change_w)

b.assign_sub(change_b)

step+=1

if step % disply_step == 0:

print(step,"--->",loss_)

#SGD

#for epochs in range(training_epochs):

#for xs,ys in zip(x_data,y_data):

#loss_ = loss(xs,w,b,ys)

#loss_list.append(loss_)

#delta_w,delta_b = grad(xs,w,b,ys)

#change_w = delta_w * learning_rate

#change_b = delta_b * learning_rate

#w.assign_sub(change_w)

#b.assign_sub(change_b)

#step+=1

#if step % disply_step == 0:

#print(step,"--->",loss_)

print("w",w.numpy())

print("b",b.numpy())

#plt.plot(x_data,x_data*w.numpy() + b.numpy())

plt.plot(loss_list)