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basic-operation-13.py
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basic-operation-13.py
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import tensorflow as tf
import numpy as np
import os
import matplotlib.pyplot as plt
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
xdata = np.linspace(0,1,100)
ydata = 2 * xdata + 1 + np.random.normal(20,6,xdata.shape)*0.02
print("init modole ...")
X = tf.placeholder("float",name="X")
Y = tf.placeholder("float",name="Y")
W = tf.Variable(-3., name="W")
B = tf.Variable(3., name="B")
linearmodel = tf.add(tf.multiply(X,W),B)
lossfunc = (tf.pow(Y - linearmodel, 2))
learningrate = 0.01
print("set Optimizer")
trainoperation = tf.train.GradientDescentOptimizer(learningrate).minimize(lossfunc)
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
index = 1
print("caculation begins ...")
for j in range(100):
for i in range(100):
sess.run(trainoperation, feed_dict={X: xdata[i], Y:ydata[i]})
if j % 10 == 0:
print("j = %s index = %s" %(j,index))
plt.subplot(2,5,index)
plt.scatter(xdata,ydata)
labelinfo="iteration: " + str(j)
plt.plot(xdata,B.eval(session=sess)+W.eval(session=sess)*xdata,'b',label=labelinfo)
plt.plot(xdata,2*xdata + 1,'r',label='expected')
baisadjust=np.mean(ydata) - np.mean(B.eval(session=sess)+W.eval(session=sess)*xdata)
plt.plot(xdata,2*xdata + 1 + baisadjust, 'y', label='adjusted')
plt.legend()
index = index + 1
print("caculation ends ...")
print("##After Caculation: ")
print(" B: " + str(B.eval(session=sess)) + ", W : " + str(W.eval(session=sess)))
plt.show()