#--*coding--:utf-8
import tensorflow as tf
import numpy
import matplotlib.pyplot as plt
rng = numpy.random
learning_rate =
0.01
training_epochs =
10000
display_step =
50
train_x = numpy.asarray(
[3.3
,4.4
,5.5
,6.71
,6.93
,4.168
,9.779
,6.182
,7.59
,2.167
,7.042
,10.791
,5.313
,7.997
,5.654
,9.27
,3.1])
train_y = numpy.asarray(
[1.7
,2.76
,2.09
,3.19
,1.694
,1.573
,3.366
,2.596
,2.53
,1.221
,2.827
,3.465
,1.65
,2.904
,2.42
,2.94
,1.3])
n_samples = train_x.shape[0]
print
("train_x:"
,train_x)
print
("train_y:"
,train_y)
train_x: [ 3.3 4.4 5.5 6.71 6.93 4.168 9.779 6.182 7.59 2.167設定placeholder7.042 10.791 5.313 7.997 5.654 9.27 3.1 ]
train_y: [1.7 2.76 2.09 3.19 1.694 1.573 3.366 2.596 2.53 1.221 2.827 3.465
1.65 2.904 2.42 2.94 1.3 ]
x = tf.placeholder(
"float"
)y = tf.placeholder(
"float"
)
w = tf.variable(rng.randn(
), name=
"weight"
)b = tf.variable(rng.randn(
), name=
"bias"
)
pred = tf.add(tf.multiply(x, w)
, b)
cost = tf.reduce_sum(tf.
pow(pred-y,2)
)/(2
*n_samples)
optimizer = tf.train.gradientdescentoptimizer(learning_rate)
.minimize(cost)
init = tf.global_variables_initializer(
)
with tf.session(
)as sess:
sess.run(init)
# 灌入所有訓練資料
for epoch in
range
(training_epochs)
:for
(x, y)
inzip
(train_x, train_y)
: sess.run(optimizer, feed_dict=
)# 列印出每次迭代的log日誌
if(epoch+1)
% display_step ==0:
c = sess.run(cost, feed_dict=
)print
("epoch:"
,'%04d'
%(epoch+1)
,"cost=",""
.format
(c), \
"w="
, sess.run(w)
,"b="
, sess.run(b)
)print
("optimization finished!"
) training_cost = sess.run(cost, feed_dict=
)print
("training cost="
, training_cost,
"w="
, sess.run(w)
,"b="
, sess.run(b)
,'\n'
)# 作圖
plt.plot(train_x, train_y,
'ro'
, label=
'original data'
) plt.plot(train_x, sess.run(w)
* train_x + sess.run(b)
, label=
'fitted line'
) plt.legend(
) plt.show(
)# 測試樣本
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