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tensorFlow Lerning process
import tensorflow as tf
import json
from urllib2 import urlopen
import api_func
#ai_func
class ai_funcClass:
def __init__(self):
print ""
def proc_run(self ,field ):
cls = api_func.api_funcClass()
#get_apiData()
#exit()
# Model parameters
W = tf.Variable([0.0], dtype=tf.float32)
b = tf.Variable([0.0], dtype=tf.float32)
# Model input and output
x = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)
linear_model = W*x + b
# loss
loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares
# optimizer
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = optimizer.minimize(loss)
# training data
y_train = cls.get_apiData(field )
cDim=[]
iCt=0
for xRow in range(len(y_train ) ):
cDim.append( float(iCt)/100.0 )
iCt +=1
x_train = cDim
print(x_train)
print(y_train)
# training loop
print('#Start traning.')
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init) # reset values to wrong
for i in range(1000):
sess.run(train, {x: x_train, y: y_train})
if i % 100 == 0:
print( i, sess.run(W), sess.run(b) )
# evaluate training accuracy
curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x: x_train, y: y_train})
if(len(curr_W)) >0:
print('W=' + str(curr_W[0]))
if(len(curr_b ) >0):
print( 'b='+ str(curr_b[0] ))
print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss))
#update
if((len(curr_W) > 0) and (len(curr_b) > 0)):
cls.update(field , curr_W[0],curr_b[0] )
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