本文通过代码展示Tensorflow实现迁移学习。
数据是使用程序产生的。该学习例子中，输入含两个分量,对应学生的单词量和阅读量，输出是英语分数。就是通过单词量、阅读量和英语分数的标注数据来学习一个模型，根据该模型从单词量和阅读量来预测英语分数。神经网络计算图如下，左分支是原始计算图，通过pb文件把该计算图算出来的网络参数和计算图保存，右边是迁移学习的计算图，它把左面学习出来的w1,b1读出来，以及使用mulAddFir计算出的向量作为新计算图中第一个节点MatMul_2的输入placeploder，然后根据右图的计算图进行训练。通过输出的w1/w2/b1/b2的新旧数值可以看到，左边图计算出来的w1/w2/b1/b2在右面图训练过程中并没有被修改，而右面图自己定义的w2new/b2new训练出来的结果和左边图的w2/b2并不一样，这符合我们对迁移学习的期待。
迁移学习代码如下：
#NN_transferLearn.py
import tensorflow as tf
import numpy as np
from traindata import *
from dataDim import *
from tensorflow.python.framework import graph_util
pbName = 'NN_transferLearn.pb'
BATCH_SIZE = 8
TRAIN_STEPS = 10000
def calAccury( ya ) : # ya is list containing predicted result
ya = np.array( ya )
y_a = Y_T
y_a = np.array( y_a )
accury =np.array(ya - y_a)
a = ( abs( accury ) < TRAIN_THRESHOLD )
af = a.astype( np.float32 )
right = af.sum()
per = right/VALIDATE_SIZE
return per , a
''' main for training using tensorflow '''
def tensorflowMain() :
x = tf.placeholder(tf.float32, shape=(None, 2), name='input')
y_= tf.placeholder(tf.float32, shape=(None, 1) , name = 'label' )
w1= tf.Variable( [[1.0,1.0,1.0],[2.0,1.0,1.0]], name = 'w1')
w2= tf.Variable([[2.0],[3.0],[5.0]] , name ='w2')
b1 = tf.Variable( [0.0 , 0.0 , 0.0 ] , name ='b1' )
b2 = tf.Variable( [0.0 ] , name ='b2' )
a = tf.add ( tf.matmul(x, w1) , b1 , name = 'mulAddFir' )
y = tf.add ( tf.matmul(a, w2) , b2 , name = 'mulAddSec' )
#2定义损失函数及反向传播方法。
loss_mse = tf.reduce_mean(tf.square(y-y_))
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(loss_mse)
#3生成会话，训练STEPS轮
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
# 训练模型。
STEPS = TRAIN_STEPS
for i in range(STEPS):
start = (i*BATCH_SIZE) % SAMPLE_SIZE
end = start + BATCH_SIZE
sess.run(train_step, feed_dict={x: X[start:end], y_: Y_[start:end]})
if (i+1) % 10000 == 0 :
total_loss = sess.run(loss_mse, feed_dict={x: X[start:end], y_: Y_[start:end]})
#print ("w1 after trained " , i+1 , " time(s) by tensorflow:\n", sess.run(w1))
#print ("w2 after trained " , i +1, " time(s) by tensorflow:\n", sess.run(w2))
#print("After %d training step(s), loss_mse is %g" % (i+1, total_loss))
print( "训练结果(Tensorflow)：" )
r_w1 = sess.run(w1)
r_w2 = sess.run(w2)
r_b1 = sess.run( b1)
r_b2 = sess.run( b2 )
print("w1:", r_w1 )
print("w2:", r_w2 )
print ("b1:", r_b1 )
print ("b2:", r_b2 )
constant_graph = graph_util.convert_variables_to_constants( sess, sess.graph_def , ['mulAddFir', 'label' , 'mulAddSec' ] )
with open( pbName, mode='wb') as f:
f.write(constant_graph.SerializeToString())
#validate
rv = sess.run( y , feed_dict={x:XT } )
per , accAr = calAccury( rv )
print("Accury :" , per )
return r_w1 , r_w2 , r_b1 , r_b2
def transferLearn() :
print("start transferLearn:" )
with tf.Graph().as_default():
graph0 = tf.GraphDef()
with open( pbName, mode='rb') as f:
graph0.ParseFromString( f.read() )
tf.import_graph_def( graph0 , name = '' )
with tf.Session() as sess :
init = tf.global_variables_initializer()
sess.run(init)
x = sess.graph.get_tensor_by_name('input:0' )
y_ = sess.graph.get_tensor_by_name('label:0' )
y = sess.graph.get_tensor_by_name('mulAddSec:0' )
rv = sess.run( y , feed_dict={x:XT } )
per , accAr = calAccury( rv )
print("Accury re-using model:" , per )
def transferLearnRetrainLayer2() :
print("start transferLearnRetrainLayer2:" )
with tf.Graph().as_default():
graph0 = tf.GraphDef()
with open( pbName, mode='rb') as f:
graph0.ParseFromString( f.read() )
tf.import_graph_def( graph0 , name = '' )
yFirst = tf.placeholder( tf.float32 )
w2new= tf.Variable([[2.0],[3.0],[5.0]] , name ='w2new')
b2new = tf.Variable( [0.0 ] , name ='b2new' )
ynew = tf.matmul( yFirst , w2new ) + b2new
with tf.Session() as sess :
init = tf.global_variables_initializer()
sess.run(init)
x = sess.graph.get_tensor_by_name('input:0' )
y_ = sess.graph.get_tensor_by_name('label:0' )
layer1Tensor = sess.graph.get_tensor_by_name('mulAddFir:0' )
loss_mse = tf.reduce_mean(tf.square(ynew-y_))
tf.summary.scalar('loss_mse', loss_mse )
train_stepH = tf.train.GradientDescentOptimizer(0.001).minimize(loss_mse)
summary_ops = tf.summary.merge_all()
STEPS = TRAIN_STEPS
summary_writer = tf.summary.FileWriter('./logs/', sess.graph)
for i in range(STEPS*10):
start = (i*BATCH_SIZE) % SAMPLE_SIZE
end = start + BATCH_SIZE
ar = sess.run( layer1Tensor , feed_dict={x:X[start:end] } )
_,val = sess.run([train_stepH,summary_ops], feed_dict={ yFirst:ar, y_: Y_[start:end]})
summary_writer.add_summary(val, global_step=i)
print( "after transfer learning：" )
w1Org = sess.graph.get_tensor_by_name('w1:0' )
b1Org = sess.graph.get_tensor_by_name('b1:0' )
w2Org = sess.graph.get_tensor_by_name('w2:0' )
b2Org = sess.graph.get_tensor_by_name('b2:0' )
r_w1Org = sess.run(w1Org)
r_w2Org = sess.run(w2Org)
r_b1Org = sess.run( b1Org)
r_b2Org = sess.run( b2Org )
print("w1Org:", r_w1Org )
print("w2Org:", r_w2Org )
print ("b1Org:", r_b1Org )
print ("b2Org:", r_b2Org )
r_w2new = sess.run(w2new)
r_b2new = sess.run( b2new )
print("w2new:", r_w2new )
print ("b2new:", r_b2new )
ar = sess.run( layer1Tensor , feed_dict={x:XT } )
rv = sess.run( ynew , feed_dict={yFirst:ar } )
per , accAr = calAccury( rv )
print("Accury after transfer learning:" , per )
def main() :
w21, w22, b21 , b22 = tensorflowMain()
transferLearn()
transferLearnRetrainLayer2()
main()
#dataDim.py
SAMPLE_SIZE = 1024
VALIDATE_SIZE = 128
HEALTHYEVAL_TRAIN_THRESHOLD = 0.3
ENGLISHSCORE_TRAIN_THRESHOLD = 5
#trandata.py
'''
X=[ Vocabulary , Reading]
Y_=[ English score ]
'''
TRAIN_THRESHOLD = 3
X=[[1.23330274,7.40049697],
[2.10652012,5.33739393],
[0.1165997 ,9.18747008],
[7.20571883,0.33421428],
[7.65559469,1.37209321],
[2.27062682,6.06083184],
[7.55380109,8.52735541],
[0.01807387,5.21226027],
[4.41630107,4.85377414],
[6.14507323,1.60716753],
[6.1164836 ,0.20809798],
[1.08168143,1.16273017],
[2.47918068,6.71452645],
[3.76983823,8.16168298],
[2.31669427,7.33125978],
[5.62097884,3.27569476],
[2.67718023,9.78058079],
[4.99665689,9.50313525],
[6.1398052 ,8.25009253],
[3.25312241,4.51308411],
[3.20505302,9.9513816 ],
[1.42051341,9.62596903],
[3.35400216,4.24052447],
[3.70519096,3.73723149],
[3.7240648 ,0.35168261],
[0.67418136,7.32520698],
[5.08959995,0.27907789],
[2.40136048,2.20852521],
[0.44015995,5.23246071],
[3.33095726,0.4821875 ],
[4.57923612,8.03516651],
[0.90881376,2.77117214],
[5.13342473,4.92737305],
[4.05244861,4.6144026 ],
[7.15794459,6.05777693],
[4.82684483,4.43871856],
[3.84037427,8.88447527],
[1.66800391,9.44581462],
[0.58776035,5.95152457],
[0.24920857,6.65257433],
[5.09908396,8.62465162],
[7.5331017 ,4.45627053],
[5.35966106,9.243307 ],
[4.95538773,3.22585733],
[2.55581049,2.91456685],
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[5.51142133,0.03287969],
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[3.08173584,5.95741894],
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[3.92763976,5.4394341 ],
[3.2436351 ,7.42410313],
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[1.19378948,7.4658379 ],
[5.13158076,2.98231029],
[3.00662316,9.88627865],
[2.75675952,3.43445001],
[4.90922764,6.18392713],
[0.10682738,5.74432795],
[6.22143562,9.49882077],
[5.62385738,1.16205733],
[1.97281182,4.18938681],
[5.05625237,0.95470253],
[3.12247368,3.21967588],
[7.77060235,9.6918238 ],
[5.94734375,5.17689295],
[1.31583965,9.4938157 ],
[7.37460764,8.825707 ],
[0.4083088 ,7.74940215],
[0.84716122,9.05395867],
[4.70309188,9.88089758],
[3.42929085,6.29849979],
[6.21025067,2.59817488],
[5.11130455,8.26799975],
[4.17044207,4.24485139],
[4.2480243 ,9.45978123],
[7.9941893 ,6.70923589],
[6.3166091 ,9.81587555],
[1.1324673,0.2628702],
[5.97154209,6.16588074],
[0.79363759,5.21902675],
[4.05945585,2.85693026],
[1.98750791,1.8034918 ],
[4.67058454,1.53918607],
[5.6938207 ,8.58752405],
[5.49153097,2.65129609],
[3.17626165,5.13016025],
[2.75363377,5.45602327],
[5.48893388,2.99692895],
[1.72286259,7.8830879 ],
[6.20679157,0.41725951],
[5.16388296,1.28193103],
[4.38122002,2.91523447],
[6.60676093,0.97352372],
[7.18009529,3.41989146],
[1.05668244,4.57582948],
[0.67550246,7.40226593],
[2.90990255,3.33561162],
[0.85139731,1.8614562 ],
[4.15399044,1.12622279],
[3.87140472,9.31488377],
[3.19084156,1.80391985],
[6.44393632,6.60735462],
[4.90748092,1.23882155],
[2.81233237,4.73099802],
[3.0270824 ,2.21923666],
[1.81504232,1.42828145],
[5.83540097,1.23011609],
[2.55822379,3.91659621],
[0.30352976,1.55011287],
[1.36519205,7.70312206],
[1.91358833,7.47170087],
[2.77995348,6.61213368],
[5.236432 ,2.32975943],
[3.36779816,6.76543516],
[4.84276229,6.08680507],
[0.31586737,9.05435955],
[7.11245105,1.78681283],
[3.10322823,9.07634594],
[3.6170524 ,4.33293137],
[5.67689815,1.60902138],
[6.91001461,0.13862581],
[0.59274568,3.75915743],
[1.99490818,5.12623345],
[1.14784206,6.41655501],
[4.31284403,8.29914176],
[2.15299926,1.71055722],
[4.21146212,2.41855409],
[3.6497129 ,1.16482011],
[4.79845302,8.36634893],
[7.29865529,9.50627072],
[4.65223495,5.14690306],
[6.2137297 ,6.72097326],
[1.31030033,4.27558355],
[1.23514129,5.66701597],
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[1.21140623,6.07674752],
[3.82563451,8.28624446],
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[0.2141363 ,8.90270818],
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[3.01840928,8.18280934],
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[2.48679109,4.11583571],
[1.80210603,2.70602881],
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[1.9678407 ,6.33851716],
[2.88350312,0.04862688],
[1.97776718,8.29907195],
[3.28928258,0.89914584],
[7.7979 ,6.52362264],
[5.24924469,9.27516271],
[7.46418095,8.78256589],
[3.2122025 ,2.78949661],
[3.84739997,7.56820831],
[4.07106064,3.75273189],
[4.78201331,6.42163185],
[7.82823877,6.21538195],
[3.14720345,9.34838372],
[5.85456597,4.30569632],
[3.67613701,9.98790409],
[7.70878547,0.06519603],
[6.76764136,3.62481134],
[3.17829292,2.94459646],
[4.91836933,1.76635579],
[1.6670548 ,2.16288225],
[4.49141459,1.03087327],
[2.49060782,8.0688157 ],
[3.88828052,9.55734313],
[5.71645607,1.15245334],
[3.58289436,5.2676948 ],
[7.1706949 ,9.20253152],
[0.42626918,9.09700668],
[5.51258768,5.74440804],
[7.34661288,4.05030625],
[5.3796048 ,8.25485597],
[6.52806692,5.60362554],
[4.4775171 ,8.58012639],
[1.64427694,7.15740917],
[6.52697234,1.19276523],
[6.7893773 ,1.25222268],
[0.42971482,4.70630658],
[7.51000517,9.47538312],
[4.69418401,8.13135592],
[2.01003657,6.41131566],
[1.32624286,2.53322696],
[7.45084401,8.97331603],
[5.94325061,4.73503363],
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[4.06294181,5.99180647],
[0.29795171,8.15538265],
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[7.46432993,7.87817505],
[6.61528249,3.03062952],
[1.26840188,3.6644971 ],
[3.14604394,9.35833524],
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[5.13597616,5.25811094],
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[1.11912428,0.60284305],
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[1.91442103,3.42183085],
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[4.83287356,7.95191081],
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[0.97231186,4.37698617],
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[0.52533882,0.95585837],
[0.21999324,8.01688117],
[2.82740947,2.0628799 ],
[0.02043357,2.67863284],
[1.13897429,8.74587464],
[0.89428602,7.65228728],
[4.13521388,0.39329984],
[1.5932598 ,3.42959737],
[2.9177437 ,2.97071155],
[5.13655112,0.92049791],
[0.66280618,5.3911986 ],
[2.47713695,6.71638541],
[2.25483831,6.79321526],
[7.0395755 ,3.46588451],
[2.05724037,7.45573767],
[1.09616766,5.23151913],
[7.4003326 ,2.91996821],
[6.00683568,4.06455446],
[6.70853565,7.45950911],
[0.21436231,8.94119526],
[1.34847055,8.80234116],
[3.16522116,0.52404247],
[7.06071845,6.32653419],
[2.318346 ,8.67481624],
[2.07264537,3.29302072],
[4.43255327,6.01873642],
[3.76397689,7.91504387],
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[0.62926213,9.04517621],
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[6.93949031,5.49256678],
[1.28049984,6.36370456],
[7.0397608 ,5.86954388],
[6.74891579,8.53586015],
[6.88722293,0.84904126],
[2.85792347,2.09148298],
[7.86696295,6.71882743],
[7.27928712,5.56058945],
[2.12381358,1.0946047 ],
[3.62935232,6.12073357],
[5.54958769,9.78602432],
[0.84208591,4.21949374],
[7.31386517,5.21658701],
[5.44760161,8.48666965],
[4.06560398,8.25916379],
[1.72538173,7.05580972],
[2.66013199,8.02823566],
[2.78701843,6.98208987],
[6.60795359,8.41855273],
[1.98474087,9.95451346],
[0.03421155,4.06932504],
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XT=[[2.44985744,2.65063566],
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Y_T=[[37.55097442],
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[39.5171227],
[25.03156627],
[80.45529093]
]
输出结果：
训练结果(Tensorflow)：
w1: [[1.0515914 1.8256049 2.6668158 ]
[0.582871 0.6132111 0.90144134]]
w2: [[1.0773216]
[1.585386 ]
[2.32953 ]]
b1: [-0.14707229 -0.069271 -0.02620909]
b2: [5.1552486]
Accury : 0.828125
start transferLearn:
Accury re-using model: 0.828125
start transferLearnRetrainLayer2:
after transfer learning：
w1Org: [[1.0515914 1.8256049 2.6668158 ]
[0.582871 0.6132111 0.90144134]]
w2Org: [[1.0773216]
[1.585386 ]
[2.32953 ]]
b1Org: [-0.14707229 -0.069271 -0.02620909]
b2Org: [5.1552486]
w2new: [[0.2837332]
[1.3128309]
[2.8824754]]
b2new: [4.708034]
Accury after transfer learning: 0.890625