Validation 为什么模型嵌入不同（tensorflow）？
我已经训练了一个循环神经网络，现在我正在分析验证集的结果 我使用了一个受VGG-16启发的模型来生成图像嵌入（类似于分类，但不是在最后一层应用softmax，而是使用Validation 为什么模型嵌入不同（tensorflow）？,validation,tensorflow,conv-neural-network,Validation,Tensorflow,Conv Neural Network,我已经训练了一个循环神经网络，现在我正在分析验证集的结果 我使用了一个受VGG-16启发的模型来生成图像嵌入（类似于分类，但不是在最后一层应用softmax，而是使用tf.nn.l2_规范化并输出其结果） 以下是我评估验证集结果的方式： for q in range(total_batch): s = q * batch_size e = (q+1) *batch_size input1,input2, input3 = training.n
tf.nn.l2_规范化for q in range(total_batch):
s = q * batch_size
e = (q+1) *batch_size
input1,input2, input3 = training.next_batch(s,e)
m1 = sess.run([model1], feed_dict={x_anchor:input1})
model 1[[ 1. -0.99999994 1. -1. -1. -1. -1.
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--------------------------------------
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--------------------------------------
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--------------------------------------
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0.9842636 -0.39281276 0.01813768 0.40322316 -0.15943591 0.65748084
0.99194562 0.00900941 0.37577793 0.05430508 0.99913377 0.97713834
-0.22025895 -0.9998731 0.12711462 0.99385047 -0.99903864 0.01264404
-0.62551564 -0.01279757 -0.99958265 0.99910063 0.98519951 -0.99925339
-0.99950749 -0.996903 -0.0805502 -0.99981397 0.38385507 -0.18567207
0.5348646 -0.01446672 -0.9839977 -0.28439516 0.36341196 0.00541824
0.98680389 -0.99527711 0.10289539 -0.01316902 -0.23070674 -0.99711412
-0.99885732 0.98736358 0.02993833 0.9998821 0.28457668 0.9865762
-0.53744942 -0.97667766 0.44472823 -0.24988614 0.27212018 -0.9888674
-0.72734493 -0.23736458 0.99992937 0.23534702 -0.22993524 0.99917501
0.51845711 -0.00524166 -0.05228702 -0.06423581 -0.98239625 -0.02956322
0.0584744 0.39755991 -0.99894112 -0.99989706 -0.99938661 -0.3585794
-0.13538241 0.98722047 0.11465792 0.98612529 0.99145955 -0.95644069
0.99284977 0.99533743 0.46626735 0.27194008 0.99905449 -0.00283846
-0.9909308 -0.98994917 -0.13649452 0.98846346 0.99939793 -0.67014861
0.99926412 0.29988277 -0.16668546 0.99772769 0.03549499 0.3593924
0.99155712 -0.24595471 0.34436712 0.98489523 -0.11648855 0.96064514
0.99585754 0.9995473 -0.0284851 0.99564117 -0.98909426 -0.13297313
-0.98252243 -0.99982309 0.96479797 -0.99903357 0.26361924 0.07331257
0.16411291 -0.99969572]]
[[ 0.04818952 -0.05918095 0.01802165 ..., 0.05679128 0.11442375
-0.01418886]
[ 0.05519219 -0.1301478 0.01969874 ..., 0.07767208 0.18997246
-0.01425523]
[ 0.08626937 -0.25595629 0.02016677 ..., 0.63242364 0.43534788
-0.01190863]
...,
[-0.14989793 0.49290144 0.18958355 ..., 0.01846622 -0.25266901
-0.22219713]
[ 0.09586276 -0.28965887 0.02260818 ..., 0.69641638 0.4886305
-0.01328262]
[-0.01896885 0.5706526 0.03627656 ..., 0.0253126 -0.36303344
-0.05107375]]
model 1[ 5.86070597e-01 1.95135430e-01 2.46683449e-01 -1.15100272e-01
-8.81946564e-01 -8.91003788e-01 -9.79653239e-01 4.03030038e-01
-3.37121964e-01 -8.15378204e-02 -1.51473030e-01 -6.80846691e-01
-9.61061358e-01 9.99767303e-01 9.17174935e-01 -9.18523490e-01
4.61293280e-01 -2.48836592e-01 -6.79437101e-01 8.90483081e-01
-9.21952069e-01 -9.33306396e-01 3.34582895e-01 -9.64680552e-01
2.17209637e-01 -9.24504027e-02 -8.05327177e-01 -9.81654227e-01
-5.13453305e-01 8.98176014e-01 4.65111285e-01 -8.81239653e-01
-2.21239150e-01 1.82802275e-01 -9.89428520e-01 -3.78754020e-01
-1.94648057e-01 -9.60758328e-01 9.99629676e-01 -1.34037957e-01
5.10413982e-02 9.51475441e-01 -1.16250336e-01 -9.02551413e-01
9.75457489e-01 3.81219059e-01 6.40182137e-01 8.98657262e-01
-9.81229663e-01 3.48009765e-01 -8.25251520e-01 -8.78372252e-01
7.62746096e-01 3.04450452e-01 -3.40841383e-01 -9.91277814e-01
8.71233225e-01 -5.17561100e-04 4.97255594e-01 -1.69827744e-01
-1.96575001e-01 9.88305330e-01 -3.83750439e-01 -1.71618521e-01
1.40758827e-01 7.19896436e-01 -3.79732788e-01 -5.24435103e-01
7.77314454e-02 8.57556820e-01 5.83529353e-01 3.34895849e-01
-8.37150455e-01 -8.86918783e-01 -8.40985894e-01 -8.68736804e-01
-1.96579075e-03 -8.52129221e-01 8.46933126e-01 6.71221852e-01
-6.03814125e-01 -1.12122051e-01 -2.26175249e-01 -5.59274018e-01
5.39124966e-01 -3.51777464e-01 -8.13132882e-01 -1.37575284e-01
-2.99482316e-01 7.41403461e-01 8.68456364e-01 -4.78453755e-01
3.74983132e-01 1.13467500e-01 1.33464202e-01 -8.81694794e-01
7.77389646e-01 3.82896155e-01 -9.17372942e-01 -3.26944888e-01
3.77652109e-01 -1.51620105e-01 2.89691836e-01 -9.90572333e-01
3.18472356e-01 8.29179347e-01 8.44251931e-01 7.09410369e-01
-9.93203878e-01 -4.48296890e-02 -9.96632695e-01 8.17295071e-03
9.04701591e-01 -7.95541167e-01 -4.05060053e-01 1.68647662e-01
-9.02341783e-01 -8.14558685e-01 9.71956789e-01 9.01448905e-01
9.96791184e-01 -5.61384344e-03 -7.10371912e-01 -4.87548530e-01
4.36763577e-02 8.43983173e-01 -1.29987687e-01 -1.70727238e-01]
def train(x_anchor, x_positive, x_negative, idx_model):
global weights, bias, batch_size, keep_rate, suma, nr
sess = tf.Session()
saver = tf.train.import_meta_graph('/home/bogdan/triplet/model_85_la_suta/my-model_'+str(idx_model)+'.meta')
saver.restore(sess,tf.train.latest_checkpoint('./triplet/model_85_la_suta/'))
graph = tf.get_default_graph()
with tf.device('/gpu:1'):
with tf.variable_scope("siamese") as scope:
model1 = siamese_convnet(x_anchor, graph)
scope.reuse_variables()
model2 = siamese_convnet(x_positive, graph)
scope.reuse_variables()
model3 = siamese_convnet(x_negative, graph)
eps = 1e-10
d_pos = tf.sqrt(tf.reduce_sum(tf.square(model1 - model2), 1) + eps)
d_neg = tf.sqrt(tf.reduce_sum(tf.square(model1 - model3), 1) + eps)
training = lfw_generated_test.inputData()
training.shuffle_epoca();
nr_training_examples = training.get_nr_training()
print ("nr_training_examples "+str(nr_training_examples))
total_batch = int(nr_training_examples/batch_size)
print("tb "+str(total_batch))
avg_acc_test = 0;
for q in range(total_batch):
s = q * batch_size
e = (q+1) *batch_size
input1,input2, input3 = training.next_batch(s,e)
distance1, distance2, m1, m2, m3 = sess.run([d_pos, d_neg, model1, model2, model3], feed_dict={x_anchor:input1, x_positive:input2, x_negative:input3})
'''print("input 1"+str(input1))
print("--------------------------------------")'''
'''print("dist1 = "+str(distance1))
print("--------------------------------------")
print("dist2 = "+str(distance2))
print("--------------------------------------")
print("--------------------------------------")'''
''''print(np.shape(distance1))
print(np.shape(distance2)) '''
print("model 1"+str(m1[0]))
print("--------------------------------------")
''''print("--------------------------------------")
print("model 2"+str(m2))
print("--------------------------------------")
print("--------------------------------------")
print("model 3"+str(m3))
print("--------------------------------------")
print("--------------------------------------")'''
''''print(m2)
print(m3)'''
''''print(np.shape(model1))
print(np.shape(model2))
print(np.shape(model3))'''
#print(str(q)+" ----------------------------------------------------------------")
test_acc = compute_accuracy(distance1, distance2)
avg_acc_test +=test_acc*100
print('Accuract TEST set %0.2f' % (avg_acc_test/(total_batch)))
batch_size = 2
#70x70 images
x_anchor = tf.placeholder('float', [None, 4900])
x_positive = tf.placeholder('float', [None, 4900])
x_negative = tf.placeholder('float', [None, 4900])
labels = tf.placeholder(tf.float32, [None, 1]) #0 sau 1 (impostor sau genuine)
train(x_anchor, x_positive, x_negative, 99);
你在使用批处理规范化吗？你确定你的模型中没有任何随机性吗？@nessuno我没有使用批处理normalization@AvijitDasgupta我使用的是一个保存的模型（已经训练过），权重和偏差已经计算好了，我用graph.get_tensor_by_name检索它们，所以不，没有任何随机性。@Helloli当您向批处理中添加一个以上的图像时，输出是否会更改？从2点到3点？