我试图做一个多标签分类器。它有5个标签。我通过ImageDataGenerators进行了培训，然后我想手动测试系统，但是我无法执行该操作，因为系统给了我奇怪的答案，看起来像是概率。它们可以是负数或大于1。我不知道如何纠正它或在哪里犯了错误。
xception = Xception(include_top=False, weights='imagenet', pooling='avg')
x = Dense(5, name='prediction')(xception.output)
model = Model(xception.inputs, x)
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
# # Creating Image Generators
# Based on ImageDataGenerator class.
train_dir = 'processed_dataset/train'
val_dir = 'processed_dataset/val'
test_dir = 'processed_dataset/test'
img_width, img_height = 300, 300
input_shape = (img_width, img_height, 3)
epochs = 40
batch_size = 8
nb_train_samples = 442
nb_validation_samples = 23
nb_test_samples = 23
datagen = ImageDataGenerator(
rotation_range=40,
width_shift_range=0.2,
height_shift_range=0.2,
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True,
fill_mode='nearest')
# Generator for training
train_generator = datagen.flow_from_directory(
train_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='categorical')
# Generator for validation
val_generator = datagen.flow_from_directory(
val_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='categorical')
# Generator for testing
test_generator = datagen.flow_from_directory(
test_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='categorical')
# Learning the model using generators
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=val_generator,
validation_steps=nb_validation_samples // batch_size)
# Evaluating the accuracy score of the system
scores = model.evaluate_generator(test_generator, nb_test_samples // batch_size)
print("Accuracy on the test set: %.2f%%" % (scores[1]*100))
test_imgs, test_labels = next(test_generator)
test_labels
predictions = model.predict_generator(test_generator, steps=1, verbose=0)
predictions
现在，test_labels给我以下输出：
array([[0., 0., 1., 0., 0.],
[0., 0., 0., 0., 1.],
[1., 0., 0., 0., 0.],
[0., 0., 1., 0., 0.],
[1., 0., 0., 0., 0.],
[0., 0., 1., 0., 0.],
[0., 1., 0., 0., 0.],
[0., 0., 1., 0., 0.]], dtype=float32)
预测给了我这个：
array([[[[ 7.52097219e-02, 5.41056037e-01, -4.95246649e-02,
-8.79141092e-02, -5.37679076e-01],
[ 8.59603286e-02, 4.42841858e-01, 2.17923790e-01,
9.83133465e-02, -1.15531743e-01],
[ 6.49649799e-02, 6.98908925e-01, 1.38851732e-01,
-6.28874898e-01, -2.87516713e-01],
...,
[-5.38311005e-02, 3.90918076e-01, 8.76815915e-02,
-6.06191695e-01, -4.36177373e-01],
[ 1.97946265e-01, -1.32776499e-01, 2.11007819e-02,
-1.76965401e-01, -3.89797568e-01],
[ 2.52179474e-01, -2.43290961e-01, -4.47124951e-02,
-2.76472978e-02, -5.58846176e-01]],
[[ 1.79345787e-01, -2.39991426e-01, -7.36931980e-01,
-2.05338299e-02, -2.79670715e-01],
[ 1.93363309e-01, 6.22152686e-02, -3.61416578e-01,
-5.90571404e-01, 2.43387222e-02],
[ 4.81128693e-04, 1.02495873e+00, -5.00306845e-01,
-1.19032454e+00, -3.84473145e-01],
...,
[ 1.01383895e-01, 5.54112792e-01, -4.92577612e-01,
-9.82546687e-01, -3.58846277e-01],
[ 1.44275457e-01, 2.45612651e-01, -2.55063474e-02,
-1.36065081e-01, -5.27489424e-01],
[-4.50903028e-02, 2.70254016e-02, -1.32244378e-01,
-1.68490663e-01, -5.82786441e-01]],
[[-1.83367491e+00, -1.40455127e+00, -2.88795042e+00,
-8.70022953e-01, -3.66152048e-01],
[-1.96281028e+00, 1.41523457e+00, -1.92235279e+00,
-2.13001728e-01, -1.25367165e-01],
[-7.24048257e-01, 2.31916142e+00, -4.72892284e-01,
2.84373224e-01, 2.22759843e-01],
...,
[ 4.44787741e-01, 1.41101933e+00, -3.08741093e-01,
-1.77997321e-01, -6.85059667e-01],
[ 3.86560917e-01, 7.81786919e-01, -1.02287531e-01,
2.26198703e-01, -7.42644906e-01],
[ 4.92909461e-01, 4.09601361e-01, -1.24008060e-02,
4.90530610e-01, -3.58241975e-01]],
...,
[[ 1.34195596e-01, 9.05789196e-01, -3.27457100e-01,
-2.72707641e-02, -5.75808585e-02],
[ 3.48491669e-02, 7.73825049e-01, -8.10453951e-01,
-4.55976486e-01, 7.19135404e-02],
[ 9.90181565e-02, 1.65191579e+00, -8.58390450e-01,
-1.44752055e-01, 1.11153495e+00],
...,
[ 1.02726197e+00, 7.24114060e-01, -5.04094720e-01,
-1.10842264e+00, 7.84971535e-01],
[ 5.04721522e-01, 1.01551962e+00, -9.06502008e-01,
-1.18128228e+00, 7.52095342e-01],
[-5.78828692e-01, 4.63467002e-01, -7.49408603e-01,
-3.87779921e-01, -6.45416617e-01]],
[[-4.17019367e-01, 3.16981822e-02, -5.89770079e-03,
4.02818799e-01, -9.62504297e-02],
[-1.35728955e-01, -4.12908316e-01, 3.20006192e-01,
1.73515067e-01, -1.40590072e-01],
[ 4.33863699e-01, -3.86119306e-01, -7.91982114e-02,
-3.16189915e-01, 5.20472348e-01],
...,
[ 1.82109082e+00, 1.94166586e-01, -5.79492450e-01,
-3.93155515e-01, 5.31377137e-01],
[ 1.08623457e+00, 7.89248347e-01, -9.01886463e-01,
-4.46968585e-01, -1.74912810e-01],
[-3.41510653e-01, 4.51059341e-01, -7.65451312e-01,
-1.59630507e-01, -4.96455640e-01]],
[[-1.37914598e-01, -2.57007927e-01, -2.20677495e-01,
1.29387960e-01, 7.42561668e-02],
[ 3.57518733e-01, -2.02521026e-01, -3.28127176e-01,
1.83442786e-01, 3.61326337e-03],
[ 4.92624342e-01, -1.78903997e-01, 9.70426947e-02,
1.15486935e-01, 4.27795678e-01],
...,
[ 1.41865325e+00, -1.02405399e-02, -9.81141925e-02,
-2.44841397e-01, 2.14931667e-02],
[ 5.51047087e-01, 4.96876895e-01, -8.94319654e-01,
-3.99587899e-02, 2.81938583e-01],
[-5.39022684e-01, 2.43451580e-01, -5.92756987e-01,
1.12866879e-01, -4.50303912e-01]]],
[[[-1.83097810e-01, 6.84144497e-01, -7.69550502e-01,
8.68627727e-01, -2.22709656e-01],
[-5.92741728e-01, 1.51784450e-01, -3.88990521e-01,
1.01064312e+00, -5.72405517e-01],
[-1.39353657e+00, -1.49367034e-01, -5.02825260e-01,
1.03664172e+00, -7.53440440e-01],
...,
[ 8.51202607e-01, 7.41849661e-01, -9.04791594e-01,
-2.36053631e-01, 5.04501641e-01],
[ 2.12731928e-01, -2.85548568e-01, -9.00105357e-01,
8.05069506e-02, -4.06455994e-01],
[ 2.55032003e-01, -4.90147203e-01, -1.21454895e-01,
-2.00196147e-01, 1.09439880e-01]],
[[-8.17323148e-01, 4.44754750e-01, -8.37389469e-01,
5.46341121e-01, 4.94321972e-01],
[-2.02778125e+00, -8.33413243e-01, -2.61943251e-01,
-4.83462214e-03, 3.62069070e-01],
[-1.57397735e+00, -1.21108556e+00, -6.78366661e-01,
5.15946925e-01, 4.34342623e-02],
...,
[-2.30410457e-01, 1.23731506e+00, -2.72230577e+00,
-4.95598316e-01, 9.59013224e-01],
[-5.49526811e-01, 3.68986040e-01, -1.85951960e+00,
-3.30176592e-01, 1.01189268e+00],
[ 8.02167132e-02, -2.48757601e-02, -5.89860201e-01,
7.26686716e-02, 4.63002503e-01]],
[[-5.70342183e-01, 1.00894876e-01, -7.74569750e-01,
2.41944373e-01, -1.19897217e-01],
[-1.20720732e+00, -9.24345016e-01, -5.86426437e-01,
-1.53923392e-01, -4.62331712e-01],
[-7.13050365e-01, -1.33217061e+00, -7.86076546e-01,
-2.22010046e-01, -6.32698417e-01],
...,
[ 2.41711795e-01, 1.15276492e+00, -2.77725124e+00,
-8.71360838e-01, 1.21103406e+00],
[-5.39063096e-01, 1.46569526e+00, -3.03291845e+00,
-7.49805629e-01, 1.23009539e+00],
[ 4.72337872e-01, 7.43141711e-01, -9.98269618e-01,
-2.55082250e-01, 6.97237730e-01]],
...,
[[-8.97521913e-01, -2.18475014e-01, -4.22037899e-01,
3.81799519e-01, -4.04932141e-01],
[-5.91429949e-01, 3.82492617e-02, -1.24508452e+00,
6.72841191e-01, 1.30095959e-01],
[-1.81367898e+00, -3.50646377e-01, -1.30041862e+00,
-6.32874966e-01, -7.77092576e-03],
...,
[-1.37562728e+00, 1.80816591e-01, -1.30899990e+00,
-1.52513552e+00, -2.86536902e-01],
[-1.20952237e+00, 2.19834709e+00, -2.42691755e+00,
-3.71934325e-01, 8.93743873e-01],
[-5.38375974e-01, 7.26018965e-01, -1.27759957e+00,
4.40124482e-01, 7.08007872e-01]],
[[-1.10443616e+00, -4.09026861e-01, -1.15125775e-02,
4.27500665e-01, 4.57156301e-02],
[-8.55174303e-01, -3.66052747e-01, -5.04867315e-01,
7.32684374e-01, 2.99848855e-01],
[-1.01622462e+00, 1.60967886e-01, -5.75568676e-01,
-6.35250032e-01, -1.73604071e-01],
...,
[-7.85465896e-01, 8.28000069e-01, -2.58620930e+00,
-2.36077642e+00, 5.02218843e-01],
[-2.12391466e-02, 1.83450246e+00, -2.75204587e+00,
-6.81238770e-01, 1.00321698e+00],
[ 4.83697116e-01, 5.87888479e-01, -1.36865020e+00,
4.94212985e-01, 2.40334690e-01]],
[[-6.12910569e-01, -2.89814770e-01, 1.23900667e-01,
6.29055619e-01, -3.00407231e-01],
[-2.37547278e-01, -5.33289671e-01, -3.78314912e-01,
1.12823749e+00, 3.17725033e-01],
[-3.00657779e-01, -4.78583246e-01, -6.42191708e-01,
2.08819807e-01, 1.84970975e-01],
...,
[-7.69586027e-01, 1.42028713e+00, -1.61659014e+00,
-2.11683798e+00, 1.05798185e-01],
[-4.80629385e-01, 1.58191848e+00, -9.51906025e-01,
-7.43460655e-01, -2.61611581e-01],
[-2.59383619e-01, 6.07047081e-01, -2.16549337e-01,
4.02391434e-01, 8.53861272e-02]]],
[[[ 2.19264716e-01, 1.80342942e-01, -6.79925084e-04,
-3.85786474e-01, 1.73509344e-02],
[-9.20660198e-02, 2.60823786e-01, -4.38521773e-01,
-4.17137653e-01, -5.87219298e-01],
[ 3.35484505e-01, 4.86201435e-01, -5.05722404e-01,
1.83470428e-01, -2.42839843e-01],
...,
[ 9.95260626e-02, -6.76741302e-01, -1.55062914e+00,
-1.93915224e+00, 1.85599029e-01],
[-2.43680790e-01, -1.12695360e+00, -2.04525042e+00,
-1.72817576e+00, -3.29362571e-01],
[-2.97525853e-01, -8.73168290e-01, -1.41158104e+00,
-9.66536701e-01, -5.90973377e-01]],
[[-5.66102564e-02, 1.69532537e-01, -2.65555143e-01,
-3.28358322e-01, -3.69028330e-01],
[-2.68736541e-01, 6.79090619e-03, -3.59403759e-01,
-7.96117127e-01, -6.02259636e-01],
[-3.09453249e-01, 2.22328603e-01, -9.17185783e-01,
-5.27001619e-02, -5.10420263e-01],
...,
[-1.29235530e+00, -3.13198209e-01, -2.82991719e+00,
-2.57532525e+00, 1.07784474e+00],
[-1.09459186e+00, -5.51856875e-01, -3.68284655e+00,
-2.13552904e+00, 1.02776110e+00],
[-7.74189532e-02, -5.14578819e-01, -3.71007538e+00,
-1.45961499e+00, -7.62989104e-01]],
[[ 4.66368049e-01, 2.97895849e-01, 3.04840118e-01,
-3.83194745e-01, -7.50639558e-01],
[-2.68740952e-01, -1.41490519e-01, 2.21021920e-01,
1.15534648e-01, -1.30901277e+00],
[ 6.58517361e-01, 7.23964572e-02, -3.64375710e-02,
1.22396636e+00, -6.63648307e-01],
...,
[-1.56414342e+00, -3.52336824e-01, -3.14347076e+00,
-1.75173032e+00, 8.34757686e-01],
[-1.93781996e+00, 2.78123438e-01, -4.93014908e+00,
-1.47512770e+00, 1.46738148e+00],
[-6.08991683e-01, 7.94276834e-01, -4.98504829e+00,
-1.10413778e+00, 1.01895213e-01]],
...,
[[-5.20243824e-01, -5.51843703e-01, -2.58033603e-01,
-2.22365141e-01, -4.29325223e-01],
[-1.79190075e+00, -2.04155016e+00, 7.25815177e-01,
-4.35511291e-01, -8.17587554e-01],
[-1.64208114e+00, -1.63805628e+00, 6.83233500e-01,
3.00381541e-01, -4.50374305e-01],
...,
[-3.75656545e-01, -2.51724213e-01, -1.88207686e-01,
-3.86871547e-02, 5.79151750e-01],
[-2.75305390e-01, -8.69545639e-02, -8.39082479e-01,
-7.92900324e-02, 9.91229773e-01],
[-3.25641781e-01, -2.26463795e-01, -1.20980537e+00,
-1.17907688e-01, 2.10901588e-01]],
[[-8.56359422e-01, -1.08248591e+00, 2.49749631e-01,
-1.08880021e-01, 2.67413259e-02],
[-1.47091806e+00, -1.88285410e+00, 1.09791517e+00,
-3.22843760e-01, -1.02435136e+00],
[-1.48095536e+00, -7.62316525e-01, 1.27657712e+00,
-5.97443223e-01, -8.11216831e-01],
...,
[-3.45795989e-01, 3.11716199e-01, 1.23298287e-01,
3.41657698e-02, 5.40752113e-01],
[-5.38865387e-01, 1.06941797e-01, -1.32307172e-01,
-3.10861677e-01, 1.95661172e-01],
[-3.76878858e-01, 1.13853976e-01, -7.75436386e-02,
-3.13877136e-01, -1.24399483e-01]],
[[-4.66729999e-01, 1.28450096e-01, -4.86393988e-01,
-2.50240207e-01, -1.04141450e+00],
[-8.86957943e-01, -4.68761235e-01, -5.47460616e-02,
-3.33873957e-01, -1.32679033e+00],
[-1.26665640e+00, 3.19139212e-02, 1.09328461e+00,
-6.06237233e-01, -8.13867748e-01],
...,
[-5.84207177e-01, 5.84086537e-01, 3.98347676e-01,
2.33056396e-02, 7.90394545e-02],
[-3.50602686e-01, 1.33500606e-01, 2.33889878e-01,
3.76633927e-02, -2.46354312e-01],
[ 1.42287880e-01, 4.76878285e-01, 3.29360783e-01,
2.78180242e-02, -1.29359767e-01]]],
...,
[[[ 1.46021217e-01, 5.15166163e-01, 6.42497092e-03,
2.10047305e-01, -5.99671364e-01],
[ 2.76231736e-01, 2.98982233e-01, 2.88115889e-01,
2.32200429e-01, -6.39735460e-02],
[-4.44781214e-01, 1.45912302e+00, 4.93384957e-01,
1.35454625e-01, -2.79690027e-02],
...,
[ 8.27333927e-01, 1.83713055e+00, 1.44557953e-01,
-7.67674565e-01, 2.45759535e+00],
[ 6.98996067e-01, 4.54693258e-01, -5.64688072e-02,
-1.54869109e-01, 2.50767410e-01],
[ 9.59822536e-02, 2.38060236e-01, -1.56232610e-01,
8.41926336e-02, -2.30810076e-01]],
[[-1.09137297e-01, 1.13186799e-01, 1.16710458e-02,
-1.58278719e-02, -2.54899174e-01],
[-2.95275331e-01, 5.24003863e-01, 1.60899147e-01,
-6.97276220e-02, 2.15586126e-01],
[-4.35572237e-01, 8.93657327e-01, -3.47001195e-01,
1.43726617e-01, 1.47935033e-01],
...,
[ 9.16206092e-02, 8.24900270e-01, -3.53681266e-01,
3.79393935e-01, 1.69526446e+00],
[ 2.91864783e-01, 3.97536218e-01, -5.22625268e-01,
-8.21618140e-02, 3.25788319e-01],
[-9.19831097e-02, 2.37887740e-01, -2.47211754e-01,
-2.40494609e-02, -1.15600847e-01]],
[[-4.17404234e-01, 4.26486492e-01, -4.19323146e-03,
-4.21829522e-03, 5.19865155e-02],
[-3.43898237e-01, 1.25907421e+00, 2.32054144e-01,
7.06444740e-01, -1.23097919e-01],
[-6.78077221e-01, 2.31465411e+00, 7.59923756e-01,
8.89178813e-01, -6.57979429e-01],
...,
[-7.21525788e-01, 1.90616703e+00, -2.44078100e-01,
1.15303183e+00, 3.68118733e-01],
[-4.75895643e-01, 9.99899983e-01, -4.59014952e-01,
1.76822886e-01, 3.67279142e-01],
[-4.19832826e-01, 5.09469032e-01, -2.28665054e-01,
-8.56926441e-02, -7.13551939e-02]],
...,
[[-1.09364796e+00, 7.47790754e-01, -6.30517185e-01,
9.64763820e-01, -9.49166656e-01],
[-8.89622092e-01, 1.04416966e+00, -1.11987680e-01,
1.17960978e+00, -7.91518927e-01],
[-1.01954508e+00, 1.65915430e+00, -2.90566325e-01,
9.16439474e-01, -6.82465136e-01],
...,
[-5.70804358e-01, 1.44986546e+00, 4.54881340e-01,
1.22483063e+00, -2.18070298e-02],
[-6.16563737e-01, 1.58624887e+00, 7.11415172e-01,
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4.96826828e-01, -1.29960561e+00],
[ 7.67562211e-01, 4.83283401e-02, -6.67566299e-01,
2.28160113e-01, -1.28627563e+00]]]], dtype=float32)
这些数字是什么意思，我如何使它们看起来像正常概率？
附言projections.shape给出（8，8，8，5）作为输出。不确定这意味着什么，但是如果有帮助，我很高兴。
最佳答案
我认为您的问题在于这条线
x = Dense(5, name='prediction')(xception.output)
您尚未设置激活功能。尝试：
x = Dense(5, activation='softmax', name='prediction')(xception.output)