我试图做一个简单的概念验证，在那里我可以看到给定预测的不同类的概率。
但是，我尝试的所有内容似乎只输出预测的类，即使我使用softmax激活。我是机器学习的新手，所以我不确定我是否犯了一个简单的错误，或者这是否是Keras中没有的功能。
我正在使用Keras + TensorFlow。我已经改编了Keras给出的basic examples之一来对MNIST数据集进行分类。
下面的代码与示例完全相同，只是一些（注释的）额外行将模型导出到本地文件。
'''Trains a simple deep NN on the MNIST dataset.
Gets to 98.40% test accuracy after 20 epochs
(there is *a lot* of margin for parameter tuning).
2 seconds per epoch on a K520 GPU.
'''
from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop
import h5py # added import because it is required for model.save
model_filepath = 'test_model.h5' # added filepath config
batch_size = 128
num_classes = 10
epochs = 20
# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))
model.summary()
model.compile(loss='categorical_crossentropy',
optimizer=RMSprop(),
metrics=['accuracy'])
history = model.fit(x_train, y_train,
batch_size=batch_size,
epochs=epochs,
verbose=1,
validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
model.save(model_filepath) # added saving model
print('Model saved') # added log
然后，第二部分是一个简单的脚本，它应该导入模型，预测某些给定数据的类，并打印出每个类的概率。 （我使用Keras代码库中包含的相同的mnist类来尽可能简单地制作示例。）
import keras
from keras.datasets import mnist
from keras.models import Sequential
import keras.backend as K
import numpy
# loading model saved locally in test_model.h5
model_filepath = 'test_model.h5'
prev_model = keras.models.load_model(model_filepath)
# these lines are copied from the example for loading MNIST data
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 784)
# for this example, I am only taking the first 10 images
x_slice = x_train[slice(1, 11, 1)]
# making the prediction
prediction = prev_model.predict(x_slice)
# logging each on a separate line
for single_prediction in prediction:
print(single_prediction)
如果我运行第一个脚本来导出模型，然后是第二个脚本来分类一些例子，我得到以下输出：
[ 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
[ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]
[ 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
[ 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]
[ 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]
这非常适合查看每个类的预测，但如果我想查看每个示例的每个类的相对概率怎么办？我正在寻找更像这样的东西：
[ 0.94 0.01 0.02 0. 0. 0.01 0. 0.01 0.01 0.]
[ 0. 0. 0. 0. 0.51 0. 0. 0. 0.49 0.]
...
换句话说，我需要知道每个预测的确定程度，而不仅仅是预测本身。我认为看到相对概率是模型中使用softmax激活的一部分，但我似乎无法在Keras文档中找到任何可以给出概率而不是预测答案的东西。我是否犯了某种愚蠢的错误，或者这个功能不可用？
答案 0 :(得分：8)
事实证明问题是我没有完全规范化预测脚本中的数据。
我的预测脚本应该有以下几行：
# these lines are copied from the example for loading MNIST data
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(60000, 784)
x_train = x_train.astype('float32') # this line was missing
x_train /= 255 # this line was missing too
因为数据没有被转换为float，除以255（因此它将介于0和1之间），它只是显示为1和0。
答案 1 :(得分：3)
Keras predict确实返回了概率，而不是类。
无法使用我的系统配置重现您的问题：
Python version 2.7.12
Tensorflow version 1.3.0
Keras version 2.0.9
Numpy version 1.13.3
以下是我的x_slice预测输出与加载的模型（训练20个时期，如代码中所示）：
print(prev_model.predict(x_slice))
# Result:
[[ 1.00000000e+00 3.31656316e-37 1.07806675e-21 7.11765177e-30
2.48000320e-31 5.34837679e-28 3.12470132e-24 4.65175406e-27
8.66994134e-31 5.26426367e-24]
[ 0.00000000e+00 5.34361977e-30 3.91144999e-35 0.00000000e+00
1.00000000e+00 0.00000000e+00 1.05583665e-36 1.01395577e-29
0.00000000e+00 1.70868685e-29]
[ 3.99137559e-38 1.00000000e+00 1.76682222e-24 9.33333581e-31
3.99846307e-15 1.17745576e-24 1.87529709e-26 2.18951752e-20
3.57518280e-17 1.62027896e-28]
[ 6.48006586e-26 1.48974980e-17 5.60530329e-22 1.81973780e-14
9.12573406e-10 1.95987500e-14 8.08566866e-27 1.17901132e-12
7.33970447e-13 1.00000000e+00]
[ 2.01602060e-16 6.58242856e-14 1.00000000e+00 6.84244084e-09
1.19809885e-16 7.94907624e-14 3.10690434e-19 8.02848586e-12
4.68330721e-11 5.14736501e-15]
[ 2.31014903e-35 1.00000000e+00 6.02224725e-21 2.35928828e-23
7.50006509e-15 4.06930881e-22 1.13288827e-24 4.20440718e-17
4.95182972e-17 1.85492109e-18]
[ 0.00000000e+00 0.00000000e+00 0.00000000e+00 1.00000000e+00
0.00000000e+00 6.30200370e-27 0.00000000e+00 5.19937755e-33
1.63205659e-31 1.21508034e-20]
[ 1.44608573e-26 1.00000000e+00 1.78712268e-18 6.84598301e-19
1.30042071e-11 2.53873986e-14 5.83169942e-17 1.20201071e-12
2.21844570e-14 3.75015198e-15]
[ 0.00000000e+00 6.29184453e-34 9.22474943e-29 0.00000000e+00
1.00000000e+00 3.05067233e-34 1.43097161e-28 1.34234082e-29
4.28647272e-36 9.29760838e-34]
[ 4.68828449e-30 5.55172479e-20 3.26705529e-19 9.99999881e-01
3.49577992e-22 1.27715460e-11 4.99185615e-36 1.19164204e-20
4.21086124e-16 1.52631387e-07]]
我怀疑打印时会出现一些舍入问题（或者你已经训练了更多的时代，而且训练集的概率已经非常接近1）...
为了说服自己确实得到了概率而不是阶级预测，我建议尝试从你的模型中获得单个时代的预测;通常情况下，您应该会看到更少1.0个 - 这里的model培训epochs=1就是这种情况：
print(model.predict(x_slice))
# Result:
[[ 9.99916673e-01 5.36548761e-08 6.10747229e-05 8.21199933e-07
6.64725164e-08 6.78853041e-07 9.09637220e-06 4.56192402e-06
1.62688798e-06 5.23997733e-06]
[ 7.59836894e-07 1.78043920e-05 1.79073555e-04 2.95592145e-05
9.98031914e-01 1.75839632e-05 5.90557102e-06 1.27705920e-03
3.94643757e-06 4.36416740e-04]
[ 4.48473330e-08 9.99895334e-01 2.82608235e-05 5.33154832e-07
9.78453227e-06 1.58954310e-06 3.38150176e-06 5.26260410e-05
8.09341054e-06 3.28643267e-07]
[ 7.38236849e-07 4.80247072e-05 2.81726116e-05 4.77648537e-05
7.21933879e-03 2.52177160e-05 3.88786475e-07 3.56770557e-04
2.83472677e-04 9.91990149e-01]
[ 5.03611082e-05 2.69402866e-04 9.92011130e-01 4.68175858e-03
9.57477605e-05 4.26214538e-04 7.66683661e-05 7.05923303e-04
1.45670515e-03 2.26032615e-04]
[ 1.36330849e-10 9.99994516e-01 7.69141934e-07 1.44130311e-07
9.52201333e-07 1.45219332e-07 4.43408908e-07 6.93398249e-07
2.18685204e-06 1.50741769e-07]
[ 2.39427478e-09 3.75754922e-07 3.89349816e-06 9.99889374e-01
1.85837867e-09 1.16176770e-05 1.89989760e-11 3.12301523e-07
1.13220040e-05 8.29571582e-05]
[ 1.45760115e-08 9.99900222e-01 3.67058942e-06 4.04857201e-06
1.97999962e-05 7.85745397e-06 8.13850420e-06 1.87294081e-05
2.81870762e-05 9.38157609e-06]
[ 7.52560858e-09 8.84437856e-09 9.71140025e-07 5.20911703e-10
9.99986649e-01 3.12135370e-07 1.06521384e-05 1.25693066e-06
7.21853368e-08 5.21001624e-08]
[ 8.67672298e-08 2.17907742e-04 2.45352840e-06 9.95455265e-01
1.43749105e-06 1.51766278e-03 1.83744309e-08 3.83995541e-07
9.90309782e-05 2.70584645e-03]]