我试图制作一个简单的概念验证，以便查看给定预测的不同类别的概率。
然而，我尝试的所有方法似乎只输出预测的类别，即使我使用softmax激活。由于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Keras中没有提供的功能。
我正在使用Keras + TensorFlow。我已经改编了Keras为MNIST数据集分类提供的基本示例之一。
我的代码与示例完全相同，只是有几行（已注释）额外的代码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
然后这个内容的第二部分是一个简单的脚本，应该导入模型，针对给定的一些数据进行分类预测，并打印出每个类别的概率。(我使用了包含在Keras代码库中的相同mnist类别，以尽可能简单的例子来说明)
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导出模型，然后运行第二个脚本对一些示例进行分类，我将得到以下输出：
[ 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.]
这对于查看每个类别的预测非常好，但是如果我想看到每个示例的每个类别的相对概率怎么办？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.]
...