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我正在与Keras NN与Theanos后端，我正在处理与14输出类的分类问题。我想要预测的类加上相关的概率。问题是predict_proba（）的概率似乎不符合predict（）的预测类，下面是代码加上1个样本的结果输出。Keras分类器predict_proba（）不符合预测（）
PPRANK = ['pp1', 'pp2', 'pp3', 'pp4', 'pp5', 'pp6', 'pp7', 'pp8', 'pp9', 'pp10', 'pp11', 'pp12', 'pp13', 'pp14', 'pp15']
FEATURES = (PPRANK)
# fix random seed for reproducibility
seed = 7
np.random.seed(seed)
data_df = pd.DataFrame.from_csv("data.csv")
X = np.array(data_df[FEATURES].values)
Y = (data_df["bres"].replace(14,13).values)
# define baseline model
def baseline_model():
# create model
model = Sequential()
model.add(Dense(8, input_dim=(len(FEATURES)), init='normal', activation='relu'))
model.add(Dense(14, init='normal', activation='softmax'))
# Compile model
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
#build model
estimator = KerasClassifier(build_fn=baseline_model, nb_epoch=200, batch_size=5, verbose=0)
#split train and test
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.1, random_state=seed)
estimator.fit(X_train, Y_train)
#get probabilities
predictions = estimator.predict_proba(X_test)
#convert expon to floats
probs = [[] for x in range(21)]
tick2 = 0
for i in range(len(predictions)):
tick = 0
for x in xrange(14):
(predictions[i][(tick)]) = '%.4f' % (predictions[i][(tick)])
probs[(tick2)].append((predictions[i][(tick)]))
tick += 1
tick2 += 1
# pprint probabilities
pp = pprint.PrettyPrinter(indent=0)
pp.pprint(probs)
#print class predictions
print estimator.predict(X_test)
print Y_test
概率
[0.00000，0.00030，0.02360，0.04329，0.00019，0.00069，0.00120，0.00030，0.00559，0.00410，0.00510，0.91549，0.0，0.0]
预测类
实际的类
它显示12具有来自predict_proba（）的最高概率，而不是来自predict（）的11。感谢您的任何帮助。