如何使用keras predict_proba 输出2列概率？

Question

我用这段代码预测x_test中0和1的概率，但结果只有一列概率。真不知道这个列的概率是0的概率还是1的概率。

import numpy as np
from keras.models import Sequential
from keras.layers import Dense

data_train = np.array([
[0, 0, 0],
[0, 1, 0],
[0, 2, 0],
[0, 3, 0],
[1, 0, 0],
[2, 0, 0],
[3, 0, 0],
[1, 1, 1],
[2, 1, 1],
[1, 2, 1],
[3, 1, 1],
])

data_test = np.array([
[1, 3],
[0, 4],
[5, 0]
])

x_train = data_train[:, :-1]
y_train = data_train[:, -1]
x_test = data_test

model = Sequential()
model.add(Dense(512, activation='relu', input_dim=2))
model.add(Dense(200, activation='relu'))
model.add(Dense(200, activation='relu'))
model.add(Dense(128, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop',
              loss='binary_crossentropy',
              metrics=['binary_accuracy'])

model.fit(x_train, y_train, epochs=5, batch_size=1, verbose=1)
predict = model.predict_proba(x_test, batch_size=1)
print(predict)

结果只有 1 列：

[[0.9431795]
 [0.47065434]
 [0.08615088]]

我要2列概率，第一列是0的概率，第二列是1的概率，比如这样：

 [[0.23334,0.76267]
    ……
 [0.84984,0.15685]
 [0.16663,0.83291]]

如何解决？

Answer 1

首先，你需要将y_train转换为one-hot encoding by

from sklearn.preprocessing import LabelEncoder
from keras.utils import np_utils

encoder = LabelEncoder()
encoder.fit(y_train)
encoded_y = encoder.transform(y_train)
y_train = np_utils.to_categorical(encoded_y)

运行这段代码，y_train 会变成

array([[1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [1., 0.],
       [0., 1.],
       [0., 1.],
       [0., 1.],
       [0., 1.]], dtype=float32)

其次，需要将输出层改为

model.add(Dense(2, activation='softmax'))

通过这两个修改，您将获得所需的输出。