Keras对标签的二元分类概率

二元分类的Keras预测输出是概率。不是类，即1或0。 例如，下面的代码生成概率

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

# Generate dummy data
x_train = np.random.random((100, 20))
y_train = np.random.randint(2, size=(100, 1))
x_test = np.random.random((10, 20))
y_test = np.random.randint(2, size=(10, 1))

model = Sequential()
model.add(Dense(64, input_dim=20, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy',optimizer='rmsprop',metrics=['accuracy'])

model.fit(x_train, y_train,  epochs=20,    batch_size=128)
y_predicted = model.predict(x_test)
print(y_predicted)

输出为：

Epoch 1/20
100/100 [==============================] - 1s 5ms/step - loss: 0.8134 - acc: 0.4300
Epoch 2/20
100/100 [==============================] - 0s 17us/step - loss: 0.7429 - acc: 0.4600
Epoch 3/20
100/100 [==============================] - 0s 20us/step - loss: 0.7511 - acc: 0.4300
Epoch 4/20
100/100 [==============================] - 0s 18us/step - loss: 0.7408 - acc: 0.5000
Epoch 5/20
100/100 [==============================] - 0s 21us/step - loss: 0.6922 - acc: 0.5700
Epoch 6/20
100/100 [==============================] - 0s 31us/step - loss: 0.6874 - acc: 0.5600
Epoch 7/20
100/100 [==============================] - 0s 29us/step - loss: 0.7005 - acc: 0.5600
Epoch 8/20
100/100 [==============================] - 0s 23us/step - loss: 0.6960 - acc: 0.5200
Epoch 9/20
100/100 [==============================] - 0s 24us/step - loss: 0.6988 - acc: 0.5200
Epoch 10/20
100/100 [==============================] - 0s 26us/step - loss: 0.7276 - acc: 0.4000
Epoch 11/20
100/100 [==============================] - 0s 20us/step - loss: 0.6967 - acc: 0.5000
Epoch 12/20
100/100 [==============================] - 0s 30us/step - loss: 0.7085 - acc: 0.5000
Epoch 13/20
100/100 [==============================] - 0s 24us/step - loss: 0.6993 - acc: 0.5500
Epoch 14/20
100/100 [==============================] - 0s 26us/step - loss: 0.7278 - acc: 0.4600
Epoch 15/20
100/100 [==============================] - 0s 27us/step - loss: 0.6665 - acc: 0.5500
Epoch 16/20
100/100 [==============================] - 0s 24us/step - loss: 0.6784 - acc: 0.5500
Epoch 17/20
100/100 [==============================] - 0s 24us/step - loss: 0.7259 - acc: 0.4800
Epoch 18/20
100/100 [==============================] - 0s 26us/step - loss: 0.7093 - acc: 0.5500
Epoch 19/20
100/100 [==============================] - 0s 28us/step - loss: 0.6911 - acc: 0.5700
Epoch 20/20
100/100 [==============================] - 0s 34us/step - loss: 0.6771 - acc: 0.5500
[[0.4875336 ]
 [0.47847825]
 [0.4808622 ]
 [0.5032022 ]
 [0.4556646 ]
 [0.48644704]
 [0.4600153 ]
 [0.47782585]
 [0.49664593]
 [0.5001673 ]]

现在，我怎样才能从概率中得到类呢？我尝试手动设置如下阈值：

print([1 if x >0.4 else 0 for x in y_predicted])

---

Keras API是否有其他方法来实现这一点？我找不到。

是的，model.predict\u类

model.predict_classes(x_test)

预测课程

对于二进制分类，它使用0.5的阈值；对于多类分类，它使用argmax