keras - 获得每个 class 的概率

我试图从keras模型中获得每个类的概率 . 请在下面找到样本keras模型：

width = 80
height = 80
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=( width, height, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(Activation('relu'))
#model.add(Dropout(0.5))
model.add(Dense(2))
model.add(Activation('softmax'))

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

但是，在训练模型后，我通过以下方式加载要预测的图像：

img = image.load_img('Test2.jpg', target_size=(80, 80))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
images = np.vstack([x])
classes = model.predict_proba(images, batch_size=1)
print(classes)

[[ 0.  1.]]

我仍然得到类标签，而不是概率 . 有什么提示我做错了什么？

EDIT 这就是模型的训练方式：

train_datagen = ImageDataGenerator(
        rotation_range=40,
        width_shift_range=0.2,
        height_shift_range=0.2,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True,
        fill_mode='nearest')

# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(
        rotation_range=40,
        width_shift_range=0.2,
        height_shift_range=0.2,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True,
        fill_mode='nearest')


train_generator = train_datagen.flow_from_directory(
        '.\\train',  # this is the target directory
        target_size=(width, height),  # all images will be resized to 150x150
        batch_size=batch_size,
        class_mode='binary',
        shuffle=True)  # since we use binary_crossentropy loss, we need binary labels

# this is a similar generator, for validation data
validation_generator = test_datagen.flow_from_directory(
        '.\\validate',
        target_size=(width, height),
        batch_size=batch_size,
        class_mode='binary',
        shuffle=True)

model.fit_generator(
        train_generator,
        steps_per_epoch=4000,
        epochs=2,
        validation_data=validation_generator,
        validation_steps=1600)