Python 最后一层上的Keras特征提取

我想在展平后保存图层的特征向量。我该怎么做？作为输入，我想给出所有测试图像，让它预测结果，但在分类层之前，我需要提取网络学习的特征，并将其保存为向量。可能吗

这是我的密码：

from keras.datasets import mnist
from keras.utils import to_categorical
from keras import layers
from keras import models

(train_img,train_label), (test_img, test_label) = mnist.load_data()

#preprocessing
train_img = train_img.reshape((60000,28,28,1))
train_img = train_img.astype('float32')/255

test_img = test_img.reshape((10000,28,28,1))
test_img = test_img.astype('float32')/255


train_label = to_categorical(train_label)
test_label = to_categorical(test_label)

# model

model = models.Sequential()
model.add(layers.Conv2D(32, (3,3), activation='relu', input_shape=(28,28,1)))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64,(3,3), activation='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64,(3,3),activation='relu'))
#check summary for output
#model.summary()

model.add(layers.Flatten())

# !!! I need the a vector of features that this layer learned!!!!
model.add(layers.Dense(64,activation='relu'))


model.add(layers.Dense(10,activation='softmax'))

#model.summary()
model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

# training

model.fit(train_img, train_label, epochs=5, batch_size=64)

---

可以为特定图层设置名称：

model.add(layers.Dense(64,activation='relu', name='features'))

完成训练后，您可以获得重量：

model.get_layer('features').get_weights()[0]