Question

我使用以下代码（由here提供）运行CNN来训练MNIST图像：

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

batch_size = 128
num_classes = 10
epochs = 1

# input image dimensions
img_rows, img_cols = 28, 28

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)

x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])
model.fit(x_train, y_train,
          batch_size=batch_size,
          epochs=epochs,
          verbose=1,
          validation_data=(x_test, y_test))

print(model.save_weights('file.txt')) # <<<<<----

score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

我的目标是使用CNN模型将MNIST特征提取到数据集中，我可以将其用作另一个分类器的输入。在这个例子中，我不关心分类操作，因为我需要的只是训练图像的特征。我找到的唯一方法是save_weights：

print(model.save_weights('file.txt'))

如何从keras模型中将特征提取到数据集中？

Answer 1

训练或加载现有训练模型后，您可以创建另一个模型：

extract = Model(model.inputs, model.layers[-3]) # Dense(128,...)
features = extract.predict(data)

并使用.predict方法从特定图层返回矢量，在这种情况下，每个图像将变为（128，），Dense（128，...）图层的输出。

您还可以使用functional API与2个输出一起训练这些网络。按照指南进行操作，您将看到可以将模型链接在一起，并且每个输出可能有单独的损失。这将允许您的模型学习共享功能，这对于同时对MNIST图像和任务进行分类非常有用。

从Keras模型中将特征提取到数据集中

1 个答案: