在运行时使用keras从自动编码器模型的隐藏图层中保存要素

Question

我正在训练一个自动编码器模型，并希望在运行时从编码器部分保存每个图像的特征，并在以后用于特征匹配。

我的模型结构是-

train_data_dir = '/train'
test_data_dir = '/test'
nb_train_samples = 100
nb_validation_samples = 25
nb_epoch = 2
batch_size = 5
input_img = Input(shape=( img_width, img_height,3))

x = Conv2D(128, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)

x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (3, 3), activation='relu',padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(64, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(128, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')

def fixed_generator(generator):
    for batch in generator:
        yield (batch, batch)

train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)
# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
        train_data_dir,
        target_size=(img_width, img_height),
        batch_size=batch_size,
        class_mode=None)
#print(type(train_generator))
test_generator = test_datagen.flow_from_directory(
        test_data_dir,
        target_size=(img_width, img_height),
        batch_size=batch_size,
        class_mode=None)

autoencoder.fit_generator(
        fixed_generator(train_generator),
        epochs=nb_epoch,
        samples_per_epoch=nb_train_samples,
        validation_data=fixed_generator(test_generator),
        validation_steps=nb_validation_samples
        )

我如何在模型拟合期间从编码器零件保存特征。有什么办法，请建议

Answer 1

要从自动编码器模型中保存特征，首先需要加载模型并从模型中提取最后一个编码器层。

这里是提取编码器层并保存图像特征的代码：

autoencoder= load_model('model_weights.h5')
encoder = Model(autoencoder.input, autoencoder.layers[-1].output)

# Read your input image for which you need to extract features

img = cv2.imread(img)
newimg = cv2.resize(img,(512,512))
encoded_imgs = encoder.predict(newimg[None])[0]

# Save your features as an numpy array

np.save('features.npy', encoded_imgs)

将特征保存为输入图像后，您需要找到用于特征匹配的欧式距离。

file = 'sample.npy'
file = np.load(file)
file1 = "features.npy"
file1 = np.load(file1)
distance = np.linalg.norm(file-file1)
print(distance)

此代码将从图像中提取特征并计算两个numpy数组之间的距离。

Answer 2

Keras Functional API是关键字，甚至在自动编码器上也有非常有见地的blog post。

以下是该示例摘自博客文章的重点：

input_img = Input(shape=(784,))
encoded = Dense(encoding_dim, activation='relu')(input_img)
decoded = Dense(784, activation='sigmoid')(encoded)
# this model maps an input to its reconstruction
autoencoder = Model(input_img, decoded)
# this model maps an input to its encoded representation
encoder = Model(input_img, encoded)

现在您可以将autoencoder.fit(...,epochs=1)方法放入for循环中，并使用encoder.predict(your_data)查看编码的表示形式以保存特征。