我正在训练一个自动编码器模型，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
)
如何在模型拟合期间保存编码器部件的特征。有什么办法吗，请建议
要保存自动编码器模型中的功能，首先需要加载模型并从模型中提取最后一个编码器层。
以下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)
在为输入图像保存特征后，您需要找到用于特征匹配的欧氏距离。
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。
Keras Functional API是关键词，甚至还有一篇关于自动编码器的非常有见地的博客文章。
以下是取自博客文章的示例的要点：
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)查看编码表示以保存特征。
在一个类似的文本数据任务中，我从一个经过训练的模型中提取编码器并保存下来。我加载编码器并执行encoder.predict(input_sample)以生成编码矢量(用于检索目的(。然而，每次我加载和预测时，输出都会发生变化(保持相同的输入(。这种行为正常吗？
我认为你应该使用
encoder = Model(input_img , encoded)
而不是
encoder = Model(autoencoder.input, autoencoder.layers[-1].output)
因为autoencoder.layers[-1].output表示解码器层，它是重建的原始图像