1
我想在用新數據微調keras上的Inception v3 CNN之後，從增加的密集層中提取特徵向量。基本上，我加載網絡結構和它的重量，從網絡的僅僅一些部分添加兩個緻密層（我的數據爲2類的問題），並更新權重，如下所述代碼顯示：如何在keras中的微調網絡中提取特徵向量
# create the base pre-trained model
base_model = InceptionV3(weights='imagenet', include_top=False)
# add a global spatial average pooling layer
x = base_model.output
x = GlobalAveragePooling2D()(x)
# let's add a fully-connected layer
x = Dense(64, activation='relu')(x)
# and a logistic layer -- I have 2 classes only
predictions = Dense(2, activation='softmax')(x)
# this is the model to train
model = Model(inputs=base_model.input, outputs=predictions)
# first: train only the top layers (which were randomly initialized)
# i.e. freeze all convolutional InceptionV3 layers
for layer in base_model.layers:
layer.trainable = False
# compile the model (should be done *after* setting layers to non-trainable)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
#load new training data
x_train, x_test, y_train, y_test =load_data(train_data, test_data, train_labels, test_labels)
datagen = ImageDataGenerator()
datagen.fit(x_train)
epochs=1
batch_size=32
# train the model on the new data for a few epochs
model.fit_generator(datagen.flow(x_train, y_train,
batch_size=batch_size),
steps_per_epoch=x_train.shape[0] //
batch_size,
epochs=epochs,
validation_data=(x_test, y_test))
# at this point, the top layers are well trained and
#I can start fine-tuning convolutional layers from inception V3.
#I will freeze the bottom N layers and train the remaining top layers.
#I chose to train the top 2 inception blocks, i.e. I will freeze the
#first 249 layers and unfreeze the rest:
for layer in model.layers[:249]:
layer.trainable = False
for layer in model.layers[249:]:
layer.trainable = True
# I need to recompile the model for these modifications to take effect
# I use SGD with a low learning rate
from keras.optimizers import SGD
model.compile(optimizer=SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy', metrics=['binary_accuracy'])
# I train our model again (this time fine-tuning the top 2 inception blocks alongside the top Dense layers
model.fit_generator(datagen.flow(x_train, y_train,
batch_size=batch_size),
steps_per_epoch=x_train.shape[0] //
batch_size,
epochs=epochs,
validation_data=(x_test, y_test))
此代碼運行得非常好，它不是我的問題。
我的問題是，經過微調這個網絡，我想從我的列車上的最後一個圖層輸出，並測試數據，因爲我想使用這個新的網絡作爲特徵提取器。我想從網絡上的這部分輸出，你可以在上面的代碼中看到：
x = Dense(64, activation='relu')(x)
我試着下面的代碼，但它不工作：
from keras import backend as K
inputs = [K.learning_phase()] + model.inputs
_convout1_f = K.function(inputs, model.get_layer(dense_1).output)
的錯誤是以下
_convout1_f = K.function(inputs, model.get_layer(dense_1).output)
NameError: global name 'dense_1' is not defined
如何在我的新數據中對預先訓練好的網絡進行微調之後，從添加的新圖層中提取特徵？我在這裏做錯了什麼？