【发布时间】：2018-03-25 14:22:02
【问题描述】：
在使用新数据对 keras 上的 Inception v3 CNN 进行微调后，我正在尝试从添加的 Dense 层中提取特征向量。基本上，我加载网络结构及其权重，添加两个密集层（我的数据是针对两类问题的）并仅从网络的某些部分更新权重，如下面的代码所示：
# 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))
这段代码运行得很好，不是我的问题。
我的问题是，在微调了这个网络之后，我想要训练和测试数据上最后一层的输出，因为我想使用这个新网络作为特征提取器。我想要你可以在上面的代码中看到的这部分网络的输出：
x = Dense(64, activation='relu')(x)
我尝试了以下代码，但它不起作用：
from keras import backend as K
inputs = [K.learning_phase()] + model.inputs
_convout1_f = K.function(inputs, model.get_layer(dense_1).output)
错误如下
_convout1_f = K.function(inputs, model.get_layer(dense_1).output)
NameError: global name 'dense_1' is not defined
在对新数据中的预训练网络进行微调后，如何从添加的新层中提取特征？我在这里做错了什么？
【问题讨论】：
标签： python tensorflow deep-learning keras