Keras VGG16微调

3 回答

• 7

  我认为vgg网络描述的权重不适合你的模型，错误源于此 . 无论如何，有一种更好的方法来使用网络本身，如（https://keras.io/applications/#vgg16）中所述 .

  你可以使用：

  base_model = keras.applications.vgg16.VGG16(include_top=False, weights='imagenet', input_tensor=None, input_shape=None)
  

  实例化预训练的vgg网 . 然后你可以冻结图层并使用模型类来实例化你自己的模型，如下所示：

  x = base_model.output
  x = Flatten()(x)
  x = Dense(your_classes, activation='softmax')(x) #minor edit
  new_model = Model(input=base_model.input, output=x)
  

  要结合底部和顶部网络，您可以使用以下代码段 . 使用以下函数（输入层（https://keras.io/getting-started/functional-api-guide/）/ load_model（https://keras.io/getting-started/faq/#how-can-i-save-a-keras-model）和keras的功能API）：

  final_input = Input(shape=(3, 224, 224))
  base_model = vgg...
  top_model = load_model(weights_file)
  
  x = base_model(final_input)
  result = top_model(x)
  final_model = Model(input=final_input, output=result)
  

  回复于 2024-05-03T18:18:50+08:00
• 3

  我认为您可以通过执行以下操作来连接两者：

  #load vgg model
  vgg_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(150, 150, 3))
  print('Model loaded.')
  
  #initialise top model
  top_model = Sequential()
  top_model.add(Flatten(input_shape=vgg_model.output_shape[1:]))
  top_model.add(Dense(256, activation='relu'))
  top_model.add(Dropout(0.5))
  top_model.add(Dense(1, activation='sigmoid'))
  
  
  top_model.load_weights(top_model_weights_path)
  
  # add the model on top of the convolutional base
  
  model = Model(input= vgg_model.input, output= top_model(vgg_model.output))
  

  此解决方案引用示例Fine-tuning the top layers of a a pre-trained network . 完整代码可以找到here .

  回复于 2024-05-03T18:18:50+08:00
• 1

  好吧，我想托马斯和Gowtham发布了正确的（更简洁的答案），但我想分享代码，我能够成功运行：

  def train_finetuned_model(lr=1e-5, verbose=True):
      file_path = get_file('vgg16.h5', VGG16_WEIGHTS_PATH, cache_subdir='models')
      if verbose:
          print('Building VGG16 (no-top) model to generate bottleneck features.')
  
      vgg16_notop = build_vgg_16()
      vgg16_notop.load_weights(file_path)
      for _ in range(6):
          vgg16_notop.pop()
      vgg16_notop.compile(optimizer=RMSprop(lr=lr), loss='categorical_crossentropy', metrics=['accuracy'])    
  
      if verbose:
          print('Bottleneck features generation.')
  
      train_batches = get_batches('train', shuffle=False, class_mode=None, batch_size=BATCH_SIZE)
      train_labels = np.array([0]*1000 + [1]*1000)
      train_bottleneck = vgg16_notop.predict_generator(train_batches, steps=2000 // BATCH_SIZE)
      valid_batches = get_batches('valid', shuffle=False, class_mode=None, batch_size=BATCH_SIZE)
      valid_labels = np.array([0]*400 + [1]*400)
      valid_bottleneck = vgg16_notop.predict_generator(valid_batches, steps=800 // BATCH_SIZE)
  
      if verbose:
          print('Training top model on bottleneck features.')
  
      top_model = Sequential()
      top_model.add(Flatten(input_shape=train_bottleneck.shape[1:]))
      top_model.add(Dense(4096, activation='relu'))
      top_model.add(Dropout(0.5))
      top_model.add(Dense(4096, activation='relu'))
      top_model.add(Dropout(0.5))
      top_model.add(Dense(2, activation='softmax'))
      top_model.compile(optimizer=RMSprop(lr=lr), loss='categorical_crossentropy', metrics=['accuracy'])
      top_model.fit(train_bottleneck, to_categorical(train_labels),
                    batch_size=32, epochs=10,
                    validation_data=(valid_bottleneck, to_categorical(valid_labels)))
  
      if verbose:
          print('Concatenate new VGG16 (without top layer) with pretrained top model.')
  
      vgg16_fine = build_vgg_16()
      vgg16_fine.load_weights(file_path)
      for _ in range(6):
          vgg16_fine.pop()
      vgg16_fine.add(Flatten(name='top_flatten'))    
      vgg16_fine.add(Dense(4096, activation='relu'))
      vgg16_fine.add(Dropout(0.5))
      vgg16_fine.add(Dense(4096, activation='relu'))
      vgg16_fine.add(Dropout(0.5))
      vgg16_fine.add(Dense(2, activation='softmax'))
      vgg16_fine.compile(optimizer=RMSprop(lr=lr), loss='categorical_crossentropy', metrics=['accuracy'])
  
      if verbose:
          print('Loading pre-trained weights into concatenated model')
  
      for i, layer in enumerate(reversed(top_model.layers), 1):
          pretrained_weights = layer.get_weights()
          vgg16_fine.layers[-i].set_weights(pretrained_weights)
  
      for layer in vgg16_fine.layers[:26]:
          layer.trainable = False
  
      if verbose:
          print('Layers training status:')
          for layer in vgg16_fine.layers:
              print('[%6s] %s' % ('' if layer.trainable else 'FROZEN', layer.name))        
  
      vgg16_fine.compile(optimizer=RMSprop(lr=1e-6), loss='binary_crossentropy', metrics=['accuracy'])
  
      if verbose:
          print('Train concatenated model on dogs/cats dataset sample.')
  
      train_datagen = ImageDataGenerator(rescale=1./255,
                                         shear_range=0.2,
                                         zoom_range=0.2,
                                         horizontal_flip=True)
      test_datagen = ImageDataGenerator(rescale=1./255)
      train_batches = get_batches('train', gen=train_datagen, class_mode='categorical', batch_size=BATCH_SIZE)
      valid_batches = get_batches('valid', gen=test_datagen, class_mode='categorical', batch_size=BATCH_SIZE)
      vgg16_fine.fit_generator(train_batches, epochs=100,
                               steps_per_epoch=2000 // BATCH_SIZE,
                               validation_data=valid_batches,
                               validation_steps=800 // BATCH_SIZE)
      return vgg16_fine
  

  它有点过于冗长，并且可以手动完成所有操作（即将重量从预训练层复制到连接模型），但它或多或少都有效 .

  虽然我发布的这个代码有一个低精度的问题（大约70％），但这是一个不同的故事 .

  回复于 2024-05-03T18:18:50+08:00