Python Keras：如何部分加载重量？

如何部分加载模型权重？例如，我只想使用原始图像净重（

VGG19权重\u tf\u dim\u排序\u tf\u内核\u notop.h5

）加载

VGG19

模型的

block1

：

---

此代码产生错误：

ValueError:您试图将包含16层的权重文件加载到一个包含2层的模型中。

来自Keras的vgg19应用程序模块默认具有imagenet的权重，因此我使用它加载我们在自定义模型中感兴趣的权重

input_shape = (224,224,3)

full_vgg19 = tf.keras.applications.VGG19(include_top=False, weights='imagenet', input_shape=input_shape)

def VGG19_part(full_vgg19, input_shape=None):
    
    img_input = tf.keras.layers.Input(shape=input_shape)

    # Block 1
    x = tf.keras.layers.Conv2D(64, (3, 3),
                      activation='linear',
                      padding='same',
                      name='block1_conv1')(img_input)
    x = tf.keras.layers.Activation('relu')(x)
    x = tf.keras.layers.Conv2D(64, (3, 3),
                      activation='linear',
                      padding='same',
                      name='block1_conv2')(x)
    x = tf.keras.layers.Activation('relu')(x)
    x = tf.keras.layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

    model = tf.keras.Model(img_input, x, name='vgg19')
    model.set_weights(full_vgg19.get_weights()[:4])
    
    return model

part_vgg19 = VGG19_part(full_vgg19, input_shape)

### check if the weights/bias are the same:
[(i == j).all() for i,j in zip(part_vgg19.get_weights()[:4],full_vgg19.get_weights()[:4])] # True True True True

input_shape = (224,224,3)

full_vgg19 = tf.keras.applications.VGG19(include_top=False, weights='imagenet', input_shape=input_shape)

def VGG19_part(full_vgg19, input_shape=None):
    
    img_input = tf.keras.layers.Input(shape=input_shape)

    # Block 1
    x = tf.keras.layers.Conv2D(64, (3, 3),
                      activation='linear',
                      padding='same',
                      name='block1_conv1')(img_input)
    x = tf.keras.layers.Activation('relu')(x)
    x = tf.keras.layers.Conv2D(64, (3, 3),
                      activation='linear',
                      padding='same',
                      name='block1_conv2')(x)
    x = tf.keras.layers.Activation('relu')(x)
    x = tf.keras.layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)

    model = tf.keras.Model(img_input, x, name='vgg19')
    model.set_weights(full_vgg19.get_weights()[:4])
    
    return model

part_vgg19 = VGG19_part(full_vgg19, input_shape)

### check if the weights/bias are the same:
[(i == j).all() for i,j in zip(part_vgg19.get_weights()[:4],full_vgg19.get_weights()[:4])] # True True True True