常见与训练模型使用方式

发表于 | 分类于
  • 生成特征图
  • 加GlobeAveragePooling
  • 输出比较

生成特征图

见上一篇文章，也可参见此

直接使用

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#导入Xception
conv_base = Xception(weights=
'imagenet',#指向模型初始化的权重检查点
                  include_top=False,#指定模型是否包含密集连接分类器
                  input_shape=(299, 299, 3)#输入到网络的张量形状
                 )
conv_base.summary()
#解冻
# conv_base.trainable = True
# set_trainable = False
# for layer in conv_base.layers:
# if layer.name == 'block13_pool (MaxPooling2D) ':
# set_trainable = True
# if set_trainable:
# layer.trainable = True
# else:
# layer.trainable = False

FREEZE_LAYERS = 2
for layer in conv_base.layers[:FREEZE_LAYERS]:
    layer.trainable = False
for layer in conv_base.layers[FREEZE_LAYERS:]:
    layer.trainable = True
    
#构建
from keras import models
from keras import layers

model = models.Sequential()
model.add(conv_base)
model.add(layers.GlobalMaxPooling2D())
model.add(layers.Dropout(0.3))
model.add(layers.Dense(12, activation='softmax'))#categorical的话用‘softmax’

输出比较、生成特征向量法②

我的解释：

  • 这里包括了最后的全连层
  • 但是模型只取到{‘layer’:’avg_pool’}获得特征向量，在加了Dense全连层
  • 输出了Xception，InceptionV3处理后的预测结果等四个预测结果
  • 预测结果比较

参考链接

示例代码

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input_tensor = Input(shape=(299, 299, 3))
    base_model1 = Xception(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)
    base_model1 = Model(inputs=[base_model1.input], outputs=[base_model1.get_layer('avg_pool').output], name='xception')

    base_model2 = InceptionV3(include_top=True, weights='imagenet', input_tensor=None, input_shape=None)
    base_model2 = Model(inputs=[base_model2.input], outputs=[base_model2.get_layer('avg_pool').output],
                        name='inceptionv3')
    
    img1 = Input(shape=(299, 299, 3), name='img_1')
    
    feature1 = base_model1(img1)
    feature2 = base_model2(img1)
    
    # let's add a fully-connected layer
    category_predict1 = Dense(100, activation='softmax', name='ctg_out_1')(
        Dropout(0.5)(
            feature1
        )
    )
    
    category_predict2 = Dense(100, activation='softmax', name='ctg_out_2')(
        Dropout(0.5)(
            feature2
        )
    )
    
    category_predict = Dense(100, activation='softmax', name='ctg_out')(
        concatenate([feature1, feature2])
    )
    max_category_predict = maximum([category_predict1, category_predict2])
    
    model = Model(inputs=[img1], outputs=[category_predict1, category_predict2, category_predict, max_category_predict])

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