一個例子：

print("Loading vgg19 weights...")

vgg_model = VGG19(include_top=False, weights='imagenet')

from_vgg = dict() # 因為模型定義中的layer的名字與原始vgg名字不同，所以需要調整

from_vgg['conv1_1'] = 'block1_conv1'

from_vgg['conv1_2'] = 'block1_conv2'

from_vgg['conv2_1'] = 'block2_conv1'

from_vgg['conv2_2'] = 'block2_conv2'

from_vgg['conv3_1'] = 'block3_conv1'

from_vgg['conv3_2'] = 'block3_conv2'

from_vgg['conv3_3'] = 'block3_conv3'

from_vgg['conv3_4'] = 'block3_conv4'

from_vgg['conv4_1'] = 'block4_conv1'

from_vgg['conv4_2'] = 'block4_conv2'

for layer in model.layers:

if layer.name in from_vgg:

vgg_layer_name = from_vgg[layer.name]

layer.set_weights(vgg_model.get_layer(vgg_layer_name).get_weights())

print("Loaded VGG19 layer: " + vgg_layer_name)

densenet.load_weights('model/densenet_weight/densenet_bottom.h5')

# densenet.save_weights('densenet_bottom.h5')

# print(densenet.weights)# 獲得模型所有權值

t=densenet.get_layer('densenet_conv1/bn')

print(t)

print(densenet.get_weights()[2])

以上這篇keras獲得某一層或者某層權重的輸出實例就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持腳本之家。

總結

以上是生活随笔為你收集整理的keras提取模型中的某一层_keras获得某一层或者某层权重的输出实例的全部內容，希望文章能夠幫你解決所遇到的問題。

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