keras获得某一层或者某层权重的输出实例

一个例子：

    print("Loading vgg19 weights...")

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

    from_vgg = dict()  # 因为模型定义中的layer的名字与原始vgg名字不同，所以需要调整
    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)# 获得模型所有权值
t=densenet.get_layer('densenet_conv1/bn')
print(t)
print(densenet.get_weights()[2])

以上这篇keras获得某一层或者某层权重的输出实例就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持python博客。