我运行此代码以使用ImageNet:预训练的ResNet50
from keras.applications import ResNet50
conv_base = ResNet50()
print(conv_base.summary())
但是，每一层的名称与最初的ResNet50(互联网接入(不同。
例如：
我的结果：(未更正(
activation_95 (Activation) (None, None, None, 5 0 bn5c_branch2a[0][0]
__________________________________________________________________________________________________
res5c_branch2b (Conv2D) (None, None, None, 5 2359808 activation_95[0][0]
__________________________________________________________________________________________________
bn5c_branch2b (BatchNormalizati (None, None, None, 5 2048 res5c_branch2b[0][0]
__________________________________________________________________________________________________
activation_96 (Activation) (None, None, None, 5 0 bn5c_branch2b[0][0]
__________________________________________________________________________________________________
res5c_branch2c (Conv2D) (None, None, None, 2 1050624 activation_96[0][0]
__________________________________________________________________________________________________
bn5c_branch2c (BatchNormalizati (None, None, None, 2 8192 res5c_branch2c[0][0]
原始结果：(已更正(
conv5_block3_1_bn (BatchNormali (None, 7, 7, 512) 2048 conv5_block3_1_conv[0][0]
__________________________________________________________________________________________________
conv5_block3_1_relu (Activation (None, 7, 7, 512) 0 conv5_block3_1_bn[0][0]
__________________________________________________________________________________________________
conv5_block3_2_conv (Conv2D) (None, 7, 7, 512) 2359808 conv5_block3_1_relu[0][0]
__________________________________________________________________________________________________
conv5_block3_2_bn (BatchNormali (None, 7, 7, 512) 2048 conv5_block3_2_conv[0][0]
__________________________________________________________________________________________________
conv5_block3_2_relu (Activation (None, 7, 7, 512) 0 conv5_block3_2_bn[0][0]
__________________________________________________________________________________________________
conv5_block3_3_conv (Conv2D) (None, 7, 7, 2048) 1050624 conv5_block3_2_relu[0][0]
__________________________________________________________________________________________________
conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048) 8192 conv5_block3_3_conv[0][0]
__________________________________________________________________________________________________
conv5_block3_add (Add) (None, 7, 7, 2048) 0 conv5_block2_out[0][0]
conv5_block3_3_bn[0][0]
__________________________________________________________________________________________________
conv5_block3_out (Activation) (None, 7, 7, 2048) 0 conv5_block3_add[0][0]
安装不同版本的python，但不正确！
请帮帮我。
我做了这一步，它纠正了：
- 卸载anacoda导航器
- 卸载所有版本的python
- 从python网站下载并安装python 3.7.0
- 使用Pip安装软件包
- 安装Cudatoolkit和Cudnn(帮助(
- 添加环境变量的路径(帮助(
非常感谢。