![]() ![]() Here are two common transfer learning blueprint involving Sequential models. If you aren't familiar with it, make sure to read our guide Transfer learning consists of freezing the bottom layers in a model and only training Transfer learning with a Sequential model ones (( 1, 250, 250, 3 )) features = feature_extractor ( x ) output, ) # Call feature extractor on test input. get_layer ( name = "my_intermediate_layer" ). Sequential ( ) feature_extractor = keras. These attributes can be used to do neat things, likeĬreating a model that extracts the outputs of all intermediate layers in a This means that every layer has an inputĪnd output attribute. Once a Sequential model has been built, it behaves like a Functional API Guide to multi-GPU and distributed training.įeature extraction with a Sequential model
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