![]() ![]() I would like it to accept not only a sequence of nn.Modules, but also a sequence of either nn.Modules, or tuples of class and dict of kwargs, e.g. number of input channels of nn.Conv2d is inferrable from input shape).Īfter that, I would like to extend an nn.Sequential class. Also, I need a method (let's call it infer_init_params) that would take an input shape and return a dict with arguments of _init_, that are inferrable from this input shape (e.g. My idea is to first of all, implement a method infer_output_shape in every class inherited from nn.Module, which would take an input shape and return an output shape. in nn.Sequential container, it seems possible and would be quite handy. However in the case, when the order of layers is predefined, e.g. Of course, it is a consequence of a dynamic-graph paradigm. In this case one require to look into documentation page for exact formula. in conv layers with non-default padding / output_padding / stride. Inferring shapes of subsequent layers require manual calculations. Model.layers and set layer.A modification of nn.Sequential class that would infer some input parameters for containing modules. In this case, you would simply iterate over Here are two common transfer learning blueprint involving Sequential models.įirst, let's say that you have a Sequential model, and you want to freeze all 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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