![]() If true, require that the provided weights exactly match those strict (boolean) Strict loading model: whether extraneous weights or missing.fetchFunc (Function) A function used to override the window.fetch function.onProgress (OnProgressCallback) Progress callback.requestInit (RequestInit) RequestInit (options) for HTTP requests.įor detailed information on the supported fields, see.options (Object) Options for the HTTP request, which allows to send.modelUrl (string|io.IOHandler) The url or an io.IOHandler that loads the model.For example, the following line runs prediction with the model on // some fake data. ![]() The model can be used for training, evaluation and prediction. ![]() First layer must have an input shape defined. Tf.model() is more generic and supports an arbitrary graph (without Tf.sequential() is less generic, supporting only a linear stack of layers. The key difference between tf.model() and tf.sequential() is that (recurrent, Dense.) an inputDim argument. InputShape or batchInputShape argument, or for some type of layers What that means is that it should have received an This means that the first layer passed to a tf.Sequential model should haveĪ defined input shape. Topology is a simple 'stack' of layers, with no branching or skipping. Outputs of one layer are the inputs to the next layer, i.e. A sequential model is any model where the E.g., equationĬreates a tf.Sequential model.
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