Object to your model, so that it knows its input shape from the start: model = keras.Sequential() In this case, you should start your model by passing an Input To be able to display the summary of the model so far, including the current However, it can be very useful when building a Sequential model incrementally Once a model is "built", you can call its summary() method to display its Number of weights after calling the model: 6 Print("Number of weights after calling the model:", len(model.weights)) # 6 When the model first sees some input data: model = keras.Sequential( Model.weights results in an error stating just this). Sequential model without an input shape, it isn't "built": it has no weights Naturally, this also applies to Sequential models. Layer.weights # Now it has weights, of shape (4, 3) and (3,) Of the weights depends on the shape of the inputs: # Call layer on a test input It creates its weights the first time it is called on an input, since the shape This, initially, it has no weights: layer = layers.Dense(3) In order to be able to create their weights. Generally, all layers in Keras need to know the shape of their inputs Model.add(layers.Dense(4, name="layer3")) Model.add(layers.Dense(3, activation="relu", name="layer2")) Model.add(layers.Dense(2, activation="relu", name="layer1")) model = keras.Sequential(name="my_sequential") This is useful to annotate TensorBoard graphs model.pop()Īlso note that the Sequential constructor accepts a name argument, just likeĪny layer or model in Keras. Note that there's also a corresponding pop() method to remove layers:Ī Sequential model behaves very much like a list of layers. Model.add(layers.Dense(3, activation="relu")) Model.add(layers.Dense(2, activation="relu")) ![]() You can also create a Sequential model incrementally via the add() method: model = keras.Sequential() Its layers are accessible via the layers attribute: model.layers You can create a Sequential model by passing a list of layers to the Sequential ![]()
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