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A Guide to Sequential Models in Keras

• Sequential models are appropriate for a simple stack of layers with one input and one output tensor.
• The Sequential constructor can be used to create a Sequential model by passing a list of layers.
• Layers in a Sequential model can be accessed using the layers attribute or added incrementally using the add() method.
• The input shape of the model needs to be specified in advance for the layers to create their weights.
• The summary() method can be used to display the contents of a built Sequential model.
• An Input object can be passed to the model to specify the input shape from the start.
• Debugging a Sequential model can be done by incrementally adding layers and printing the model summary.
• Once a Sequential model is built, it can be used for feature extraction using the input and output attributes of each layer.
• Transfer learning can be done by freezing the bottom layers and only training the top layers or stacking a pre-trained model with newly initialized classification layers.
• Freezing layers can be done by iterating over model.layers and setting layer.trainable = False on each layer except the last one.
• Stacking a pre-trained model with classification layers can be done by creating a new Sequential model and adding the pre-trained model's layers followed by the new classification layers.
• It is recommended to specify the input shape of a Sequential model in advance if possible.

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