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What is the name argument in a Sequential model? | Sequential constructor accepts a name argument, just like any layer or model in Ker | Keras: Sequential Model
Keras: Sequential Model
The Sequential model
The Sequential model
A Sequential model is appropriate for a plain stack of layers where each layer has exactly one input tensor and one output tensor.
Equivalent to:
A Sequential model is not appropriate when:
Your model has multiple inputs or mul... |
What is useful to annotate TensorBoard graphs with semantically meaningful | This is useful to annotate TensorBoard graphs with semantically meaningful | This is useful to annotate TensorBoard graphs with semantically meaningful names.
Specifying the input shape in advance
Specifying the input shape in advance
Generally, all layers in Keras need to know the shape of their inputs in order to be able to create their weights |
What does this apply to? | Sequential models | So when you create a layer like this, initially, it has no weights:
It creates its weights the first time it is called on an input, since the shape of the weights depends on the shape of the inputs:
Naturally, this also applies to Sequential models |
What happens when a Sequential model is not built? | instantiate a Sequential model without an input shape | When you instantiate a Sequential model without an input shape, it isn't "built": it has no weights (and calling model.weights results in an error stating just this) |
What is the current output shape of a Sequential model? | the summary of the model so far, including the current output shape | The weights are created when the model first sees some input data:
Once a model is "built", you can call its summary() method to display its contents:
model.summary()
However, it can be very useful when building a Sequential model incrementally to be able to display the summary of the model so far, including the cu... |
What is a common debugging workflow? | add() + summary() | In this case, you should start your model by passing an Input object to your model, so that it knows its input shape from the start:
Note that the Input object is not displayed as part of model.layers, since it isn't a layer:
model.layers
A simple alternative is to just pass an input_shape argument to your first layer... |
What does Feature extraction do once a Sequential model behave like? | a Functional API model | For instance, this enables you to monitor how a stack of Conv2D and MaxPooling2D layers is downsampling image feature maps:
What to do once you have a model
What to do once you have a model
Once your model architecture is ready, you will want to:
Train your model, evaluate it, and run inference.
Save your model to d... |
What does this mean? | every layer has an input and output attribute | This means that every layer has an input and output attribute |
What can be done to create a model that extracts the outputs of all intermediate layers | quickly creating a model that extracts the outputs of all intermediate layers in a Sequ | These attributes can be used to do neat things, like quickly creating a model that extracts the outputs of all intermediate layers in a Sequential model:
Here's a similar example that only extract features from one layer:
Transfer learning & fine-tuning
Transfer learning & fine-tuning
Transfer learning consists of t... |
What is the list of those that aren't meant to be trained? | non_trainable_weights | They will learn to turn the old features into predictions on a new dataset.
Train the new layers on your dataset.
A last, optional step, is fine-tuning, which consists of unfreezing the entire model you obtained above (or part of it), and re-training it on the new data with a very low learning rate.
This can potentiall... |
What is the typical transfer learning workflow? | Instantiate a base model and load pre-trained weights into it | Typically they are updated by the model during the forward pass.
The typical transfer-learning workflow
The typical transfer-learning workflow
This leads us to how a typical transfer learning workflow can be implemented in Keras:
Instantiate a base model and load pre-trained weights into it.
Freeze all layers in the ... |
What is the feature extraction? | every layer has an input and output attribute | This is called feature extraction.
Use that output as input data for a new, smaller model.
A key advantage of that second workflow is that you only run the base model once one your data, rather than once per epoch of training |
What is the problem with Transfer learning with a Sequential model? | taking features learned on one problem, and leveraging them on a new, similar problem | So it's a lot faster & cheaper.
An issue with that second workflow, though, is that it doesn't allow you to dynamically modify the input data of your new model during training, which is required when doing data augmentation, for instance.
Transfer learning with a Sequential model
Transfer learning with a Sequential ... |
What is the most common blueprint for a Sequential model? | a Sequential model to stack a pre-trained model and some freshly initialized | In this case, you would simply iterate over model.layers and set layer.trainable = False on each layer, except the last one.
Another common blueprint is to use a Sequential model to stack a pre-trained model and some freshly initialized classification layers.
Thanks |
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