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What does Sequential constructor accept a name argument? | annotate TensorBoard graphs with semantically meaningful names | 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 the name argument that a Sequential model accepts? | Sequential constructor | 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 the name argument in Keras? | Sequential constructor | 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... |
How are all layers in Keras able to create weights? | need to know the shape of their inputs | 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 is useful for annotating TensorBoard graphs? | semantically meaningful names | 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 |
How can all layers in Keras be able to create their weights? | by passing a list of layers to the Sequential constructor | 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 is the result of a layer like this? | initial, it has no weights | 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 does this mean? | Specifying the input shape in advance | 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 does this mean for Sequential models? | It has no weights | 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 you instantiate a Sequential model without an input shape? | it has no weights | 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 a result of an error in stating the weight? | 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 happens when a Sequential model is instantiated without an input shape? | it isn't "built" | 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 size of a Sequential model? | one input tensor and one output | 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 can be useful when building a Sequential model incrementally to display the summary of the model so far? | summary() method | 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... |
How can the summary function of a Sequential model be used to view output? | by passing a list of layers to the Sequential constructor | 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? | Specifying the input shape in advance | 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 is the most common debugging workflow? | Specifying the input shape in advance | 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 is the main reason for changing a Sequential architecture? | a list of layers | 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 list of layers | 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... |
How does the Sequential model behave? | behaves very much like a list of layers | 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 is the function of a Sequential model? | pass a list of layers to the Sequential constructor | 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 the output attribute mean? | The layers are accessible via the layers attribute: model.layers | This means that every layer has an input and output attribute |
What is the name for each layer of the layer? | input tensor | This means that every layer has an input and output attribute |
What does this mean when a layer is in the same layer? | isn't built | This means that every layer has an input and output attribute |
What can be used to do neat things? | annotate TensorBoard graphs with semantically meaningful names | 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 attributes can be used to do neat things? | layers attribute: model.layers | 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 can be done to make a model that extracts the outputs of all intermediate layers in a Sequential model? | pass a list of layers to the Sequential constructor | 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 are not meant to be trained? | Its layers are accessible via the layer attribute: model.layers | 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... |
How can you improve a new dataset? | by passing an Input object to your model, so that it knows its input shape | 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 last step of fine tuning? | Specifying the input shape in advance | 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 process for automatic transfer learning workflows? | passing a list of layers to the Sequential constructor | 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 process of updating a base model during the forward pass? | passing an Input object to your model | 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 ... |
How can a different transfer learning workflow be implemented in Keras? | pass a list of layers to the Sequential constructor | 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 advantage of feature extraction? | annotate TensorBoard graphs with semantically meaningful names | 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 main advantage of feature extraction? | Specifying the input shape in advance | 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 a feature extraction system called? | a Sequential model | 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 one reason for a Sequential model to be less expensive? | a plain stack of layers | 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 a problem with Transfer learning with a Sequential model? | there's also a corresponding pop() method to remove layers | 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 a common blueprint for stacking pre-trained models? | a plain stack of layers | 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 |
What would be the most common blueprint for this example? | Specifying the input shape in advance | 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 |
What does a Sequential model do to stack a pre-trained model? | passing a list of layers to the Sequential constructor | 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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