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What does the 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 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... |
What is the name argument that a Sequential constructor accepts? | a name argument | 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 names? | 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 best way to create weights in Keras? | first time it is called on an input | 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 happen to? | When you instantiate a Sequential model without an input shape, 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 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 does this also apply to when you create a layer like this? | 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 a Sequential model isn't 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 happens when an input shape is used to instantiate a Sequential model without input? | 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 happens if a model is not built? | 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 does the summary() method use to display its contents? | model.summary() | 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 useful tool to build a Sequential model? | 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 the current output shape of a Sequential model? | a Sequential model incrementally to be able to display 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()? | pop() method | 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 important part of 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 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 happens if a Sequential model has been built? | instantiate a Sequential model without an input shape | 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 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... |
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... |
What does every layer have? | one input tensor and one output tensor | This means that every layer has an input and output attribute |
What attribute can be used to do neat things? | 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 used to create a model that extracts the outputs of all intermediate layers in a Sequential model? | the add() method | 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? | layer sharing | 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? | multiple inputs or multiple outputs | 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 list of those that aren't meant to be trained? | Layers are accessible via the layers 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... |
What happens when a new model is updated? | You can call its summary() method to display its contents | 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 do Keras users do to improve their workflow? | Specifying the input shape in advance | 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 case with a model's workflow? | you should start your model by passing an Input object to your model, so that it knows the summary of the 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 ... |
What is a feature extraction? | every layer has exactly one input tensor and one output tensor | 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 feature extraction process called? | 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 happens when you freeze all layers? | need to know the shape of their inputs in order to be able to create their weights | 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 ... |
How does the second workflow do you want to freeze all layers? | by passing an Input object to your model | 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 ... |
How is it done? | by passing a list of layers to the Sequential constructor | 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 would happen to the Sequential model that requires a Sequential model? | you can create a Sequential model by 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 |
What is the only way to do a Sequential model stack a pre-trained model? | by passing a list 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 iterate over model.layers and set layer.trainable = False on each layer? | one input tensor and one output tensor | 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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