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What is the name argument in a Sequential model? | 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 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... |
How are the layers in Keras able to create their weights? | all layers in Keras 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 to annotate TensorBoard graphs with semantically meaningful names? | Sequential constructor accepts a name argument | 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 annotate to have semantically meaningful names? | TensorBoard graphs | 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 same as Sequential models? | list of layers | 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 the shape of the inputs depend on? | the shape of the inputs | 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 is the name of the layer that has no weights? | model | 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 |
Why is a Sequential model 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 happens when an input shape is created? | when the model first sees some input data | 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 resulted in a error from the instantiation of an input shape? | calling model.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 great example of a Sequential model? | Keras: Sequential Model The Sequential model | 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 a Sequential model be able to display its summary? | by calling its 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 are the weights created when a model first sees input data? | The weights are created when the model first sees some input data | 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 does add() use to produce model summaries? | a corresponding 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 an example of a standard example of an Input object? | model | 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... |
What does the 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 each layer have? | one input tensor and one output tensor | This means that every layer has an input and output attribute |
What does this mean for each layer? | exactly one input tensor and one output tensor | This means that every layer has an input and output attribute |
What does a layer have to do to their output? | have exactly one input tensor and one output tensor | This means that every layer has an input and output attribute |
What is the most common example of transfer learning? | a list of 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 in a Sequential model? | passing 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 do layers have to be freed? | multiple inputs or multiple outputs | 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? | the Sequential constructor | 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 non-trainable weights? | It creates its weights the first time it is called on an input | 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 does the transfer learning workflow do in Keras? | annotate TensorBoard graphs with semantically meaningful names | 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 are the types of changes to the model? | multiple inputs or multiple outputs | 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 is a typical transfer learning workflow implemented? | all layers in Keras need to know the shape of their inputs | 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 does feature extraction work? | 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 a 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 the term for feature extraction? | a plain stack of layers | 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 another reason for you being able to use a Sequential model to freeze all layers? | 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 the problem with transfer learning with a Sequential model? | does not | 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 problem with Transfer learning with a Sequential model? | It has no 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 ... |
What is a common blueprint to use to stack a pre-trained model? | annotate TensorBoard graphs with semantically meaningful names | 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 used to iterate a model? | 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 does it mean to use a Sequential model to stack pre-trained models and some freshly initialized classification layers? | 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 |
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