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What is the importance of MODEL.

Keras: 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.

Summarize ANY.

Equivalent to: A Sequential model is not appropriate when: Your model has multiple inputs or multiple outputs Any of your layers has multiple inputs or multiple outputs You need to do layer sharing You want non-linear topology Creating a Sequential model You can create a Sequential model by passing a list of layers to the Sequential constructor: Its layers are accessible via the layers attribute: model.layers You can also create a Sequential model incrementally via the add() method: There's also a corresponding pop() method to remove layers: a Sequential model behaves very much like a list of layers.

Describe the process of 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 would you explain 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.

Elaborate on 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 are the applications of SUMMARY OF THE 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 current output shape.

Discuss the significance of INPUT.

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: Models built with a predefined input shape like this always have weights (even before seeing any data) and always have a defined output shape.

Describe the process of INPUT SHAPE.

In general, it's a recommended best practice to always specify the input shape of a Sequential model in advance if you know what it is.

Describe the process of DEBUGGING WORKFLOW.

A common debugging workflow: add() + summary() When building a new Sequential architecture, it's useful to incrementally stack layers with add() and frequently print model summaries.

Discuss the significance of STACK OF CONV2D.

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 Once your model architecture is ready, you will want to: Train your model, evaluate it, and run inference.

How would you explain FEATURE EXTRACTION.

Feature extraction with a Sequential model Once a Sequential model has been built, it behaves like a Functional API model.

Describe the process of TRANSFER.

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 consists of taking features learned on one problem, and leveraging them on a new, similar problem.

What are the applications of MODEL MEANT.

For instance, features from a model that has learned to identify racoons may be useful to kick-start a model meant to identify tanukis.

Describe the process of TRANSFER LEARNING.

Transfer learning is usually done for tasks where your dataset has too little data to train a full-scale model from scratch.

What do you mean by INCARNATION OF TRANSFER LEARNING.

The most common incarnation of transfer learning in the context of deep learning is the following worfklow: Take layers from a previously trained model.

Explain in detail ATTRIBUTE LAYERS.

Freezing layers: understanding the trainable attribute Layers & models have three weight attributes: weights is the list of all weights variables of the layer.

What is the importance of GRADIENT DESCENT.

trainable_weights is the list of those that are meant to be updated (via gradient descent) to minimize the loss during training.

How would you explain KERAS.

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.

Elaborate on MODEL ON TOP.

Create a new model on top of the output of one (or several) layers from the base model.

Write a short note on NOTE THAT AN ALTERNATIVE.

Note that an alternative, more lightweight workflow could also be: Instantiate a base model and load pre-trained weights into it.

What are the main points of BASE MODEL.

Run your new dataset through it and record the output of one (or several) layers from the base model.

Write a short note on EPOCH OF TRAINING.

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.

Describe the process of MODEL DURING TRAINING.

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.

Discuss the significance of SEQUENTIAL.

Transfer learning with a Sequential model First, let's say that you have a Sequential model, and you want to freeze all layers except the last one.

What are the main points of FALSE.

In this case, you would simply iterate over model.layers and set layer.trainable = False on each layer, except the last one.