I am sure many of you have heard about deep learning, and many tools like Keras Functional API go into the functioning of deep learning. The Keras Python library is the one that makes the deep learning models super easy and quick. Isn’t it amazing? We have more to tell you, but only if you keep reading till the end. So, keep reading to know more.
Sequential Keras Functional API:
The sequential API is an API model that allows you to create models layer by layer for most problems. There is also a limitation to that. It is limited in allowing you to create models which share multiple inputs or outputs or are available in layers. It is an alternative way of creating models that offer flexibility, including creating more complex models.
Keras Functional API Models:
Many of you here are Python enthusiasts, and it is not surprising that you are eagerly waiting to know about the Keras functional API models. Well, in this section, we will cover just that.
Keras Functional Models:
This is one of the Keras functional API models that provides more flexible ways to define the models. It also specifically allows you to define the multiple input or output models which share layers. It also allows you to define ad hoc acrylic network graphs.
A model is defined by creating instances of layers as well as connecting them with each other in pairs. Then, the model specifies the layers acting as the input or output of the model.
Keras Sequential Models:
Keras also provides a sequential Keras sequential model for API.
The sequential model by the Keras API is a way of creating deep learning models where an instance of the sequential class is created. The models are created and added to it.
The sequential model API is also a great way to develop deep learning models in most situations. However, like the previous Keras functional API model, it has some limitations.
For example, it is a complex way of defining the models, which may have multiple different sources of input. It can produce multiple output destinations or models which come in re-use layers.
I am sure folks, you all are keen to know more about the Keras functional API; well, keep reading!
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Meaning of Input:
This is unlike the sequential model, where you will be asked to create and define a standalone input layer that will specify the shape of the input data.
The input layer also takes a shape argument, a tuple that indicates the dimensionality of the input data.
In the case of one-dimensional input data, such as the multilayer Perceptron. The shape of this model will explicitly leave room for the shape of the mini-batch size, which is used when splitting the data when training the network.
Hence, the shape of a tuple is always defined by the last dimension when the input is one-dimensional.
What Do You Mean by Connecting Layers?
These are the layers in the Keras functional API model connected pairwise.
This takes place by specifying where the inputs come from while defining each new layer. A bracket notation is usually used, and the layers are created afterward. The layer from which the input to the current comes gets specified.
I am sure you are all getting deep insights about the Keras functional API. We have more to share with you, so keep reading!
What are Standard Network Models?
When starting with the Keras functional API, it is always a good idea to see how some standard neural network models are defined.
For example, you can look at multilayer Perceptron, convolutional neural networks, and recurrent neural networks.
Let’s know more about them.
Multilayer Perceptron:
This Keras functional API model has 10 inputs, 3 hidden layers with 10, 20, and 10 neurons, and an output layer with 1 output. The rectified linear activation functions are used in each of the hidden layers and also, a sigmoid activation function is used in the output layer. This is for binary classification.
Convolutional Neural Network:
This Keras functional API model receives black ad white 64×64 images as input; then, it has a sequence of two convolutional and pooling layers as feature extractors. This is followed by a fully connected layer to interpret the features and an output layer with sigmoid activation for two-class predictions.
Recurrent Neural Network:
This Keras functional API model has 100-time steps of one feature as input. This model of Keras functional API has a single LSTM hidden layout for extracting features from the sequence. This is followed by a fully connected layer for interpreting the LSTM layout. This is then followed by an output layer for making binary predictions.
Multiple Input and Output Models:
This is one of the Keras functional API models, which is a more complex model. It has multiple inputs and is possible with different modalities. It can also be used to develop models which produce multiple outputs.
What do you think, folks, about these different Keras functional API models which we have shared with you? These are the best Keras functional API models, which can be complex but valuable. I hope you have a good insight into the different Keras functional API models.
Conclusion:
That’s all, folks. I hope the article helped you in getting all the information you needed.
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