Output layer

As explained in Grokking Machine Learning

The neural-network layer that produces the model's final outputs or predictions.

The output layer is the final layer of a neural network. Its nodes produce the model’s predictions; its size and activation can vary with the example architecture. section 10.1 section 10.3

Classification outputs

A Keras classifier for two classes ends with two softmax units; an MNIST digit classifier ends with ten. The predicted class can be selected from the output values. section 10.3

Figure 10.21 The architecture that we will use to classify our dataset. It contains two hidden layers: one of 128 and one of 64 nodes. The activation function between them is a ReLU, and the final activation function is a softmax.

See the network diagram, including its final softmax layer. section 10.3

How Grokking Machine Learning covers it

How deep it goes

Defines its position and illustrates output sizes and activations in classification architectures.

Examples

Uses a one-node output in a network diagram and two- and ten-unit softmax outputs for classification.

What you can do after

Apply it via Keras by adding a Dense output layer with two or ten softmax units for classification.

How it connects

Links neural-network architecture to hidden layers, activation functions, and classification predictions.

liveBookmanning.com

Read Grokking Machine Learning on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Classic Computer Science Problems in Swift: Essential techniques for practicing programmers
• 

  liveBookmanning.com
  Inside AIAn output layer is the neural-network layer that produces the model’s final prediction or output.
• 

  liveBookmanning.com
  Grokking AI Algorithms, Second EditionAn output layer is the final neural-network layer whose units produce the network's externally returned results.
• 

  liveBookmanning.com
  Data Without LabelsAn output layer is the final neural-network layer that transforms preceding activations into a prediction or other task output.
• 

  liveBookmanning.com
  Reinforcement Learning from Human FeedbackA scalar head is a model output component that maps hidden representations or logits to a single numerical prediction.

Compare how 6 books explain output layer →

Related concepts

• deep learningpart of · Deep learning is a machine-learning approach that uses neural networks with multiple computational layers to learn representations from data.
• neural network layerkind of · A neural network layer is a computational stage or component that transforms input representations into outputs within a neural network.
• language modelpart of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
• linear layerimplemented by · A linear layer is a neural-network layer that maps input features to outputs by a learned affine transformation, typically connecting each output to every input feature.
• transformer architecturepart of · A transformer architecture is a neural-network architecture that processes sequences or other structured inputs using attention mechanisms and related transformations.
• embedding layerequivalent to · An embedding layer maps discrete identifiers, such as tokens or categories, to learned dense numerical vectors.

This explainer was generated by Manning's knowledge graph from the text of Grokking Machine Learning. The section links point to the parts of the book it draws on.