Basic Feedforward Neural Network Diagram

Basic Feedforward Neural Network

A feedforward neural network moves input features through one or more hidden layers to produce an output prediction. Each hidden layer learns a new representation of the data, using the weights and activation functions learned during training. This diagram gives a readable first view of that flow: features enter on the left, transformations occur in the hidden layers, and a prediction leaves on the right. It is suitable for a lesson, an architecture overview, or a model card introduction. It is not a detailed neuron-level drawing, so it does not show individual weights, bias terms, or activations. Add those only when the audience needs to inspect the mathematical model.

類型 方塊圖標準 Ogata / standard controls textbook convention引擎 schematex-blockdiagram更新時間 2026/9/24

需求

“Create a neural network diagram with input features, two hidden layers, and one output prediction.”

接著試試Add a third hidden layer.Rename the output prediction to customer churn probability.

這張圖裡有什麼

看懂背後的決策。

01Inputs

Name the features entering the model.

02Hidden layers

Show the learned transformation stages.

03Output

Identify the specific prediction returned.

Use this simple form when teaching the basic direction of information flow in a feedforward neural network.

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常見問題

What is a hidden layer?

It is a layer between the inputs and outputs that learns intermediate patterns from the training data.

Why is it called feedforward?

Information moves from input to output without a feedback connection to an earlier layer.

What is an output prediction?

It is the model result, such as a class, probability, or numeric estimate.