ParametersConcepts
Model parameters
1 · In one line
Parameters are the model values that training can change to improve how inputs map to outputs.
1 · What it is
TensorFlow uses parameter as a broad name for model values learned from data. In its dense-layer example, the kernel and bias are parameters of the affine mapping.
Training decides how much to change each learnable parameter. PyTorch places learnable weights and biases in a model’s parameters.
The architecture specifies which layers a model contains and how they are connected.
TensorFlow describes a machine learning model as a function with learnable parameters.
Store learningTensorFlow says optimal parameters are obtained by training the model on data.
Shape mattersDense-layer parameters include a kernel and a bias.
Count parametersModel summaries can report the total number of parameters.
Follow one dense layer from parameter shapes to an output.
- 1 · shapeThe layer defines a parameter tensor with a known shape.
- 2 · inspectInspect the framework's reported parameter total.
- 3 · computeThe layer multiplies the input by the kernel and adds the bias.
- 4 · updateTraining decides how much to change each learnable parameter.
Frameworks expose parameter tensor shapes.
| Who | What they ask | What it works with |
|---|---|---|
| Model engineer | “Which tensors will this layer learn?” | Parameter names, shapes and trainable status |
| Training engineer | “Which values should the optimizer update?” | The trainable parameter collection |
| Deployment engineer | “Which model state must be restored?” | Saved parameter tensors |
| Researcher | “Did model capacity change between experiments?” | Architecture and parameter count |
solves
- Parameters give training values it can adjust from data.
- A dense layer's kernel and bias are parameters of its affine mapping.
- A framework can mark a layer weight as trainable.
- A saved parameter dictionary can restore learned model state.
doesn't solve
- Not every item in model state is trainable.
6 · Go deeper
Sources used
This explainer is written in original language. The links below support its factual claims.
- docsTraining models, TensorFlow · read 28 Sept 2026
- docsIntroduction to modules, layers, and models, TensorFlow · read 28 Sept 2026
- docsMaking new layers and models via subclassing, Keras · read 28 Sept 2026
- docsSaving and Loading Models, PyTorch · read 28 Sept 2026
- docsSave, serialize, and export models, TensorFlow · read 28 Sept 2026