Keras Deep Learning Cheat Sheet
A Keras reference for tensors and layers, Sequential and Functional models, compilation, training, callbacks, evaluation, saving and inference.
What this reference covers
The Keras reference follows a model from tensor shapes and layers through Sequential or Functional organization, compilation, training, callbacks, evaluation, saving and inference. It is designed for quick lookup while keeping data preparation and model behavior explicit.
Start with shapes and labels
Record sample, feature, channel and sequence dimensions, and make sure label types match the output layer. Run a forward pass on a small batch before adding depth or training for a long time. Shape errors are often data-contract errors rather than layer-count problems.
Control training and preserve the inference contract
Choose loss and metrics for the task, configure validation, early stopping, learning-rate and checkpoint callbacks, then review training curves. Save preprocessing, feature order, label mapping, random seed, hardware and data version with the model so a restored model sees the same input contract.
Maintenance note
Training metrics can be affected by leakage, imbalance, overfitting and distribution change. Model output also needs domain and privacy review. Content review date: 2026-08-23.
Save to your cloud drive
Save the complete collection first so files remain together and are easier to access across devices.
Quark Cloud Drive
RecommendedSave Keras Deep Learning Cheat Sheet to this cloud drive
Baidu Netdisk
Save Keras Deep Learning Cheat Sheet to this cloud drive
Keras deep learning study guide
Verify input and output shapes first, choose the model structure and training contract, then evaluate and restore the model with matching preprocessing.
Before you start
- Prepare a PDF reader and a small data set with checked labels.
- Understand training, validation, batches, epochs, loss and metrics.
- Record feature order, preprocessing steps and output label mapping.
Quick start
- 01
Check input and output shapes
Record sample, feature, channel or sequence dimensions, verify label type and output shape, and run a small forward pass.
- 02
Choose the model structure
Use Sequential for a simple stack and Functional for multiple inputs, outputs, shared layers or skip connections, naming branches clearly.
- 03
Compile and control training
Select task-appropriate loss and metrics, configure validation and callbacks, and preserve the training curves for review.
- 04
Evaluate and save the inference path
Use independent data, verify prediction shapes and preprocessing, then save the model with labels, configuration and version information.
Usage tips
- Training and inference must use the same preprocessing, feature order and label mapping.
- Run a small model and data set through the full save and restore path before scaling up.
- Record random seed, hardware, batch size, epochs, learning rate and data version.
Troubleshooting and uninstall
Why does the model report an input shape mismatch?
Print shapes layer by layer and compare preprocessing, batch dimension, channel order and the declared model input.
Why does training loss improve while validation gets worse?
Check overfitting, split rules, augmentation and learning rate, then consider early stopping or regularization while keeping an independent test set.
Frequently asked questions
Which Keras workflows are covered?
The sheet covers tensors and layers, Sequential and Functional models, compilation, training, callbacks, evaluation, saving and inference.
When should Functional models be used?
Use Functional for multiple inputs or outputs, shared layers, skip connections or other non-linear model relationships.
Why can training and inference differ?
Preprocessing, feature order, label mapping, batch dimensions or training and inference modes may not match.
Why save the training configuration?
A model file may not include data version, preprocessing, labels, random seed or hardware, all of which can affect reproducible inference.