Author: Awais Farooq
-
Training-related questions
What do “sample”, “batch”, and “epoch” mean? Below are some common definitions that are necessary to know and understand to correctly utilize Keras fit():
-
How can I install HDF5 or h5py to save my models?
In order to save your Keras models as HDF5 files, Keras uses the h5py Python package. It is a dependency of Keras and should be installed by default. On Debian-based distributions, you will have to additionally install libhdf5: If you are unsure if h5py is installed you can open a Python shell and load the module…
-
What are my options for saving models?
Note: it is not recommended to use pickle or cPickle to save a Keras model. 1) Whole-model saving (configuration + weights) Whole-model saving means creating a file that will contain: The default and recommended way to save a whole model is to just do: model.save(your_file_path.keras). After saving a model in either format, you can reinstantiate it…
-
How can I obtain reproducible results using Keras during development?
There are four sources of randomness to consider: To make both Keras and the current backend framework deterministic, use this: To make Python deterministic, you need to set the PYTHONHASHSEED environment variable to 0 before the program starts (not within the program itself). This is necessary in Python 3.2.3 onwards to have reproducible behavior for certain hash-based operations (e.g.,…
-
Where is the Keras configuration file stored?
The default directory where all Keras data is stored is: $HOME/.keras/ For instance, for me, on a MacBook Pro, it’s /Users/fchollet/.keras/. Note that Windows users should replace $HOME with %USERPROFILE%. In case Keras cannot create the above directory (e.g. due to permission issues), /tmp/.keras/ is used as a backup. The Keras configuration file is a JSON file stored at $HOME/.keras/keras.json. The default…
-
How can I train a Keras model on TPU?
TPUs are a fast & efficient hardware accelerator for deep learning that is publicly available on Google Cloud. You can use TPUs via Colab, Kaggle notebooks, and GCP Deep Learning VMs (provided the TPU_NAME environment variable is set on the VM). All Keras backends (JAX, TensorFlow, PyTorch) are supported on TPU, but we recommend JAX or TensorFlow…
-
Documentation and Community
Refer to the Keras documentation, GitHub issues, and community forums (like Stack Overflow) for troubleshooting, updates, and best practices shared by experienced users.
-
GPU Optimization
Ensure Keras utilizes GPUs effectively by setting up TensorFlow with GPU support and optimizing batch sizes to fully leverage GPU memory.
-
Custom Loss and Metrics
Implement custom loss functions or metrics using TensorFlow operations for tasks where standard losses or metrics are insufficient.
-
Model Ensembling
Combine predictions from multiple models (trained on the same or different data) to improve accuracy and robustness.