The Keras “Pneumonia Classification on TPU” example trains a convolutional neural network to label chest X-rays as NORMAL or PNEUMONIA, using TensorFlow’s TPU distribution strategy. It is a teaching example. Its own held-out test result, a binary accuracy of 0.7901, is well below the roughly 95% validation accuracy the tutorial reports, and the example does not show that the model is clinically validated or suitable for diagnosis.
What the example is built to show
The tutorial, written by Amy MiHyun Jang and first published on 2020-07-28 (last modified 2024-02-12), is aimed at Python and machine-learning learners who want a concrete pattern for running a Keras image classifier on a TPU. Its scope is narrow: one binary image task, one model, one training run. The reader takes away four things: how TFRecord files can be read and turned into labeled image tensors, how a class-imbalanced dataset is handled with class weights, how a Keras model is placed under a TPU strategy, and how precision and recall are reported alongside accuracy.
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Running the example on a TPU
The tutorial states that it must be run in Google Colab with the TPU runtime selected. Its setup code tries to connect to a TPU and create a TensorFlow TPUStrategy; if no TPU is found, it falls back to the default strategy.
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- Open the tutorial on the Keras examples site, Pneumonia Classification on TPU, and launch its Colab notebook.
- In Colab, go to Runtime > Change runtime type.
- Set Hardware accelerator to TPU and select Save.
- Run the TPU setup cell first and read its output. Confirm that it reports a TPU connection before running training.
- Build the model inside the strategy scope and run training.
Step 4 matters more than it looks. The fallback path lets the notebook run on a machine without a TPU, which is useful for debugging but means the training cells will not use TPU cores. If the setup output shows the default strategy, you are not running the TPU version of the example, and timing results from that session say nothing about TPU performance.
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How the data is read and prepared
The example reads ChestXRay2017 train and test splits stored as TFRecord files on Google Cloud. Each image record is zipped with a path record. The class label is taken from the directory name in the path: NORMAL maps to 0 and PNEUMONIA maps to 1. Each image is decoded as a three-channel JPEG and resized to 180 × 180 pixels.
The shuffled training dataset is split into 4,200 training examples, with the remaining examples used for validation. The validation split therefore comes from the training files, not from the separate test split, which is why validation and test numbers can diverge so sharply (see the results section).
- Read the image records and path records from the TFRecord files.
- Zip the two streams so each image is paired with its path.
- Derive the label from the path’s class directory (NORMAL = 0, PNEUMONIA = 1).
- Decode JPEG data as three channels and resize to 180 × 180.
- Shuffle, then take 4,200 training examples; the remainder becomes validation.
The tutorial does not explain how patients were assigned to splits, so readers should not assume that the validation set is independent at the patient level. That detail belongs to the dataset’s own documentation, which the tutorial links but this article has not verified.
Class imbalance and class weights
The training data is uneven. The tutorial counts the following images, and it uses class weights to offset the imbalance during training:
| Class (label) | Training images in this tutorial | Class weight shown in the tutorial |
|---|---|---|
| NORMAL (0) | 1,349 | 1.94 |
| PNEUMONIA (1) | 3,883 | 0.67 |
These counts describe this tutorial’s training files. They are not population rates of pneumonia, and they should not be used to estimate prevalence in any clinical setting.
The model and training setup
The model rescales pixel values from 0–255 to 0–1. It then stacks convolution and separable-convolution blocks, each followed by max pooling and batch normalization, with dropout applied to reduce overfitting. The features are flattened, passed through dense layers, and end in a single sigmoid unit that outputs the probability of PNEUMONIA.
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The model is compiled with:
- the Adam optimizer, with exponential learning-rate decay;
- binary cross-entropy as the loss;
- binary accuracy, precision, and recall as metrics.
Training uses a model checkpoint and early stopping, so the saved model is not necessarily the one from the final epoch.
Why precision and recall are logged
Accuracy alone can hide which kind of error a model makes. Precision shows how many of the images predicted as pneumonia really were pneumonia. Recall shows how many real pneumonia images the model caught. The tutorial reports both so that the trade-off is visible.
TPU-specific choices
Three details are specific to the TPU version:
- Batch size is set to 25 times the number of TPU replicas in the strategy, so the global batch grows with the hardware.
- Caching and prefetching. The tutorial caches the dataset in memory and prefetches batches so the accelerator is not left waiting for input.
- Caching caveat. The tutorial itself warns: “Please note that large image datasets should not be cached in memory. We do it here because the dataset is not very large and we want to train on TPU.” Do not copy the in-memory cache into a pipeline built on a large image collection.
The current Keras FAQ gives the general TensorFlow pattern: connect with TPUClusterResolver, create a TPUStrategy, construct the model inside strategy.scope(), and make sure the input pipeline reads data fast enough to keep the TPU busy. The Keras FAQ on training on TPU also notes that all Keras backends (JAX, TensorFlow, PyTorch) are supported on TPU, while recommending JAX or TensorFlow for this case. The example uses TensorFlow, so it follows that recommendation.
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Reading the results
The tutorial’s test evaluation is the number to read first. It reports the following on the held-out test set for its own training run:
| Metric (test set, tutorial run) | Value | What it means here |
|---|---|---|
| Binary accuracy | 0.7901 | About 79% of test images were labeled correctly. |
| Precision | 0.7524 | About 75% of images predicted as PNEUMONIA were PNEUMONIA; roughly a quarter of positive predictions were wrong. |
| Recall | 0.9897 | About 99% of the PNEUMONIA images in the test set were flagged. |
The training log and the tutorial’s discussion report validation accuracy of around 95%. That gap of roughly 15 percentage points between validation and test accuracy is the most important number in the example. Keras’s own text says the lower test accuracy may indicate overfitting. It reads the higher recall than precision as the model detecting many pneumonia images while also producing false positives among NORMAL images.
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What the example does not establish
- Clinical validity. The tutorial is an image-classification exercise. It provides no evidence of clinical performance, radiologist comparison, or safety for medical decisions.
- Generalization. The test gap shows the model did not transfer as well to the test images as the validation numbers suggested. The example does not test other hospitals, scanners, or patient populations.
- Speed or accuracy versus other setups. The tutorial does not compare TPU against GPU or CPU, nor JAX or PyTorch against TensorFlow. Claims that one accelerator or backend is faster or more accurate need separate evidence.
- Dataset construction. Patient-level splitting, representativeness, and labeling quality of the underlying chest X-ray collection are not covered in the tutorial.
How to use the example
Treat the notebook as a template for the TPU mechanics and the data pipeline: TFRecord parsing, label derivation, strategy scoping, class weighting, and metric reporting. If you adapt it, the practical changes that matter most are: split by patient rather than by image, evaluate on a separate test set that you never tune against, report precision and recall per class, and check the TPU connection before trusting any timing result.
The example is a useful piece of TensorFlow and Keras teaching code. Its own reported test numbers show that a model trained this way can look strong on validation data and still perform noticeably worse on held-out data, so any version of it that touches medical images needs clinical evaluation far beyond what this tutorial provides.
Hardware access for the tutorial is through Colab, which the tutorial requires with the TPU runtime selected. The Keras FAQ also lists Kaggle notebooks and Google Cloud Deep Learning VMs as TPU routes; availability and pricing on those services change, so check them directly before planning a run.
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