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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteTensorFlow classification follows a repeatable workflow: define the labels, prepare representative data, build an input pipeline, match the model output and loss to the label format, train with validation, evaluate on an untouched test set, inspect errors, and save a reproducible inference procedure. This guide uses multiclass image classification as the main example, then shows how the same decisions change for binary, multilabel, tabular, and text problems.
You can run the examples in Google Colab or locally. The official installation page identified TensorFlow 2.21.0 as the latest stable release on March 12, 2026; check the live installation instructions for current Python and platform compatibility.
What classification means
A classification model predicts a discrete label, not a continuous number. Binary classification chooses between two classes such as spam and not spam. Multiclass classification chooses one class from several, such as cat, dog, or bird. Multilabel classification allows several labels to be true for one example, such as an image containing both a person and a dog.
A neural network’s final values are usually logits. Applying softmax converts multiclass logits into values that sum to one, called probabilities; the largest value gives the predicted class. A high softmax value is not automatically a trustworthy probability: calibration, data quality, and distribution shift still matter.
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Prerequisites and setup
- Basic Python, functions, imports, and simple NumPy operations.
- Familiarity with features, labels, batches, epochs, loss, and accuracy.
- A notebook or command-line Python environment.
For the least setup, open a TensorFlow tutorial and choose Run in Google Colab, connect to a runtime, then use Runtime → Run all. A local virtual environment is better when you need persistent files and reproducible development.
Local CPU installation
python3 -m venv tf
source tf/bin/activate
python -m pip install --upgrade pip
python -m pip install tensorflow
python -c "import tensorflow as tf; print(tf.__version__)"On Linux, the current guide documents this GPU installation command:
python3 -m pip install 'tensorflow[and-cuda]'
python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"Small datasets do not require a GPU. Native-Windows official GPU support ends with TensorFlow 2.10; newer GPU workflows generally use WSL2 or another supported environment. Verify platform details at TensorFlow’s installation guide.
The end-to-end workflow
- Define classes and the prediction target.
- Collect, label, inspect, and split representative data.
- Load and preprocess it consistently.
- Choose output units, loss, optimizer, and metrics that match the labels.
- Train while monitoring validation behavior.
- Evaluate once on an untouched test set and inspect errors.
- Save the model, class ordering, and preprocessing assumptions.
Choose and organize an image dataset
For a directory-based project, use one subdirectory per class:
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dataset/
├── cats/
│ ├── cat_001.jpg
│ └── cat_002.jpg
├── dogs/
│ ├── dog_001.jpg
│ └── dog_002.jpg
└── birds/
├── bird_001.jpg
└── bird_002.jpgTensorFlow’s image tutorial uses image_dataset_from_directory, which infers integer class IDs from subdirectory names.
- Keep names stable and unambiguous; save their ordering with the model.
- Remove corrupt, mislabeled, duplicate, and near-duplicate files.
- Use images resembling the real lighting, devices, backgrounds, and users.
- Check class counts. If related images come from one person, patient, video, or device, split by that source to prevent leakage.
- Use validation during development and keep a separate test set untouched until final reporting.
Load, split, and inspect the data
import tensorflow as tf
IMG_HEIGHT = 180
IMG_WIDTH = 180
BATCH_SIZE = 32
SEED = 123
train_ds = tf.keras.utils.image_dataset_from_directory(
"dataset",
validation_split=0.2,
subset="training",
seed=SEED,
image_size=(IMG_HEIGHT, IMG_WIDTH),
batch_size=BATCH_SIZE,
)
val_ds = tf.keras.utils.image_dataset_from_directory(
"dataset",
validation_split=0.2,
subset="validation",
seed=SEED,
image_size=(IMG_HEIGHT, IMG_WIDTH),
batch_size=BATCH_SIZE,
)
print(train_ds.class_names)
for images, labels in train_ds.take(1):
print(images.shape, labels.shape, labels.dtype)Use the identical validation fraction and seed in both calls. This creates training and validation subsets, not an independent test set. For serious reporting, create a carefully designed third partition.
Input-pipeline performance
AUTOTUNE = tf.data.AUTOTUNE
train_ds = train_ds.cache().shuffle(1000).prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)cache() can consume substantial memory; omit it or provide a cache filename when the dataset does not fit in memory.
Build a small convolutional classifier
num_classes = len(train_ds.class_names)
model = tf.keras.Sequential([
tf.keras.Input(shape=(IMG_HEIGHT, IMG_WIDTH, 3)),
tf.keras.layers.Rescaling(1.0 / 255),
tf.keras.layers.Conv2D(16, 3, padding="same", activation="relu"),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D(32, 3, padding="same", activation="relu"),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Conv2D(64, 3, padding="same", activation="relu"),
tf.keras.layers.MaxPooling2D(),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation="relu"),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(num_classes),
])Inputdeclares height, width, and three RGB channels.Rescalingchanges pixel values from approximately 0–255 to 0–1.Conv2Dlearns local visual patterns; pooling reduces spatial size.Flattenconverts feature maps to a vector for dense layers.Dropoutregularizes the dense representation.- The final dense layer emits one logit per class and deliberately has no softmax.
Compile with a loss that matches the labels
model.compile(
optimizer="adam",
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=["accuracy"],
)Integer class IDs require sparse categorical cross-entropy. With no softmax in the model, from_logits=True lets the loss perform a numerically stable calculation. TensorFlow’s beginner quickstart explains why adding softmax before this loss is unnecessary.
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| Task | Labels | Output | Typical loss |
|---|---|---|---|
| Binary | 0/1 | One sigmoid unit | Binary cross-entropy |
| Binary | 0/1 | Two logits | Sparse categorical cross-entropy |
| Multiclass | Integer class ID | One logit per class | Sparse categorical cross-entropy with from_logits=True |
| Multiclass | One-hot vector | One logit per class | Categorical cross-entropy |
| Multilabel | 0/1 vector | One sigmoid unit per label | Binary cross-entropy |
Do not mix integer labels with a loss expecting one-hot vectors, one-hot labels with sparse loss, or from_logits=True with a model that already applies softmax.
Train with validation and callbacks
callbacks = [
tf.keras.callbacks.EarlyStopping(
monitor="val_loss", patience=3, restore_best_weights=True
),
tf.keras.callbacks.ModelCheckpoint(
"best_model.keras", monitor="val_accuracy",
mode="max", save_best_only=True
),
]
history = model.fit(
train_ds,
validation_data=val_ds,
epochs=30,
callbacks=callbacks,
)An epoch is one pass through the training data; a batch is the group processed in one step. Training metrics describe data the optimizer has seen. Validation metrics estimate performance on held-out data and guide decisions, so they are not a substitute for a final test.
Recognize and reduce overfitting
Overfitting commonly appears when training accuracy keeps rising while validation accuracy plateaus or falls, or when training loss falls while validation loss rises.
data_augmentation = tf.keras.Sequential([
tf.keras.layers.RandomFlip("horizontal"),
tf.keras.layers.RandomRotation(0.1),
tf.keras.layers.RandomZoom(0.1),
])Apply random augmentation only to training examples, either in the model or input pipeline. Other remedies include more representative data, removing leakage, a smaller model, dropout, regularization, early stopping, class reweighting, and transfer learning. A from-scratch CNN teaches mechanics but often needs more labeled data; transfer learning is usually stronger on small visual datasets, although base-model preprocessing, fine-tuning, and weight provenance add complexity. TensorFlow’s learning resources cover transfer-learning options at tensorflow.org/learn.
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Evaluate beyond accuracy
After development, evaluate exactly once on an untouched test dataset:
test_loss, test_accuracy = model.evaluate(test_ds, verbose=2)
print(test_loss, test_accuracy)Accuracy is informative when classes are reasonably balanced and error costs are similar. Otherwise inspect precision, recall, F1 score, confusion matrices, per-class results, and (where appropriate) ROC-AUC or PR-AUC. Review false positives, false negatives, low-score cases, and examples with unusual lighting, backgrounds, devices, or demographics.
Report the split method, class balance, preprocessing, random seed, software and hardware environment, and evaluation protocol with any metric. A test score is not evidence of deployment reliability unless the test distribution represents deployment. For safety-sensitive uses, define a confidence threshold below which the system abstains or requests human review.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Save and reload the classifier
model.save("classifier.keras")
restored_model = tf.keras.models.load_model("classifier.keras")The .keras format stores architecture, weights, training configuration, and optimizer state. A weights-only checkpoint requires recreating the architecture; SavedModel remains useful for TensorFlow Serving and some deployment workflows. See TensorFlow’s save and load guide and the Keras model-format guide. Store class names, image dimensions, channel order, scaling, library versions, and the data-split definition beside the file.
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Make predictions on a new image
import numpy as np
from tensorflow.keras.utils import load_img, img_to_array
probability_model = tf.keras.Sequential([
model,
tf.keras.layers.Softmax()
])
img = load_img("example.jpg", target_size=(IMG_HEIGHT, IMG_WIDTH))
x = img_to_array(img)
x = tf.expand_dims(x, axis=0)
probabilities = probability_model.predict(x, verbose=0)[0]
predicted_index = int(np.argmax(probabilities))
predicted_name = train_ds.class_names[predicted_index]
confidence = float(probabilities[predicted_index])
print(predicted_name, confidence)The model’s embedded rescaling layer ensures this path uses the same pixel scaling as training. Preserve RGB channels, dimensions, and the exact class-name order. The largest probability is a ranking signal, not proof of correctness.
Troubleshoot common failures
Installation errors
- Check the Python version against the current installation page and upgrade
pip. - Use a fresh virtual environment and install with
pip. - For GPU issues, verify supported operating system, drivers, and TensorFlow release rather than assuming a GPU is available.
Shape or label errors
print(model.input_shape)
for images, labels in train_ds.take(1):
print(images.shape, labels.shape, labels.dtype)Typical causes are grayscale images sent to a three-channel model, a missing batch dimension, inconsistent resizing, or labels with the wrong dtype or shape.
High accuracy but bad real predictions
- Look for leakage, duplicates, class imbalance, mislabeled files, and an unrepresentative test set.
- Check train–deployment distribution shift and inference preprocessing.
- Verify that the class-name mapping was saved and restored correctly.
Out-of-memory errors
- Reduce batch size or image dimensions.
- Use a smaller model and avoid caching the whole dataset.
- Stream data and remove unnecessary workers or cached tensors.
Unstable validation accuracy
Inspect validation-set size and class counts, correlated samples, random seeds, augmentation strength, learning rate, and leakage.
Adapt the pattern to other problems
Built-in sanity checks
MNIST or Fashion-MNIST is a fast way to verify that TensorFlow, labels, loss, and training work. The official quickstart uses pixel scaling, a sequential model, dropout, ten output logits, Adam, five epochs, and final test evaluation; those are teaching defaults, not guarantees for other datasets. Start at the beginner quickstart.
Tabular and text classification
Replace image decoding and convolution layers with numeric feature preprocessing or tokenization and embedding layers. The output/loss decision remains the same: one sigmoid for binary output, one sigmoid per independent multilabel target, or one logit per mutually exclusive class.
Deployment choices
- TensorFlow Lite (also referred to in current materials as LiteRT) for mobile and edge inference.
- TensorFlow.js for browser inference.
- TensorFlow Serving for server-side prediction.
- TFX for production pipelines, validation, and lifecycle management.
TensorFlow lists these environments and managed options in its learning materials, with production pipeline details at tensorflow.org/tfx. Cloud training is documented at TensorFlow Cloud; it adds billing, storage, permissions, and resource-cleanup responsibilities and is unnecessary for a first CPU or Colab experiment.
Quick Recap
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