TFDS now supports the Croissant 🥐 format! Read the documentation to know more.
Training a neural network on MNIST with Keras
Stay organized with collections
Save and categorize content based on your preferences.
This simple example demonstrates how to plug TensorFlow Datasets (TFDS) into a Keras model.
View on TensorFlow.org Run in Google Colab View source on GitHub Download notebookimporttensorflowastf
importtensorflow_datasetsastfds
2025年10月04日 11:16:16.699239: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered WARNING: All log messages before absl::InitializeLog() is called are written to STDERR E0000 00:00:1759576576.724018 11705 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered E0000 00:00:1759576576.732199 11705 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered W0000 00:00:1759576576.751752 11705 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1759576576.751774 11705 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1759576576.751777 11705 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once. W0000 00:00:1759576576.751780 11705 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.
Step 1: Create your input pipeline
Start by building an efficient input pipeline using advices from:
- The Performance tips guide
- The Better performance with the
tf.dataAPI guide
Load a dataset
Load the MNIST dataset with the following arguments:
shuffle_files=True: The MNIST data is only stored in a single file, but for larger datasets with multiple files on disk, it's good practice to shuffle them when training.as_supervised=True: Returns a tuple(img, label)instead of a dictionary{'image': img, 'label': label}.
(ds_train, ds_test), ds_info = tfds.load(
'mnist',
split=['train', 'test'],
shuffle_files=True,
as_supervised=True,
with_info=True,
)
2025年10月04日 11:16:21.222353: E external/local_xla/xla/stream_executor/cuda/cuda_platform.cc:51] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
Build a training pipeline
Apply the following transformations:
tf.data.Dataset.map: TFDS provide images of typetf.uint8, while the model expectstf.float32. Therefore, you need to normalize images.tf.data.Dataset.cacheAs you fit the dataset in memory, cache it before shuffling for a better performance.
Note: Random transformations should be applied after caching.tf.data.Dataset.shuffle: For true randomness, set the shuffle buffer to the full dataset size.
Note: For large datasets that can't fit in memory, usebuffer_size=1000if your system allows it.tf.data.Dataset.batch: Batch elements of the dataset after shuffling to get unique batches at each epoch.tf.data.Dataset.prefetch: It is good practice to end the pipeline by prefetching for performance.
defnormalize_img(image, label):
"""Normalizes images: `uint8` -> `float32`."""
return tf.cast(image, tf.float32) / 255., label
ds_train = ds_train.map(
normalize_img, num_parallel_calls=tf.data.AUTOTUNE)
ds_train = ds_train.cache()
ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples)
ds_train = ds_train.batch(128)
ds_train = ds_train.prefetch(tf.data.AUTOTUNE)
Build an evaluation pipeline
Your testing pipeline is similar to the training pipeline with small differences:
- You don't need to call
tf.data.Dataset.shuffle. - Caching is done after batching because batches can be the same between epochs.
ds_test = ds_test.map(
normalize_img, num_parallel_calls=tf.data.AUTOTUNE)
ds_test = ds_test.batch(128)
ds_test = ds_test.cache()
ds_test = ds_test.prefetch(tf.data.AUTOTUNE)
Step 2: Create and train the model
Plug the TFDS input pipeline into a simple Keras model, compile the model, and train it.
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(28, 28)),
tf.keras.layers.Dense(128, activation='relu'),
tf.keras.layers.Dense(10)
])
model.compile(
optimizer=tf.keras.optimizers.Adam(0.001),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],
)
model.fit(
ds_train,
epochs=6,
validation_data=ds_test,
)
/tmpfs/src/tf_docs_env/lib/python3.9/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(**kwargs) Epoch 1/6 469/469 ━━━━━━━━━━━━━━━━━━━━ 4s 4ms/step - loss: 0.6073 - sparse_categorical_accuracy: 0.8348 - val_loss: 0.1852 - val_sparse_categorical_accuracy: 0.9468 Epoch 2/6 469/469 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - loss: 0.1737 - sparse_categorical_accuracy: 0.9514 - val_loss: 0.1307 - val_sparse_categorical_accuracy: 0.9619 Epoch 3/6 469/469 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - loss: 0.1192 - sparse_categorical_accuracy: 0.9661 - val_loss: 0.1063 - val_sparse_categorical_accuracy: 0.9697 Epoch 4/6 469/469 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - loss: 0.0948 - sparse_categorical_accuracy: 0.9727 - val_loss: 0.0951 - val_sparse_categorical_accuracy: 0.9723 Epoch 5/6 469/469 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - loss: 0.0744 - sparse_categorical_accuracy: 0.9786 - val_loss: 0.0892 - val_sparse_categorical_accuracy: 0.9738 Epoch 6/6 469/469 ━━━━━━━━━━━━━━━━━━━━ 1s 3ms/step - loss: 0.0599 - sparse_categorical_accuracy: 0.9830 - val_loss: 0.0794 - val_sparse_categorical_accuracy: 0.9772 <keras.src.callbacks.history.History at 0x7fbc39d63880>