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Load NumPy data

<table class="tfo-notebook-buttons" align="left"> <td> <a target="_blank" href="https://www.tensorflow.org/tutorials/load_data/numpy">View on TensorFlow.org</a> </td> <td> <a target="_blank" href="https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/load_data/numpy.ipynb">Run in Google Colab</a> </td> <td> <a target="_blank" href="https://github.com/tensorflow/docs/blob/master/site/en/tutorials/load_data/numpy.ipynb">View source on GitHub</a> </td> <td> <a href="https://storage.googleapis.com/tensorflow_docs/docs/site/en/tutorials/load_data/numpy.ipynb">Download notebook</a> </td> </table>

This tutorial provides an example of loading data from NumPy arrays into a tf.data.Dataset.

This example loads the MNIST dataset from a .npz file. However, the source of the NumPy arrays is not important.

Setup

 
import numpy as np
import tensorflow as tf

Load from `.npz` file

DATA_URL = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz'

path = tf.keras.utils.get_file('mnist.npz', DATA_URL)
with np.load(path) as data:
  train_examples = data['x_train']
  train_labels = data['y_train']
  test_examples = data['x_test']
  test_labels = data['y_test']

Load NumPy arrays with `tf.data.Dataset`

Assuming you have an array of examples and a corresponding array of labels, pass the two arrays as a tuple into tf.data.Dataset.from_tensor_slices to create a tf.data.Dataset.

train_dataset = tf.data.Dataset.from_tensor_slices((train_examples, train_labels))
test_dataset = tf.data.Dataset.from_tensor_slices((test_examples, test_labels))

Use the datasets

Shuffle and batch the datasets

BATCH_SIZE = 64
SHUFFLE_BUFFER_SIZE = 100

train_dataset = train_dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)
test_dataset = test_dataset.batch(BATCH_SIZE)

Build and train a model

model = tf.keras.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.RMSprop(),
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['sparse_categorical_accuracy'])

model.fit(train_dataset, epochs=10)

model.evaluate(test_dataset)

you may not use this file except in compliance with the License.