Load NumPy data

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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

from __future__ import absolute_import, division, print_function, unicode_literals
 
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)
Train for 938 steps
Epoch 1/10
938/938 [==============================] - 3s 3ms/step - loss: 3.5939 - sparse_categorical_accuracy: 0.8841
Epoch 2/10
938/938 [==============================] - 2s 2ms/step - loss: 0.5411 - sparse_categorical_accuracy: 0.9275
Epoch 3/10
938/938 [==============================] - 2s 2ms/step - loss: 0.3962 - sparse_categorical_accuracy: 0.9440
Epoch 4/10
938/938 [==============================] - 2s 2ms/step - loss: 0.3183 - sparse_categorical_accuracy: 0.9539
Epoch 5/10
938/938 [==============================] - 2s 2ms/step - loss: 0.2943 - sparse_categorical_accuracy: 0.9595
Epoch 6/10
938/938 [==============================] - 2s 2ms/step - loss: 0.2649 - sparse_categorical_accuracy: 0.9649
Epoch 7/10
938/938 [==============================] - 2s 2ms/step - loss: 0.2402 - sparse_categorical_accuracy: 0.9673
Epoch 8/10
938/938 [==============================] - 2s 2ms/step - loss: 0.2256 - sparse_categorical_accuracy: 0.9705
Epoch 9/10
938/938 [==============================] - 2s 2ms/step - loss: 0.2013 - sparse_categorical_accuracy: 0.9739
Epoch 10/10
938/938 [==============================] - 2s 2ms/step - loss: 0.1998 - sparse_categorical_accuracy: 0.9741

<tensorflow.python.keras.callbacks.History at 0x7ff5fbb08080>
model.evaluate(test_dataset)
157/157 [==============================] - 0s 2ms/step - loss: 0.6454 - sparse_categorical_accuracy: 0.9606

[0.645447734135479, 0.9606]