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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
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)
Epoch 1/10 938/938 [==============================] - 2s 2ms/step - loss: 2.8712 - sparse_categorical_accuracy: 0.8771 Epoch 2/10 938/938 [==============================] - 2s 2ms/step - loss: 0.4812 - sparse_categorical_accuracy: 0.9299 Epoch 3/10 938/938 [==============================] - 2s 2ms/step - loss: 0.3514 - sparse_categorical_accuracy: 0.9471 Epoch 4/10 938/938 [==============================] - 2s 2ms/step - loss: 0.3048 - sparse_categorical_accuracy: 0.9546 Epoch 5/10 938/938 [==============================] - 2s 2ms/step - loss: 0.2752 - sparse_categorical_accuracy: 0.9617 Epoch 6/10 938/938 [==============================] - 2s 2ms/step - loss: 0.2505 - sparse_categorical_accuracy: 0.9652 Epoch 7/10 938/938 [==============================] - 2s 2ms/step - loss: 0.2375 - sparse_categorical_accuracy: 0.9676 Epoch 8/10 938/938 [==============================] - 2s 2ms/step - loss: 0.2188 - sparse_categorical_accuracy: 0.9705 Epoch 9/10 938/938 [==============================] - 2s 2ms/step - loss: 0.2007 - sparse_categorical_accuracy: 0.9725 Epoch 10/10 938/938 [==============================] - 2s 2ms/step - loss: 0.1964 - sparse_categorical_accuracy: 0.9757 <tensorflow.python.keras.callbacks.History at 0x7fbc9cbffe80>
model.evaluate(test_dataset)
157/157 [==============================] - 0s 2ms/step - loss: 0.6592 - sparse_categorical_accuracy: 0.9554 [0.6592006087303162, 0.9553999900817871]