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Load NumPy data: 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)