Copyright 2019 The TensorFlow Authors.



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使用 tf.data 加载 NumPy 数据

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在 Github 上查看源代码

下载此 notebook

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本教程提供了将数据从 NumPy 数组加载到 tf.data.Dataset 的示例本示例从一个 .npz 文件中加载 MNIST 数据集。但是，本实例中 NumPy 数据的来源并不重要。

安装



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import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds

从 `.npz` 文件中加载



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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']

使用 `tf.data.Dataset` 加载 NumPy 数组

假设您有一个示例数组和相应的标签数组，请将两个数组作为元组传递给 tf.data.Dataset.from_tensor_slices 以创建 tf.data.Dataset 。



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

使用该数据集

打乱和批次化数据集



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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)

建立和训练模型



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model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer=tf.keras.optimizers.RMSprop(),
                loss=tf.keras.losses.SparseCategoricalCrossentropy(),
                metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])



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model.fit(train_dataset, epochs=10)



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model.evaluate(test_dataset)