使用 tf.data 加载 NumPy 数据

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使用 tf.data 加载 NumPy 数据#

在 Tensorflow.org 上查看 在 Google Colab 运行 在 Github 上查看源代码 下载笔记本

本教程提供了一个将数据从 NumPy 数组加载到 tf.data.Dataset 中的示例。

此示例从 .npz 文件加载 MNIST 数据集。但是，NumPy 数组的来源并不重要。

安装#

 
import numpy as np
import tensorflow as tf

从 .npz 文件中加载#

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 。

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#

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#

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)