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104 lines
4.1 KiB
Markdown
104 lines
4.1 KiB
Markdown
# 使用 tf.data 加载 NumPy 数据
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> 原文：[https://tensorflow.google.cn/tutorials/load_data/numpy](https://tensorflow.google.cn/tutorials/load_data/numpy)
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**Note:** 我们的 TensorFlow 社区翻译了这些文档。因为社区翻译是尽力而为， 所以无法保证它们是最准确的，并且反映了最新的 [官方英文文档](https://tensorflow.google.cn/?hl=en)。如果您有改进此翻译的建议， 请提交 pull request 到 [tensorflow/docs](https://github.com/tensorflow/docs) GitHub 仓库。要志愿地撰写或者审核译文，请加入 [docs-zh-cn@tensorflow.org Google Group](https://groups.google.com/a/tensorflow.org/forum/#!forum/docs-zh-cn)。
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本教程提供了将数据从 NumPy 数组加载到 [`tf.data.Dataset`](https://tensorflow.google.cn/api_docs/python/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。
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## 安装
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```py
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import numpy as np
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import tensorflow as tf
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import tensorflow_datasets as tfds
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```
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### 从 `.npz` 文件中加载
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```py
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DATA_URL = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz'
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path = tf.keras.utils.get_file('mnist.npz', DATA_URL)
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with np.load(path) as data:
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train_examples = data['x_train']
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train_labels = data['y_train']
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test_examples = data['x_test']
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test_labels = data['y_test']
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```
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## 使用 [`tf.data.Dataset`](https://tensorflow.google.cn/api_docs/python/tf/data/Dataset) 加载 NumPy 数组
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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`](https://tensorflow.google.cn/api_docs/python/tf/data/Dataset#from_tensor_slices) 以创建 [`tf.data.Dataset`](https://tensorflow.google.cn/api_docs/python/tf/data/Dataset) 。
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```py
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train_dataset = tf.data.Dataset.from_tensor_slices((train_examples, train_labels))
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test_dataset = tf.data.Dataset.from_tensor_slices((test_examples, test_labels))
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```
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## 使用该数据集
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### 打乱和批次化数据集
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```py
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BATCH_SIZE = 64
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SHUFFLE_BUFFER_SIZE = 100
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train_dataset = train_dataset.shuffle(SHUFFLE_BUFFER_SIZE).batch(BATCH_SIZE)
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test_dataset = test_dataset.batch(BATCH_SIZE)
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```
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### 建立和训练模型
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```py
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model = tf.keras.Sequential([
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tf.keras.layers.Flatten(input_shape=(28, 28)),
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tf.keras.layers.Dense(128, activation='relu'),
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tf.keras.layers.Dense(10, activation='softmax')
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])
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model.compile(optimizer=tf.keras.optimizers.RMSprop(),
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loss=tf.keras.losses.SparseCategoricalCrossentropy(),
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metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])
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```
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```py
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model.fit(train_dataset, epochs=10)
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```
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```py
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Epoch 1/10
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938/938 [==============================] - 2s 2ms/step - loss: 3.1713 - sparse_categorical_accuracy: 0.8769
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Epoch 2/10
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938/938 [==============================] - 2s 2ms/step - loss: 0.5085 - sparse_categorical_accuracy: 0.9271
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Epoch 3/10
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938/938 [==============================] - 2s 2ms/step - loss: 0.3764 - sparse_categorical_accuracy: 0.9466
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Epoch 4/10
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938/938 [==============================] - 2s 2ms/step - loss: 0.3165 - sparse_categorical_accuracy: 0.9550
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Epoch 5/10
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938/938 [==============================] - 2s 2ms/step - loss: 0.2812 - sparse_categorical_accuracy: 0.9599
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Epoch 6/10
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938/938 [==============================] - 2s 2ms/step - loss: 0.2587 - sparse_categorical_accuracy: 0.9645
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Epoch 7/10
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938/938 [==============================] - 2s 2ms/step - loss: 0.2530 - sparse_categorical_accuracy: 0.9674
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Epoch 8/10
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938/938 [==============================] - 2s 2ms/step - loss: 0.2192 - sparse_categorical_accuracy: 0.9707
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Epoch 9/10
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938/938 [==============================] - 2s 2ms/step - loss: 0.2116 - sparse_categorical_accuracy: 0.9721
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Epoch 10/10
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938/938 [==============================] - 2s 2ms/step - loss: 0.2014 - sparse_categorical_accuracy: 0.9747
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<tensorflow.python.keras.callbacks.History at 0x7fe4f37d1470>
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```
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```py
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model.evaluate(test_dataset)
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```
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```py
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157/157 [==============================] - 0s 2ms/step - loss: 0.5586 - sparse_categorical_accuracy: 0.9568
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[0.5586389303207397, 0.9567999839782715]
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``` |