TensorFlow 2官方教程 . 针对专业人员的 TensorFlow 2.0 入门

写在前面

此篇博客转载自tensorflow官方教程中文翻译版:
https://www.tensorflow.org/tutorials/quickstart/advanced
笔者有删改，同时笔者会以注释的形式将可能由环境问题带来的BUG写出来

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Note: 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。如果您有改进此翻译的建议， 请提交 pull request 到 tensorflow/docs GitHub 仓库。 要志愿地撰写或者审核译文，请加入 [email protected] Google Group。

↑ 以上链接部分需要科学上网↑

此博客教程可在tensorflow官方github 上下载：
https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/quickstart/advanced.ipynb

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针对专业人员的 TensorFlow 2.0 入门

下载并安装 TensorFlow 2.0 Beta 软件包：(笔者注: 其实这一步可以不用做)

try:
    # Colab only
    %tensorflow_version 2.x
except Exception:
    pass

将 Tensorflow 导入您的程序：

from __future__ import absolute_import, division, print_function, unicode_literals

import tensorflow as tf

from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model

载入并准备好 MNIST 数据集。

mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

# 添加通道维数(Add a channels dimension)
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]

使用tf.data来将数据集切分为 batch 以及混淆数据集(笔者注: 混淆的意思是将数据打乱)：

train_ds = tf.data.Dataset.from_tensor_slices(
                    (x_train, y_train)
            ).shuffle(10000).batch(32)
test_ds = tf.data.Dataset.from_tensor_slices((x_test, y_test)).batch(32)

使用 Keras 模型子类化（model subclassing） API 构建 tf.keras 模型：

class MyModel(Model):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = Conv2D(32, 3, activation='relu')
        self.flatten = Flatten()
        self.d1 = Dense(128, activation='relu')
        self.d2 = Dense(10, activation='softmax')

    def call(self, x):
        x = self.conv1(x)
        x = self.flatten(x)
        x = self.d1(x)
        return self.d2(x)

model = MyModel()

为训练选择优化器与损失函数：

loss_object = tf.keras.losses.SparseCategoricalCrossentropy()

optimizer = tf.keras.optimizers.Adam()

选择衡量指标来度量模型的损失值（loss）和准确率（accuracy）。这些指标在 epoch 上累积值，然后打印出整体结果。

train_loss = tf.keras.metrics.Mean(name='train_loss')
train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')

test_loss = tf.keras.metrics.Mean(name='test_loss')
test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')

使用 tf.GradientTape 来训练模型：

@tf.function
def train_step(images, labels):
    with tf.GradientTape() as tape:
        predictions = model(images)
        loss = loss_object(labels, predictions)
    gradients = tape.gradient(loss, model.trainable_variables)
    optimizer.apply_gradients(zip(gradients, model.trainable_variables))

    train_loss(loss)
    train_accuracy(labels, predictions)

测试数据:

@tf.function
def test_step(images, labels):
    predictions = model(images)
    t_loss = loss_object(labels, predictions)

    test_loss(t_loss)
    test_accuracy(labels, predictions)

EPOCHS = 5

for epoch in range(EPOCHS):
    # 在下一个epoch开始时，重置评估指标
    train_loss.reset_states()
    train_accuracy.reset_states()
    test_loss.reset_states()
    test_accuracy.reset_states()

    for images, labels in train_ds:
        train_step(images, labels)

    for test_images, test_labels in test_ds:
        test_step(test_images, test_labels)

    template = 'Epoch {}, Loss: {}, Accuracy: {}, Test Loss: {}, Test Accuracy: {}'
    print (template.format(epoch+1,
                           train_loss.result(),
                           train_accuracy.result()*100,
                           test_loss.result(),
                           test_accuracy.result()*100))

运行模型：(笔者注: CPU上运行很慢)

WARNING:tensorflow:Layer my_model is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.

If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.

To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.

Epoch 1, Loss: 0.13130314648151398, Accuracy: 96.03833770751953, Test Loss: 0.06053972616791725, Test Accuracy: 97.91999816894531
Epoch 2, Loss: 0.042836885899305344, Accuracy: 98.63333129882812, Test Loss: 0.05354950577020645, Test Accuracy: 98.23999786376953
Epoch 3, Loss: 0.023272410035133362, Accuracy: 99.25, Test Loss: 0.0571180060505867, Test Accuracy: 98.29000091552734
Epoch 4, Loss: 0.013985390774905682, Accuracy: 99.51000213623047, Test Loss: 0.061239469796419144, Test Accuracy: 98.3499984741211
Epoch 5, Loss: 0.008612685836851597, Accuracy: 99.70166778564453, Test Loss: 0.060723815113306046, Test Accuracy: 98.44999694824219

该图片分类器现在在此数据集上训练得到了接近 98% 的准确率（accuracy）。要了解更多信息，请继续阅读。

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以上便是tensorflow官方教程《针对专业人员的 TensorFlow 2.0 入门》全部内容：
https://www.tensorflow.org/tutorials/quickstart/advanced