最新版本：http://www.mashangxue123.com/tensorflow/tf2-tutorials-quickstart-advanced.html
英文版本：https://tensorflow.google.cn/alpha/tutorials/quickstart/advanced
翻译建议PR：https://github.com/mashangxue/tensorflow2-zh/edit/master/r2/tutorials/quickstart/beginner.md
初学者入门教程中，使用tf.keras.Sequential模型，只是简单的堆叠模型。
本文是专家级入门，使用 Keras 模型子类 API 构建模型，会使用更底层一点的的函数接口，自定义模型、损失、评估指标和梯度下降控制等，流程清晰。
开始，请将TensorFlow库导入您的程序：
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf # 安装命令 `pip install tensorflow-gpu==2.0.0-alpha0`
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
加载并准备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
# 添加一个通道维度
x_train = x_train[..., tf.newaxis]
x_test = x_test[..., tf.newaxis]
使用tf.data批处理和随机打乱数据集：
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)
通过使用Keras模型子类 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()
选择优化器和损失函数进行训练：
loss_object = tf.keras.losses.SparseCategoricalCrossentropy()
optimizer = tf.keras.optimizers.Adam()
选择指标（metrics）以衡量模型的损失和准确性。这些指标累积超过周期的值，然后打印整体结果。
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)
现在测试模型：
@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):
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))
Epoch 1, Loss: 0.13177014887332916, Accuracy: 96.06000518798828, Test Loss: 0.05814294517040253, Test Accuracy: 98.04999542236328
...
Epoch 5, Loss: 0.042211469262838364, Accuracy: 98.72000122070312, Test Loss: 0.05708516761660576, Test Accuracy: 98.3239974975586
现在，图像分类器在该数据集上的准确度达到约98％。要了解更多信息，请阅读 TensorFlow教程.。
最新版本：http://www.mashangxue123.com/tensorflow/tf2-tutorials-quickstart-advanced.html
英文版本：https://tensorflow.google.cn/alpha/tutorials/quickstart/advanced
翻译建议PR：https://github.com/mashangxue/tensorflow2-zh/edit/master/r2/tutorials/quickstart/beginner.md
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