 在 TensorFlow.org 上查看 |  在 Google Colab 中运行 |  在 GitHub 上查看源代码 |  下载笔记本 |
这是一个 Google Colaboratory 笔记本（notebook）文件。Python 程序直接在浏览器中运行——这是一种学习和使用 Tensorflow 的好方法。要学习本教程，请单击本页顶部按钮,在 Google Colab 中运行笔记本（notebook）.
- 在 Colab 中，连接到 Python 运行时：在菜单栏右上角，选择连接（CONNECT）。
- 运行所有笔记本（notebook）代码单元格：选择运行时（Runtime） > 运行所有（Run all）。
下载并安装 TensorFlow 2。将 TensorFlow 导入您的程序：
注：升级 pip 以安装 TensorFlow 2 软件包。请参阅安装指南了解详细信息。
将 Tensorflow 导入您的程序：
import tensorflow as tf print("TensorFlow version:", tf.__version__) 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 # Add a channels dimension x_train = x_train[..., tf.newaxis].astype("float32") x_test = x_test[..., tf.newaxis].astype("float32") 使用 tf.data 来将数据集切分为 batch 以及混淆数据集：
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) 使用 Keras 模型子类化 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) def call(self, x): x = self.conv1(x) x = self.flatten(x) x = self.d1(x) return self.d2(x) # Create an instance of the model model = MyModel() 为训练选择优化器与损失函数：
loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) 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: # training=True is only needed if there are layers with different # behavior during training versus inference (e.g. Dropout). predictions = model(images, training=True) 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) 测试模型：
@tf.function def test_step(images, labels): # training=False is only needed if there are layers with different # behavior during training versus inference (e.g. Dropout). predictions = model(images, training=False) t_loss = loss_object(labels, predictions) test_loss(t_loss) test_accuracy(labels, predictions) EPOCHS = 5 for epoch in range(EPOCHS): # Reset the metrics at the start of the next epoch 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) print( f'Epoch {epoch + 1}, ' f'Loss: {train_loss.result()}, ' f'Accuracy: {train_accuracy.result() * 100}, ' f'Test Loss: {test_loss.result()}, ' f'Test Accuracy: {test_accuracy.result() * 100}' ) 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，请阅读 TensorFlow 教程。