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针对专业人员的 TensorFlow 2.0 入门

在 tensorflow.google.cn 上查看 在 Google Colab 中运行 在 GitHub 上查看源代码 下载 notebook

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).

  1. 在 Colab 中,连接到 Python 运行时:在菜单栏右上角,选择连接(CONNECT)
  2. 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

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(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)

  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}'
  )
Epoch 1, Loss: 0.13267311453819275, Accuracy: 96.02333068847656, Test Loss: 0.061965566128492355, Test Accuracy: 98.06999969482422
Epoch 2, Loss: 0.042059458792209625, Accuracy: 98.72000122070312, Test Loss: 0.048135414719581604, Test Accuracy: 98.3499984741211
Epoch 3, Loss: 0.023382706567645073, Accuracy: 99.24333190917969, Test Loss: 0.05048979073762894, Test Accuracy: 98.3499984741211
Epoch 4, Loss: 0.01350458711385727, Accuracy: 99.57333374023438, Test Loss: 0.0620846264064312, Test Accuracy: 98.25
Epoch 5, Loss: 0.009131859056651592, Accuracy: 99.69999694824219, Test Loss: 0.06238056719303131, Test Accuracy: 98.33999633789062

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