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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。