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207 lines
9.4 KiB
Markdown
207 lines
9.4 KiB
Markdown
# 卷积神经网络（Convolutional Neural Network, CNN）
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> 原文：[https://tensorflow.google.cn/tutorials/images/cnn](https://tensorflow.google.cn/tutorials/images/cnn)
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**Note:** 我们的 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](https://tensorflow.google.cn/?hl=en)。如果您有改进此翻译的建议， 请提交 pull request 到 [tensorflow/docs-l10n](https://github.com/tensorflow/docs-l10n) GitHub 仓库。要志愿地撰写或者审核译文，请加入 [docs-zh-cn@tensorflow.org Google Group](https://groups.google.com/a/tensorflow.org/forum/#!forum/docs-zh-cn)。
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### 导入 TensorFlow
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```py
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import tensorflow as tf
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from tensorflow.keras import datasets, layers, models
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import matplotlib.pyplot as plt
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```
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### 下载并准备 CIFAR10 数据集
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CIFAR10 数据集包含 10 类，共 60000 张彩色图片，每类图片有 6000 张。此数据集中 50000 个样例被作为训练集，剩余 10000 个样例作为测试集。类之间相互度立，不存在重叠的部分。
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```py
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(train_images, train_labels), (test_images, test_labels) = datasets.cifar10.load_data()
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# 将像素的值标准化至 0 到 1 的区间内。
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train_images, test_images = train_images / 255.0, test_images / 255.0
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```
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### 验证数据
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我们将测试集的前 25 张图片和类名打印出来，来确保数据集被正确加载。
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```py
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class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
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'dog', 'frog', 'horse', 'ship', 'truck']
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plt.figure(figsize=(10,10))
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for i in range(25):
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plt.subplot(5,5,i+1)
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plt.xticks([])
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plt.yticks([])
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plt.grid(False)
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plt.imshow(train_images[i], cmap=plt.cm.binary)
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# 由于 CIFAR 的标签是 array，
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# 因此您需要额外的索引（index）。
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plt.xlabel(class_names[train_labels[i][0]])
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plt.show()
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```
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### 构造卷积神经网络模型
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下方展示的 6 行代码声明了了一个常见卷积神经网络，由几个 [Conv2D](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/Conv2D) 和 [MaxPooling2D](https://tensorflow.google.cn/api_docs/python/tf/keras/layers/MaxPool2D) 层组成。
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CNN 的输入是张量 (Tensor) 形式的 (image_height, image_width, color_channels)，包含了图像高度、宽度及颜色信息。不需要输入 batch size。如果您不熟悉图像处理，颜色信息建议您使用 RGB 色彩模式，此模式下，`color_channels` 为 `(R,G,B)` 分别对应 RGB 的三个颜色通道（color channel）。在此示例中，我们的 CNN 输入，CIFAR 数据集中的图片，形状是 `(32, 32, 3)`。您可以在声明第一层时将形状赋值给参数 `input_shape` 。
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```py
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model = models.Sequential()
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model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
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model.add(layers.MaxPooling2D((2, 2)))
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model.add(layers.Conv2D(64, (3, 3), activation='relu'))
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model.add(layers.MaxPooling2D((2, 2)))
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model.add(layers.Conv2D(64, (3, 3), activation='relu'))
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```
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我们声明的 CNN 结构是：
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```py
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model.summary()
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```
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```py
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Model: "sequential"
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_________________________________________________________________
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Layer (type) Output Shape Param #
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=================================================================
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conv2d (Conv2D) (None, 30, 30, 32) 896
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_________________________________________________________________
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max_pooling2d (MaxPooling2D) (None, 15, 15, 32) 0
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_________________________________________________________________
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conv2d_1 (Conv2D) (None, 13, 13, 64) 18496
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_________________________________________________________________
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max_pooling2d_1 (MaxPooling2 (None, 6, 6, 64) 0
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_________________________________________________________________
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conv2d_2 (Conv2D) (None, 4, 4, 64) 36928
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=================================================================
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Total params: 56,320
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Trainable params: 56,320
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Non-trainable params: 0
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_________________________________________________________________
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```
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在上面的结构中，您可以看到每个 Conv2D 和 MaxPooling2D 层的输出都是一个三维的张量 (Tensor)，其形状描述了 (height, width, channels)。越深的层中，宽度和高度都会收缩。每个 Conv2D 层输出的通道数量 (channels) 取决于声明层时的第一个参数（如：上面代码中的 32 或 64）。这样，由于宽度和高度的收缩，您便可以（从运算的角度）增加每个 Conv2D 层输出的通道数量 (channels)。
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### 增加 Dense 层
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*Dense 层等同于全连接 (Full Connected) 层。*
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在模型的最后，您将把卷积后的输出张量（本例中形状为 (4, 4, 64)）传给一个或多个 Dense 层来完成分类。Dense 层的输入为向量（一维），但前面层的输出是 3 维的张量 (Tensor)。因此您需要将三维张量展开 (flatten) 到 1 维，之后再传入一个或多个 Dense 层。CIFAR 数据集有 10 个类，因此您最终的 Dense 层需要 10 个输出及一个 softmax 激活函数。
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```py
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model.add(layers.Flatten())
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model.add(layers.Dense(64, activation='relu'))
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model.add(layers.Dense(10))
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```
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查看完整的 CNN 结构：
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```py
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model.summary()
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```
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```py
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Model: "sequential"
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_________________________________________________________________
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Layer (type) Output Shape Param #
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=================================================================
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conv2d (Conv2D) (None, 30, 30, 32) 896
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_________________________________________________________________
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max_pooling2d (MaxPooling2D) (None, 15, 15, 32) 0
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_________________________________________________________________
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conv2d_1 (Conv2D) (None, 13, 13, 64) 18496
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_________________________________________________________________
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max_pooling2d_1 (MaxPooling2 (None, 6, 6, 64) 0
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_________________________________________________________________
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conv2d_2 (Conv2D) (None, 4, 4, 64) 36928
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_________________________________________________________________
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flatten (Flatten) (None, 1024) 0
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_________________________________________________________________
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dense (Dense) (None, 64) 65600
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_________________________________________________________________
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dense_1 (Dense) (None, 10) 650
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=================================================================
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Total params: 122,570
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Trainable params: 122,570
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Non-trainable params: 0
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_________________________________________________________________
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```
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可以看出，在被传入两个 Dense 层之前，形状为 (4, 4, 64) 的输出被展平成了形状为 (1024) 的向量。
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### 编译并训练模型
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```py
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model.compile(optimizer='adam',
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loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
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metrics=['accuracy'])
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history = model.fit(train_images, train_labels, epochs=10,
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validation_data=(test_images, test_labels))
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```
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```py
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Epoch 1/10
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1563/1563 [==============================] - 5s 3ms/step - loss: 1.5143 - accuracy: 0.4469 - val_loss: 1.2281 - val_accuracy: 0.5585
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Epoch 2/10
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1563/1563 [==============================] - 5s 3ms/step - loss: 1.1625 - accuracy: 0.5855 - val_loss: 1.2102 - val_accuracy: 0.5660
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Epoch 3/10
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1563/1563 [==============================] - 5s 3ms/step - loss: 1.0049 - accuracy: 0.6458 - val_loss: 0.9935 - val_accuracy: 0.6511
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Epoch 4/10
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1563/1563 [==============================] - 5s 3ms/step - loss: 0.9089 - accuracy: 0.6801 - val_loss: 0.9658 - val_accuracy: 0.6536
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Epoch 5/10
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1563/1563 [==============================] - 5s 3ms/step - loss: 0.8341 - accuracy: 0.7066 - val_loss: 0.9890 - val_accuracy: 0.6581
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Epoch 6/10
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1563/1563 [==============================] - 5s 3ms/step - loss: 0.7797 - accuracy: 0.7272 - val_loss: 0.8948 - val_accuracy: 0.6891
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Epoch 7/10
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1563/1563 [==============================] - 5s 3ms/step - loss: 0.7287 - accuracy: 0.7437 - val_loss: 0.9004 - val_accuracy: 0.6947
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Epoch 8/10
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1563/1563 [==============================] - 5s 3ms/step - loss: 0.6858 - accuracy: 0.7609 - val_loss: 0.8284 - val_accuracy: 0.7191
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Epoch 9/10
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1563/1563 [==============================] - 5s 3ms/step - loss: 0.6448 - accuracy: 0.7736 - val_loss: 0.8752 - val_accuracy: 0.7096
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Epoch 10/10
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1563/1563 [==============================] - 5s 3ms/step - loss: 0.6117 - accuracy: 0.7855 - val_loss: 0.8524 - val_accuracy: 0.7204
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```
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### 评估模型
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```py
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plt.plot(history.history['accuracy'], label='accuracy')
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plt.plot(history.history['val_accuracy'], label = 'val_accuracy')
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plt.xlabel('Epoch')
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plt.ylabel('Accuracy')
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plt.ylim([0.5, 1])
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plt.legend(loc='lower right')
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plt.show()
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test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
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```
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```py
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313/313 - 1s - loss: 0.8524 - accuracy: 0.7204
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```
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```py
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print(test_acc)
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```
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```py
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0.7203999757766724
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```
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我们搭建的简单的 CNN 模型在测试集上可以达到 70% 的准确率。对于只有几行的代码来说效果不错！对于另一种 CNN 结构可参考另一个使用的基于 Keras 子类 API 和 [`tf.GradientTape`](https://tensorflow.google.cn/api_docs/python/tf/GradientTape) 的样例 [here](https://tensorflow.google.cn/tutorials/quickstart/advanced)。 |