CNN python

在Python中，使用卷积神经网络（CNN）进行图像分类的步骤如下：

1. 导入所需的库：

   import tensorflow as tf
   from tensorflow.keras import datasets, layers, models

2. 加载MNIST手写数字图片数据集：

   (train_images, train_labels), (test_images, test_labels) = datasets.mnist.load_data()

3. 数据预处理：

   train_images = train_images.reshape((60000, 28, 28, 1))
   test_images = test_images.reshape((10000, 28, 28, 1))
   train_images, test_images = train_images / 255.0, test_images / 255.0

4. 构建CNN模型：

   model = models.Sequential()
   model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
   model.add(layers.MaxPooling2D((2, 2)))
   model.add(layers.Conv2D(64, (3, 3), activation='relu'))
   model.add(layers.MaxPooling2D((2, 2)))
   model.add(layers.Conv2D(64, (3, 3), activation='relu'))
   model.add(layers.Flatten())
   model.add(layers.Dense(64, activation='relu'))
   model.add(layers.Dense(10, activation='softmax'))

5. 编译模型：

   model.compile(optimizer='adam',
                 loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
                 metrics=['accuracy'])

6. 训练模型：

   history = model.fit(train_images, train_labels, epochs=10,
                       validation_data=(test_images, test_labels))

7. 评估模型性能：

   test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
   print('Test accuracy:', test_acc)

通过上述步骤，您可以在Python中使用CNN对MNIST手写数字图片数据集进行分类。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
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