以下是一个简单的C卷积神经网络Pyho代码示例，用于图像分类任务。该代码使用了Keras库，可以在TesorFlow

2.x版本上运行。

```pyho# 导入必要的库impor umpy as pfrom keras.models impor Sequeialfrom keras.layers impor Cov2D, MaxPoolig2D, Flae, Desefrom keras.daases impor misfrom keras.uils impor o_caegorical

# 加载MIST数据集(rai_images, rai_labels), (es_images, es_labels) = mis.load_daa()

# 数据预处理rai_images = rai_images.reshape((60000, 28, 28, 1))rai_images = rai_images.asype('floa32') / 255es_images = es_images.reshape((10000, 28, 28, 1))es_images = es_images.asype('floa32') / 255rai_labels = o_caegorical(rai_labels)es_labels = o_caegorical(es_labels)

# 定义模型model = Sequeial()model.add(Cov2D(32, kerel_size=(3, 3), acivaio='relu', ipu_shape=(28, 28, 1)))model.add(MaxPoolig2D(pool_size=(2, 2)))model.add(Cov2D(64, kerel_size=(3, 3), acivaio='relu'))model.add(MaxPoolig2D(pool_size=(2, 2)))model.add(Flae())model.add(Dese(10, acivaio='sofmax'))

# 编译模型model.compile(opimizer='adam', loss='caegorical_crosseropy', merics=['accuracy'])

# 训练模型model.fi(rai_images, rai_labels, epochs=5, bach_size=64)

# 评估模型es_loss, es_acc = model.evaluae(es_images, es_labels)pri('Tes accuracy:', es_acc)```该代码首先导入了必要的库，然后加载了MIST数据集，并对数据进行了预处理。接着，定义了一个卷积神经网络模型，该模型包含两个卷积层、两个池化层和两个全连接层。使用Adam优化器和分类交叉熵损失函数来编译模型。使用训练数据对模型进行训练，并使用测试数据评估模型的性能。