这里我们将利用TensorFlow库来创建一个基本的卷积神经网络(CNN)，用于识别MNIST手写数字数据集中的图片。这个例子展示了如何轻松地应用深度学习技术解决问题。

```python

import tensorflow as tf

from tensorflow.keras import datasets, layers, models

import matplotlib.pyplot as plt

加载数据

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

数据预处理

train_images = train_images.reshape((60000, 28, 28, 1))

test_images = test_images.reshape((10000, 28, 28, 1))

归一化像素值到 [0, 1] 区间

train_images, test_images = train_images / 255.0, test_images / 255.0

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'))

添加全连接层

model.add(layers.Flatten())

model.add(layers.Dense(64, activation='relu'))

model.add(layers.Dense(10))

编译模型

model.compile(optimizer='adam',

loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),

metrics=['accuracy'])

训练模型

history = model.fit(train_images, train_labels, epochs=5,

validation_data=(test_images, test_labels))

评估模型

test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)

print('\nTest accuracy:', test_acc)

绘制训练过程中的损失和准确率变化

plt.figure(figsize=(12, 4))

plt.subplot(1, 2, 1)

plt.plot(history.history['loss'], label='Training Loss')

plt.plot(history.history['val_loss'], label='Validation Loss')

plt.legend()

plt.title('Loss over Epochs')

plt.subplot(1, 2, 2)

plt.plot(history.history['accuracy'], label='Training Accuracy')

plt.plot(history.history['val_accuracy'], label='Validation Accuracy')