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')
plt.legend()
plt.title('Accuracy over Epochs')
plt.show()
```
这段代码首先加载了MNIST数据集并对图像进行了预处理；接着定义了一个包含三个卷积层和两个全连接层的简单CNN架构；最后训练该模型并在测试集上评估其性能。此外，还绘制了训练过程中损失值及准确率的变化曲线图，以便直观地了解模型的学习情况。