【深度学习入门实战】基于Keras的手写数字识别实战（附完整可视化分析）

import numpy as np
import matplotlib.pyplot as plt
from tensorflow import keras
from tensorflow.keras import layers
from sklearn.metrics import confusion_matrix
import seaborn as sns

# 加载内置MNIST数据集
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

print("训练集形状:", x_train.shape)  
print("测试集形状:", x_test.shape)   

plt.figure(figsize=(10,5))
for i in range(15):
    plt.subplot(3,5,i+1)
    plt.imshow(x_train[i], cmap='gray')
    plt.title(f"Label: {
     y_train[i]}")
    plt.axis('off')
plt.tight_layout()
plt.savefig('mnist_samples.png', dpi=300)
plt.show()

# 将像素值缩放到0-1范围
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255

# 将图像展平为784维向量
x_train = x_train.reshape(-1,