CNN和DNN · Owlbay

深度学习_MNIST手写数字识别_CNN与DNN对比实战

使用 CNN（卷积神经网络） 和 DNN（全连接神经网络） 训练 MNIST 数据集的完整代码，包括：

✅ 数据加载 & 预处理 ✅ CNN 模型定义 & 训练 ✅ DNN 模型定义 & 训练 ✅ 模型评估 & 预测 ✅ 可视化预测结果

📌 代码结构

CNN 模型（用于图像分类，适用于 MNIST）
DNN 模型（全连接网络，适用于基本分类任务）
对比 CNN 和 DNN 的性能
展示预测效果

📌 代码实现

import numpy as np
import os
import keras
import matplotlib.pyplot as plt
from sklearn.metrics import accuracy_score

# 设置 Keras 后端
os.environ["KERAS_BACKEND"] = "tensorflow"

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

# 归一化数据
x_train = x_train.astype("float32") / 255
x_test = x_test.astype("float32") / 255

# 调整形状以适应 CNN 结构
x_train_cnn = np.expand_dims(x_train, -1)
x_test_cnn = np.expand_dims(x_test, -1)

# 调整形状以适应 DNN 结构（展平 28x28 为 784）
x_train_dnn = x_train.reshape(-1, 28 * 28)
x_test_dnn = x_test.reshape(-1, 28 * 28)

# 设定类别数
num_classes = 10

# 将标签转换为 one-hot 编码（仅 DNN 需要）
y_train_onehot = keras.utils.to_categorical(y_train, num_classes)
y_test_onehot = keras.utils.to_categorical(y_test, num_classes)

# ---------------------------------------
# 📌 CNN 模型定义
# ---------------------------------------
def build_cnn_model():
    model = keras.Sequential([
        keras.layers.Conv2D(64, kernel_size=(3, 3), activation="relu", input_shape=(28, 28, 1)),
        keras.layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
        keras.layers.MaxPooling2D(pool_size=(2, 2)),
        keras.layers.Conv2D(128, kernel_size=(3, 3), activation="relu"),
        keras.layers.Conv2D(128, kernel_size=(3, 3), activation="relu"),
        keras.layers.GlobalAveragePooling2D(),
        keras.layers.Dropout(0.5),
        keras.layers.Dense(num_classes, activation="softmax"),
    ])
    return model

# ---------------------------------------
# 📌 DNN 模型定义
# ---------------------------------------
def build_dnn_model():
    model = keras.Sequential([
        keras.layers.Dense(512, activation="relu", input_shape=(28 * 28,)),
        keras.layers.Dropout(0.2),
        keras.layers.Dense(512, activation="relu"),
        keras.layers.Dropout(0.2),
        keras.layers.Dense(num_classes, activation="softmax"),
    ])
    return model

# ---------------------------------------
# 📌 训练 CNN
# ---------------------------------------
cnn_model = build_cnn_model()
cnn_model.compile(
    loss=keras.losses.SparseCategoricalCrossentropy(),
    optimizer=keras.optimizers.Adam(learning_rate=1e-3),
    metrics=["accuracy"],
)

cnn_model.fit(
    x_train_cnn, y_train, batch_size=128, epochs=5, validation_split=0.15
)

# ---------------------------------------
# 📌 训练 DNN
# ---------------------------------------
dnn_model = build_dnn_model()
dnn_model.compile(
    loss="categorical_crossentropy",
    optimizer=keras.optimizers.Adam(learning_rate=1e-3),
    metrics=["accuracy"],
)

dnn_model.fit(
    x_train_dnn, y_train_onehot, batch_size=128, epochs=5, validation_split=0.15
)

# ---------------------------------------
# 📌 评估 CNN 和 DNN
# ---------------------------------------
cnn_score = cnn_model.evaluate(x_test_cnn, y_test, verbose=0)
dnn_score = dnn_model.evaluate(x_test_dnn, y_test_onehot, verbose=0)

print(f"CNN 测试集准确率: {cnn_score[1]:.4f}")
print(f"DNN 测试集准确率: {dnn_score[1]:.4f}")

# ---------------------------------------
# 📌 CNN 和 DNN 进行预测
# ---------------------------------------
cnn_predictions = np.argmax(cnn_model.predict(x_test_cnn), axis=1)
dnn_predictions = np.argmax(dnn_model.predict(x_test_dnn), axis=1)

# ---------------------------------------
# 📌 可视化 CNN 和 DNN 预测结果
# ---------------------------------------
def plot_predictions(preds, model_name):
    num_images = 10
    plt.figure(figsize=(10, 5))
    for i in range(num_images):
        plt.subplot(2, 5, i + 1)
        plt.imshow(x_test[i], cmap="gray")  # 显示灰度图
        plt.title(f"{model_name} Pred: {preds[i]}\nTrue: {y_test[i]}", fontsize=10)
        plt.axis("off")
    plt.tight_layout()
    plt.show()

print("\n🎯 CNN 预测结果：")
plot_predictions(cnn_predictions, "CNN")

print("\n🎯 DNN 预测结果：")
plot_predictions(dnn_predictions, "DNN")

# ---------------------------------------
# 📌 计算 CNN 和 DNN 预测错误的示例
# ---------------------------------------
def plot_wrong_predictions(preds, model_name):
    wrong_preds = np.where(preds != y_test)[0]
    num_errors = min(10, len(wrong_preds))

    plt.figure(figsize=(10, 5))
    for i in range(num_errors):
        idx = wrong_preds[i]
        plt.subplot(2, 5, i + 1)
        plt.imshow(x_test[idx], cmap="gray")
        plt.title(f"Pred: {preds[idx]}\nTrue: {y_test[idx]}", fontsize=10, color="red")
        plt.axis("off")

    plt.tight_layout()
    plt.show()

print("\n🚨 CNN 预测错误示例：")
plot_wrong_predictions(cnn_predictions, "CNN")

print("\n🚨 DNN 预测错误示例：")
plot_wrong_predictions(dnn_predictions, "DNN")

📌 代码解析

1️⃣ 数据预处理

CNN 需要 (28, 28, 1) 格式输入，因此使用 np.expand_dims()
DNN 需要 展平为 (784,) 形状，因此使用 reshape(-1, 28*28)

2️⃣ 模型定义

CNN 模型
- Conv2D 卷积层
- MaxPooling2D 池化层
- GlobalAveragePooling2D
- Dropout 防止过拟合
- Dense 输出层（Softmax 分类）
DNN 模型
- Dense(512, activation="relu") 两层
- Dropout(0.2) 防止过拟合
- Dense(10, activation="softmax") 输出层