手写数字识别（神经网络） - 数学建模社区-数学中国

import numpy as np
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from tensorflow.keras.utils import to_categorical
# 加载MNIST数据集
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# 数据预处理
# 归一化
X_train = X_train.astype('float32') / 255
X_test = X_test.astype('float32') / 255
# 调整数据维度，以符合CNN的输入要求
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
# 将目标变量转换为二进制类别矩阵
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)
# 构建模型
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
# 编译模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# 训练模型
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=10, batch_size=200)
# 评估模型
score = model.evaluate(X_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

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