keras mnist python 机器学习深度学习

最新推荐文章于 2024-06-22 19:13:31 发布

原创最新推荐文章于 2024-06-22 19:13:31 发布 · 289 阅读

0 ·

CC 4.0 BY-SA版权

本文为LifelongLearningPan博主原创文章，转载请注明出处。

文章标签：

#keras #mnist #python #机器学习 #深度学习

机器学习专栏收录该内容

3 篇文章

订阅专栏

本文详细介绍了如何利用Python的Keras库进行机器学习，特别是针对MNIST手写数字识别的深度学习模型训练。通过构建、编译及训练神经网络，展示了一个完整的深度学习流程。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

from keras.datasets import mnist
from keras.utils import np_utils
import numpy as np
np.random.seed(10);

(x_Train,y_Train),(x_Test,y_Test)=mnist.load_data();

x_Train4D=x_Train.reshape(x_Train.shape[0],28,28,1).astype('float32');
x_Test4D=x_Test.reshape(x_Test.shape[0],28,28,-1).astype('float32');

x_Train4D_normalize=x_Train4D/255;
x_Test4D_normalize=x_Test4D/255;

y_TrainOneHot=np_utils.to_categorical(y_Train);
y_TestOneHot=np_utils.to_categorical(y_Test);

from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten,Conv2D,MaxPooling2D

model=Sequential();
model.add(Conv2D(filters=16,kernel_size=(5,5),padding='same',input_shape=(28,28,1),activation='relu'));
model.add(MaxPooling2D(pool_size=(2,2)));
model.add(Conv2D(filters=36,kernel_size=(5,5),padding="same",activation='relu'));
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'));

#print(model.summary());

model.compile(loss="categorical_crossentropy",optimizer="adam",metrics=['accuracy']);
train_history=model.fit(x=x_Train4D_normalize,y=y_TrainOneHot,batch_size=32,validation_split=0.2,epochs=5,verbose=2);

loss, accuracy=model.evaluate(x_Test4D_normalize,y_TestOneHot,verbose=0);
print('Test loss:', loss)
print('Accuracy:', accuracy)

model.save('keras_mnist_model.h5')