大家好，又见面了，我是你们的朋友全栈君。如果您正在找激活码,请点击查看最新教程,关注关注公众号 “全栈程序员社区” 获取激活教程,可能之前旧版本教程已经失效.最新Idea2022.1教程亲测有效,一键激活。
Jetbrains全系列IDE使用 1年只要46元 售后保障 童叟无欺
一、概述
- 手写数字识别通常作为第一个深度学习在计算机视觉方面应用的示例，Mnist数据集在这当中也被广泛采用，可用于进行训练及模型性能测试；
- 模型的输入： 32*32的手写字体图片，这些手写字体包含0~9数字，也就是相当于10个类别的图片
- 模型的输出： 分类结果，0~9之间的一个数
- 下面通过多层感知器模型以及卷积神经网络的方式进行实现
二、基于多层感知器的手写数字识别
- 多层感知器的模型如下，其具有一层影藏层：
| 784个神经元 | 784个神经元 | 10个神经元 |
|---|---|---|
| 输入层 | 影藏层 | 输出层 |
- Mnist数据集此前可通过mnist.load_data()进行下载，但网址打不开，因此通过其他方式将数据集下载到本地，并在本地进行读取，数据集下载链接为：链接: https://pan.baidu.com/s/1ZlktkjqEGEJ0aZGQBQuqXg 提取码: br96
- 改编后的数据读取方式如下：
import numpy as np def loadData(path="mnist.npz"): f = np.load(path) x_train, y_train = f['x_train'], f['y_train'] x_test, y_test = f['x_test'], f['y_test'] f.close() return (x_train, y_train), (x_test, y_test) # 从Keras导入Mnist数据集 (x_train, y_train), (x_validation, y_validation) = loadData() - 完整的实现代码如下：
import matplotlib.pyplot as plt import numpy as np from keras.models import Sequential from keras.layers import Dense from keras.utils import np_utils def loadData(path="mnist.npz"): f = np.load(path) x_train, y_train = f['x_train'], f['y_train'] x_test, y_test = f['x_test'], f['y_test'] f.close() return (x_train, y_train), (x_test, y_test) # 从Keras导入Mnist数据集 (x_train, y_train), (x_validation, y_validation) = loadData() # 显示4张手写数字图片 plt.subplot(221) plt.imshow(x_train[0], cmap=plt.get_cmap('gray')) plt.subplot(222) plt.imshow(x_train[1], cmap=plt.get_cmap('gray')) plt.subplot(223) plt.imshow(x_train[2], cmap=plt.get_cmap('gray')) plt.subplot(224) plt.imshow(x_train[3], cmap=plt.get_cmap('gray')) plt.show() # 设定随机种子 seed = 7 np.random.seed(seed) num_pixels = x_train.shape[1] * x_train.shape[2] print(num_pixels) x_train = x_train.reshape(x_train.shape[0], num_pixels).astype('float32') x_validation = x_validation.reshape(x_validation.shape[0], num_pixels).astype('float32') # 格式化数据到0~1 x_train = x_train/255 x_validation = x_validation/255 # 进行one-hot编码 y_train = np_utils.to_categorical(y_train) y_validation = np_utils.to_categorical(y_validation) num_classes = y_validation.shape[1] print(num_classes) # 定义基准MLP模型 def create_model(): model = Sequential() model.add(Dense(units=num_pixels, input_dim= num_pixels,kernel_initializer='normal', activation='relu')) model.add(Dense(units=num_classes, kernel_initializer='normal', activation='softmax')) # 编译模型 model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model model = create_model() model.fit(x_train, y_train, epochs=10, batch_size=200) score = model.evaluate(x_validation, y_validation) print('MLP: %.2f%%' % (score[1]*100)) - 程序运行结果如下
784 10 Epoch 1/10 200/60000 [..............................] - ETA: 4:32 - loss: 2.3038 - acc: 0.1100 600/60000 [..............................] - ETA: 1:37 - loss: 2.0529 - acc: 0.3283 1000/60000 [..............................] - ETA: 1:02 - loss: 1.8041 - acc: 0.4710 ... 9472/10000 [===========================>..] - ETA: 0s 10000/10000 [==============================] - 1s 112us/step MLP: 98.07% 三、基于卷积神经网络的手写数字识别
- 构建的卷积神经网络结构如下：
| 1 x 28 x 28个输入 | 32maps, 5 x 5 | 2 x 2 | 20% | 128个 | 10个 | |
|---|---|---|---|---|---|---|
| 输入层 | 卷积层 | 池化层 | Dropout层 | Flatten层 | 全连接层 | 输出层 |
Flatten层: Flatten层用来将输入“压平”，即把多维的输入一维化，常用在从卷积层到全连接层的过渡,举例如下
| input size | —->> | output size |
|---|---|---|
| 32 x 32 x 3 | Flatten–> | 3072 |
- 完整的实现代码如下：
import numpy as np from keras.models import Sequential from keras.layers import Dense from keras.layers import Dropout from keras.layers import Flatten from keras.layers.convolutional import Conv2D from keras.layers.convolutional import MaxPooling2D from keras.utils import np_utils from keras import backend backend.set_image_data_format('channels_first') def loadData(path="mnist.npz"): f = np.load(path) x_train, y_train = f['x_train'], f['y_train'] x_test, y_test = f['x_test'], f['y_test'] f.close() return (x_train, y_train), (x_test, y_test) # 从Keras导入Mnist数据集 (x_train, y_train), (x_validation, y_validation) = loadData() # 设定随机种子 seed = 7 np.random.seed(seed) x_train = x_train.reshape(x_train.shape[0], 1, 28, 28).astype('float32') x_validation = x_validation.reshape(x_validation.shape[0], 1, 28, 28).astype('float32') # 格式化数据到0~1 x_train = x_train/255 x_validation = x_validation/255 # 进行one-hot编码 y_train = np_utils.to_categorical(y_train) y_validation = np_utils.to_categorical(y_validation) # 定义模型 def create_model(): model = Sequential() model.add(Conv2D(32, (5, 5), input_shape=(1, 28, 28), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.2)) model.add(Flatten()) model.add(Dense(units=128, activation='relu')) model.add(Dense(units=10, activation='softmax')) # 编译模型 model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return model model = create_model() model.fit(x_train, y_train, epochs=10, batch_size=200, verbose=2) score = model.evaluate(x_validation, y_validation, verbose=0) print('CNN_Small: %.2f%%' % (score[1]*100)) - 运行结果如下（明显感觉到运行时间较长）：
Epoch 1/10 - 165s - loss: 0.2226 - acc: 0.9367 Epoch 2/10 - 163s - loss: 0.0713 - acc: 0.9785 Epoch 3/10 - 165s - loss: 0.0512 - acc: 0.9841 Epoch 4/10 - 165s - loss: 0.0391 - acc: 0.9880 Epoch 5/10 - 166s - loss: 0.0325 - acc: 0.9900 Epoch 6/10 - 162s - loss: 0.0268 - acc: 0.9917 Epoch 7/10 - 164s - loss: 0.0221 - acc: 0.9928 Epoch 8/10 - 161s - loss: 0.0190 - acc: 0.9943 Epoch 9/10 - 162s - loss: 0.0156 - acc: 0.9950 Epoch 10/10 - 162s - loss: 0.0143 - acc: 0.9959 CNN_Small: 98.87% 版权声明：本文内容由互联网用户自发贡献，该文观点仅代表作者本人。本站仅提供信息存储空间服务，不拥有所有权，不承担相关法律责任。如发现本站有涉嫌侵权/违法违规的内容， 请联系我们举报，一经查实，本站将立刻删除。
发布者：全栈程序员-站长，转载请注明出处：https://javaforall.net/194000.html原文链接：https://javaforall.net
![jQuery和Vue的区别[通俗易懂]](https://javaforall.net/wp-content/uploads/2020/11/2020110817443450-480x300.jpg)
