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⭐ 手写数字分类: Keras + MNIST 数据集

手写数字分类任务

任务：将手写数字的灰度图像（28像素×28像素）划分到10个类别中（0~9）

MNIST数据集包含60 000张训练图像和10 000张测试图像，由美国国家标准与技术研究院（National Institute of Standards and Technology，即 MNIST 中的NIST）在20世纪80年代收集得到

• 样本示例如下：(hint: 显示数据集的第一个数字的代码：plt.imshow(train_images[0], cmap=plt.cm.binary))

💜步骤一 ： 加载Keras中的MNIST数据集

from keras.datasets import mnist 
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()  # 包括4个Numpy数组# 准备数据 
train_images = train_images.reshape((60000, 28 * 28)) 
train_images = train_images.astype('float32') / 255 
test_images  = test_images.reshape((10000, 28 * 28)) 
test_images  = test_images.astype('float32') / 255# 准备标签
from keras.utils import to_categorical 
train_labels = to_categorical(train_labels) 
test_labels  = to_categorical(test_labels)

💜步骤二 ： 构建网络架构 (两层全连接层为例)

from keras import models 
from keras import layers 
network = models.Sequential() 
network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,))) 
network.add(layers.Dense(10, activation='softmax'))

💜步骤三： 编译步骤 (optimizer + loss + metrics)

network.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])

💜步骤四：训练网络

network.fit(train_images, train_labels, epochs=5, batch_size=128)

💜步骤五：测试网络

 test_loss, test_acc = network.evaluate(test_images, test_labels) 

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完整代码参考：

from keras.datasets import mnist 
from keras import models 
from keras import layers (train_images, train_labels), (test_images, test_labels) = mnist.load_data()  # 包括4个Numpy数组# 准备数据 
train_images = train_images.reshape((60000, 28 * 28)) 
train_images = train_images.astype('float32') / 255 
test_images  = test_images.reshape((10000, 28 * 28)) 
test_images  = test_images.astype('float32') / 255# 准备标签
from keras.utils import to_categorical 
train_labels = to_categorical(train_labels) 
test_labels  = to_categorical(test_labels)# 构建网络架构
network = models.Sequential() 
network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,))) 
network.add(layers.Dense(10, activation='softmax'))# 编译步骤
network.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])# 训练网络
network.fit(train_images, train_labels, epochs=5, batch_size=128)# 测试网络
test_loss, test_acc = network.evaluate(test_images, test_labels) print("Loss: {}, Acc: {}".format(test_loss, test_acc))

----- 结束后会得到类似如下结果：

Epoch 1/5
469/469 [==============================] - 2s 5ms/step - loss: 0.2598 - accuracy: 0.9253
Epoch 2/5
469/469 [==============================] - 2s 5ms/step - loss: 0.1041 - accuracy: 0.9692
Epoch 3/5
469/469 [==============================] - 2s 5ms/step - loss: 0.0684 - accuracy: 0.9795
Epoch 4/5
469/469 [==============================] - 2s 5ms/step - loss: 0.0492 - accuracy: 0.9848
Epoch 5/5
469/469 [==============================] - 2s 5ms/step - loss: 0.0367 - accuracy: 0.9892
313/313 [==============================] - 0s 702us/step - loss: 0.0665 - accuracy: 0.9803
Loss: 0.06652633100748062, Acc: 0.9803000092506409

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参考书籍：Python 深度学习