手把手用keras分类mnist数据集

实战流程获得数据,并将数据处理成合适的格式按照自己的设计搭建神经网络设定合适的参数训练神经网络在测试集上评价训练效果一、认识mnist数据集fromkeras.utilsimportto_categoricalfromkerasimportmodels,layers,regularizersfromkeras.optimizersimportRMSprop…

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博主将整个实战做成了视频讲解,视频链接:https://www.bilibili.com/video/BV16g4y1z7Qu/

实战流程

  • 获得数据,并将数据处理成合适的格式
  • 按照自己的设计搭建神经网络
  • 设定合适的参数训练神经网络
  • 在测试集上评价训练效果

一、认识mnist数据集

from keras.utils import to_categorical
from keras import models, layers, regularizers
from keras.optimizers import RMSprop
from keras.datasets import mnist
import matplotlib.pyplot as plt

# 加载数据集
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()
print(train_images.shape, test_images.shape)
print(train_images[0])
print(train_labels[0])
plt.imshow(train_images[0])
plt.show()

将图片由二维铺开成一维

train_images = train_images.reshape((60000, 28*28)).astype('float')
test_images = test_images.reshape((10000, 28*28)).astype('float')
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

二、搭建一个神经网络

在这里插入图片描述

network = models.Sequential()
network.add(layers.Dense(units=15, activation='relu', input_shape=(28*28, ),))
network.add(layers.Dense(units=10, activation='softmax'))

三、神经网络训练

1、编译:确定优化器和损失函数等

# 编译步骤
network.compile(optimizer=RMSprop(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])

2、训练网络:确定训练的数据、训练的轮数和每次训练的样本数等

# 训练网络,用fit函数, epochs表示训练多少个回合, batch_size表示每次训练给多大的数据
network.fit(train_images, train_labels, epochs=20, batch_size=128, verbose=2)

四、用训练好的模型进行预测,并在测试集上做出评价

# 来在测试集上测试一下模型的性能吧
y_pre = network.predict(test_images[:5])
print(y_pre, test_labels[:5])
test_loss, test_accuracy = network.evaluate(test_images, test_labels)
print("test_loss:", test_loss, " test_accuracy:", test_accuracy)

五、完整代码

1、使用全连接神经网络

from keras.utils import to_categorical
from keras import models, layers, regularizers
from keras.optimizers import RMSprop
from keras.datasets import mnist
import matplotlib.pyplot as plt

# 加载数据集
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

train_images = train_images.reshape((60000, 28*28)).astype('float')
test_images = test_images.reshape((10000, 28*28)).astype('float')
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

network = models.Sequential()
network.add(layers.Dense(units=128, activation='relu', input_shape=(28*28, ),
                         kernel_regularizer=regularizers.l1(0.0001)))
network.add(layers.Dropout(0.01))
network.add(layers.Dense(units=32, activation='relu',
                         kernel_regularizer=regularizers.l1(0.0001)))
network.add(layers.Dropout(0.01))
network.add(layers.Dense(units=10, activation='softmax'))

# 编译步骤
network.compile(optimizer=RMSprop(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])

# 训练网络,用fit函数, epochs表示训练多少个回合, batch_size表示每次训练给多大的数据
network.fit(train_images, train_labels, epochs=20, batch_size=128, verbose=2)

# 来在测试集上测试一下模型的性能吧
test_loss, test_accuracy = network.evaluate(test_images, test_labels)
print("test_loss:", test_loss, " test_accuracy:", test_accuracy)

2、使用卷积神经网络

from keras.utils import to_categorical
from keras import models, layers
from keras.optimizers import RMSprop
from keras.datasets import mnist
# 加载数据集
(train_images, train_labels), (test_images, test_labels) = mnist.load_data()

# 搭建LeNet网络
def LeNet():
    network = models.Sequential()
    network.add(layers.Conv2D(filters=6, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))
    network.add(layers.AveragePooling2D((2, 2)))
    network.add(layers.Conv2D(filters=16, kernel_size=(3, 3), activation='relu'))
    network.add(layers.AveragePooling2D((2, 2)))
    network.add(layers.Conv2D(filters=120, kernel_size=(3, 3), activation='relu'))
    network.add(layers.Flatten())
    network.add(layers.Dense(84, activation='relu'))
    network.add(layers.Dense(10, activation='softmax'))
    return network
network = LeNet()
network.compile(optimizer=RMSprop(lr=0.001), loss='categorical_crossentropy', metrics=['accuracy'])

train_images = train_images.reshape((60000, 28, 28, 1)).astype('float') / 255
test_images = test_images.reshape((10000, 28, 28, 1)).astype('float') / 255
train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

# 训练网络,用fit函数, epochs表示训练多少个回合, batch_size表示每次训练给多大的数据
network.fit(train_images, train_labels, epochs=10, batch_size=128, verbose=2)
test_loss, test_accuracy = network.evaluate(test_images, test_labels)
print("test_loss:", test_loss, " test_accuracy:", test_accuracy)

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