15-手写数字识别-小数据集

1.手写数字数据集

from sklearn.datasets import load_digits
digits = load_digits()

2.图片数据预处理

x：归一化MinMaxScaler()
y：独热编码OneHotEncoder()或to_categorical
训练集测试集划分
张量结构

from sklearn.datasets import load_digits
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import OneHotEncoder
import numpy as np
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt

digits=load_digits() #获取数据集

X_data=digits.data.astype(np.float32)#样本数据
print("样本数据:
",X_data)
#对x归一化处理MinMaxScaler()
scaler=MinMaxScaler()
X_data=scaler.fit_transform(X_data)
print("归一化后处理后的样本数据:
",X_data)

Y_data=digits.target.astype(np.float32).reshape(-1,1)#将Y_data变为一列
print("样本标签:
",Y_data)
#对y进行独热编码OneHotEncoder()
Y=OneHotEncoder().fit_transform(Y_data).todense()
print("独热编码后的样本标签:
",Y)

#转换为图片的格式
X=X_data.reshape(-1,8,8,1)

#训练集测试集划分
x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.2,random_state=0,stratify=Y)
print('训练集数据：',x_train.shape,'测试集数据：',x_test.shape)
print('
训练集标签：',y_train.shape,'测试集标签：',y_test.shape)

运行结果：

3.设计卷积神经网络结构

绘制模型结构图，并说明设计依据。

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
# 建立模型
model = Sequential()

# C1卷积层
model.add(
     Conv2D(
        filters=16,#输出空间的维度
        kernel_size=(5, 5),#卷积核大小
        padding='same',#填充边界 
        input_shape=x_train.shape[1:],
        activation='relu'))

# S2池化层
model.add(MaxPool2D(pool_size=(2, 2)))

# drop层(防止过拟合)
model.add(Dropout(0.25))

# C3卷积层
model.add(
    Conv2D(
        filters=32,
        kernel_size=(5, 5), 
        padding='same',
        activation='relu'))


# S4池化层
model.add(MaxPool2D(pool_size=(2, 2)))

model.add(Dropout(0.25))

#C5卷积层
model.add(
    Conv2D(
        filters=64,
        kernel_size=(5, 5), 
        padding='same',
        activation='relu'))

#C6卷积层
model.add(
    Conv2D(
        filters=128,
        kernel_size=(5, 5), 
        padding='same',
        activation='relu'))

# S7池化层
model.add(MaxPool2D(pool_size=(2, 2)))

model.add(Dropout(0.25))

#平坦层
model.add(Flatten())

#全连接层
model.add(Dense(128, activation='relu'))

model.add(Dropout(0.25)) 

#激活函数
model.add(Dense(10, activation='softmax'))

model.summary()

运行结果：

4.模型训练

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
train_history = model.fit(x=X_train,y=y_train,validation_split=0.2, batch_size=300,epochs=10,verbose=2)

#模型训练
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
train_history = model.fit(x=x_train,y=y_train,
                          validation_split=0.2, 
                          batch_size=300,#每次训练的数据量
                          epochs=10,#训练迭代次数
                          verbose=2)

运行结果：

5.模型评价

model.evaluate()
交叉表与交叉矩阵
pandas.crosstab
seaborn.heatmap

# 模型评价
score =model.evaluate(x_test,y_test)
print("模型评价:",score)

#预测值
y_pred=model.predict_classes(x_test)
print("预测值:",y_pred)

#交叉矩阵查看预测数据与原数据对比
import pandas as pd
import seaborn as sns
#标签数值化
y_test1=np.argmax(y_test,axis=1).reshape(-1)
y_ture=np.array(y_test1[0]).reshape(-1)

a=pd.crosstab(y_ture,y_pred,rownames=['lables'],colnames=['predict'])
#转换为数据框
df=pd.DataFrame(a)
#绘制热力图
sns.heatmap(df,annot=True,cmap="YlGnBu",linewidths=0.2,linecolor='G')
plt.show()