1.手写数字数据集

  • from sklearn.datasets import load_digits
  • digits = load_digits()

 

from sklearn.datasets import load_digits
import numpy as np
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
# SKlearn 数据预处理:归一化
from sklearn.preprocessing import MinMaxScaler
X_data = digits.data.astype(np.float32)
scaler = MinMaxScaler()
X_data = scaler.fit_transform(X_data)
print("归一化后",X_data)
# 转化为图片的格式(batch,height,width,channels)
X=X_data.reshape(-1,8,8,1)
# OneHotEncoder独热编码
from sklearn.preprocessing import OneHotEncoder
# y = digits.target.reshape(-1,1)
y = digits.target.astype(np.float32).reshape(-1,1)  #将Y_data变为一列
Y = OneHotEncoder().fit_transform(y).todense() #张量结构todense
print("独热编码:",Y)
# 切分数据集
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test = train_test_split(X,Y,test_size=0.2,random_state=0,stratify=Y)
print(X_train,X_test,y_train,y_test)
print("X_data.shape:",X_data.shape)
print("X.shape",X.shape)

 

 

 

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Dropout,Conv2D,MaxPool2D,Flatten
#3、建立模型
model = Sequential()
ks = (3, 3)  # 卷积核的大小
input_shape = X_train.shape[1:]
# 一层卷积,padding='same',tensorflow会对输入自动补0
model.add(Conv2D(filters=16, kernel_size=ks, padding='same', input_shape=input_shape, activation='relu'))
# 池化层1
model.add(MaxPool2D(pool_size=(2, 2)))
# 防止过拟合,随机丢掉连接
model.add(Dropout(0.25))
# 二层卷积
model.add(Conv2D(filters=32, kernel_size=ks, padding='same', activation='relu'))
# 池化层2
model.add(MaxPool2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
# 三层卷积
model.add(Conv2D(filters=64, kernel_size=ks, padding='same', activation='relu'))
# 四层卷积
model.add(Conv2D(filters=128, kernel_size=ks, padding='same', activation='relu'))
# 池化层3
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softmax
model.add(Dense(10, activation='softmax'))
print(model.summary())

 

 3.模型训练

 

# 3、模型训练
# 画Train History图
plt.rcParams['font.sans-serif'] = ['FangSong'] # 指定字体
def show_train_history(train_history, train, validation):
    plt.plot(train_history.history[train])
    plt.plot(train_history.history[validation])
    plt.ylabel('train')
    plt.xlabel('epoch')
    plt.legend(['train', 'validation'], loc='upper left')
    plt.show()
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)
show_train_history(train_history,"accuracy","val_accuracy")  # 准确率
show_train_history(train_history,"loss","val_loss")  # 损失率

 


 

5.模型评价

  • model.evaluate()
  • 交叉表与交叉矩阵
  • pandas.crosstab
  • seaborn.heatmap
复制代码
# 5、模型评价
import pandas as pd
import seaborn as sns
# model.evaluate()
score = model.evaluate(X_test, y_test)
print('score:', score)
# 预测值
y_pred = model.predict_classes(X_test)
print('y_pred:', y_pred[:10])
# 交叉表与交叉矩阵
y_test1 = np.argmax(y_test, axis=1).reshape(-1)
y_true = np.array(y_test1)[0]
# 交叉表查看预测数据与原数据对比
# pandas.crosstab
pd.crosstab(y_true, y_pred, rownames=['true'], colnames=['predict'])
# 交叉矩阵
# seaborn.heatmap
y_test1 = y_test1.tolist()[0]
a = pd.crosstab(np.array(y_test1), y_pred, rownames=['Lables'], colnames=['Predict'])
# 转换成属dataframe
df = pd.DataFrame(a)
sns.heatmap(df, annot=True, cmap="Reds", linewidths=0.2, linecolor='G')
plt.show()

 

 

 
 

 

 


 


 

 

 

posted on 2020-06-10 15:40  zlkang  阅读(196)  评论(0编辑  收藏  举报