机器学习15 手写数字识别-小数据集

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系统重装了,先缓缓

1.手写数字数据集

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

 

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

 

import numpy as np
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)
X=X_data.reshape(-1,8,8,1)
from sklearn.preprocessing import OneHotEncoder
y = digits.target.astype(np.float32).reshape(-1,1)
Y = OneHotEncoder().fit_transform(y).todense()
print("独热编码:",Y)
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_data.shape:",X_data.shape)
print("X.shape:",X.shape)

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,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())

4.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
  • import pandas as pd
    import seaborn as sns
    score = model.evaluate(X_test, y_test)
    print(‘score:‘, score)
    y_pred = model.predict_classes(X_test)
    print(‘y_pred:‘, y_pred[:10])
    y_test1 = np.argmax(y_test, axis=1).reshape(-1)
    y_true = np.array(y_test1)[0]
    pd.crosstab(y_true, y_pred, rownames=[‘true‘], colnames=[‘predict‘])
    y_test1 = y_test1.tolist()[0]
    a = pd.crosstab(np.array(y_test1), y_pred, rownames=[‘Lables‘], colnames=[‘Predict‘])
    df = pd.DataFrame(a)
    sns.heatmap(df, annot=True, cmap="Blues", linewidths=0.2, linecolor=‘G‘)
    plt.show()

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