补交作业：第十二次作业--垃圾邮件分类：https://www.cnblogs.com/sgczw/p/13060726.html
第十三次作业--垃圾邮件分类2：https://www.cnblogs.com/sgczw/p/13060743.html
（这两个作业电脑显卡炸了，返厂修了两周，所以没有完成，只能在手机上直播课，谢谢老师）
第六次作业--逻辑回归：https://www.cnblogs.com/sgczw/p/12759682.html （这个作业我4月23日就已经弄好了，但是忘记交了，老师可以查看一下这篇博客的日期）
1.手写数字数据集
- from sklearn.datasets import load_digits
- digits = load_digits()
digits = load_digits() X_data = digits.data.astype(np.float32) Y_data = digits.target.astype(np.float32).reshape(-1, 1)
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
# 将属性缩放到一个指定的最大和最小值（通常是1-0之间） # x：归一化MinMaxScaler() scaler = MinMaxScaler() X_data = scaler.fit_transform(X_data) X = X_data.reshape(-1, 8, 8, 1) print("MinMaxScaler_trans_X_data：") print(X_data) # y：独热编码OneHotEncoder 张量结构todense # 进行oe-hot编码 Y = OneHotEncoder().fit_transform(Y_data).todense() print("one-hot_Y：") print(Y) # 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('X_train.shape, X_test.shape, y_train.shape, y_test.shape：', X_train.shape, X_test.shape, y_train.shape, y_test.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
- 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)
import matplotlib.pyplot as plt # 画Train History图 def show_train_history(train_history, train, validation): plt.plot(train_history.history[train]) plt.plot(train_history.history[validation]) plt.title('Train History') plt.ylabel('train') plt.xlabel('epoch') plt.legend(['train', 'validation'], loc='upper left') plt.show() # 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) # 准确率 show_train_history(train_history, 'accuracy', 'val_accuracy') # 损失率 show_train_history(train_history, 'loss', 'val_loss')
5.模型评价
- model.evaluate()
- 交叉表与交叉矩阵
- pandas.crosstab
- seaborn.heatmap
# 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()
