1.手写数字数据集及预处理
# 1、手写数字数据集及预处理 from sklearn.datasets import load_digits digits = load_digits() # 读取手写数字数据集 X_data = digits.data.astype(np.float32) Y_data = digits.target.astype(np.float32).reshape(-1,1) # 对X_data进行归一化MinMaxScaler scaler = MinMaxScaler() X_data = scaler.fit_transform(X_data) print("X_data归一化后：",X_data) # 对Y进行独热编码OneHotEncoder Y = OneHotEncoder().fit_transform(Y_data).todense() print("Y独热编码后：",Y) X = X_data.reshape(-1,8,8,1) # 转换为图片的格式 # 划分训练集和测试集 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_train.shape) # 查看维度 print("x_test.shape：",x_test.shape) # 查看维度 print("y_train.shape：",y_train.shape) # 查看维度 print("y_test.shape：",y_test.shape) # 查看维度
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2.设计卷积神经网络结构
模型结构图：
# 2、设计卷积神经网络结构 model = Sequential() ks = (3,3) # 设置卷积核的大小 input_shape = x_train.shape[1:] # 第一层卷积 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 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())
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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
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4.模型评价
# 4、模型评价 score = model.evaluate(x_test,y_test) print("score:",score) # 预测值 pre = model.predict_classes(x_test) print("预测值为：",pre[:10]) # 交差表与交叉矩阵 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 pd.crosstab(y_true,pre,rownames=['true'],colnames=['predict']) # 交叉矩阵 y_test1 = y_test1.tolist()[0] a = pd.crosstab(np.array(y_test1),pre,rownames=['Lables'],colnames=['Predict']) # 转换成dataframe df = pd.DataFrame(a) sns.heatmap(df,annot=True,cmap="Oranges",linewidths=0.2,linecolor="G")
运行结果：
