1.手写数字数据集
- from sklearn.datasets import load_digits
- digits = load_digits()
#1.手写数字数据集 from sklearn.datasets import load_digits import numpy as np 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
#2.图片预处理 #x:归一化 from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() X_data = scaler.fit_transform(X_data) print("MinMaxScaler_X_data:") print(X_data) #y:独热编码 from sklearn.preprocessing import OneHotEncoder Y = OneHotEncoder().fit_transform(Y_data).todense() print("one-hot_Y:") print(Y)
#4.训练集测试集划分 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)
3.设计卷积神经网络结构
模型结构图:
4.模型训练
#5.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()) #7.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)
5.模型评价
- model.evaluate()
- 交叉表与交叉矩阵
- pandas.crosstab
- seaborn.heatmap
#8.模型评价 import numpy as np 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()