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)# 将Y_data变为一列
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） scaler = MinMaxScaler() X_data = scaler.fit_transform(X_data) print("MinMaxScaler_trans_X_data：") print(X_data) Y = OneHotEncoder().fit_transform(Y_data).todense()# 进行oe-hot编码 print("one-hot_Y：") print(Y) # 转换为图片的格式（batch, height, width, channels） X = X_data.reshape(-1, 8, 8, 1) 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)
x：归一化MinMaxScaler()
y：独热编码OneHotEncoder()或to_categorical
训练集测试集划分
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。
model = Sequential() ks = (3, 3) # 卷积核的大小 input_shape = X_train.shape[1:] model.add(Conv2D(filters=16, kernel_size=ks, padding='same', input_shape=input_shape, activation='relu'))# 一层卷积，padding='same',tensorflow会对输入自动补0 model.add(MaxPool2D(pool_size=(2, 2)))# 池化层1 model.add(Dropout(0.25))# 防止过拟合，随机丢掉连接 model.add(Conv2D(filters=32, kernel_size=ks, padding='same', activation='relu'))# 二层卷积 model.add(MaxPool2D(pool_size=(2, 2)))# 池化层2 model.add(Dropout(0.25)) model.add(Conv2D(filters=64, kernel_size=ks, padding='same', activation='relu'))# 三层卷积 model.add(Conv2D(filters=128, kernel_size=ks, padding='same', activation='relu'))# 四层卷积 model.add(MaxPool2D(pool_size=(2, 2)))# 池化层3 model.add(Dropout(0.25)) model.add(Flatten())# 平坦层 model.add(Dense(128, activation='relu'))# 全连接层 model.add(Dropout(0.25)) model.add(Dense(10, activation='softmax'))# 激活函数softmax model.summary()
4.模型训练
# 训练模型 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) score = model.evaluate(X_test,y_test) score
第一次运行损失率loss:1.2753，准确率accuracy：0.6750
第二次运行
训练参数可视化
# 定义训练参数可视化 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() # 准确率 show_train_history(train_history, 'accuracy', 'val_accuracy') # 损失率 show_train_history(train_history, 'loss', 'val_loss')
准确率
损失率
5.模型评价
- model.evaluate()
- 交叉表与交叉矩阵
- pandas.crosstab
- seaborn.heatmap
# 模型评价 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 pd.crosstab(y_true, y_pred, rownames=['true'], colnames=['predict']) # 交叉矩阵 y_test1 = y_test1.tolist()[0] a = pd.crosstab(np.array(y_test1), y_pred) df = pd.DataFrame(a) sns.heatmap(df, annot=True, cmap="YlGnBu", linewidths=0.2, linecolor='G') plt.show()
模型评价score，预测值y_pred
交叉表查看预测数据与原数据对比
代码如下：
import matplotlib.pyplot as plt import numpy as np import pandas as pd from sklearn.datasets import load_digits#小数据集8*8 from sklearn.model_selection import train_test_split# 训练集测试集划分 from sklearn.preprocessing import OneHotEncoder#独热编码 from sklearn.preprocessing import MinMaxScaler#归一化 import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense,Dropout,Flatten,Conv2D,MaxPool2D from sklearn.metrics import accuracy_score import seaborn as sns digits = load_digits() X_data = digits.data.astype(np.float32) Y_data = digits.target.astype(np.float32).reshape(-1, 1)# 将Y_data变为一列 # 将属性缩放到一个指定的最大和最小值（通常是1-0之间） scaler = MinMaxScaler() X_data = scaler.fit_transform(X_data) print("MinMaxScaler_trans_X_data：") print(X_data) Y = OneHotEncoder().fit_transform(Y_data).todense()# 进行oe-hot编码 print("one-hot_Y：") print(Y) # 转换为图片的格式（batch, height, width, channels） X = X_data.reshape(-1, 8, 8, 1) 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) model = Sequential() ks = (3, 3) # 卷积核的大小 input_shape = X_train.shape[1:] model.add(Conv2D(filters=16, kernel_size=ks, padding='same', input_shape=input_shape, activation='relu'))# 一层卷积，padding='same',tensorflow会对输入自动补0 model.add(MaxPool2D(pool_size=(2, 2)))# 池化层1 model.add(Dropout(0.25))# 防止过拟合，随机丢掉连接 model.add(Conv2D(filters=32, kernel_size=ks, padding='same', activation='relu'))# 二层卷积 model.add(MaxPool2D(pool_size=(2, 2)))# 池化层2 model.add(Dropout(0.25)) model.add(Conv2D(filters=64, kernel_size=ks, padding='same', activation='relu'))# 三层卷积 model.add(Conv2D(filters=128, kernel_size=ks, padding='same', activation='relu'))# 四层卷积 model.add(MaxPool2D(pool_size=(2, 2)))# 池化层3 model.add(Dropout(0.25)) model.add(Flatten())# 平坦层 model.add(Dense(128, activation='relu'))# 全连接层 model.add(Dropout(0.25)) model.add(Dense(10, activation='softmax'))# 激活函数softmax model.summary() # 训练模型 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) score = model.evaluate(X_test,y_test) score # 定义训练参数可视化 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() # 准确率 show_train_history(train_history, 'accuracy', 'val_accuracy') # 损失率 show_train_history(train_history, 'loss', 'val_loss') # 模型评价 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 pd.crosstab(y_true, y_pred, rownames=['true'], colnames=['predict']) # 交叉矩阵 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="YlGnBu", linewidths=0.2, linecolor='G') plt.show()
