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 = digits.data.astype(np.float32) Y = digits.target.astype(np.float32).reshape(-1, 1) # 将y变为一列
结果如图：
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、图片数据预处理 from sklearn.preprocessing import MinMaxScaler from sklearn.preprocessing import OneHotEncoder from sklearn.model_selection import train_test_split scaler = MinMaxScaler() x_data = scaler.fit_transform(X) x = x_data.reshape(-1, 8, 8, 1) # 转换为图片格式 y = OneHotEncoder().fit_transform(Y).todense() # y : 独热编码 # 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)
结果如图：
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。
#3.设计卷积神经网络结构 import tensorflow tensorflow.__version__ #导入相关包 from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense,Dropout,Flatten,Conv2D,MaxPool2D #建立模型 model = Sequential() ks = [3,3] #卷积核大小 #第一卷积层输入数据的shape要指定，其他层的数据shape框架会制动推导 model.add(Conv2D(filters=16,kernel_size=ks,padding='same',input_shape=x_train.shape[1:],activation='relu')) #池化层 model.add(MaxPool2D(pool_size=(2,2))) model.add(Dropout(0.25)) #二层卷积 model.add(Conv2D(filters=32,kernel_size=ks,padding='same',activation='relu')) model.add(MaxPool2D(pool_size=(2,2))) model.add(Dropout(0.25)) model.add(Conv2D(filters=64,kernel_size=ks,padding='same',activation='relu')) model.add(Conv2D(filters=128,kernel_size=ks,padding='same',activation='relu')) 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 model.add(Dense(10,activation='softmax')) model.summary()
结果如图：
4.模型训练
# 4、模型训练
import matplotlib.pyplot as plt
# 损失函数:categorical_crossentropy,优化器:adam ,用准确率accuracy衡量模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# 划分20%作为验证数据,每次训练300个数据,训练迭代300轮
train_history = model.fit(x=x_train, y=y_train, validation_split=0.2, batch_size=300, epochs=10, verbose=2)
# 定义可视化
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, 'acc', 'val_acc')
# 损失率
show_train_history(train_history, 'loss', 'val_loss')
结果如图：
5.模型评价
- model.evaluate()
- 交叉表与交叉矩阵
- pandas.crosstab
- seaborn.heatmap
#5、模型评估
import pandas as pd
import seaborn as sns
score = model.evaluate(x_test, y_test)[1]
print('模型准确率=',score)
# 预测值
y_pre = model.predict_classes(x_test)
y_pre[:10]
# 交叉表和交叉矩阵
y_test1 = np.argmax(y_test, axis=1).reshape(-1)
y_true = np.array(y_test1)[0]
y_true.shape
# 交叉表查看预测数据与原数据对比
pd.crosstab(y_true, y_pre, rownames=['true'], colnames=['predict'])
# 交叉矩阵
y_test1 = y_test1.tolist()[0]
a = pd.crosstab(np.array(y_test1), y_pre, rownames=['Lables'], colnames=['predict'])
df = pd.DataFrame(a)
print(df)
sns.heatmap(df, annot=True, cmap="pink_r", linewidths=0.2, linecolor='G')
结果如图：
