1.手写数字数据集
- from sklearn.datasets import load_digits
- digits = load_digits()
import numpy as np
from sklearn.datasets import load_digits
digits = load_digits()
x_data = digits.data.astype(np.float32)
x_target = digits.target.astype(np.float32).reshape(-1, 1)
print("data-----")
print(x_data)
print("-"*10)
print("target"+"-"*10)
print(x_target)
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
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, OneHotEncoder
#归一化
scaler = MinMaxScaler()
X_data = scaler.fit_transform(x_data)
print("data归一化"+"-"*100)
print(X_data)
print("-"*100)
# one-hot编码
X_target = OneHotEncoder().fit_transform(x_target).todense()
print("tagert独热编码"+"-"*100)
print(X_target)
print("-"*100)
# 转换为图片的格式
X_data_1 = X_data.reshape(-1, 8, 8, 1)
#训练集测试集划分
X_train, X_test, y_train, y_test = train_test_split(X_data_1, X_target, test_size=0.2, random_state=0, stratify=X_target)
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。
rom tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
model = Sequential()
ks = [3, 3] # 卷积核
model.add(Conv2D(filters=16, kernel_size=ks, padding='same', input_shape=X_train.shape[1:], activation='relu'))# 一层卷积
model.add(MaxPool2D(pool_size=(2, 2)))# 池化层
model.add(Dropout(0.25))
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
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
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
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
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)
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')
5.模型评价
- model.evaluate()
- 交叉表与交叉矩阵
- pandas.crosstab
- seaborn.heatmap
import matplotlib.pyplot as plt
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)
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')