15.手写数字识别-小数据集
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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) y_data = digits.target.astype(np.float32).reshape(-1, 1) #手写数字数据集
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 scaler = MinMaxScaler() #归一化 X_data = scaler.fit_transform(x_data) print(X_data) Y = OneHotEncoder().fit_transform(y_data).todense() # oe-hot编码 print(Y) X = X_data.reshape(-1, 8, 8, 1) # 转换为图片的格式 batch、height、width和channels 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)#训练集测试集划分
归一化后:
oe-hot:
划分后:
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。
from 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 #载入新的包 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‘)
定义训练参数可视化:
准确率图:
损失率图:
5.模型评价
- model.evaluate()
- 交叉表与交叉矩阵
- pandas.crosstab
- seaborn.heatmap
import pandas as pd import seaborn as sns #加载两个新包 score = model.evaluate(X_test, y_test) print(‘score:‘, score) y_pred = model.predict_classes(X_test) print(‘y_pred:‘, y_pred[:10]) y_test1 = np.argmax(y_test, axis=1).reshape(-1) y_true = np.array(y_test1)[0] # 数据对比 pd.crosstab(y_true, y_pred, rownames=[‘true‘], colnames=[‘predict‘])# 交叉表与交叉矩阵 y_test1 = y_test1.tolist()[0] a = pd.crosstab(np.array(y_test1), y_pred, rownames=[‘Lables‘], colnames=[‘Predict‘]) df = pd.DataFrame(a) # 转换DataFrame sns.heatmap(df, annot=True, cmap="GnBu_r", linewidths=0.2, linecolor=‘G‘)# seaborn.heatmap ,选择蓝色来画图 plt.show()
模型score,y_pred的预测值:
最终画出来的图:
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