15 手写数字识别-小数据集
1.手写数字数据集
- from sklearn.datasets import load_digits
- digits = load_digits()
from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense,Dropout,Flatten,Conv2D,MaxPool2D import matplotlib.pyplot as plt import pandas as pd import tensorflow as tf from sklearn.metrics import accuracy_score 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
scaler = MinMaxScaler() X_data = scaler.fit_transform(X_data) print("MinMaxScaler_trans_X_data:") print(X_data) Y = OneHotEncoder().fit_transform(Y_data).todense() print("one-hot_Y:") print(Y) 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)
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。
模型结构图如下:
设计依据:
(1)模型是层的堆叠,参考VGGnet模型,一条路走到黑,小卷积核,小池化核。
(2)模型使用了四层卷积,三个池化层,所以加入Dropout层来防止过拟合。
model = Sequential() ks = (3, 3) input_shape = X_train.shape[1:] model.add(Conv2D(filters=16, kernel_size=ks, padding='same', input_shape=input_shape, activation='relu')) model.add(MaxPool2D(pool_size=(2, 2))) model.add(Dropout(0.25)) 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)) model.add(Flatten()) model.add(Dense(128, activation='relu')) model.add(Dropout(0.25)) model.add(Dense(10, activation='softmax')) print(model.summary())
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4.模型训练
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() 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) show_train_history(train_history, 'accuracy', 'val_accuracy') 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
score = model.evaluate(X_test, y_test) print('score:', score) 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] pd.crosstab(y_true, y_pred, rownames=['true'], colnames=['predict']) # seaborn.heatmap 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="Reds", linewidths=0.2, linecolor='G') plt.show()