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()

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

scaler = MinMaxScaler()
X_data = scaler.fit_transform(X_data) #归一化
print("MinMaxScaler_trans_X_data：",X_data)
Y = OneHotEncoder().fit_transform(Y_data).todense()# 对Y进行oe-hot编码，张量结构todense
print("one-hot_Y：",Y)
# 转换为图片的格式
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。

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',保证卷积核大小，不够补零
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
# 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, rownames=['Lables'], colnames=['Predict'])
df = pd.DataFrame(a)
sns.heatmap(df, annot=True, cmap="Purples", linewidths=0.2, linecolor='G')
plt.show()