最近在阅读一些AI项目，写入markdown，持续更新，算是之后也能回想起做法

项目 https://github.com/calssion/Fun_AI

 

Digit Recognizer(数字识别)

kaggle：https://www.kaggle.com/c/digit-recognizer

kernal-tutorial(教程)：https://www.kaggle.com/yassineghouzam/introduction-to-cnn-keras-0-997-top-6

Goal(任务)

The goal in this competition is to take an image of a handwritten single digit, and determine what that digit is.(比赛任务是识别手写的单一数字)
For every ImageId in the test set, you should predict the correct label.(对于训练集的每个图片ID，应该预测其正确标签)

Metric(评估)

This competition is evaluated on the categorization accuracy of your predictions (the percentage of images you get correct).
(用分类正确率进行评估，正确分类的图片的占比)

Introduction to CNN Keras - Acc 0.997 (top 8%)(介绍CNN-keras)

1. Introduction(介绍)

2. Data preparation(数据预处理)

2.1 Load data(加载数据)

2.2 Check for null and missing values(查空值和缺省值)

2.3 Normalization(标准化)

2.4 Reshape(调整尺寸)

2.5 Label encoding(标签编码)

2.6 Split training and valdiation set(切分训练集和验证集)

3. CNN

3.1 Define the model(建模)

3.2 Set the optimizer and annealer(优化算法和退火)

3.3 Data augmentation(数据增强)

4. Evaluate the model(模型评估)

4.1 Training and validation curves(训练和验证曲线图)

4.2 Confusion matrix(混淆矩阵)

5. Prediction and submition(预测和提交)

5.1 Predict and Submit results(预测并提交结果)

import seaborn as sns
sns.countplot(Y_train)

plt.imshow(X_train[0][:,:,0])

CNN

model = Sequential()
model.add(Conv2D(filters = 32,kernel_size = (5,5),padding = 'Same', activation ='relu', input_shape = (28,28,1)))
model.add(Conv2D(filters = 32,kernel_size = (5,5),padding = 'Same', activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(filters = 64,kernel_size = (3,3),padding = 'Same', activation ='relu'))
model.add(Conv2D(filters = 64,kernel_size = (3,3),padding = 'Same', activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation ="relu"))
model.add(Dropout(0.5))
model.add(Dense(10, activation = "softmax"))

optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)

model.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"])

In order to make the optimizer converge faster and closest to the global minimum of the loss function, i used an annealing method of the learning rate (LR).(为了使优化算法更快更近地达到代价函数的全局最小，我使用了学习率(LR)的退火方式)

Its better to have a decreasing learning rate during the training to reach efficiently the global minimum of the loss function.(一种较好的方式是,在训练中到达代价函数的全局最小,用逐渐变小的学习率)

To keep the advantage of the fast computation time with a high LR, i decreased the LR dynamically every X steps (epochs) depending if it is necessary (when accuracy is not improved).(为了保持高学习率的计算更快的优势，当必要时，即准确率无法提高，我动态地每x步减小学习率)

With the ReduceLROnPlateau function from Keras.callbacks, i choose to reduce the LR by half if the accuracy is not improved after 3 epochs.(用Keras.callbacks里的ReduceLROnPlateau函数，当准确率无法在3次迭代之后提高，我选择让学习率减半)

learning_rate_reduction =ReduceLROnPlateau(monitor='val_acc', patience=3, verbose=1, factor=0.5, min_lr=0.00001)

Data augmentation(数据增强)

The improvement is important :(这一步骤很重要)

Without data augmentation i obtained an accuracy of 98.114%(没有数据增强仅获得准确率98.114%)

With data augmentation i achieved 99.67% of accuracy(数据增强之后达到准确率99.67%)

Evaluate the model(模型评估)

plot the error classification(观察错误分类样本)

get the accuracy 0.997(准确率)