Keras achieve CNN: Handwritten digit recognition accuracy rate of 99.6%

After Tensorflow installed, the default installation Keras TF as the rear end, a convolutional code is realized Keras network is very simple, and keras class provides the method for detecting callback model training process variable can be detected in a timely manner in accordance with the variable learning efficiency and adjust the model parameters. The following examples, MNIST as the test data

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as pimg
import seaborn as sb         # 一个构建在matplotlib上的绘画模块，支持numpy,pandas等数据结构
%matplotlib inline

from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix     # 混淆矩阵

import itertools
#  keras
from keras.utils import to_categorical         #数字标签转化成one-hot编码
from keras.models import Sequential
from keras.layers import Dense,Dropout,Flatten,Conv2D,MaxPool2D
from keras.optimizers import RMSprop
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ReduceLROnPlateau

Using TensorFlow backend.

# 设置绘画风格
sb.set(style='white', context='notebook', palette='deep')

# 加载数据
train_data = pd.read_csv('data/train.csv')
test_data = pd.read_csv('data/test.csv')
#train_x = train_data.drop(labels=['label'],axis=1)  # 去掉标签列
train_x = train_data.iloc[:,1:]
train_y = train_data.iloc[:,0]
del  train_data   # 释放一下内存

# 观察一下训练数据的分布情况
g = sb.countplot(train_y)
train_y.value_counts()

1    4684
7    4401
3    4351
9    4188
2    4177
6    4137
0    4132
4    4072
8    4063
5    3795
Name: label, dtype: int64

train_x.isnull().describe() # 检查是否存在确实值

 

train_x.isnull().any().describe()

count       784
unique        1
top       False
freq        784
dtype: object

test_data.isnull().any().describe()

count       784
unique        1
top       False
freq        784
dtype: object

# 归一化
train_x =  train_x/255.0
test_x = test_data/255.0

del test_data

shape data conversion

# reshape trian_x, test_x
#train_x = train_x.values.reshape(-1, 28, 28, 1)
#test_x = test_x.values.reshape(-1, 28, 28, 1)
train_x = train_x.as_matrix().reshape(-1, 28, 28, 1)
test_x = test_x.as_matrix().reshape(-1, 28, 28, 1)

# 吧标签列转化为one-hot 编码格式
train_y = to_categorical(train_y, num_classes = 10)

Separated from the data verification data

#从训练数据中分出十分之一的数据作为验证数据
random_seed = 3
train_x , val_x , train_y, val_y = train_test_split(train_x, train_y, test_size=0.1, random_state=random_seed)

A training sample

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

 

 

Use Keras build CNN

 

model = Sequential()
# 第一个卷积层，32个卷积核，大小５x5，卷积模式SAME,激活函数relu,输入张量的大小
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'))
# 池化层,池化核大小２x2
model.add(MaxPool2D(pool_size=(2,2)))
# 随机丢弃四分之一的网络连接，防止过拟合
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))
# 全连接层,展开操作，
model.add(Flatten())
# 添加隐藏层神经元的数量和激活函数
model.add(Dense(256, activation='relu'))    
model.add(Dropout(0.25))
# 输出层
model.add(Dense(10, activation='softmax'))  

# 设置优化器
# lr :学习效率，　decay :lr的衰减值
optimizer = RMSprop(lr = 0.001, decay=0.0)

# 编译模型
# loss:损失函数，metrics：对应性能评估函数
model.compile(optimizer=optimizer, loss = 'categorical_crossentropy',metrics=['accuracy'])

Create an instance of a callback class

# keras的callback类提供了可以跟踪目标值，和动态调整学习效率
# moitor : 要监测的量，这里是验证准确率
# matience: 当经过３轮的迭代，监测的目标量，仍没有变化，就会调整学习效率
# verbose : 信息展示模式，去０或１
# factor :　每次减少学习率的因子，学习率将以lr = lr*factor的形式被减少
# mode：‘auto’，‘min’，‘max’之一，在min模式下，如果检测值触发学习率减少。在max模式下，当检测值不再上升则触发学习率减少。
# epsilon：阈值，用来确定是否进入检测值的“平原区”
# cooldown：学习率减少后，会经过cooldown个epoch才重新进行正常操作
# min_lr：学习率的下限
learning_rate_reduction = ReduceLROnPlateau(monitor = 'val_acc', patience = 3,
                                            verbose = 1, factor=0.5, min_lr = 0.00001)

epochs = 40
batch_size = 100

Enhanced data processing

# 数据增强处理，提升模型的泛化能力，也可以有效的避免模型的过拟合
# rotation_range : 旋转的角度
# zoom_range : 随机缩放图像
# width_shift_range : 水平移动占图像宽度的比例
# height_shift_range 
# horizontal_filp : 水平反转
# vertical_filp : 纵轴方向上反转
data_augment = ImageDataGenerator(rotation_range= 10,zoom_range= 0.1,
                                  width_shift_range = 0.1,height_shift_range = 0.1,
                                  horizontal_flip = False, vertical_flip = False)

Trainer

history = model.fit_generator(data_augment.flow(train_x, train_y, batch_size=batch_size),
                             epochs= epochs, validation_data = (val_x,val_y),
                             verbose =2, steps_per_epoch=train_x.shape[0]//batch_size,
                             callbacks=[learning_rate_reduction])

Epoch 1/40
359s - loss: 0.4529 - acc: 0.8498 - val_loss: 0.0658 - val_acc: 0.9793
Epoch 2/40
375s - loss: 0.1188 - acc: 0.9637 - val_loss: 0.0456 - val_acc: 0.9848
Epoch 3/40
374s - loss: 0.0880 - acc: 0.9734 - val_loss: 0.0502 - val_acc: 0.9845
Epoch 4/40
375s - loss: 0.0750 - acc: 0.9767 - val_loss: 0.0318 - val_acc: 0.9902
Epoch 5/40
374s - loss: 0.0680 - acc: 0.9800 - val_loss: 0.0379 - val_acc: 0.9888
Epoch 6/40
369s - loss: 0.0584 - acc: 0.9823 - val_loss: 0.0267 - val_acc: 0.9910
Epoch 7/40
381s - loss: 0.0556 - acc: 0.9832 - val_loss: 0.0505 - val_acc: 0.9824
Epoch 8/40
381s - loss: 0.0531 - acc: 0.9842 - val_loss: 0.0236 - val_acc: 0.9912
Epoch 9/40
376s - loss: 0.0534 - acc: 0.9839 - val_loss: 0.0310 - val_acc: 0.9910
Epoch 10/40
379s - loss: 0.0537 - acc: 0.9848 - val_loss: 0.0274 - val_acc: 0.9917
Epoch 11/40
375s - loss: 0.0501 - acc: 0.9856 - val_loss: 0.0254 - val_acc: 0.9931
Epoch 12/40
382s - loss: 0.0492 - acc: 0.9860 - val_loss: 0.0212 - val_acc: 0.9924
Epoch 13/40
380s - loss: 0.0482 - acc: 0.9864 - val_loss: 0.0259 - val_acc: 0.9919
Epoch 14/40
373s - loss: 0.0488 - acc: 0.9858 - val_loss: 0.0305 - val_acc: 0.9905
Epoch 15/40

Epoch 00014: reducing learning rate to 0.000500000023749.
370s - loss: 0.0493 - acc: 0.9853 - val_loss: 0.0259 - val_acc: 0.9919
Epoch 16/40
367s - loss: 0.0382 - acc: 0.9888 - val_loss: 0.0176 - val_acc: 0.9936
Epoch 17/40
376s - loss: 0.0376 - acc: 0.9891 - val_loss: 0.0187 - val_acc: 0.9945
Epoch 18/40
376s - loss: 0.0410 - acc: 0.9885 - val_loss: 0.0220 - val_acc: 0.9926
Epoch 19/40
371s - loss: 0.0385 - acc: 0.9886 - val_loss: 0.0194 - val_acc: 0.9933
Epoch 20/40
372s - loss: 0.0345 - acc: 0.9894 - val_loss: 0.0186 - val_acc: 0.9938
Epoch 21/40

Epoch 00020: reducing learning rate to 0.000250000011874.
375s - loss: 0.0395 - acc: 0.9888 - val_loss: 0.0233 - val_acc: 0.9945
Epoch 22/40
369s - loss: 0.0313 - acc: 0.9907 - val_loss: 0.0141 - val_acc: 0.9955
Epoch 23/40
376s - loss: 0.0308 - acc: 0.9910 - val_loss: 0.0187 - val_acc: 0.9945
Epoch 24/40
374s - loss: 0.0331 - acc: 0.9908 - val_loss: 0.0170 - val_acc: 0.9940
Epoch 25/40
372s - loss: 0.0325 - acc: 0.9904 - val_loss: 0.0166 - val_acc: 0.9948
Epoch 26/40

Epoch 00025: reducing learning rate to 0.000125000005937.
373s - loss: 0.0319 - acc: 0.9904 - val_loss: 0.0167 - val_acc: 0.9943
Epoch 27/40
372s - loss: 0.0285 - acc: 0.9915 - val_loss: 0.0138 - val_acc: 0.9950
Epoch 28/40
375s - loss: 0.0280 - acc: 0.9913 - val_loss: 0.0150 - val_acc: 0.9950
Epoch 29/40

Epoch 00028: reducing learning rate to 6.25000029686e-05.
377s - loss: 0.0281 - acc: 0.9924 - val_loss: 0.0158 - val_acc: 0.9948
Epoch 30/40
374s - loss: 0.0265 - acc: 0.9920 - val_loss: 0.0134 - val_acc: 0.9952
Epoch 31/40
378s - loss: 0.0270 - acc: 0.9922 - val_loss: 0.0128 - val_acc: 0.9957
Epoch 32/40
372s - loss: 0.0237 - acc: 0.9930 - val_loss: 0.0133 - val_acc: 0.9957
Epoch 33/40
375s - loss: 0.0237 - acc: 0.9931 - val_loss: 0.0138 - val_acc: 0.9955
Epoch 34/40
371s - loss: 0.0276 - acc: 0.9920 - val_loss: 0.0135 - val_acc: 0.9962
Epoch 35/40
373s - loss: 0.0259 - acc: 0.9920 - val_loss: 0.0136 - val_acc: 0.9952
Epoch 36/40
369s - loss: 0.0249 - acc: 0.9924 - val_loss: 0.0126 - val_acc: 0.9952
Epoch 37/40
370s - loss: 0.0257 - acc: 0.9923 - val_loss: 0.0130 - val_acc: 0.9960
Epoch 38/40

Epoch 00037: reducing learning rate to 3.12500014843e-05.
374s - loss: 0.0252 - acc: 0.9926 - val_loss: 0.0136 - val_acc: 0.9950
Epoch 39/40
372s - loss: 0.0246 - acc: 0.9927 - val_loss: 0.0134 - val_acc: 0.9957
Epoch 40/40
371s - loss: 0.0247 - acc: 0.9929 - val_loss: 0.0139 - val_acc: 0.9950

Among the training process, there are several conditions for learning efficiency attenuation triggered whenever val_acc 3 consecutive rounds no growth, will bring the efficiency of learning to adjust to the current half, after adjustment, val_acc have significant growth, but in the final rounds The model may have converged.

# learning curves
fig,ax = plt.subplots(2,1,figsize=(10,10))
ax[0].plot(history.history['loss'], color='r', label='Training Loss')
ax[0].plot(history.history['val_loss'], color='g', label='Validation Loss')
ax[0].legend(loc='best',shadow=True)
ax[0].grid(True)


ax[1].plot(history.history['acc'], color='r', label='Training Accuracy')
ax[1].plot(history.history['val_acc'], color='g', label='Validation Accuracy')
ax[1].legend(loc='best',shadow=True)
ax[1].grid(True)

# 混淆矩阵
def plot_sonfusion_matrix(cm, classes, normalize=False, title='Confusion matrix',cmap=plt.cm.Blues):
    plt.imshow(cm, interpolation='nearest', cmap=cmap)
    plt.title(title)
    plt.colorbar()
    tick_marks = np.arange(len(classes))
    plt.xticks(tick_marks, classes, rotation=45)
    plt.yticks(tick_marks, classes)
    if normalize:
        cm = cm.astype('float')/cm.sum(axis=1)[:,np.newaxis]
    thresh = cm.max()/2.0
    for i,j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
        plt.text(j,i,cm[i,j], horizontalalignment='center',color='white' if cm[i,j] > thresh else 'black')
    plt.tight_layout()
    plt.ylabel('True label')
    plt.xlabel('Predict label')

Confuse proof verification data

pred_y = model.predict(val_x)
pred_label = np.argmax(pred_y, axis=1)
true_label = np.argmax(val_y, axis=1)

confusion_mat = confusion_matrix(true_label, pred_label)

plot_sonfusion_matrix(confusion_mat, classes = range(10))

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