大家好，今天跟大家學習 利用Keras中的卷積神經網路（Convolutional Neural Networks，CNN)來進行數字辨識。

謎之聲:說好的ASP.NET C#呢?
作者:今天吃喜酒，只好把以前的筆記拿來當作鐵人賽文章 (掩面

本文開始

(1)準備資料
#宣告要使用到的函式庫

from keras.utils import np_utils
import numpy as np
from keras.datasets import mnist
from keras.layers import Dense,Dropout,Flatten,Conv2D,MaxPooling2D
import matplotlib.pyplot as plt
from keras.models import Sequential
np.random.seed(10)

#讀入Mnist資料

(x_train ,y_train),(x_test,y_test)=mnist.load_data()

#將28x28的圖片矩陣轉為784個數字(將28x28的圖片轉為矩陣)

x_train4=x_train.reshape(x_train.shape[0],28,28,1).astype('float32')
x_test4=x_test.reshape(x_test.shape[0],28,28,1).astype('float32')

#將資料正規化

x_train4_nor = x_train4 /255
x_test_nor=x_test4 / 255

28*28將圖片歸類為0~9的數字

y_train_one=np_utils.to_categorical(y_train)
y_test_one=np_utils.to_categorical(y_test)

如果沒有mnist資料：需要點選Downloading data from下載的連結
https://s3.amazonaws.com/img-datasets/mnist.npz

(2)建立模型

#建立一個線性堆疊的模型
model = Sequential()
#建立卷積層 1
model.add(Conv2D(filters=16,
kernel_size=(5,5),
                 padding='same',
input_shape=(28,28,1),
                 activation='relu'
                ))
#建立池化層1。
model.add(MaxPooling2D(pool_size=(2,2)))
#建立卷積層2
model.add(Conv2D(filters=36,
kernel_size=(5,5),
                 padding='same',
                 activation='relu'))

#filters：濾鏡的層數
#kernel_size(a,b)：濾鏡的大小為axb
#padding='same'：使影像經過卷積計算後大小不變
#input_shape(a,b,c)：a,b為輸入影像的大小，c為單色或彩色
#activation：設定激勵函數
#建立池化層1
model.add(MaxPooling2D(pool_size=(2,2)))
#避免overfitting
model.add(Dropout(0.25))
#建立平坦層
model.add(Flatten())
#建立隱藏層，且避免overfitting
model.add(Dense(128,activation='relu'))
model.add(Dropout(0.25))
#建立輸出層
model.add(Dense(10,activation='softmax'))

(3)定義訓練方式

#設定損失函數，最佳化方法，以及評估模型等。
model.compile(loss='categorical_crossentropy',
          optimizer='adam',
          metrics=['accuracy']
         )
         

(4)開始訓練

    train_history=model.fit(x=x_train4_nor,y=y_train_one,validation_split=0.1,epochs=15,batch_size=300,verbose=2)

#說明：
#x=>影像特徵值
#y=>影像實際值
#validation_split=>設定訓練及驗證資料比例
#epochs=>訓練周期
#batch_size=>每一批次多少筆資料
#verbose=>顯示訓練過程
#acc是指模型訓練精度，val_acc是指模型在驗證集上的精度，loss是訓練的的損失值。

(5) 一、訓練週期15次

Train on 54000 samples, validate on 6000 samples
Epoch 1/15
 - 59s - loss: 0.3893 - acc: 0.8806 - val_loss: 0.0755 - val_acc: 0.9783
Epoch 2/15
 - 57s - loss: 0.1047 - acc: 0.9680 - val_loss: 0.0527 - val_acc: 0.9850
Epoch 3/15
 - 66s - loss: 0.0723 - acc: 0.9778 - val_loss: 0.0424 - val_acc: 0.9888
Epoch 4/15
 - 75s - loss: 0.0590 - acc: 0.9818 - val_loss: 0.0372 - val_acc: 0.9890
Epoch 5/15
 - 63s - loss: 0.0489 - acc: 0.9851 - val_loss: 0.0335 - val_acc: 0.9908
Epoch 6/15
 - 66s - loss: 0.0433 - acc: 0.9866 - val_loss: 0.0321 - val_acc: 0.9897
Epoch 7/15
 - 66s - loss: 0.0398 - acc: 0.9875 - val_loss: 0.0345 - val_acc: 0.9902
Epoch 8/15
 - 60s - loss: 0.0334 - acc: 0.9891 - val_loss: 0.0278 - val_acc: 0.9925
Epoch 9/15
 - 65s - loss: 0.0320 - acc: 0.9896 - val_loss: 0.0310 - val_acc: 0.9913
Epoch 10/15
 - 57s - loss: 0.0275 - acc: 0.9913 - val_loss: 0.0266 - val_acc: 0.9928
Epoch 11/15
 - 57s - loss: 0.0243 - acc: 0.9921 - val_loss: 0.0245 - val_acc: 0.9928
Epoch 12/15
 - 56s - loss: 0.0221 - acc: 0.9930 - val_loss: 0.0285 - val_acc: 0.9918
Epoch 13/15
 - 44s - loss: 0.0220 - acc: 0.9929 - val_loss: 0.0256 - val_acc: 0.9912
Epoch 14/15
 - 63s - loss: 0.0185 - acc: 0.9938 - val_loss: 0.0274 - val_acc: 0.9923
Epoch 15/15
 - 54s - loss: 0.0186 - acc: 0.9933 - val_loss: 0.0248 - val_acc: 0.9927

二、訓練週期15次 (第16~30次)

Train on 54000 samples, validate on 6000 samples
Epoch 1/15
 - 59s - loss: 0.0169 - acc: 0.9942 - val_loss: 0.0276 - val_acc: 0.9923
Epoch 2/15
 - 53s - loss: 0.0151 - acc: 0.9948 - val_loss: 0.0261 - val_acc: 0.9922
Epoch 3/15
 - 50s - loss: 0.0161 - acc: 0.9946 - val_loss: 0.0262 - val_acc: 0.9927
Epoch 4/15
 - 56s - loss: 0.0141 - acc: 0.9954 - val_loss: 0.0251 - val_acc: 0.9930
Epoch 5/15
 - 58s - loss: 0.0140 - acc: 0.9953 - val_loss: 0.0227 - val_acc: 0.9940
Epoch 6/15
 - 56s - loss: 0.0133 - acc: 0.9958 - val_loss: 0.0224 - val_acc: 0.9935
Epoch 7/15
 - 59s - loss: 0.0119 - acc: 0.9959 - val_loss: 0.0278 - val_acc: 0.9938
Epoch 8/15
 - 63s - loss: 0.0117 - acc: 0.9961 - val_loss: 0.0254 - val_acc: 0.9932
Epoch 9/15
 - 64s - loss: 0.0104 - acc: 0.9966 - val_loss: 0.0278 - val_acc: 0.9920
Epoch 10/15
 - 52s - loss: 0.0105 - acc: 0.9962 - val_loss: 0.0252 - val_acc: 0.9940
Epoch 11/15
 - 66s - loss: 0.0112 - acc: 0.9960 - val_loss: 0.0243 - val_acc: 0.9932
Epoch 12/15
 - 64s - loss: 0.0098 - acc: 0.9967 - val_loss: 0.0231 - val_acc: 0.9937
Epoch 13/15
 - 49s - loss: 0.0103 - acc: 0.9964 - val_loss: 0.0238 - val_acc: 0.9932
Epoch 14/15
 - 59s - loss: 0.0085 - acc: 0.9972 - val_loss: 0.0263 - val_acc: 0.9943
Epoch 15/15
 - 66s - loss: 0.0088 - acc: 0.9969 - val_loss: 0.0236 - val_acc: 0.9937

(6)畫出圖形

defplot_images_labels(images,labels,prediction,idx,num=10):
    fig=plt.gcf()
fig.set_size_inches(12,14)
    if num>25:
num=15
    for i in range(0,num):
        ax=plt.subplot(5,5,1+i)
ax.imshow(np.reshape(images[idx],(28,28)), cmap='binary')
        title="label=" +str(labels[idx])
        if len(prediction)>0:
            title+=",predict="+str(prediction[idx])
ax.set_title(title,fontsize=10)
ax.set_xticks([])
ax.set_yticks([])
idx=idx+1
plt.show()

(7)進行預測

#輸入影像，並儲存結果。
prediction=model.predict_classes(x_test_nor)
#印出預測值，從第300筆開始，印10筆，也就是（300-309）
plot_images_labels(x_test,y_test,prediction,idx=300)

(8)顯示混淆矩陣

import pandas as pd
pd.crosstab(y_test,prediction,rownames=['label'],colnames=['predict'])

參考資料：https://ithelp.ithome.com.tw/articles/10197257
https://morvanzhou.github.io/tutorials/machine-learning/keras/2-2-classifier/