今天要跟大家分享的適用CNN架構作阿拉伯數字的辨識，順便比較一下跟之前用Neural Network有甚麼不同

建置環境

from __future__ import print_function
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K
num_classes = 10
img_rows, img_cols = 28, 28
(x_train, y_train), (x_test, y_test) = mnist.load_data()

資料的處理

y_test_org = y_test
# channels_first: 色彩通道(R/G/B)資料(深度)放在第2維度，第3、4維度放置寬與高
if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else: # channels_last: 色彩通道(R/G/B)資料(深度)放在第4維度，第2、3維度放置寬與高
    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
    input_shape = (img_rows, img_cols, 1)
# 轉換色彩 0~255 資料為 0~1
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

建立捲基層

model = Sequential()
# 建立卷積層，filter=32, Kernal Size: 3x3, activation function 採用 relu
model.add(Conv2D(32, kernel_size=(3, 3),
                 activation='relu',
                 input_shape=input_shape))
# 建立卷積層，filter=64,即 output size, Kernal Size: 3x3, activation function 採用 relu
model.add(Conv2D(64, (3, 3), activation='relu'))

建立池化層

# 池化大小=2x2，取最大值
model.add(MaxPooling2D(pool_size=(2, 2)))
# Dropout層隨機斷開輸入神經元，用於防止過度擬合
model.add(Dropout(0.25))
# Flatten層把多維的輸入一維化，常用在從卷積層到全連接層的過渡。
model.add(Flatten())
# 全連接層: 128個output
model.add(Dense(128, activation='relu'))
# Dropout層隨機斷開輸入神經元，用於防止過度擬合
model.add(Dropout(0.5))
# 使用 softmax activation function，將結果分類
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
              optimizer=keras.optimizers.Adadelta(),
              metrics=['accuracy'])
train_history = model.fit(x_train, y_train,
          batch_size=128,
          epochs=12,
          verbose=1,
          validation_data=(x_test, y_test))
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

這個程式碼我是參考https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py
裡面的註解也是我東查西想的如果有錯，還請大家糾正我
訓練結果準確率達 90.11%，比單純使用簡單的 Neural Network 高多了，但執行時間也相對較長，但只要將模型結果儲存，我們就只要訓練這次就夠了，之後直接載入模型及參數，就可以直接進行預測了。
參考
https://ithelp.ithome.com.tw/articles/10191924