Keras學習（四）——CNN卷積神經網路
阿新 • • 發佈：2018-10-31
本文主要介紹使用keras實現CNN對手寫資料集進行分類。
示例程式碼：
import numpy as np from keras.datasets import mnist from keras.utils import np_utils from keras.models import Sequential from keras.layers import Dense, Activation, Convolution2D, MaxPooling2D, Flatten from keras.optimizers import Adam # 使多次生成的隨機數相同 np.random.seed(1337) # 下載資料集 # X_shape(60000 28x28),y shape(10000) (X_train, y_train), (X_test, y_test) = mnist.load_data() # 預處理資料 ''' X_train.reshape(X_train.shape[0], -1) 將60000個28x28的資料變為60000x784 /255：把資料標準化到[0,1] ''' X_train = X_train.reshape(-1, 1, 28, 28) # -1:sample個數， 1：channel, 28x28：長寬 X_test = X_test.reshape(-1, 1, 28, 28) # 將標籤變為one-hot形式 y_train = np_utils.to_categorical(y_train, num_classes=10) y_test = np_utils.to_categorical(y_test, num_classes=10) # 搭建網路 model = Sequential() # conv1 layer model.add(Convolution2D( nb_filter=32, # 濾波器 nb_row=5, # filter寬度 nb_col=5, # filter高度 border_mode='same', # padding的方法 input_shape=(1, # channel的個數 28, 28), # width和height )) model.add(Activation('relu')) # 池化層 pooling model.add( MaxPooling2D( pool_size=(2, 2), strides=(2, 2), border_mode='same', # padding method )) # Conv2 layer model.add(Convolution2D(64, 5, 5, border_mode='same')) model.add(Activation('relu')) # pooling2 layer model.add(MaxPooling2D(pool_size=(2, 2), border_mode='same')) # 展開 model.add(Flatten()) # 全連線層1 model.add(Dense(1024)) model.add(Activation('relu')) # fc2 model.add(Dense(10)) model.add(Activation('softmax')) # 用於分類的啟用函式 # 優化器 adam = Adam(lr=1e-4) # 啟用模型 model.compile(optimizer=adam, loss='categorical_crossentropy', metrics=['accuracy']) # 訓練 print('Training...') model.fit(X_train, y_train, nb_epoch=2, batch_size=32) # 測試 print('\nTesing....\n') loss, accuracy = model.evaluate(X_test, y_test) print('\ntest loss', loss) print('\ntest accuracy', accuracy)
執行結果：
58560/60000 [============================>.] - ETA: 1s - loss: 0.0993 - acc: 0.9691 58624/60000 [============================>.] - ETA: 1s - loss: 0.0992 - acc: 0.9691 58688/60000 [============================>.] - ETA: 1s - loss: 0.0992 - acc: 0.9692 58752/60000 [============================>.] - ETA: 1s - loss: 0.0992 - acc: 0.9692 58816/60000 [============================>.] - ETA: 1s - loss: 0.0996 - acc: 0.9691 58880/60000 [============================>.] - ETA: 1s - loss: 0.0996 - acc: 0.9692 58944/60000 [============================>.] - ETA: 1s - loss: 0.0995 - acc: 0.9692 59008/60000 [============================>.] - ETA: 1s - loss: 0.0994 - acc: 0.9692 59072/60000 [============================>.] - ETA: 0s - loss: 0.0993 - acc: 0.9692 59136/60000 [============================>.] - ETA: 0s - loss: 0.0993 - acc: 0.9692 59200/60000 [============================>.] - ETA: 0s - loss: 0.0993 - acc: 0.9692 59264/60000 [============================>.] - ETA: 0s - loss: 0.0993 - acc: 0.9693 59328/60000 [============================>.] - ETA: 0s - loss: 0.0992 - acc: 0.9693 59392/60000 [============================>.] - ETA: 0s - loss: 0.0992 - acc: 0.9693 59456/60000 [============================>.] - ETA: 0s - loss: 0.0992 - acc: 0.9693 59520/60000 [============================>.] - ETA: 0s - loss: 0.0992 - acc: 0.9693 59584/60000 [============================>.] - ETA: 0s - loss: 0.0991 - acc: 0.9693 59648/60000 [============================>.] - ETA: 0s - loss: 0.0991 - acc: 0.9693 59712/60000 [============================>.] - ETA: 0s - loss: 0.0990 - acc: 0.9694 59776/60000 [============================>.] - ETA: 0s - loss: 0.0989 - acc: 0.9694 59840/60000 [============================>.] - ETA: 0s - loss: 0.0989 - acc: 0.9694 59904/60000 [============================>.] - ETA: 0s - loss: 0.0990 - acc: 0.9694 59968/60000 [============================>.] - ETA: 0s - loss: 0.0989 - acc: 0.9694 60000/60000 [==============================] - 64s 1ms/step - loss: 0.0989 - acc: 0.9694 Tesing.... 32/10000 [..............................] - ETA: 28s 256/10000 [..............................] - ETA: 5s 480/10000 [>.............................] - ETA: 3s 704/10000 [=>............................] - ETA: 3s 928/10000 [=>............................] - ETA: 2s 1152/10000 [==>...........................] - ETA: 2s 1376/10000 [===>..........................] - ETA: 2s 1600/10000 [===>..........................] - ETA: 2s 1792/10000 [====>.........................] - ETA: 2s 2016/10000 [=====>........................] - ETA: 2s 2240/10000 [=====>........................] - ETA: 2s 2464/10000 [======>.......................] - ETA: 2s 2656/10000 [======>.......................] - ETA: 2s 2816/10000 [=======>......................] - ETA: 2s 3040/10000 [========>.....................] - ETA: 1s 3264/10000 [========>.....................] - ETA: 1s 3488/10000 [=========>....................] - ETA: 1s 3712/10000 [==========>...................] - ETA: 1s 3872/10000 [==========>...................] - ETA: 1s 4096/10000 [===========>..................] - ETA: 1s 4320/10000 [===========>..................] - ETA: 1s 4544/10000 [============>.................] - ETA: 1s 4768/10000 [=============>................] - ETA: 1s 4960/10000 [=============>................] - ETA: 1s 5184/10000 [==============>...............] - ETA: 1s 5376/10000 [===============>..............] - ETA: 1s 5600/10000 [===============>..............] - ETA: 1s 5824/10000 [================>.............] - ETA: 1s 6048/10000 [=================>............] - ETA: 1s 6272/10000 [=================>............] - ETA: 0s 6496/10000 [==================>...........] - ETA: 0s 6720/10000 [===================>..........] - ETA: 0s 6944/10000 [===================>..........] - ETA: 0s 7168/10000 [====================>.........] - ETA: 0s 7360/10000 [=====================>........] - ETA: 0s 7584/10000 [=====================>........] - ETA: 0s 7808/10000 [======================>.......] - ETA: 0s 8000/10000 [=======================>......] - ETA: 0s 8224/10000 [=======================>......] - ETA: 0s 8448/10000 [========================>.....] - ETA: 0s 8640/10000 [========================>.....] - ETA: 0s 8864/10000 [=========================>....] - ETA: 0s 9088/10000 [==========================>...] - ETA: 0s 9216/10000 [==========================>...] - ETA: 0s 9376/10000 [===========================>..] - ETA: 0s 9536/10000 [===========================>..] - ETA: 0s 9728/10000 [============================>.] - ETA: 0s 9920/10000 [============================>.] - ETA: 0s 10000/10000 [==============================] - 3s 269us/step test loss 0.08740828046086244 test accuracy 0.9717