Keras實現LeNet-5網路,並可視化網路結構圖
阿新 • • 發佈:2018-12-30
模型源自Yann LeCun(1998)的論文《Gradient-Based Learning Applied to Document Recognition》,用於MNIST資料集。模型輸入為32X32的灰度影象,第一層為6個5X5卷積核,不擴充套件邊界;第二層為2X2的最大值池化層,步進為2X2;第三層為16個5X5卷積核,不擴充套件邊界;第四層為2X2的最大值池化層,步進為2X2;第五層為展平層,並全連線120個節點;第六層為全連線層,84個節點;第七層為全連線softmax層,輸出結果。
原論文中第二層池化層和第三層卷積層之間為是部分連線。本文中並未考慮,而是做成全連線,模型結構如下圖所示。
模型採用keras的Sequential實現,源資料分為train和test兩個資料夾,每個資料夾下有十個子資料夾,分別方有各數字對應的灰度圖。實現程式碼如下:
import os import cv2 from numpy import * from keras.models import Sequential from keras.layers import Dense from keras.layers import Conv2D, MaxPooling2D, Flatten from keras.optimizers import SGD from keras.utils import np_utils from keras.utils.vis_utils import plot_model def loadData(path): data = [] labels = [] for i in range(10): dir = './'+path+'/'+str(i) listImg = os.listdir(dir) for img in listImg: data.append([cv2.imread(dir+'/'+img, 0)]) labels.append(i) print path, i, 'is read' return data, labels trainData, trainLabels = loadData('train') testData, testLabels = loadData('test') trainLabels = np_utils.to_categorical(trainLabels, 10) testLabels = np_utils.to_categorical(testLabels, 10) model = Sequential() model.add(Conv2D(filters=6, kernel_size=(5,5), padding='valid', input_shape=(1,28,28), activation='tanh')) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Conv2D(filters=16, kernel_size=(5,5), padding='valid', activation='tanh')) model.add(MaxPooling2D(pool_size=(2,2))) model.add(Flatten()) model.add(Dense(120, activation='tanh')) model.add(Dense(84, activation='tanh')) model.add(Dense(10, activation='softmax')) sgd = SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True) model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy']) model.fit(trainData, trainLabels, batch_size=500, epochs=20, verbose=1, shuffle=True) plot_model(model, to_file='model.png', show_shapes=True, show_layer_names=False)
網路結構圖如下: