从Keras加载vgg16
from keras.applications import VGG16import osconv_base = VGG16(weights = 'imagenet',include_top = False,input_shape = (128, 128, 3))conv_base.summary()'''VGG16卷积层结构'''_________________________________________________________________Layer (type) Output Shape Param # =================================================================input_1 (InputLayer) (None, 128, 128, 3) 0 _________________________________________________________________block1_conv1 (Conv2D) (None, 128, 128, 64)1792_________________________________________________________________block1_conv2 (Conv2D) (None, 128, 128, 64)36928_________________________________________________________________block1_pool (MaxPooling2D) (None, 64, 64, 64) 0 _________________________________________________________________block2_conv1 (Conv2D) (None, 64, 64, 128) 73856_________________________________________________________________block2_conv2 (Conv2D) (None, 64, 64, 128) 147584 _________________________________________________________________block2_pool (MaxPooling2D) (None, 32, 32, 128) 0 _________________________________________________________________block3_conv1 (Conv2D) (None, 32, 32, 256) 295168 _________________________________________________________________block3_conv2 (Conv2D) (None, 32, 32, 256) 590080 _________________________________________________________________block3_conv3 (Conv2D) (None, 32, 32, 256) 590080 _________________________________________________________________block3_pool (MaxPooling2D) (None, 16, 16, 256) 0 _________________________________________________________________block4_conv1 (Conv2D) (None, 16, 16, 512) 1180160 _________________________________________________________________block4_conv2 (Conv2D) (None, 16, 16, 512) 2359808 _________________________________________________________________block4_conv3 (Conv2D) (None, 16, 16, 512) 2359808 _________________________________________________________________block4_pool (MaxPooling2D) (None, 8, 8, 512) 0 _________________________________________________________________block5_conv1 (Conv2D) (None, 8, 8, 512) 2359808 _________________________________________________________________block5_conv2 (Conv2D) (None, 8, 8, 512) 2359808 _________________________________________________________________block5_conv3 (Conv2D) (None, 8, 8, 512) 2359808 _________________________________________________________________block5_pool (MaxPooling2D) (None, 4, 4, 512) 0 =================================================================
添加全连接层
《Python深度学习》中提到了使用这个卷积网络的两种方法，第一种是先让图片经过VGG16卷积层，保存得到的向量，再在全连接层上训练，这样前面的卷积层运算只需要进行一次。第二种是直接连接上全连接层，将卷积层“冻结”，对全连接层进行训练。这里采用简单的第二种方法，不需要对数据进行处理，但是计算成本比较高。
from keras import models, layersmodel = models.Sequential()model.add(conv_base)model.add(layers.Flatten())model.add(layers.Dropout(0.5))model.add(layers.Dense(256,activation = 'relu'))model.add(layers.Dense(1,activation = 'sigmoid'))conv_base.trainable = Falsemodel.summary()
进行训练
from keras import optimizersfrom keras.preprocessing.image import ImageDataGeneratorbase_dir = r'dir\Desktop\Einstein'train_dir = os.path.join(base_dir, 'train')validation_dir = os.path.join(base_dir, 'v')pile(loss='binary_crossentropy',optimizer=optimizers.RMSprop(lr=1e-4),metrics=['acc'])train_datagen = ImageDataGenerator(rescale=1./255)validation_datagen = ImageDataGenerator(rescale=1./255)train_generator = train_datagen.flow_from_directory(train_dir,target_size=(128,128),batch_size=5,class_mode='binary')validation_generator = validation_datagen.flow_from_directory(validation_dir,target_size=(128, 128),batch_size=5,class_mode='binary')history = model.fit_generator(train_generator,steps_per_epoch=128,epochs=2 ,validation_data=validation_generator,validation_steps=50)model.save('EMvgg.h5')#绘制训练曲线import matplotlib.pyplot as pltacc = history.history['acc']val_acc = history.history['val_acc']loss = history.history['loss']val_loss = history.history['val_loss']epochs = range(1, len(acc) + 1)plt.plot(epochs, acc, 'bo', label='Training acc')plt.plot(epochs, val_acc, 'b', label='Validation acc')plt.title('Training and validation accuracy')plt.legend()plt.figure()plt.plot(epochs, loss, 'bo', label='Training loss')plt.plot(epochs, val_loss, 'b', label='Validation loss')plt.title('Training and validation loss')plt.legend()plt.show()
最终的结果是
loss： 0.0015 - acc: 0.9996 - val_loss: 2.8083 - val_acc: 0.7217
可以看到也过拟合，验证集的正确率在振荡，我们关注的是模型的泛化能力。可以选择提前结束训练，第二次的验证集的识别正确率很可观。让模型在第二次停止训练。正确率达到了86%，相比自己搭建的网络提高了15%的正确率。
loss: 0.2335 - acc: 0.9016 - val_loss: 0.3862 - val_acc: 0.8600
检验
最终的目的还是为了检验这张图片，这次有更大的把握。
from keras.preprocessing import imagefrom keras import modelsimport numpy as npimg = image.load_img(r'dir\Einstein\EM.jpg',target_size=(128,128,3))img = np.array(img)img = img/255model = models.load_model(r'dir\Einstein\EMvgg_1.h5')img = img.reshape(1,128,128,3)pre = model.predict(img)print('预测结果：',pre)预测结果： [[0.00213499]]
也可以使用predict_classes函数，直接输出类别。
pre = model.predict_classes(img)print('预测结果：',pre)预测结果： [[0]]
0也就是爱因斯坦图像的标签。
结论
综合两次实验，可以知道，卷积神经网络认为这张图片中包含着更多的爱因斯坦的特征。
