Keras卷积神经网络玛丽莲梦露与爱因斯坦的识别Part2：使用预训练模型VGG16
从Keras加载vgg16
from keras.applications import VGG16
import os
conv_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, layers
model = 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 = False
model.summary()
进行训练
from keras import optimizers
from keras.preprocessing.image import ImageDataGenerator
base_dir = r'dir\Desktop\Einstein'
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'v')
model.compile(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 plt
acc = 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 image
from keras import models
import numpy as np
img = image.load_img(r'dir\Einstein\EM.jpg',target_size=(128,128,3))
img = np.array(img)
img = img/255
model = 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。