Keras利用卷积神经网络玛丽莲梦露与爱因斯坦的识别Part1

目的
突发奇想想会认为下面这张图片究竟是玛丽莲梦露还是爱因斯坦，主要目的顺便实践练习《Python深度学习》书中的例子，只采用了很小批量的数据，也没有深究如何提高正确率，解决过拟合的问题。详细可以参见《python深度学习》第五章前两节。

在这里插入图片描述
数据准备

从百度图片中找到了风格各异的爱因斯坦的图片，直接采用下载整个网页的方式获取图片。选的量不多，100张作为训练，25张用于验证。本来是留有测试的数据，不小心删掉了就跳过在新数据上测试的步骤。（数据量太小也是一个严重的问题）
手动删掉一些不合适的图片，分别放到train和validation文件夹下的E，M两个文件中。

在这里插入图片描述

构建网络
建立序列模型，采用这个网络是因为之前在一个SAR图像的识别中表现优异，预测准确率达到96%以上（尽管并不能说明它在区分爱因斯坦和玛丽莲梦露也能表现得很好）

import os
from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(64, (3, 3), activation='relu',input_shape=(88, 88,3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(1, activation='sigmoid'))
model.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 86, 86, 64)        1792      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 43, 43, 64)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 41, 41, 64)        36928     
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 20, 20, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 18, 18, 128)       73856     
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 9, 9, 128)         0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 7, 7, 128)         147584    
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 3, 3, 128)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 1152)              0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 1152)              0         
_________________________________________________________________
dense_1 (Dense)              (None, 512)               590336    
_________________________________________________________________
dropout_2 (Dropout)          (None, 512)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 512)               262656    
_________________________________________________________________
dense_3 (Dense)              (None, 1)                 513       
=================================================================
Total params: 1,113,665
Trainable params: 1,113,665
Non-trainable params: 0

读入图片并训练

base_dir = r'dir\Einstein'
train_dir = os.path.join(base_dir, 'train')
validation_dir = os.path.join(base_dir, 'v')from keras import optimizers
model.compile(loss='binary_crossentropy',optimizer=optimizers.RMSprop(lr=1e-4),metrics=['acc'])from keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(rescale=1. / 255)
test_datagen = ImageDataGenerator(rescale=1. / 255)train_generator = train_datagen.flow_from_directory(train_dir,target_size=(88,88),batch_size=20,class_mode='binary')validation_generator = test_datagen.flow_from_directory(validation_dir,target_size=(88, 88),batch_size=20,class_mode='binary')
history = model.fit_generator(train_generator,steps_per_epoch=128,epochs=20,validation_data=validation_generator,validation_steps=50)#保存模型
model.save('EM.h5')

训练结果
绘制性能曲线

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()

在这里插入图片描述
l最终结果：
oss: 0.0050 - acc: 0.9996 -
val_loss: 2.4635 - val_acc: 0.6614
结果显然过拟合了，预测的正确率只有66%。但不妨碍用于用的预测。
预测

from keras.preprocessing import image
import matplotlib.image as mpimg
from keras import models
import numpy as np
img = image.load_img(r'dir\Einstein\EM.jpg',target_size=(88,88,3))
img = np.array(img)
img = img/255
model = models.load_model(r'dir\Einstein\EM.h5')
img = img.reshape(1,88,88,3)
pre = model.predict(img)
print('预测结果：'，pre)预测结果： [[0.00376787]]