深度学习实战-使用Kera预测人物年龄

问题描述

我们的任务是从一个人的面部特征来预测他的年龄(用“Young”“Middle ”“Old”表示)，我们训练的数据集大约有19906多张照片及其每张图片对应的年龄（全是阿三的头像。。。），测试集有6636张图片，首先我们加载数据集，然后我们通过深度学习框架Keras建立、编译、训练模型，预测出6636张人物头像对应的年龄

引入所需要模块

import osimport randomimport pandas as pdimport numpy as npfrom PIL import Image

加载数据集

root_dir=os.path.abspath('E:/data/age')train=pd.read_csv(os.path.join(root_dir,'train.csv'))test=pd.read_csv(os.path.join(root_dir,'test.csv'))print(train.head())print(test.head())

          ID   Class0    377.jpg  MIDDLE1  17814.jpg   YOUNG2  21283.jpg  MIDDLE3  16496.jpg   YOUNG4   4487.jpg  MIDDLE          ID0  25321.jpg1    989.jpg2  19277.jpg3  13093.jpg4   5367.jpg

随机读取一张图片试下（☺）

i=random.choice(train.index)img_name=train.ID[i]print(img_name)img=Image.open(os.path.join(root_dir,'Train',img_name))img.show()print(train.Class[i])

20188.jpgMIDDLE

难点

我们随机打开几张图片之后，可以发现图片之间的差别比较大。大家感受下：

1. 质量好的图片：

   • Middle:
     
     

     **Middle**

   • Young:
     
     

     **Young**

   • Old:
     
     

     **Old**

2. 质量差的：

   • Middle:
     
     

     **Middle**

下面是我们需要面临的问题：

1. 图片的尺寸差别：有的图片的尺寸是66x46,而另一张图片尺寸为102x87
2. 人物面貌角度不同：

   • 侧脸：
     
     

   • 正脸：
     
     

3. 图片质量不一（直接上图）:

   
   
   
   

   插图

4. 亮度和对比度的差异

   
   
   
   

   亮度
   
   
   
   

   对比度
   
   

   现在，我们只专注下图片尺寸处理，将每一张图片尺寸重置为32x32

格式化图片尺寸和将图片转换成numpy数组

temp=[]for img_name in train.ID:    img_path=os.path.join(root_dir,'Train',img_name)    img=Image.open(img_path)    img=img.resize((32,32))    array=np.array(img)    temp.append(array.astype('float32'))train_x=np.stack(temp)print(train_x.shape)print(train_x.ndim)

(19906, 32, 32, 3)4

temp=[]for img_name in test.ID:    img_path=os.path.join(root_dir,'Test',img_name)    img=Image.open(img_path)    img=img.resize((32,32))    array=np.array(img)    temp.append(array.astype('float32'))test_x=np.stack(temp)print(test_x.shape)

(6636, 32, 32, 3)

另外我们再归一化图像，这样会使模型训练的更快

train_x = train_x / 255.test_x = test_x / 255.

我们看下图片年龄大致分布

train.Class.value_counts(normalize=True)

MIDDLE    0.542751YOUNG     0.336883OLD       0.120366Name: Class, dtype: float64

test['Class'] = 'MIDDLE'test.to_csv('sub01.csv', index=False)

将目标变量处理虚拟列，能够使模型更容易接受识别它

import kerasfrom sklearn.preprocessing import LabelEncoderlb=LabelEncoder()train_y=lb.fit_transform(train.Class)print(train_y)train_y=keras.utils.np_utils.to_categorical(train_y)print(train_y)print(train_y.shape)

[0 2 0 ..., 0 0 0][[ 1.  0.  0.] [ 0.  0.  1.] [ 1.  0.  0.] ...,  [ 1.  0.  0.] [ 1.  0.  0.] [ 1.  0.  0.]](19906, 3)

创建模型

#构建神经网络input_num_units=(32,32,3)hidden_num_units=500output_num_units=3epochs=5batch_size=128

from keras.models import Sequentialfrom keras.layers import Dense,Flatten,InputLayermodel=Sequential({    InputLayer(input_shape=input_num_units),    Flatten(),    Dense(units=hidden_num_units,activation='relu'),    Dense(input_shape=(32,32,3),units=output_num_units,activation='softmax')})model.summary()

_________________________________________________________________Layer (type)                 Output Shape              Param #   =================================================================input_23 (InputLayer)        (None, 32, 32, 3)         0         _________________________________________________________________flatten_23 (Flatten)         (None, 3072)              0         _________________________________________________________________dense_45 (Dense)             (None, 500)               1536500   _________________________________________________________________dense_46 (Dense)             (None, 3)                 1503      =================================================================Total params: 1,538,003Trainable params: 1,538,003Non-trainable params: 0_________________________________________________________________

编译模型

# model.compile(optimizer='sgd',loss='categorical_crossentropy',metrics=['accuracy'])model.compile(optimizer='sgd',loss='categorical_crossentropy', metrics=['accuracy'])model.fit(train_x,train_y,batch_size=batch_size,epochs=epochs,verbose=1)

Epoch 1/519906/19906 [==============================] - 4s - loss: 0.8878 - acc: 0.5809     Epoch 2/519906/19906 [==============================] - 4s - loss: 0.8420 - acc: 0.6077     Epoch 3/519906/19906 [==============================] - 4s - loss: 0.8210 - acc: 0.6214     Epoch 4/519906/19906 [==============================] - 4s - loss: 0.8149 - acc: 0.6194     Epoch 5/519906/19906 [==============================] - 4s - loss: 0.8042 - acc: 0.6305     <keras.callbacks.History at 0x1d3803e6278>

model.fit(train_x, train_y, batch_size=batch_size,epochs=epochs,verbose=1, validation_split=0.2)

Train on 15924 samples, validate on 3982 samplesEpoch 1/515924/15924 [==============================] - 3s - loss: 0.7970 - acc: 0.6375 - val_loss: 0.7854 - val_acc: 0.6396Epoch 2/515924/15924 [==============================] - 3s - loss: 0.7919 - acc: 0.6378 - val_loss: 0.7767 - val_acc: 0.6519Epoch 3/515924/15924 [==============================] - 3s - loss: 0.7870 - acc: 0.6404 - val_loss: 0.7754 - val_acc: 0.6534Epoch 4/515924/15924 [==============================] - 3s - loss: 0.7806 - acc: 0.6439 - val_loss: 0.7715 - val_acc: 0.6524Epoch 5/515924/15924 [==============================] - 3s - loss: 0.7755 - acc: 0.6519 - val_loss: 0.7970 - val_acc: 0.6346<keras.callbacks.History at 0x1d3800a4eb8>

优化

我们使用最基本的模型来处理这个年龄预测结果，并且最终的预测结果为0.6375。接下来，从以下角度尝试优化：

1. 使用更好的神经网络模型
2. 增加训练次数
3. 将图片进行灰度处理（因为对于本问题而言，图片颜色不是一个特别重要的特征。）

optimize1 使用卷积神经网络

添加卷积层之后，预测准确率有所上涨，从6.3到6.7；最开始epochs轮数是5，训练轮数增加到10，此时准确率为6.87；然后将训练轮数增加到20，结果没有发生变化。

Conv2D层

keras.layers.convolutional.Conv2D(filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)

• filters:输出的维度
• strides:卷积的步长

更多关于Conv2D的介绍请看Keras文档Conv2D层

#参数初始化filters=10filtersize=(5,5)epochs =10batchsize=128input_shape=(32,32,3)

from keras.models import Sequentialmodel = Sequential()model.add(keras.layers.InputLayer(input_shape=input_shape))model.add(keras.layers.convolutional.Conv2D(filters, filtersize, strides=(1, 1), padding='valid', data_format="channels_last", activation='relu'))model.add(keras.layers.MaxPooling2D(pool_size=(2, 2)))model.add(keras.layers.Flatten())model.add(keras.layers.Dense(units=3, input_dim=50,activation='softmax'))model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])model.fit(train_x, train_y, epochs=epochs, batch_size=batchsize,validation_split=0.3)model.summary()

Train on 13934 samples, validate on 5972 samplesEpoch 1/1013934/13934 [==============================] - 9s - loss: 0.8986 - acc: 0.5884 - val_loss: 0.8352 - val_acc: 0.6271Epoch 2/1013934/13934 [==============================] - 9s - loss: 0.8141 - acc: 0.6281 - val_loss: 0.7886 - val_acc: 0.6474Epoch 3/1013934/13934 [==============================] - 9s - loss: 0.7788 - acc: 0.6504 - val_loss: 0.7706 - val_acc: 0.6551Epoch 4/1013934/13934 [==============================] - 9s - loss: 0.7638 - acc: 0.6577 - val_loss: 0.7559 - val_acc: 0.6626Epoch 5/1013934/13934 [==============================] - 9s - loss: 0.7484 - acc: 0.6679 - val_loss: 0.7457 - val_acc: 0.6710Epoch 6/1013934/13934 [==============================] - 9s - loss: 0.7346 - acc: 0.6723 - val_loss: 0.7490 - val_acc: 0.6780Epoch 7/1013934/13934 [==============================] - 9s - loss: 0.7217 - acc: 0.6804 - val_loss: 0.7298 - val_acc: 0.6795Epoch 8/1013934/13934 [==============================] - 9s - loss: 0.7162 - acc: 0.6826 - val_loss: 0.7248 - val_acc: 0.6792Epoch 9/1013934/13934 [==============================] - 9s - loss: 0.7082 - acc: 0.6892 - val_loss: 0.7202 - val_acc: 0.6890Epoch 10/1013934/13934 [==============================] - 9s - loss: 0.7001 - acc: 0.6940 - val_loss: 0.7226 - val_acc: 0.6885_________________________________________________________________Layer (type)                 Output Shape              Param #   =================================================================input_6 (InputLayer)         (None, 32, 32, 3)         0         _________________________________________________________________conv2d_6 (Conv2D)            (None, 28, 28, 10)        760       _________________________________________________________________max_pooling2d_6 (MaxPooling2 (None, 14, 14, 10)        0         _________________________________________________________________flatten_6 (Flatten)          (None, 1960)              0         _________________________________________________________________dense_6 (Dense)              (None, 3)                 5883      =================================================================Total params: 6,643Trainable params: 6,643Non-trainable params: 0_________________________________________________________________

optimize2 增加神经网络的层数

我们在模型中多添加几层并且提高卷几层的输出维度，这次结果得到显著提升：0.750904

#参数初始化filters1=50filters2=100filters3=100filtersize=(5,5)epochs =10batchsize=128input_shape=(32,32,3)

from keras.models import Sequentialmodel = Sequential()model.add(keras.layers.InputLayer(input_shape=input_shape))model.add(keras.layers.convolutional.Conv2D(filters1, filtersize, strides=(1, 1), padding='valid', data_format="channels_last", activation='relu'))model.add(keras.layers.MaxPooling2D(pool_size=(2, 2)))model.add(keras.layers.convolutional.Conv2D(filters2, filtersize, strides=(1, 1), padding='valid', data_format="channels_last", activation='relu'))model.add(keras.layers.MaxPooling2D(pool_size=(2, 2)))model.add(keras.layers.convolutional.Conv2D(filters3, filtersize, strides=(1, 1), padding='valid', data_format="channels_last", activation='relu'))model.add(keras.layers.Flatten())model.add(keras.layers.Dense(units=3, input_dim=50,activation='softmax'))model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])model.fit(train_x, train_y, epochs=epochs, batch_size=batchsize,validation_split=0.3)model.summary()

Train on 13934 samples, validate on 5972 samplesEpoch 1/1013934/13934 [==============================] - 44s - loss: 0.8613 - acc: 0.5985 - val_loss: 0.7778 - val_acc: 0.6586Epoch 2/1013934/13934 [==============================] - 44s - loss: 0.7493 - acc: 0.6697 - val_loss: 0.7545 - val_acc: 0.6808Epoch 3/1013934/13934 [==============================] - 43s - loss: 0.7079 - acc: 0.6877 - val_loss: 0.7150 - val_acc: 0.6947Epoch 4/1013934/13934 [==============================] - 43s - loss: 0.6694 - acc: 0.7061 - val_loss: 0.6496 - val_acc: 0.7261Epoch 5/1013934/13934 [==============================] - 43s - loss: 0.6274 - acc: 0.7295 - val_loss: 0.6683 - val_acc: 0.7125Epoch 6/1013934/13934 [==============================] - 43s - loss: 0.5950 - acc: 0.7462 - val_loss: 0.6194 - val_acc: 0.7400Epoch 7/1013934/13934 [==============================] - 43s - loss: 0.5562 - acc: 0.7655 - val_loss: 0.5981 - val_acc: 0.7465Epoch 8/1013934/13934 [==============================] - 43s - loss: 0.5165 - acc: 0.7852 - val_loss: 0.6458 - val_acc: 0.7354Epoch 9/1013934/13934 [==============================] - 46s - loss: 0.4826 - acc: 0.7986 - val_loss: 0.6206 - val_acc: 0.7467Epoch 10/1013934/13934 [==============================] - 45s - loss: 0.4530 - acc: 0.8130 - val_loss: 0.5984 - val_acc: 0.7569_________________________________________________________________Layer (type)                 Output Shape              Param #   =================================================================input_15 (InputLayer)        (None, 32, 32, 3)         0         _________________________________________________________________conv2d_31 (Conv2D)           (None, 28, 28, 50)        3800      _________________________________________________________________max_pooling2d_23 (MaxPooling (None, 14, 14, 50)        0         _________________________________________________________________conv2d_32 (Conv2D)           (None, 10, 10, 100)       125100    _________________________________________________________________max_pooling2d_24 (MaxPooling (None, 5, 5, 100)         0         _________________________________________________________________conv2d_33 (Conv2D)           (None, 1, 1, 100)         250100    _________________________________________________________________flatten_15 (Flatten)         (None, 100)               0         _________________________________________________________________dense_7 (Dense)              (None, 3)                 303       =================================================================Total params: 379,303Trainable params: 379,303Non-trainable params: 0_________________________________________________________________

输出结果

pred=model.predict_classes(test_x)pred=lb.inverse_transform(pred)print(pred)test['Class']=predtest.to_csv('sub02.csv',index=False)

6636/6636 [==============================] - 7s     ['MIDDLE' 'YOUNG' 'MIDDLE' ..., 'MIDDLE' 'MIDDLE' 'YOUNG']

i = random.choice(train.index)img_name = train.ID[i]img=Image.open(os.path.join(root_dir,'Train',img_name))img.show()pred = model.predict_classes(train_x)print('Original:', train.Class[i], 'Predicted:', lb.inverse_transform(pred[i]))

19872/19906 [============================>.] - ETA: 0sOriginal: MIDDLE Predicted: MIDDLE

结果

image.png

还可以优化，继续探讨