机器学习（二）深度学习实战-使用Kera预测人物年龄问题描述引入所需要模块加载数据集创建模型编译模型优化optimize1 使用卷积神经网络optimize2 增加神经网络的层数输出结果结果

致Great

发布于 2018-04-11 17:50:36

1.1K0

发布于 2018-04-11 17:50:36

文章被收录于专栏：程序生活

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 os
import random
import pandas as pd
import numpy as np
from 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   Class
0    377.jpg  MIDDLE
1  17814.jpg   YOUNG
2  21283.jpg  MIDDLE
3  16496.jpg   YOUNG
4   4487.jpg  MIDDLE
          ID
0  25321.jpg
1    989.jpg
2  19277.jpg
3  13093.jpg
4   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.jpg
MIDDLE

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：

质量好的图片：
- Middle:
**Middle**
- Young:
**Young**
- Old:
**Old**
质量差的：
- Middle:
**Middle**

下面是我们需要面临的问题：

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
人物面貌角度不同：
- 侧脸：
- 正脸：
图片质量不一（直接上图）:

插图

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    0.542751
YOUNG     0.336883
OLD       0.120366
Name: 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 keras
from sklearn.preprocessing import LabelEncoder
lb=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=500
output_num_units=3
epochs=5
batch_size=128

from keras.models import Sequential
from keras.layers import Dense,Flatten,InputLayer
model=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: 1,538,003
Trainable params: 1,538,003
Non-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 1/5
19906/19906 [==============================] - 4s - loss: 0.8878 - acc: 0.5809     
Epoch 2/5
19906/19906 [==============================] - 4s - loss: 0.8420 - acc: 0.6077     
Epoch 3/5
19906/19906 [==============================] - 4s - loss: 0.8210 - acc: 0.6214     
Epoch 4/5
19906/19906 [==============================] - 4s - loss: 0.8149 - acc: 0.6194     
Epoch 5/5
19906/19906 [==============================] - 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 samples
Epoch 1/5
15924/15924 [==============================] - 3s - loss: 0.7970 - acc: 0.6375 - val_loss: 0.7854 - val_acc: 0.6396
Epoch 2/5
15924/15924 [==============================] - 3s - loss: 0.7919 - acc: 0.6378 - val_loss: 0.7767 - val_acc: 0.6519
Epoch 3/5
15924/15924 [==============================] - 3s - loss: 0.7870 - acc: 0.6404 - val_loss: 0.7754 - val_acc: 0.6534
Epoch 4/5
15924/15924 [==============================] - 3s - loss: 0.7806 - acc: 0.6439 - val_loss: 0.7715 - val_acc: 0.6524
Epoch 5/5
15924/15924 [==============================] - 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：

使用更好的神经网络模型
增加训练次数
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=10
filtersize=(5,5)

epochs =10
batchsize=128

input_shape=(32,32,3)

from keras.models import Sequential
model = 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 samples
Epoch 1/10
13934/13934 [==============================] - 9s - loss: 0.8986 - acc: 0.5884 - val_loss: 0.8352 - val_acc: 0.6271
Epoch 2/10
13934/13934 [==============================] - 9s - loss: 0.8141 - acc: 0.6281 - val_loss: 0.7886 - val_acc: 0.6474
Epoch 3/10
13934/13934 [==============================] - 9s - loss: 0.7788 - acc: 0.6504 - val_loss: 0.7706 - val_acc: 0.6551
Epoch 4/10
13934/13934 [==============================] - 9s - loss: 0.7638 - acc: 0.6577 - val_loss: 0.7559 - val_acc: 0.6626
Epoch 5/10
13934/13934 [==============================] - 9s - loss: 0.7484 - acc: 0.6679 - val_loss: 0.7457 - val_acc: 0.6710
Epoch 6/10
13934/13934 [==============================] - 9s - loss: 0.7346 - acc: 0.6723 - val_loss: 0.7490 - val_acc: 0.6780
Epoch 7/10
13934/13934 [==============================] - 9s - loss: 0.7217 - acc: 0.6804 - val_loss: 0.7298 - val_acc: 0.6795
Epoch 8/10
13934/13934 [==============================] - 9s - loss: 0.7162 - acc: 0.6826 - val_loss: 0.7248 - val_acc: 0.6792
Epoch 9/10
13934/13934 [==============================] - 9s - loss: 0.7082 - acc: 0.6892 - val_loss: 0.7202 - val_acc: 0.6890
Epoch 10/10
13934/13934 [==============================] - 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: 6,643
Trainable params: 6,643
Non-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=50
filters2=100
filters3=100

filtersize=(5,5)

epochs =10
batchsize=128

input_shape=(32,32,3)

from keras.models import Sequential

model = 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 samples
Epoch 1/10
13934/13934 [==============================] - 44s - loss: 0.8613 - acc: 0.5985 - val_loss: 0.7778 - val_acc: 0.6586
Epoch 2/10
13934/13934 [==============================] - 44s - loss: 0.7493 - acc: 0.6697 - val_loss: 0.7545 - val_acc: 0.6808
Epoch 3/10
13934/13934 [==============================] - 43s - loss: 0.7079 - acc: 0.6877 - val_loss: 0.7150 - val_acc: 0.6947
Epoch 4/10
13934/13934 [==============================] - 43s - loss: 0.6694 - acc: 0.7061 - val_loss: 0.6496 - val_acc: 0.7261
Epoch 5/10
13934/13934 [==============================] - 43s - loss: 0.6274 - acc: 0.7295 - val_loss: 0.6683 - val_acc: 0.7125
Epoch 6/10
13934/13934 [==============================] - 43s - loss: 0.5950 - acc: 0.7462 - val_loss: 0.6194 - val_acc: 0.7400
Epoch 7/10
13934/13934 [==============================] - 43s - loss: 0.5562 - acc: 0.7655 - val_loss: 0.5981 - val_acc: 0.7465
Epoch 8/10
13934/13934 [==============================] - 43s - loss: 0.5165 - acc: 0.7852 - val_loss: 0.6458 - val_acc: 0.7354
Epoch 9/10
13934/13934 [==============================] - 46s - loss: 0.4826 - acc: 0.7986 - val_loss: 0.6206 - val_acc: 0.7467
Epoch 10/10
13934/13934 [==============================] - 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: 379,303
Trainable params: 379,303
Non-trainable params: 0
_________________________________________________________________

输出结果

pred=model.predict_classes(test_x)
pred=lb.inverse_transform(pred)
print(pred)
test['Class']=pred
test.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

本文参与腾讯云自媒体同步曝光计划，分享自作者个人站点/博客。

原始发表：2017.08.03 ，如有侵权请联系 cloudcommunity@tencent.com 删除

机器学习

深度学习

本文分享自作者个人站点/博客前往查看

如有侵权，请联系 cloudcommunity@tencent.com 删除。

本文参与腾讯云自媒体同步曝光计划，欢迎热爱写作的你一起参与！

机器学习

深度学习

登录后参与评论

0 条评论

热度