深度學習實戰 | 使用Kera預測人物年齡
阿新 • • 發佈：2022-05-03
01 問題描述
我們的任務是從一個人的面部特徵來預測他的年齡(用“Young”“Middle ”“Old”表示)，我們訓練的資料集大約有19906多張照片及其每張圖片對應的年齡（全是阿三的頭像。。。），測試集有6636張圖片，首先我們載入資料集，然後我們通過深度學習框架Keras建立、編譯、訓練模型，預測出6636張人物頭像對應的年齡。
02 引入所需要的模組
import os
import random
import pandas as pd
import numpy as np
from PIL import Image03 載入資料集
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
04 隨機讀取一張圖片試下
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
MIDDLE05 難點
我們隨機開啟幾張圖片之後，可以發現圖片之間的差別比較大。大家感受下：
質量好的圖片：
Middle:
*Middle**
Young:
**Young**
Old:
*Old**
質量差的：
Middle:
**Middle**
下面是我們需要面臨的問題：
1、圖片的尺寸差別：有的圖片的尺寸是66x46,而另一張圖片尺寸為102x87
2、人物面貌角度不同：
側臉：
正臉：
3、圖片質量不一（直接上圖）:
插圖
4、亮度和對比度的差異
亮度
對比度
現在，我們只專注下圖片尺寸處理，將每一張圖片尺寸重置為32x32；
06 格式化圖片尺寸和將圖片轉換成numpy陣列
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) 4temp=[]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)
另外我們再歸一化影象，這樣會使模型訓練的更快
train_x = train_x / 255.test_x = test_x / 255.
我們看下圖片年齡大致分佈：
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)
將目標變數處理虛擬列，能夠使模型更容易接受識別它
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)07 建立模型
#構建神經網路
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_________________________________________________________________
08 編譯模型
# 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>09 優化
我們使用最基本的模型來處理這個年齡預測結果，並且最終的預測結果為0.6375。接下來，從以下角度嘗試優化：
1、使用更好的神經網路模型
2、增加訓練次數
3、將圖片進行灰度處理（因為對於本問題而言，圖片顏色不是一個特別重要的特徵。）
10 optimize1 使用卷積神經網路
添加捲積層之後，預測準確率有所上漲，從6.3到6.7；最開始epochs輪數是5，訓練輪數增加到10，此時準確率為6.87；然後將訓練輪數增加到20，結果沒有發生變化。
11 Conv2D層
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:輸出的維度
- strides:卷積的步長
更多關於Conv2D的介紹請看Keras文件Conv2D層（http://keras-cn.readthedocs.io/en/latest/layers/convolutional_layer/#conv2d）
#引數初始化
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 1/1
013934/13934 [==============================] - 9s - loss: 0.8986 - acc: 0.5884 - val_loss: 0.8352 - val_acc: 0.6271
Epoch 2/1
013934/13934 [==============================] - 9s - loss: 0.8141 - acc: 0.6281 - val_loss: 0.7886 - val_acc: 0.6474
Epoch 3/1
013934/13934 [==============================] - 9s - loss: 0.7788 - acc: 0.6504 - val_loss: 0.7706 - val_acc: 0.6551
Epoch 4/1
013934/13934 [==============================] - 9s - loss: 0.7638 - acc: 0.6577 - val_loss: 0.7559 - val_acc: 0.6626
Epoch 5/1
013934/13934 [==============================] - 9s - loss: 0.7484 - acc: 0.6679 - val_loss: 0.7457 - val_acc: 0.6710
Epoch 6/1
013934/13934 [==============================] - 9s - loss: 0.7346 - acc: 0.6723 - val_loss: 0.7490 - val_acc: 0.6780
Epoch 7/1
013934/13934 [==============================] - 9s - loss: 0.7217 - acc: 0.6804 - val_loss: 0.7298 - val_acc: 0.6795
Epoch 8/1
013934/13934 [==============================] - 9s - loss: 0.7162 - acc: 0.6826 - val_loss: 0.7248 - val_acc: 0.6792
Epoch 9/1
013934/13934 [==============================] - 9s - loss: 0.7082 - acc: 0.6892 - val_loss: 0.7202 - val_acc: 0.6890
Epoch 10/1
013934/13934 [==============================] - 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
_________________________________________________________________12 optimize2 增加神經網路的層數
我們在模型中多新增幾層並且提高卷幾層的輸出維度，這次結果得到顯著提升：0.750904
#引數初始化
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 1/1
013934/13934 [==============================] - 44s - loss: 0.8613 - acc: 0.5985 - val_loss: 0.7778 - val_acc: 0.6586
Epoch 2/1
013934/13934 [==============================] - 44s - loss: 0.7493 - acc: 0.6697 - val_loss: 0.7545 - val_acc: 0.6808
Epoch 3/1
013934/13934 [==============================] - 43s - loss: 0.7079 - acc: 0.6877 - val_loss: 0.7150 - val_acc: 0.6947
Epoch 4/1
013934/13934 [==============================] - 43s - loss: 0.6694 - acc: 0.7061 - val_loss: 0.6496 - val_acc: 0.7261
Epoch 5/1
013934/13934 [==============================] - 43s - loss: 0.6274 - acc: 0.7295 - val_loss: 0.6683 - val_acc: 0.7125
Epoch 6/1
013934/13934 [==============================] - 43s - loss: 0.5950 - acc: 0.7462 - val_loss: 0.6194 - val_acc: 0.7400
Epoch 7/1
013934/13934 [==============================] - 43s - loss: 0.5562 - acc: 0.7655 - val_loss: 0.5981 - val_acc: 0.7465
Epoch 8/1
013934/13934 [==============================] - 43s - loss: 0.5165 - acc: 0.7852 - val_loss: 0.6458 - val_acc: 0.7354
Epoch 9/1
013934/13934 [==============================] - 46s - loss: 0.4826 - acc: 0.7986 - val_loss: 0.6206 - val_acc: 0.7467
Epoch 10/1
013934/13934 [==============================] - 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
_________________________________________________________________13
輸出結果
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: MIDDLE14 結果
