卷积神经网络似乎是随机猜测 [英] Convolutional Neural Network seems to be randomly guessing

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本文介绍了卷积神经网络似乎是随机猜测的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！

问题描述

因此，我目前正在尝试使用卷积神经网络构建种族识别程序.我正在输入200px x 200px的UTKFaceRegonition版本数据集(将我的数据集放在谷歌驱动器，如果你想看看).我使用keras和tensorflow使用了8个不同的类别(4个种族* 2个性别)，每个类别都有大约700张图像，但是我用1000张就完成了.问题是，当我运行网络时，它的准确度最高为13.5％，大约为11-12.5.验证准确性的百分比，即使经过50个左右的时间，它的损失仍约为2.079-2.081，根本无法改善.我目前的假设是，它随机猜测东西/不学习，因为8/100 = 12.5％，这与得到的东西有关，而在我用3个班级制作的其他模型上，它得到的东西约为33％

So I am currently trying to build a race recognition program using a convolution neural network. I'm inputting 200px by 200px versions of the UTKFaceRegonition dataset (put my dataset on a google drive if you want to take a look). Im using 8 different classes (4 races * 2 genders) using keras and tensorflow, each having about 700 images but I have done it with 1000. The problem is when I run the network it gets at best 13.5% accuracy and about 11-12.5% validation accuracy, with a loss around 2.079-2.081, even after 50 epochs or so it won't improve at all. My current hypothesis is that it is randomly guessing stuff/not learning because 8/100=12.5%, which is about what it is getting and on other models I have made with 3 classes it was getting about 33%

我注意到验证准确度在第一个时期(有时是第二个时期)有所不同，但此后最终保持不变.我提高了像素分辨率，更改了层的数量，改变了层的类型以及每层的神经元，我尝试了优化程序(在正常lr以及非常大和小的(.1和10 ^ -6)下使用sgd，我尝试了KLDivergence等不同的损失函数，但似乎对它没有任何影响，除了KLDivergence一次运行得很好(约16％)，然后又失败了.也许与密集层的数量有关，但老实说，我不知道为什么它没有学习.

I noticed the validation accuracy is different on the first and sometimes second epoch, but after that it ends up staying constant. I've increased the pixel resolution, changed amount of layers, types of layer and neurons per layer, I've tried optimizers (sgd at the normal lr and at very large and small (.1 and 10^-6) and I've tried different loss functions like KLDivergence but nothing seems to have any effect on it except KLDivergence which on one run did pretty well (about 16%) but then it flopped again. Some ideas I had are maybe theres too much noise in the dataset or maybe it has to do with the amount of dense layers, but honestly I dont know why it is not learning.

这里有制作张量的代码

import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import os
import cv2
import random
import pickle

WIDTH_SIZE = 200
HEIGHT_SIZE = 200

CATEGORIES = []
for CATEGORY in os.listdir('./TRAINING'):
    CATEGORIES.append(CATEGORY)
DATADIR = "./TRAINING"
training_data = []
def create_training_data():
    for category in CATEGORIES:
        path = os.path.join(DATADIR, category)
        class_num = CATEGORIES.index(category)
        for img in os.listdir(path)[:700]:
            try:
                img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_COLOR)
                new_array = cv2.resize(img_array,(WIDTH_SIZE,HEIGHT_SIZE))
                training_data.append([new_array,class_num])
            except Exception as error:
                print(error)

create_training_data()

random.shuffle(training_data)
X = []
y = []

for features, label in training_data:
    X.append(features)
    y.append(label)

X = np.array(X).reshape(-1, WIDTH_SIZE, HEIGHT_SIZE, 3)
y = np.array(y)

pickle_out = open("X.pickle", "wb")
pickle.dump(X, pickle_out)
pickle_out = open("y.pickle", "wb")
pickle.dump(y, pickle_out)

在这里建立我的模型

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D
import pickle

pickle_in = open("X.pickle","rb")
X = pickle.load(pickle_in)
pickle_in = open("y.pickle","rb")
y = pickle.load(pickle_in)
X = X/255.0

model = Sequential()
model.add(Conv2D(256, (2,2), activation = 'relu', input_shape = X.shape[1:]))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(256, (2,2), activation = 'relu'))
model.add(Conv2D(256, (2,2), activation = 'relu'))
model.add(Conv2D(256, (2,2), activation = 'relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(256, (2,2), activation = 'relu'))
model.add(Conv2D(256, (2,2), activation = 'relu'))
model.add(Dropout(0.4))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(256, (2,2), activation = 'relu'))
model.add(Conv2D(256, (2,2), activation = 'relu'))
model.add(Dropout(0.4))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(256, (2,2), activation = 'relu'))
model.add(Conv2D(256, (2,2), activation = 'relu'))
model.add(Dropout(0.4))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())
model.add(Dense(8, activation="softmax"))

model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),metrics=['accuracy'])

model.fit(X, y, batch_size=16,epochs=100,validation_split=.1)

这里记录了我运行的10个纪元.

Heres a log of 10 epochs I ran.

5040/5040 [==============================] - 55s 11ms/sample - loss: 2.0803 - accuracy: 0.1226 - val_loss: 2.0796 - val_accuracy: 0.1250
Epoch 2/100
5040/5040 [==============================] - 53s 10ms/sample - loss: 2.0797 - accuracy: 0.1147 - val_loss: 2.0798 - val_accuracy: 0.1161
Epoch 3/100
5040/5040 [==============================] - 53s 10ms/sample - loss: 2.0797 - accuracy: 0.1190 - val_loss: 2.0800 - val_accuracy: 0.1161
Epoch 4/100
5040/5040 [==============================] - 53s 11ms/sample - loss: 2.0797 - accuracy: 0.1173 - val_loss: 2.0799 - val_accuracy: 0.1107
Epoch 5/100
5040/5040 [==============================] - 52s 10ms/sample - loss: 2.0797 - accuracy: 0.1183 - val_loss: 2.0802 - val_accuracy: 0.1107
Epoch 6/100
5040/5040 [==============================] - 52s 10ms/sample - loss: 2.0797 - accuracy: 0.1226 - val_loss: 2.0801 - val_accuracy: 0.1107
Epoch 7/100
5040/5040 [==============================] - 52s 10ms/sample - loss: 2.0797 - accuracy: 0.1238 - val_loss: 2.0803 - val_accuracy: 0.1107
Epoch 8/100
5040/5040 [==============================] - 54s 11ms/sample - loss: 2.0797 - accuracy: 0.1169 - val_loss: 2.0802 - val_accuracy: 0.1107
Epoch 9/100
5040/5040 [==============================] - 52s 10ms/sample - loss: 2.0797 - accuracy: 0.1212 - val_loss: 2.0803 - val_accuracy: 0.1107
Epoch 10/100
5040/5040 [==============================] - 53s 11ms/sample - loss: 2.0797 - accuracy: 0.1177 - val_loss: 2.0802 - val_accuracy: 0.1107

是的，对我的网络为何似乎只是猜测的任何帮助?谢谢！

So yeah, any help on why my network seems to be just guessing? Thank you!

推荐答案

问题出在您的网络设计中.

The problem lies in the design of you network.

  • 通常，您希望在第一层中学习高级功能并使用奇数大小的较大内核.目前，您实际上是在对相邻像素进行插值.为什么是奇数大小?阅读例如此处.
  • 当深入网络时，过滤器的数量通常会从较小的数量(例如16、32)增加到较大的值.在您的网络中，所有层都学习相同数量的过滤器.这样做的原因是，您越深入，就越想学习更细粒度的功能-从而增加了过滤器的数量.
  • 在您的ANN中，每一层还从图像中切出有价值的信息(默认情况下，您使用的是valid填充).
  • Typically you'd want in the first layers to learn high-level features and use larger kernel with odd size. Currently you're essentially interpolating neighbouring pixels. Why odd size? Read e.g. here.
  • Number of filters typically increases from small (e.g. 16, 32) number to larger values when going deeper into the network. In your network all layers learn the same number of filters. The reasoning is that the deeper you go, the more fine-grained features you'd like to learn - hence increase in number of filters.
  • In your ANN each layer also cuts out valuable information from the image (by default you are using valid padding).

这是一个非常基本的网络，可以让我在40秒和10个纪元后达到95％的训练准确度:

Here's a very basic network that gets me after 40 seconds and 10 epochs over 95% training accuracy:

import pickle
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D

pickle_in = open("X.pickle","rb")
X = pickle.load(pickle_in)
pickle_in = open("y.pickle","rb")
y = pickle.load(pickle_in)
X = X/255.0

model = Sequential()
model.add(Conv2D(16, (5,5), activation = 'relu', input_shape = X.shape[1:], padding='same'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(32, (3,3), activation = 'relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Conv2D(64, (3,3), activation = 'relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())
model.add(Dense(512))
model.add(Dense(8, activation='softmax'))

model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),metrics=['accuracy'])

建筑:

Model: "sequential_4"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_19 (Conv2D)           (None, 200, 200, 16)      1216      
_________________________________________________________________
max_pooling2d_14 (MaxPooling (None, 100, 100, 16)      0         
_________________________________________________________________
conv2d_20 (Conv2D)           (None, 100, 100, 32)      4640      
_________________________________________________________________
max_pooling2d_15 (MaxPooling (None, 50, 50, 32)        0         
_________________________________________________________________
conv2d_21 (Conv2D)           (None, 50, 50, 64)        18496     
_________________________________________________________________
max_pooling2d_16 (MaxPooling (None, 25, 25, 64)        0         
_________________________________________________________________
flatten_4 (Flatten)          (None, 40000)             0         
_________________________________________________________________
dense_7 (Dense)              (None, 512)               20480512  
_________________________________________________________________
dense_8 (Dense)              (None, 8)                 4104      
=================================================================
Total params: 20,508,968
Trainable params: 20,508,968
Non-trainable params: 0

培训:

Train on 5040 samples, validate on 560 samples
Epoch 1/10
5040/5040 [==============================] - 7s 1ms/sample - loss: 2.2725 - accuracy: 0.1897 - val_loss: 1.8939 - val_accuracy: 0.2946
Epoch 2/10
5040/5040 [==============================] - 6s 1ms/sample - loss: 1.7831 - accuracy: 0.3375 - val_loss: 1.8658 - val_accuracy: 0.3179
Epoch 3/10
5040/5040 [==============================] - 6s 1ms/sample - loss: 1.4857 - accuracy: 0.4623 - val_loss: 1.9507 - val_accuracy: 0.3357
Epoch 4/10
5040/5040 [==============================] - 6s 1ms/sample - loss: 1.1294 - accuracy: 0.6028 - val_loss: 2.1745 - val_accuracy: 0.3250
Epoch 5/10
5040/5040 [==============================] - 6s 1ms/sample - loss: 0.8060 - accuracy: 0.7179 - val_loss: 3.1622 - val_accuracy: 0.3000
Epoch 6/10
5040/5040 [==============================] - 6s 1ms/sample - loss: 0.5574 - accuracy: 0.8169 - val_loss: 3.7494 - val_accuracy: 0.2839
Epoch 7/10
5040/5040 [==============================] - 6s 1ms/sample - loss: 0.3756 - accuracy: 0.8813 - val_loss: 4.9125 - val_accuracy: 0.2643
Epoch 8/10
5040/5040 [==============================] - 6s 1ms/sample - loss: 0.3001 - accuracy: 0.9036 - val_loss: 5.6300 - val_accuracy: 0.2821
Epoch 9/10
5040/5040 [==============================] - 6s 1ms/sample - loss: 0.2345 - accuracy: 0.9337 - val_loss: 5.7263 - val_accuracy: 0.2679
Epoch 10/10
5040/5040 [==============================] - 6s 1ms/sample - loss: 0.1549 - accuracy: 0.9581 - val_loss: 7.3682 - val_accuracy: 0.2732

如您所见，验证分数很糟糕，但是重点是要证明差劲的架构可以完全阻止训练.

As you can see, validation score is terrible, but the point was to demonstrate that poor architecture can prevent training altogether.

这篇关于卷积神经网络似乎是随机猜测的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！

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