卷积神经网络似乎在随机猜测

问题描述 投票：1回答：1

所以我目前正在尝试使用卷积神经网络来构建一个种族识别程序。我正在输入200px乘200px版本的UTKFaceRegonition。数据集 (把我的数据集放在一个谷歌驱动器上，如果你想看看)。我使用keras和tensorflow对8个不同的类（4个种族*2个性别）进行分析，每个类都有大约700个图像，但我已经用1000个图像进行了分析。问题是，当我运行网络时，它的准确率最多为13.5%，验证准确率约为11-12.5%，损失约为2.079-2.081，即使在50个epochs左右后，它也不会有任何改善。我目前的假设是它在随机猜测东西不学习，因为8100=12.5%，这是它得到的结果，而在我做的其他3类模型上，它得到的是33%左右的结果。

我注意到验证精度在第一个，有时是第二个时间段是不同的，但在那之后它最终保持不变。我增加了像素分辨率，改变了层数，层的类型和每层神经元的数量，我试过优化器（在正常的lr和非常大和非常小的情况下（.1和10^-6），我试过不同的损失函数，如KLDivergence，但除了KLDivergence在一次运行中做得很好（约16%），似乎没有任何影响，但随后它又失败了。我曾有过一些想法，也许是数据集中的噪声太多，也许是与密集层的数量有关，但老实说，我不知道为什么它不学习。

以下是制作Tensors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)

这是我建造的模型

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)

这是我运行的10个纪元的日志。

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

所以是的，任何帮助，为什么我的网络似乎只是猜测？谢谢你！所以我目前正试图使用卷积神经网络建立一个比赛识别程序。

tensorflow keras deep-learning neural-network conv-neural-network
1个回答
2
投票

问题在于你的网络设计。

  • 通常情况下，你会希望在第一层学习高级特征，并使用较大的奇数大小的内核。目前你基本上是在插值相邻的像素。为什么是奇数大小？请阅读例如 这里.
  • 过滤器的数量通常从小的（如16，32）数量增加到更深的网络时的大值。在你的网络中，所有层都学习相同数量的过滤器。其原因是，你越深入，你想学习的精细特征就越多--因此过滤器的数量就越多。
  • 在您的ANN中，每一层都会从图像中剔除有价值的信息（默认情况下，您使用的是 valid padding）。)

这是一个非常基本的网络，让我在40秒和10个epochs后得到95%以上的训练精度。

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'])

架构：

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

架构： 训练。

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

正如你所看到的，验证分数是可怕的， 但问题是要证明， 糟糕的架构可以阻止训练完全。

最新问题
© www.soinside.com 2019 - 2024. All rights reserved.