为什么模型的训练精度在一定时期内会非常缓慢地增加（几乎保持稳定）？

Question

我正在研究CIFAR-10数据集，并试图获得基准或至少90％的准确性。我已经尝试了下面提到的所有方法，但是大多数方法都是相同的。这是……。[[训练准确度在某些时期后并没有提高，并且保持稳定，并且验证准确度也有所波动。] >数据集目录为：

\cifar

   \train(total 40,000 images. 4000 images per class. Total 10 classes)   
      \airplane
      \automobile......(similar structure for test and validation as well)

   \test.(total 10,000 images. 1000 images per class)

   \validation.(total 10,000 images. 1000 images per class)

code.py

我尝试使用以下参数：

优化程序：亚当，那当，阿达特塔和SGD。
1. 批量大小：16,32。
2. 最初从2个卷积层开始。首先，我与两者都有64个过滤器，然后两个卷积层都有128个过滤器。现在，我添加了第三个卷积层。
3. 这里是代码：

import keras

from keras.layers import Conv2D, MaxPool2D, Flatten, Dense, Dropout, 
Activation, BatchNormalization, GlobalAveragePooling2D

from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers, regularizers


classifier = Sequential()

classifier.add(Conv2D(filters=64, kernel_size=(3,3), input_shape= (32,32,3), use_bias=False))
classifier.add(BatchNormalization())
classifier.add(Activation('relu'))
classifier.add(MaxPool2D(pool_size=(2,2)))
classifier.add(Conv2D(filters=64, kernel_size=(3,3), use_bias=False))
classifier.add(BatchNormalization())
classifier.add(Activation('relu'))
classifier.add(MaxPool2D(pool_size=(2,2)))
classifier.add(Conv2D(filters=64, kernel_size=(3,3), use_bias=False))
classifier.add(BatchNormalization())
classifier.add(Activation('relu'))
classifier.add(MaxPool2D(pool_size=(2,2)))
classifier.add(GlobalAveragePooling2D())
classifier.add(Dense(units=10,activation='softmax'))

'''
sgd = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
'''

classifier.compile(optimizer='nadam', loss='categorical_crossentropy', metrics=['accuracy'])

train_datagen=ImageDataGenerator(rescale=1./255, 
   featurewise_center=True,featurewise_std_normalization=True, 
   shear_range=0.2,rotation_range=20, width_shift_range=0.2, 
   height_shift_range=0.2,horizontal_flip=True)

test_datagen=ImageDataGenerator(rescale=1./255, 
   featurewise_center=True,featurewise_std_normalization=True)

train_dataset=train_datagen.flow_from_directory(
   directory='cifar/train', target_size=(32,32), 
   batch_size=16, class_mode='categorical')

test_dataset=test_datagen.flow_from_directory(
   directory='cifar/validation', target_size=(32,32), 
   batch_size=16, class_mode='categorical')

classifier.fit_generator(train_dataset, 
   steps_per_epoch=2500, epochs=50, 
   validation_data=test_dataset, validation_steps=625)

这里是时代的观察结果：

Epoch 17/50 
2500/2500 [==============================] - 259s 103ms/step - loss: 
0.9305 - acc: 0.6840 - val_loss: 0.8195 - val_acc: 0.7111

Epoch 18/50
2500/2500 [==============================] - 257s 103ms/step - loss: 
0.9280 - acc: 0.6817 - val_loss: 0.9981 - val_acc: 0.6816

Epoch 19/50
2500/2500 [==============================] - 260s 104ms/step - loss: 
0.9112 - acc: 0.6896 - val_loss: 0.9393 - val_acc: 0.6786

Epoch 20/50
2500/2500 [==============================] - 257s 103ms/step - loss: 
0.9053 - acc: 0.6881 - val_loss: 0.8509 - val_acc: 0.7172

Epoch 21/50
2500/2500 [==============================] - 259s 104ms/step - loss: 
0.9110 - acc: 0.6874 - val_loss: 0.8427 - val_acc: 0.7211

Epoch 22/50
2500/2500 [==============================] - 257s 103ms/step - loss: 
0.8967 - acc: 0.6944 - val_loss: 0.7139 - val_acc: 0.7592

Epoch 23/50
2500/2500 [==============================] - 257s 103ms/step - loss: 
0.8825 - acc: 0.6967 - val_loss: 0.8611 - val_acc: 0.7066

Epoch 24/50
2500/2500 [==============================] - 260s 104ms/step - loss: 
0.8819 - acc: 0.6967 - val_loss: 0.7436 - val_acc: 0.7447

Epoch 25/50
2500/2500 [==============================] - 270s 108ms/step - loss: 
0.8780 - acc: 0.6995 - val_loss: 0.8129 - val_acc: 0.7310

Epoch 26/50
2500/2500 [==============================] - 279s 112ms/step - loss: 
0.8756 - acc: 0.7010 - val_loss: 0.7890 - val_acc: 0.7276

Epoch 27/50
2500/2500 [==============================] - 283s 113ms/step - loss: 
0.8680 - acc: 0.7027 - val_loss: 0.8185 - val_acc: 0.7307

Epoch 28/50
2500/2500 [==============================] - 287s 115ms/step - loss: 
0.8651 - acc: 0.7043 - val_loss: 0.7457 - val_acc: 0.7460

Epoch 29/50
2500/2500 [==============================] - 286s 114ms/step - loss: 
0.8531 - acc: 0.7065 - val_loss: 1.1669 - val_acc: 0.6483

Epoch 30/50
2500/2500 [==============================] - 290s 116ms/step - loss: 
0.8521 - acc: 0.7085 - val_loss: 0.7221 - val_acc: 0.7565

Epoch 31/50
2500/2500 [==============================] - 289s 116ms/step - loss: 
0.8518 - acc: 0.7072 - val_loss: 0.7308 - val_acc: 0.7549

Epoch 32/50
2500/2500 [==============================] - 291s 116ms/step - loss: 
0.8465 - acc: 0.7119 - val_loss: 0.8550 - val_acc: 0.7182

Epoch 33/50
2500/2500 [==============================] - 302s 121ms/step - loss: 
0.8406 - acc: 0.7121 - val_loss: 1.0259 - val_acc: 0.6770

Epoch 34/50
2500/2500 [==============================] - 286s 115ms/step - loss: 
0.8424 - acc: 0.7120 - val_loss: 0.6924 - val_acc: 0.7646

Epoch 35/50
2500/2500 [==============================] - 273s 109ms/step - loss: 
0.8337 - acc: 0.7143 - val_loss: 0.8744 - val_acc: 0.7220

Epoch 36/50
2500/2500 [==============================] - 285s 114ms/step - loss: 
0.8332 - acc: 0.7144 - val_loss: 1.0132 - val_acc: 0.6753

Epoch 37/50
2500/2500 [==============================] - 275s 110ms/step - loss: 
0.8382 - acc: 0.7122 - val_loss: 0.7873 - val_acc: 0.7366

我是深度学习的初学者，如果我犯了任何愚蠢的错误，请原谅我。请指导我如何继续进行。

我正在研究CIFAR-10数据集，并试图获得基准或至少90％的准确性。我已经尝试了下面提到的所有方法，但是大多数方法都会导致相同的结果，这就是.......

Answer 1

降低您的学习率，我想它会起作用