我应该如何提高转学的准确性？

如何解决我应该如何提高转学的准确性？

我正在训练古吉拉特语语言的光学字符识别模型。输入图像是字符图像。我上了37堂课。总训练图像为22200（每节课600张），测试图像为5920（每节课160张）。我输入的图片尺寸为32x32。

下面是我的代码：

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D
from tensorflow.keras.layers import MaxPooling2D
from tensorflow.keras.layers import Flatten
from tensorflow.keras.layers import Dense


from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Dropout,Activation,Flatten
from tensorflow.keras.layers import Convolution2D,MaxPooling2D
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import MaxPool2D
from tensorflow.keras.layers import ReLU

from tensorflow.keras.callbacks import TensorBoard
import pickle
import time

from datetime import datetime
from packaging import version

import tensorflow as tf
from tensorflow import keras

import numpy as np
from tensorflow.keras import layers

model = tf.keras.applications.DenseNet121(include_top=False,weights='imagenet',pooling='max',input_shape=(32,32,3))

model.trainable = True
print("Number of layers in the base model: ",len(model.layers))

fine_tune_at = 400
for layer in model.layers[:fine_tune_at]:
    layer.trainable =  False
    
base_outputs = model.layers[-1].output 

final_outputs = layers.Dense(37)(base_outputs)
new_model = keras.Model(inputs=model.input,outputs=final_outputs)
new_model.summary()  

from tensorflow.keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(
                                   shear_range=0.2,zoom_range=0.2,horizontal_flip=False)

test_datagen = ImageDataGenerator(horizontal_flip = False)

training_set = train_datagen.flow_from_directory('C:/Users/shweta/Desktop/characters/train',target_size = (32,32),batch_size = 64,class_mode = 'categorical')

test_set = test_datagen.flow_from_directory('C:/Users/shweta/Desktop/characters/test',class_mode = 'categorical')

new_model.compile(optimizer = 'adam',loss = 'categorical_crossentropy',metrics = ['accuracy'])

new_model.fit_generator(training_set,epochs = 25,validation_data = test_set,shuffle=True)
new_model.save('alphanumeric.mod')

以下是我的输出：

提前谢谢！

解决方法

重新缩放输入数据。您可以通过在ImageDataGenerator中将缩放比例因子设置为1/255来完成此操作。

,

在放入模型之前，请尝试使用ImageDataGenerator来预处理数据集。您可以在Tensorflow文档中找到完整的选项：ImageDataGenerator

如前所述，在将数据放入模型之前，请尝试重新缩放数据或对其进行随机整理