如何修复分类分类中的“val_accuracy: 0.0000e+00”？

Question

I am new to deep learning, I have 3 classes to classify, when I train my model I observed that my "val_loss > val_accuracy " means my model is overfitting how can I fix this? also I get "val_accuracy: 0.0000e+00" this way. Initially I have kept my epoch to low. I have less data to train a model.

import tensorflow as tf           
from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dropout
from keras.layers import Dense
from keras.preprocessing.image import ImageDataGenerator, image
import numpy as np
import os
from pathlib import Path
country  = "armenia"
cwd = os.getcwd()
print("cwd",cwd)
save_path = r'E://paymentz//'+country+'/'
abc  = os.listdir(r'E:/paymentz/'+country+'/training')
print("list of subfolders in directory:",abc)

model = Sequential()
model.add(Convolution2D(16, 2, 2, input_shape = ( 64, 64, 3), activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Dropout(0.5))
model.add(Convolution2D(32, 3, 3, activation = 'relu'))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Flatten())
model.add(Dropout(0.5))
model.add(Dense(output_dim= 64, activation='relu' ))
output_dim = os.listdir(r'E:/paymentz/'+country+'/training')
print(len(output_dim))
output_dim = len(output_dim)
model.add(Dense(output_dim , activation = 'softmax'))
model.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics =['accuracy'])
batch_size = 16
train_datagen = ImageDataGenerator(rescale = 1./255,
                               shear_range = 0.2,
                               zoom_range = 0.2,
                               horizontal_flip = True,
                               rotation_range = 360)
test_datagen = ImageDataGenerator(rescale = 1./255)
training_set = test_datagen.flow_from_directory(r'E:/paymentz/'+country+'/training',
                                            target_size = (64, 64),
                                            batch_size= batch_size,
                                            class_mode='categorical')
test_set = test_datagen.flow_from_directory(r'E:/paymentz/'+country+'/testing',
                                        target_size= (64, 64),
                                        batch_size= batch_size,
                                        class_mode='categorical')
training_path = Path(fr'E://paymentz//{country}//training')
training_png_count = len(list(training_path.rglob('*.png')))
training_jpg_count = len(list(training_path.rglob('*.jpg')))
training_jpeg_count = len(list(training_path.rglob('*.jpeg')))
          
training_count = training_png_count + training_jpg_count + training_jpeg_count
print("training_count ", training_count)
            
testing_path = Path(fr'E://paymentz//{country}//testing')
testing_png_count = len(list(testing_path.rglob('*.png')))
testing_jpg_count = len(list(testing_path.rglob('*.jpg')))
testing_jpeg_count = len(list(testing_path.rglob('*.jpeg')))
            
testing_count = testing_png_count + testing_jpg_count + testing_jpeg_count
print("testing_count ", testing_count)              

steps_per_epoch = (training_count// batch_size )
print("steps_per_epoch", steps_per_epoch)
validation_steps = ( testing_count // batch_size )
print("validation_steps", validation_steps) 
    
model.fit_generator(
      training_set,
      validation_data = test_set,
      samples_per_epoch = training_count, 
      epochs = 15,
      validation_steps = validation_steps,
      steps_per_epoch = steps_per_epoch)
print("training done.")
score = model.fit(test_set)
score= model.evaluate_generator(test_set)
print("test_set ",score)
#score= model.evaluate_generator(training_set)
#print("training_set ", score)
#if score[0] < 0.05 and score [1] < .85:
save_path = r'E:/paymentz/'+country+'/'
model.save(save_path+country+'.model')
model.save(save_path+country+'.model.h5')
#model.save("StatewiseDLmodel.model.h5")
print("model saved")

abc  = os.listdir(r'E:/paymentz/'+country+'/training')

model_path = r''+country+'model.h5'

#model = tf.keras.models.load_model(country+'.model.h5')
print("model trained to:",score)```

Answer 1

Why are you fitting your model again on the test set?

print("training done.")
score = model.fit(test_set) # this should be predict.
score= model.evaluate_generator(test_set)
print("test_set ",score)

remove the following line and try again.

score = model.fit(test_set)