Overfitting on my model classification images CNN-CodePudding

I tried a lot to make a change in my validation accuracy by adding layers and dropout but still, I have no change yet my accuracy is upper than 95% and my validation accuracy is always stuck in 88%.

my split :

x_train,x_validate,y_train,y_validate = train_test_split(x_train,y_train,test_size = 0.2,random_state = 42)

after splitting data (shape) :

x_train shape: (5850,)
x_train shape: (5850,)
x_validate shape: (1463,)
y_validate shape: (1463,)
x_test shape: (2441,)
y_test shape: (2441,)

width and height and number of channels :

width, height, channels = 64, 64, 3

after converting images to array (shape) :

Training set shape :  (5850, 64, 64, 3)
Validation set shape :  (1463, 64, 64, 3)
Test set shape :  (2441, 64, 64, 3)

and I have 6 classes

augmentation :

datagen = ImageDataGenerator(
        featurewise_center=True,  
        samplewise_center=True,  
        featurewise_std_normalization=True,  
        samplewise_std_normalization=True, 
        zca_whitening=False,  
        rotation_range=0.9, 
        zoom_range = 0.7, 
        width_shift_range=0.8,  
        height_shift_range=0.8,  
        horizontal_flip=True,  
        vertical_flip=True) 
datagen.fit(x_train)

my Sequential :

model = Sequential()
model.add(Conv2D(16,(3,3),input_shape = (224,224,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D())


model.add(Conv2D(32,(3,3)))  
model.add(Activation("relu"))
model.add(MaxPooling2D())


model.add(Conv2D(64,(3,3)))  
model.add(Activation("relu"))
model.add(MaxPooling2D())

model.add(Conv2D(128,(3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D())


model.add(Conv2D(256,(3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D())

model.add(Flatten())
model.add(Dense(256))
model.add(Activation("relu"))
model.add(Dropout(0.3))
model.add(Dense(256))
model.add(Activation("relu"))
model.add(Dropout(0.2))

model.add(Dense(numberOfClass)) # output
model.add(Activation("softmax"))

model.compile(loss = "binary_crossentropy",
              optimizer = "adam",
              metrics = ["accuracy"]) 
batch_size = 256

I put an early stopping in my code to make the best less validation loss, how can improve my validation accuracy to at least 92%?

Epoch 1/100
29/29 [==============================] - 62s 2s/step - loss: 0.4635 - accuracy: 0.3040 - val_loss: 0.4227 - val_accuracy: 0.4007

Epoch 00001: val_loss improved from inf to 0.42266, saving model to ./model_best_weights.h5
Epoch 2/100
29/29 [==============================] - 60s 2s/step - loss: 0.4230 - accuracy: 0.3260 - val_loss: 0.4046 - val_accuracy: 0.3314

Epoch 00002: val_loss improved from 0.42266 to 0.40463, saving model to ./model_best_weights.h5
Epoch 3/100
29/29 [==============================] - 60s 2s/step - loss: 0.3833 - accuracy: 0.4234 - val_loss: 0.3417 - val_accuracy: 0.5125

Epoch 00003: val_loss improved from 0.40463 to 0.34174, saving model to ./model_best_weights.h5
Epoch 4/100
29/29 [==============================] - 60s 2s/step - loss: 0.3351 - accuracy: 0.5040 - val_loss: 0.3108 - val_accuracy: 0.5432

Epoch 00004: val_loss improved from 0.34174 to 0.31083, saving model to ./model_best_weights.h5
Epoch 5/100
29/29 [==============================] - 59s 2s/step - loss: 0.3002 - accuracy: 0.5683 - val_loss: 0.2655 - val_accuracy: 0.6247

Epoch 00005: val_loss improved from 0.31083 to 0.26553, saving model to ./model_best_weights.h5
Epoch 6/100
29/29 [==============================] - 60s 2s/step - loss: 0.2794 - accuracy: 0.6025 - val_loss: 0.2677 - val_accuracy: 0.6194

Epoch 00006: val_loss did not improve from 0.26553
Epoch 7/100
29/29 [==============================] - 60s 2s/step - loss: 0.2606 - accuracy: 0.6374 - val_loss: 0.2524 - val_accuracy: 0.6477

Epoch 00007: val_loss improved from 0.26553 to 0.25239, saving model to ./model_best_weights.h5
Epoch 8/100
29/29 [==============================] - 59s 2s/step - loss: 0.2400 - accuracy: 0.6751 - val_loss: 0.2232 - val_accuracy: 0.6997

Epoch 00008: val_loss improved from 0.25239 to 0.22320, saving model to ./model_best_weights.h5
Epoch 9/100
29/29 [==============================] - 60s 2s/step - loss: 0.2307 - accuracy: 0.6875 - val_loss: 0.2092 - val_accuracy: 0.7181

Epoch 00009: val_loss improved from 0.22320 to 0.20916, saving model to ./model_best_weights.h5
Epoch 10/100
29/29 [==============================] - 59s 2s/step - loss: 0.2085 - accuracy: 0.7284 - val_loss: 0.2092 - val_accuracy: 0.7255

Epoch 00010: val_loss did not improve from 0.20916
Epoch 11/100
29/29 [==============================] - 60s 2s/step - loss: 0.1961 - accuracy: 0.7463 - val_loss: 0.1943 - val_accuracy: 0.7603

Epoch 00011: val_loss improved from 0.20916 to 0.19435, saving model to ./model_best_weights.h5
Epoch 12/100
29/29 [==============================] - 60s 2s/step - loss: 0.1894 - accuracy: 0.7621 - val_loss: 0.1829 - val_accuracy: 0.7669

Epoch 00012: val_loss improved from 0.19435 to 0.18294, saving model to ./model_best_weights.h5
Epoch 13/100
29/29 [==============================] - 60s 2s/step - loss: 0.1766 - accuracy: 0.7770 - val_loss: 0.1751 - val_accuracy: 0.7780

Epoch 00013: val_loss improved from 0.18294 to 0.17508, saving model to ./model_best_weights.h5
Epoch 14/100
29/29 [==============================] - 60s 2s/step - loss: 0.1606 - accuracy: 0.8006 - val_loss: 0.1666 - val_accuracy: 0.8005

Epoch 00014: val_loss improved from 0.17508 to 0.16662, saving model to ./model_best_weights.h5
Epoch 15/100
29/29 [==============================] - 60s 2s/step - loss: 0.1531 - accuracy: 0.8105 - val_loss: 0.1718 - val_accuracy: 0.7816

Epoch 00015: val_loss did not improve from 0.16662
Epoch 16/100
29/29 [==============================] - 61s 2s/step - loss: 0.1449 - accuracy: 0.8265 - val_loss: 0.1600 - val_accuracy: 0.8083

Epoch 00016: val_loss improved from 0.16662 to 0.16000, saving model to ./model_best_weights.h5
Epoch 17/100
29/29 [==============================] - 62s 2s/step - loss: 0.1309 - accuracy: 0.8419 - val_loss: 0.1609 - val_accuracy: 0.8202

Epoch 00017: val_loss did not improve from 0.16000
Epoch 18/100
29/29 [==============================] - 60s 2s/step - loss: 0.1165 - accuracy: 0.8607 - val_loss: 0.1572 - val_accuracy: 0.8222

Epoch 00018: val_loss improved from 0.16000 to 0.15722, saving model to ./model_best_weights.h5
Epoch 19/100
29/29 [==============================] - 60s 2s/step - loss: 0.1109 - accuracy: 0.8711 - val_loss: 0.1523 - val_accuracy: 0.8370

Epoch 00019: val_loss improved from 0.15722 to 0.15225, saving model to ./model_best_weights.h5
Epoch 20/100
29/29 [==============================] - 60s 2s/step - loss: 0.1008 - accuracy: 0.8877 - val_loss: 0.1405 - val_accuracy: 0.8484

Epoch 00020: val_loss improved from 0.15225 to 0.14046, saving model to ./model_best_weights.h5
Epoch 21/100
29/29 [==============================] - 60s 2s/step - loss: 0.1063 - accuracy: 0.8764 - val_loss: 0.1514 - val_accuracy: 0.8390

Epoch 00021: val_loss did not improve from 0.14046
Epoch 22/100
29/29 [==============================] - 61s 2s/step - loss: 0.0880 - accuracy: 0.8979 - val_loss: 0.1423 - val_accuracy: 0.8550

Epoch 00022: val_loss did not improve from 0.14046
Epoch 23/100
29/29 [==============================] - 60s 2s/step - loss: 0.0750 - accuracy: 0.9196 - val_loss: 0.1368 - val_accuracy: 0.8632

Epoch 00023: val_loss improved from 0.14046 to 0.13678, saving model to ./model_best_weights.h5
Epoch 24/100
29/29 [==============================] - 60s 2s/step - loss: 0.0712 - accuracy: 0.9218 - val_loss: 0.1520 - val_accuracy: 0.8521

Epoch 00024: val_loss did not improve from 0.13678
Epoch 25/100
29/29 [==============================] - 60s 2s/step - loss: 0.0664 - accuracy: 0.9288 - val_loss: 0.1600 - val_accuracy: 0.8451

Epoch 00025: val_loss did not improve from 0.13678
Epoch 26/100
29/29 [==============================] - 60s 2s/step - loss: 0.0605 - accuracy: 0.9360 - val_loss: 0.1528 - val_accuracy: 0.8636

Epoch 00026: val_loss did not improve from 0.13678
Epoch 00026: early stopping

images of my graph :

https://i.imgur.com/pNYwcE8.jpg
https://i.imgur.com/ZCSRI8e.jpg

CodePudding user response：

You should experiment more, but glancing at your code, I can give you the following tips:

according to the plot, validation accuracy is increasing a bit even in the end, maybe you can try to increase EarlyStopping patience and monitor validation accuracy instead of validation loss
add batch normalization into your architecture
increase dropout rate, maybe to some value between 0.4 and 0.7
tune learning rate, and maybe use some learning rate scheduler like ReduceLROnPlateau which might help to train even further after there is no increase in validation metrics

Good luck!