How to increase the accuracy of my cnn model?

Question

I have 4 different types of images like bicyle,car,airplane and musicalinstrument and i tried to make image classification with this dataset.Then when i train the model i get this accuracy : 0.62 What should i do to increase the accuracy?

from tensorflow import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.optimizers import RMSprop,Adam

#build the model
#set up the layers
model = Sequential()

model.add(Conv2D(filters = 8, kernel_size = (5,5),padding = 'Same', 
             activation ='relu', input_shape = (IMG_SIZE,IMG_SIZE,1)))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.25))

model.add(Conv2D(filters = 16, kernel_size = (3,3),padding = 'Same', 
             activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation = "relu"))
model.add(Dropout(0.5))
model.add(Dense(10, activation = "softmax"))

# Define the optimizer
#Adam optimizer: Change the learning rate
optimizer = Adam(lr=0.001, beta_1=0.9, beta_2=0.999)

# Compile the model
model.compile(optimizer = optimizer , loss = "categorical_crossentropy", 
metrics=["accuracy"])

#train the model
model.fit(train_images, train_labels, epochs=10,batch_size=250) 

#evaluate accuracy
val_loss, val_acc = model.evaluate(val_images, val_labels)
print('validation accuracy:', val_acc)
print('validation loss:' , val_loss)

Here is the result:

Epoch 1/10
18620/18620 [==============================] - 987s 53ms/step - loss: 2.0487 - acc: 0.39380/18620 [=======>......................] - ETA: 11:57 - loss: 4.0915 - acc: 0.278410500/18620 [===============>..............] - ETA: 7:07 - loss: 2.7013 - acc: 0.325015500/18620 [=======================>......] - ETA: 2:45 - loss: 2.2196 - acc: 0.3754
Epoch 2/10
18620/18620 [==============================] - 985s 53ms/step - loss: 1.1145 - acc: 0.5409TA: 14:05 - loss: 1.1721 - acc: 0.4987 7750/18620 [===========>..................] - ETA: 9:31 - loss: 1.1378 - acc: 0.5288 - ETA: 2:44 - loss: 1.1183 - acc: 0.5392
Epoch 3/10
18620/18620 [==============================] - 978s 53ms/step - loss: 1.0331 - acc: 0.5830TA: 14:17 - loss: 1.0323 - acc: 0.5845
Epoch 4/10
18620/18620 [==============================] - 975s 52ms/step - loss: 1.0032 - acc: 0.5942TA: 9:37 - loss: 1.0127 - acc: 0.5875 9750/18620 [==============>...............] - ETA: 7:41 - loss: 1.0119 - acc: 0.5892 - ETA: 5:19 - loss: 1.0122 - acc: 0.5902
Epoch 5/10
18620/18620 [==============================] - 973s 52ms/step - loss: 0.9680 - acc: 0.6137TA: 11:27 - loss: 0.9670 - acc: 0.6137 7000/18620 [==========>...................] - ETA: 9:58 - loss: 0.9718 - acc: 0.6066 15000/18620 [=======================>......] - ETA: 3:08 - loss: 0.9694 - acc: 0.6115
Epoch 6/10
18620/18620 [==============================] - 979s 53ms/step - loss: 0.9308 - acc: 0.62960/18620 [=============>................] - ETA: 8:36 - loss: 0.9311 - acc: 0.633110500/18620 [===============>..............] - ETA: 7:05 - loss: 0.9310 - acc: 0.6304
Epoch 7/10
18620/18620 [==============================] - 976s 52ms/step - loss: 0.9052 - acc: 0.63860/18620 [==========>...................] - ETA: 10:02 - loss: 0.9112 - acc: 0.6347 - ETA: 9:11 - loss: 0.9055 - acc: 0.6368 - ETA: 5:19 - loss: 0.9105 - acc: 0.6362
Epoch 8/10
18620/18620 [==============================] - 1008s 54ms/step - loss: 0.8755 - acc: 0.6507/18620 [==================>...........] - ETA: 5:52 - loss: 0.8746 - acc: 0.6513
Epoch 9/10
18620/18620 [==============================] - 994s 53ms/step - loss: 0.8479 - acc: 0.66140/18620 [===>..........................] - ETA: 14:12 - loss: 0.8474 - acc: 0.6560 3500/18620 [====>.........................] - ETA: 13:27 - loss: 0.8437 - acc: 0.6566 - ETA: 11:54 - loss: 0.8318 - acc: 0.6672 - ETA: 9:30 - loss: 0.8273 - acc: 0.6681 9500/18620 [==============>...............] - ETA: 8:08 - loss: 0.8390 - acc: 0.6653 - ETA: 6:09 - loss: 0.8399 - acc: 0.6660 - ETA: 1:53 - loss: 0.8473 - acc: 0.6628
Epoch 10/10
18620/18620 [==============================] - 997s 54ms/step - loss: 0.8108 - acc: 0.67490/18620 [=======>......................] - ETA: 11:54 - loss: 0.8146 - acc: 0.6650 - ETA: 10:45 - loss: 0.8196 - acc: 0.6652
4656/4656 [==============================] - 40s 9ms/stepETA: 1s
validation accuracy: 0.6265034364261168
validation loss: 0.964772748373628

Answer 1

You may need to tune the exact values of filters depending on

1. The complexity of your dataset.
2. The depth of your neural network, but I recommend starting with filters in the range [32, 64, 128] in the earlier and increasing up to [256, 512, 1024] in the deeper layers.

If your input images are greater than 128×128 you may choose to use a kernel size > 3.

1. learn larger spatial filters.
2. to help reduce volume size.

If your images are smaller than 128×128 you may want to consider sticking with strictly 1×1 and 3×3 filters.

Also use Adam optimiser with its original values.

Answer 2

At the first you can increase CNN filters (8 or 16 is not enough)and then use BatchNormalization layers after MaxPool layers. If it's not work change the optimizer (like SGD etc.)