increase validation accuracy in CNN

Question

Below is my code to classify 2 kind of classes.
The accuracy increase gradually till reach about 87%.
The problem is validation accuracy stuck between 0.5 and 0.6.
I know it is over fitting problem.
I tried to manipulate the number of parameters, but still got same problem.
Any idea about how the model can be improved?
Thanks so much

from keras.models import Sequential
from keras.layers import Conv2D, Dropout, Activation
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
import matplotlib.pyplot as plt
from keras.preprocessing.image import ImageDataGenerator


classifier = Sequential()

classifier.add(Conv2D(16, (3, 3), input_shape = (110, 110, 3)))
classifier.add(Activation('relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

classifier.add(Conv2D(32,(3,3))) 
classifier.add(Activation('relu'))
classifier.add(MaxPooling2D(pool_size =(2,2)))

classifier.add(Conv2D(64,(3,3))) 
classifier.add(Activation('relu'))
classifier.add(MaxPooling2D(pool_size =(2,2)))

classifier.add(Flatten())

classifier.add(Dense(64))
classifier.add(Activation('relu'))
classifier.add(Dropout(0.5))
classifier.add(Dense(1))
classifier.add(Activation('sigmoid'))
classifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])

train_datagen = ImageDataGenerator(rescale = 1./255,
shear_range = 0.2,
zoom_range = 0.2,
horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = 
train_datagen.flow_from_directory('/home/ccc/Downloads/Compressed/CNN/AD/train',
target_size = (110, 110),
batch_size = 10,    #10
class_mode = 'binary')

test_set = test_datagen.flow_from_directory('/home/ccc/Downloads/Compressed/CNN/AD/test',
target_size = (110, 110),
batch_size =6,  # 6
class_mode = 'binary')

hist = classifier.fit_generator(training_set,
steps_per_epoch = 1160,
epochs = 50,
validation_data = test_set,
validation_steps = 300)

plt.plot(hist.history['accuracy'])
plt.plot(hist.history['val_accuracy'])
plt.title('Model accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Epoch')
plt.legend(['Train', 'Val'], loc='upper left')
plt.show()

Answer 1

Add few more layers.Start with high learning rate and slowly decrease your learning rate. Try different optimizers. I recommend to use transfer learning technique for more validation accuracy.