CNN accuracy is not improving - tensorflow

Question

I have a dataset of images ( EEG spectrograms ) as given below
Image dimensions are 669X1026. I am using the following code for binary classification of the spectrograms.
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
# dimensions of our images.
img_width, img_height = 669, 1026
train_data_dir = '/home/spectrograms/train'
validation_data_dir = '/home/spectrograms/test'
nb_train_samples = 791
nb_validation_samples = 198
epochs = 100
batch_size = 3
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height,3)
model = Sequential()
model.add(Conv2D(128, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(256, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(512, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(256, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(16))
model.add(Activation('relu'))
# model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.summary()
# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0,
zoom_range=0,
horizontal_flip=False)
# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)
model.save_weights('CNN_model.h5')
But I am not able to get a training accuracy greater than 0.53. I have only a limited amount of data ( 790 training samples and 198 testing samples ). So increasing number of input images is not an option. What else can I do to improve the accuracy?

Answer 1

your code
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0,
zoom_range=0,
horizontal_flip=False)
Is not doing any image augmentation, only rescalling. Not sure what type of augmentation may help. Looks like your images really do not rely on color. It probably will not help accuracy but you could reduce computational expense by converting the images to gray scale. You might get some improvement by using the Keras callbacks ReduceLROnPlateau and EarlyStopping. Documentation is here. My suggested code for these callbacks is shown below
rlronp=tf.keras.callbacks.ReduceLROnPlateau(monitor="val_loss", factor=0.5, patience=1,
verbose=1, mode="auto", min_delta=0.0001, cooldown=0, min_lr=0)
estop=tf.keras.callbacks.EarlyStopping(monitor="val_loss", min_delta=0,patience=4,
verbose=1, mode="auto", baseline=None, restore_best_weights=True)
callbacks=[rlronp, estop]
You can try using transfer learning. Most of those models are trained on the imagenet dataset which is dis-similar to the type of images you are using but it might be worth a try. I recommend you use the Mobilenet model. Code for that is shown below
base_model=tf.keras.applications.mobilenet.MobileNet( include_top=False,
input_shape=input_shape, pooling='max', weights='imagenet',dropout=.4)
x=base_model.output
x = Dense(64,activation='relu')(x)
x=Dropout(.3, seed=123)(x)
output=Dense(1, activation='sigmoid')(x)
model=Model(inputs=base_model.input, outputs=output)
model.compile(Adamax(lr=.001), loss='binary_crossentropy', metrics=['accuracy'])
use the callbacks referenced above in model.fit You may get a warning the Mobilenet was trained with an image shape of 224 X 224 X 3 but it should still load the imagenet weights and work.