My model is experiencing wild and big fluctuations in the validation loss and does not converge.
I am doing an image recognition project with my three dogs i.e. classifying the dog in the image. Two dogs are very similar and the 3rd is very different. I took 10 minute videos of each dog, separately. Frames were extracted as images at each second.
This block of code is responsible for augmenting and creating the data to feed the model.
randomize = np.arange(len(imArr)) # imArr is the numpy array of all the images
np.random.shuffle(randomize) # Shuffle the images and labels
imArr = imArr[randomize]
imLab= imLab[randomize] # imLab is the array of labels of the images
lab = to_categorical(imLab, 3)
gen = ImageDataGenerator(zoom_range = 0.2,horizontal_flip = True , vertical_flip = True,validation_split = 0.25)
train_gen = gen.flow(imArr,lab,batch_size = 64, subset = 'training')
test_gen = gen.flow(imArr,lab,batch_size =64,subset = 'validation')
This picture is the result of the model below.
model = Sequential()
model.add(Conv2D(16, (11, 11),strides = 1, input_shape=(imgSize,imgSize,3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(3,3),strides = 2))
model.add(BatchNormalization(axis=-1))
model.add(Conv2D(32, (5, 5),strides = 1))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(3,3),strides = 2))
model.add(BatchNormalization(axis=-1))
model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(3,3),strides = 2))
model.add(BatchNormalization(axis=-1))
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(BatchNormalization(axis=-1))
model.add(Dropout(0.3))
#Fully connected layer
model.add(Dense(256))
model.add(Activation('relu'))
model.add(BatchNormalization())
model.add(Dropout(0.3))
model.add(Dense(3))
model.add(Activation('softmax'))
sgd = SGD(lr=0.004)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
batch_size = 64
epochs = 100
model.fit_generator(train_gen, steps_per_epoch=(len(train_gen)), epochs=epochs, validation_data=test_gen, validation_steps=len(test_gen),shuffle = True)
Things I have tried.
- High/low Learning rate ( 0.01 -> 0.0001)
- Increase Dropout to 0.5 in both Dense layers
- Increase/Decrease size of both Dense Layers ( 128 min -> 4048 max)
- Increased number of CNN layers
- Introduced Momentum
- Increased/Decreased Batch Size
Things I have not tried
- I have not used any other loss or metric
- I have not used any other optimiser.
- Have not adjusted any parameters of the CNN layers
It seems that there is some form of randomness or too many parameters in my model. I am aware that it is currently overfitting, but that should not be the cause of the volatility(?). I am not too worried about the performance of the model. I would like to achieve about a 70% accuracy. All I want to do now is to stabilise the validation accuracy and to converge.
Note:
- At some epochs, the training loss is very low ( <0.1 ) but validation loss is very high ( > 3 ).
- The videos are taken on different backgrounds, but +- the same amount on each background for each dog.
- Some images are a bit blurry.