How to avoid overfitting in CNN? - tensorflow

Question

I'm making a model for predicting the age of people by analyzing their face. I'm using this pretrained model, and maked a custom loss function and a custom metrics. So I obtain discrete result but I want to improve it. In particular, I noticed that after some epochs the model begin to overfitt on the training set then the val_loss increases. How can I avoid this? I'm already using Dropout, but this doesn't seem to be enough.
I think maybe I should use l1 and l2 but I don't know how.
def resnet_model():
model = VGGFace(model = 'resnet50')#model :{resnet50, vgg16, senet50}
xl = model.get_layer('avg_pool').output
x = keras.layers.Flatten(name='flatten')(xl)
x = keras.layers.Dense(4096, activation='relu')(x)
x = keras.layers.Dropout(0.5)(x)
x = keras.layers.Dense(4096, activation='relu')(x)
x = keras.layers.Dropout(0.5)(x)
x = keras.layers.Dense(11, activation='softmax', name='predictions')(x)
model = keras.engine.Model(model.input, outputs = x)
return model
model = resnet_model()
initial_learning_rate = 0.0003
epochs = 20; batch_size = 110
num_steps = train_x.shape[0]//batch_size
learning_rate_fn = tf.keras.optimizers.schedules.PiecewiseConstantDecay(
[3*num_steps, 10*num_steps, 16*num_steps, 25*num_steps],
[1e-4, 1e-5, 1e-6, 1e-7, 5e-7]
)
optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate_fn)
model.compile(loss=custom_loss, optimizer=optimizer, metrics=['accuracy', one_off_accuracy])
model.fit(train_x, train_y, epochs=epochs, batch_size=batch_size, validation_data=(test_x, test_y))
This is an example of result:

Answer 1

There are many regularization methods to help you avoid overfitting your model:
Dropouts:
Randomly disables neurons during the training, in order to force other neurons to be trained as well.
L1/L2 penalties:
Penalizes weights that change dramatically. This tries to ensure that all parameters will be equally taken into consideration when classifying an input.
Random Gaussian Noise at the inputs:
Adds random gaussian noise at the inputs: x = x + r where r is a random normal value from range [-1, 1]. This will confuse your model and prevent it from overfitting into your dataset, because in every epoch, each input will be different.
Label Smoothing:
Instead of saying that a target is 0 or 1, You can smooth those values (e.g. 0.1 & 0.9).
Early Stopping:
This is a quite common technique for avoiding training your model too much. If you notice that your model's loss is decreasing along with the validation's accuracy, then this is a good sign to stop the training, as your model begins to overfit.
K-Fold Cross-Validation:
This is a very strong technique, which ensures that your model is not fed all the time with the same inputs and is not overfitting.
Data Augmentations:
By rotating/shifting/zooming/flipping/padding etc. an image you make sure that your model is forced to train better its parameters and not overfit to the existing dataset.
I am quite sure there are also more techniques to avoid overfitting. This repository contains many examples of how the above techniques are deployed in a dataset:
https://github.com/kochlisGit/Tensorflow-State-of-the-Art-Neural-Networks

Answer 2

You can try incorporate image augmentation in your training, which increases the "sample size" of your data as well as the "diversity" as #Suraj S Jain mentioned. The official tutorial is here: https://www.tensorflow.org/tutorials/images/data_augmentation