Unexpected overfitting in TensorFlow model despite early stopping and regularization
I'm trying to configure I'm performance testing and I'm prototyping a solution and I'm stuck on something that should probably be simple... I'm training a neural network using TensorFlow 2.9, and I keep encountering overfitting issues despite implementing early stopping and L2 regularization. My model architecture includes two dense layers with ReLU activation and dropout, but the validation loss diverges from the training loss after a few epochs. Here's the code I'm using to build and train my model: ```python import tensorflow as tf from tensorflow import keras from tensorflow.keras import layers # Load and preprocess data (x_train, y_train), (x_val, y_val) = keras.datasets.mnist.load_data() x_train = x_train.astype('float32') / 255 x_val = x_val.astype('float32') / 255 x_train = x_train.reshape((x_train.shape[0], 28 * 28)) x_val = x_val.reshape((x_val.shape[0], 28 * 28)) # Build model model = keras.Sequential([ layers.Dense(128, activation='relu', input_shape=(28 * 28,)), layers.Dropout(0.2), layers.Dense(64, activation='relu'), layers.Dense(10, activation='softmax') ]) # Compile model with L2 regularization model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # Set up early stopping early_stopping = keras.callbacks.EarlyStopping(monitor='val_loss', patience=3) # Train model history = model.fit(x_train, y_train, epochs=30, validation_data=(x_val, y_val), callbacks=[early_stopping]) ``` After training, my training accuracy reaches around 98%, but validation accuracy stagnates at about 85% after the third epoch. The training loss continuously decreases, while the validation loss starts to increase. I've tried adjusting the dropout rate from 0.2 to 0.5, but it didn't yield any improvement. Additionally, I've ensured my data is normalized correctly, and I've even tried using data augmentation techniques, but the overfitting persists. Does anyone have suggestions on how to better manage overfitting in this scenario? Are there specific configurations or techniques in TensorFlow that I might be overlooking? How would you solve this? I recently upgraded to Python latest. Any suggestions would be helpful. I appreciate any insights! For context: I'm using Python on Debian. Thanks for your help in advance! I'm on Ubuntu 20.04 using the latest version of Python. Any pointers in the right direction?