I’m training a simple deep learning model, but it still overfits even after applying dropout and early stopping. Training accuracy is high, but validation performance drops.
import tensorflow as tf
from tensorflow.keras import layers, models
from tensorflow.keras import layers, models
model = models.Sequential([
layers.Dense(128, activation=‘relu’, input_shape=(20,)),
layers.Dropout(0.5),
layers.Dense(64, activation=‘relu’),
layers.Dense(1, activation=‘sigmoid’)
])
model.compile(optimizer=‘adam’,
loss=‘binary_crossentropy’,
metrics=[‘accuracy’])
history = model.fit(X_train, y_train,
validation_data=(X_val, y_val),
epochs=50,
batch_size=32)
What are the common reasons this still happens in practice, and how can it be mitigated beyond basic regularization?
