Hello,
can someone please suggest me about above learning curve ?
Does you think this loss curve looks good ? if yes then why MAE is noisy on validation dataset ? How can i reduce the noise ?
I have around 60K samples for training, 5K for validation and 5K for testing.
My sample code looks as below.
def createModel():
model = models.Sequential()
model.add(layers.Dense(11, activation=‘relu’, input_shape=(X_train.shape[1],)))
model.add(layers.Dense(11, activation=‘relu’))
model.add(layers.Dense(1))
return model
model = createModel()
model.compile(optimizer=‘rmsprop’, loss=‘mse’, metrics=[‘mae’])
history = model.fit(X_train,
y_train,
epochs=25,
batch_size=25,
validation_data=(X_val, y_val))