RNN Text Generation: How to balance training/test lost with validation loss? - tensorflow

Question

I'm working on a short project that involves implementing a character RNN for text generation. My model uses a single LSTM layer with varying units (messing around with between 50 and 500), dropout at a rate of 0.2, and softmax activation. I'm using RMSprop with a learning rate of 0.01.
My issue is that I can't find a good way to characterize the validation loss. I'm using a validation split of 0.3 and I'm finding that the validation loss starts to become constant after only a few epochs (maybe 2-5 or so) while the training loss keeps decreasing. Does validation loss carry much weight in this sort of problem? The purpose of the model is to generate new strings, so quantifying the validation loss with other strings seems... pointless?
It's hard for me to really find the best model since qualitatively I get the sense that the best model is trained for more epochs than it takes for the validation loss to stop changing but also for fewer epochs than it takes for the training loss to start increasing. I would really appreciate any advice you have regarding this problem as well as any general advice about RNN's for text generation, especially regarding dropout and overfitting. Thanks!
This is the code for fitting the model for every epoch. The callback is a custom callback that just prints a few tests. I'm now realizing that history_callback.history['loss'] is probably the training loss isn't it...
for i in range(num_epochs):
history_callback = model.fit(x, y,
batch_size=128,
epochs=1,
callbacks=[print_callback],
validation_split=0.3)
loss_history.append(history_callback.history['loss'])
validation_loss_history.append(history_callback.history['val_loss'])
My intention for this model isn't to replicate sentences from the training data, rather, I'd like to generate sentence from the same distribution that I'm training on.

Answer 1

Yes history_callback.history['loss'] is Training Loss and history_callback.history['val_loss'] is the Validation Loss.
Yes, Validation Loss carries weight in this sort of problem because you just don't want to replicate the sentences which are given during Training but you want to learn the patterns from the Training Data and generate new sentences when it sees a new data.
From the information you mentioned in the question and from the insights identified from comments (thanks to Brian Bartoldson), it is understood that your model is overfitting. In addition to EarlyStopping and dropout, you can try the below mentioned techniques to mitigate overfitting problem.
3.a. Shuffle the Data, by using shuffle=True in model.fit. Code is shown below
3.b. Use recurrent_dropout. For example, If we set the value of Recurrent Dropout as 0.2 in a Recurrent Layer (LSTM), it means that it will consider only 80% of the Time Steps for that Recurrent Layer (LSTM).
3.c. Use Regularization. You can try l1 Regularization or l1_l2 Regularization as well for the arguments, kernel_regularizer, recurrent_regularizer, bias_regularizer, activity_regularizer of the LSTM Layer.
Sample code to use Shuffle, Early Stopping, Recurrent_Dropout, Regularization is shown below:
from tensorflow.keras.regularizers import l2
from tensorflow.keras.models import Sequential
model = Sequential()
Regularizer = l2(0.001)
model.add(tf.keras.layers.LSTM(units = 50, activation='relu',kernel_regularizer=Regularizer ,
recurrent_regularizer=Regularizer , bias_regularizer=Regularizer , activity_regularizer=Regularizer, dropout=0.2, recurrent_dropout=0.3))
callback = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=15)
history_callback = model.fit(x, y,
batch_size=128,
epochs=1,
callbacks=[print_callback, callback],
validation_split=0.3, shuffle = True)
Hope this helps. Happy Learning!