Can Talos Work with Unsupervised Learning on LSTM/Autoencoder Model

[krwiegold]

Hi, I am trying to use Talos to optimize the hyperparameters on an unsupervised LSTM/Autoencoder model. The model works without Talos. Since I do not have y data (no known labels / dependent variables), so I created my model as follows below. And the data input is called "scaled_data".

set parameters for Talos

p = {'optimizer': ['Nadam', 'Adam', 'sgd'], 'losses': ['binary_crossentropy', 'mse'], 'activation':['relu', 'elu']}

create autoencoder model

def create_model(X_input, y_input, params): autoencoder = Sequential() autoencoder.add(LSTM(12, input_shape=(scaled_data.shape[1], scaled_data.shape[2]), activation=params['activation'], return_sequences=True, kernel_regularizer=tf.keras.regularizers.l2(0.01))) autoencoder.add(LSTM(4, activation=params['activation'])) autoencoder.add(RepeatVector(scaled_data.shape[1])) autoencoder.add(LSTM(4, activation=params['activation'], return_sequences=True)) autoencoder.add(LSTM(12, activation=params['activation'], return_sequences=True)) autoencoder.add(TimeDistributed(Dense(scaled_data.shape[2]))) autoencoder.compile(optimizer=params['optimizer'], loss=params['losses'], metrics=['acc'])

history = autoencoder.fit(X_input, y_input, epochs=10, batch_size=1, validation_split=0.0,
                          callbacks=[EarlyStopping(monitor='acc', patience=3)]).history

return autoencoder, history

scan_object = talos.Scan(x=scaled_data, y=scaled_data, params=p, model=create_model, experiment_name='LSTM')

My error says: TypeError: create_model() takes 3 positional arguments but 5 were given.

How am I passing 5 arguments? Any ideas how to fix this issue? I looked through the documents and other questions, but don't see anything with an unsupervised model. Thank you!

[mikkokotila]

def create_model(X_input, y_input, params) is wrong. You must declare it exactly like the docs explain for the input model.

For example:

def iris_model(x_train, y_train, x_val, y_val, params):

[krwiegold]

My model is unsupervised, so I do not have the "y" dataset. Does Talos only work for supervised models?

[alexcwsmith]

I am also wondering this, @krwiegold did you ever find a way to make this work?

[krwiegold]

@alexcwsmith I could never get it to work unfortunately. I had to give up on talos.

[mikkokotila]

Let's have a look into this. Some of the higher priority items like full support for multi-input models, and distributed experiments have now been completed, so I think this could very well be next. It's a very interesting problem, given there is no truth to optimize for.

[mikkokotila]

@krwiegold @alexcwsmith can you help and share one or two code complete examples in Google Colab where such a model is running without Talos. Also, had you any thoughts about the possible ways to implement the support into Talos.

[alexcwsmith]

Thanks @mikkokotila I'm not a colab user, tried to get this to run in colab for a bit but don't even know the basics so that seems like a steep learning curve to run a simple script... if that is the only way you can run this, I can have someone who knows colab get this in there for you.

The simplest example I think is the VAE example from PyTorch here:

https://github.com/pytorch/examples/tree/main/vae

And as far as possible ways to implement talos with a VAE, simply running a scan to find parameters that minimize the loss, or the KL Divergence, would be a great start.