minimal_talos_example.py

import talos
from keras.models import Sequential
from keras.layers import Dense

def minimal():

    x, y = talos.templates.datasets.iris()

    p = {'activation':['relu', 'elu'],
         'optimizer': ['Nadam', 'Adam'],
         'losses': ['categorical_crossentropy'],
         'batch_size': [20,30,40],
         'epochs': [10,20]}

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

        model = Sequential()
        model.add(Dense(32, input_dim=4, activation=params['activation']))
        model.add(Dense(3, activation='softmax'))
        model.compile(optimizer=params['optimizer'], loss=params['losses'])

        out = model.fit(x_train, y_train,
                         batch_size=params['batch_size'],
                         epochs=params['epochs'],
                         validation_data=(x_val, y_val),
                         verbose=0)

        return out, model

    scan_object = talos.Scan(x, y, model=iris_model, params=p, experiment_name='iris', fraction_limit=0.1)

    return scan_object

I think the call ta.datasets.iris() changed to ta.templates.datasets.iris() and should be updated.

Updated the script to work as of today. @mikkokotila, please pull or modify accordingly - thanks!
https://gist.github.com/ronammar/a62fe679ef156135faf5b171c39cd47e

What happens if you don't pass x_val and y_val (Like in this example)

You have to pass it.

How do i use this with the keras image data generator?

Have a look at the generator example in the docs.

@blu3r4y @ronammar @dileep3004 thanks for the corrections. It's now updated to the latest API.