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notebook.community

Model Notebook



In [1]:

    
lerning_rate = .5
dataset = get_default_dataset()



In [2]:

    
# train
model = Model()
model.fit(dataset.x, dataset.y)
score = model.mean_square_error()

Optimizer Notebook



In [1]:

    
model = Notebook('model.ipynb')

score = []
for lerning_rate in [.001, .01, .1]:
    model.ns['lerning_rate'] = lerning_rate
    model.ns['dataset'] = new_dataset
    model.run_tag('train')
    score.append(model.ns['score'])

Looper Notebook



In [1]:

    
parser = Notebook('parser.ipynb')

data = []
for filename in os.listdir():
    parser.ns['filename'] = filename
    parser.run_all(blacklist=['init_filename'])
    data.append(parser.ns['result'])
    # just in case:
    parser.restart()

Parser Notebook



In [1]:

    
# init_filename
filename = 'file0.json'

Here I do something with my json that can be very well documented and easily debugged

...

Take it for what it's worth