Example #1
0
from dae import ex

@ex.named_config
def best_bars():
    dataset = {
        'name': 'bars',
        'salt_n_pepper': 0.0
    }
    training = {
        'learning_rate': 0.768014586935404
    }
    seed = 459182787
    network_spec = "Fr100"
    net_filename = 'Networks/best_bars_dae.h5'

ex.run(named_configs=['best_bars'])


@ex.named_config
def best_corners():
    dataset = {
        'name': 'corners',
        'salt_n_pepper': 0.0
    }
    training = {
        'learning_rate': 0.0019199822609484764
    }
    seed = 158253144
    network_spec = "Fr100"
    net_filename = 'Networks/best_corners_dae.h5'
Example #2
0
nr_runs_per_dataset = 100
datasets = {
    'bars': 12, 
    'corners': 5,
    'shapes': 3,
    'multi_mnist': 3,
    'mnist_shape': 2,
    'simple_superpos':2
}
db_name = 'binding_via_rc'

# Random search
ex.observers = [MongoObserver.create(db_name=db_name, prefix='random_search')]
for ds, k in datasets.items():
    for i in range(nr_runs_per_dataset):
        ex.run(config_updates={'dataset.name': ds, 'verbose': False, 'em.k': k},
               named_configs=['random_search'])


# Multi-Train Runs
ex.observers = [MongoObserver.create(db_name=db_name, prefix='train_multi')]
for ds, k in datasets.items():
    if ds == "simple_superpos": continue
    for i in range(nr_runs_per_dataset):
        ex.run(config_updates={
            'dataset.name': ds, 
            'dataset.train_set': 'train_multi',
            'em.k': k,
            'em.e_step': 'max',
            'verbose': False}, named_configs=['random_search'])

# MSE-Likelihood Runs
Example #3
0
#!/usr/bin/env python
# coding=utf-8
from __future__ import division, print_function, unicode_literals
from dae import ex


@ex.named_config
def best_bars():
    dataset = {'name': 'bars', 'salt_n_pepper': 0.0}
    training = {'learning_rate': 0.768014586935404}
    seed = 459182787
    network_spec = "Fr100"
    net_filename = 'Networks/best_bars_dae.h5'


ex.run(named_configs=['best_bars'])


@ex.named_config
def best_corners():
    dataset = {'name': 'corners', 'salt_n_pepper': 0.0}
    training = {'learning_rate': 0.0019199822609484764}
    seed = 158253144
    network_spec = "Fr100"
    net_filename = 'Networks/best_corners_dae.h5'


ex.run(named_configs=['best_corners'])


@ex.named_config