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'
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
#!/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