layer1_yaml = open('dae_l1.yaml', 'r').read()
hyper_params_l1 = {'train_stop' : 50000,
                   'batch_size' : 100,
                   'monitoring_batches' : 5,
                   'nhid' : 500,
                   'max_epochs' : 10,
                   'save_path' : '.'}
layer1_yaml = layer1_yaml % (hyper_params_l1)
print layer1_yaml

from pylearn2.config import yaml_parse
train = yaml_parse.load(layer1_yaml)
train.main_loop()

layer2_yaml = open('dae_l2.yaml', 'r').read()
hyper_params_l2 = {'train_stop' : 50000,
                   'batch_size' : 100,
                   'monitoring_batches' : 5,
                   'nvis' : hyper_params_l1['nhid'],
                   'nhid' : 500,
                   'max_epochs' : 10,
                   'save_path' : '.'}
layer2_yaml = layer2_yaml % (hyper_params_l2)
print layer2_yaml

train = yaml_parse.load(layer2_yaml)
train.main_loop()

mlp_yaml = open('dae_mlp.yaml', 'r').read()
hyper_params_mlp = {'train_stop' : 50000,
                    'valid_stop' : 60000,
                    'batch_size' : 100,
                    'max_epochs' : 50,
                    'save_path' : '.'}
mlp_yaml = mlp_yaml % (hyper_params_mlp)
print mlp_yaml

train = yaml_parse.load(mlp_yaml)
train.main_loop()