def get_model_mlp(self):
self.dropout = False
self.input_include_probs = {}
self.input_scales = {}
self.weight_decay = False
self.weight_decays = {}
self.l1_weight_decay = False
self.l1_weight_decays = {}
nnet_layers = self.state.layers
input_space_id = self.state.input_space_id
nvis = self.nvis
self.batch_size = self.state.batch_size
# TODO: add input_space as a config option.
input_space = None
# TODO: top_view always False for the moment.
self.topo_view = False
assert nvis is not None
layers = []
for i,layer in enumerate(nnet_layers.values()):
layer = expand(layer)
layer = self.get_layer(layer, i)
layers.append(layer)
# create MLP:
print layers
model = MLP(layers=layers,input_space=input_space,nvis=nvis,
batch_size=self.batch_size)
self.mlp = model
return model
def get_model_mlp(self):
self.dropout = False
self.input_include_probs = {}
self.input_scales = {}
self.weight_decay = False
self.weight_decays = {}
self.l1_weight_decay = False
self.l1_weight_decays = {}
nnet_layers = self.state.layers
input_space_id = self.state.input_space_id
nvis = self.nvis
self.batch_size = self.state.batch_size
# TODO: add input_space as a config option.
input_space = None
# TODO: top_view always False for the moment.
self.topo_view = False
assert nvis is not None
layers = []
for i,layer in enumerate(nnet_layers.values()):
layer = expand(layer)
layer = self.get_layer(layer, i)
layers.append(layer)
# create MLP:
print layers
model = My_MLP(layers=layers,input_space=input_space,nvis=nvis,
batch_size=self.batch_size)
self.mlp = model
return model
for key in config:
setattr(state, key, config[key])
model_type = config.model
print 'Model Type: %s' % model_type
print 'Host: %s' % socket.gethostname()
print 'Command: %s' % ' '.join(sys.argv)
if config.model == 'attention':
model_attention.train_from_scratch(state, channel)
else:
raise NotImplementedError()
def main(state, channel=None):
set_config(config, state)
train_from_scratch(config, state, channel)
if __name__ == '__main__':
args = {}
try:
for arg in sys.argv[1:]:
k, v = arg.split('=')
args[k] = v
except:
print 'args must be like a=X b.c=X'
exit(1)
state = expand(args)
print 'state is :::', state
sys.exit(main(state))
for key in config:
setattr(state, key, config[key])
model_type = config.model
print 'Model Type: %s'%model_type
print 'Host: %s' % socket.gethostname()
print 'Command: %s' % ' '.join(sys.argv)
if config.model == 'attention':
model_attention.train_from_scratch(state, channel)
else:
raise NotImplementedError()
def main(state, channel=None):
set_config(config, state)
train_from_scratch(config, state, channel)
if __name__ == '__main__':
args = {}
try:
for arg in sys.argv[1:]:
k, v = arg.split('=')
args[k] = v
except:
print 'args must be like a=X b.c=X'
exit(1)
state = expand(args)
sys.exit(main(state))
