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))