semantic_args['n_hidden'] = args['semantic_tensor_n_hidden']

        _semantic_model = semantic_class(**semantic_args)

        combined_args = {
            'w_trainer':_syntactic_model,
            'v_trainer':_semantic_model,
            'vocab_size':vocab_size,
            'indices_in_intersection':list(indices_in_intersection),
            'dimensions':args['dimensions'],
            'w_loss_multiplier':args['w_loss_multiplier'],
            'other_params':args,
            'mode':args['mode']
        }
        if args['simple_joint']:
            model = Joint(**combined_args)
        else:
            combined_args['rho'] = args['rho']
            model = ADMM(**combined_args)

    def save_model(filename=None):
        if filename is None:
            filename = 'model-%d.pkl.gz' % model.k
        fname = os.path.join(args['base_dir'], filename)
        sys.stdout.write('dumping model to %s' % fname)
        sys.stdout.flush()
        with gzip.open(fname, 'wb') as f:
            cPickle.dump(model, f)
        sys.stdout.write('\r')
        sys.stdout.flush()
Exemplo n.º 2
0
        _semantic_model = SimilarityNN(**semantic_args)

        combined_args = {
            'w_trainer':_syntactic_model,
            'v_trainer':_semantic_model,
            'vocab_size':args['vocab_size'],
            'indices_in_intersection':list(indices_in_intersection),
            'dimensions':args['dimensions'],
            'w_loss_multiplier':args['w_loss_multiplier'],
            'other_params':args,
            'mode':args['mode']
        }

        if args['simple_joint']:
            model = Joint(**combined_args)
        else:
            combined_args['rho'] = args['rho']
            model = ADMM(**combined_args)

    def save_model():
        fname = os.path.join(args['base_dir'], 'model-%d.pkl.gz' % model.k)
        sys.stdout.write('dumping model to %s' % fname)
        sys.stdout.flush()
        with gzip.open(fname, 'wb') as f:
            cPickle.dump(model, f)
        sys.stdout.write('\r')
        sys.stdout.flush()

    # save the initial state
    if not model_loaded:
Exemplo n.º 3
0
        _semantic_model = SimilarityNN(**semantic_args)

        combined_args = {
            'w_trainer': _syntactic_model,
            'v_trainer': _semantic_model,
            'vocab_size': args['vocab_size'],
            'indices_in_intersection': list(indices_in_intersection),
            'dimensions': args['dimensions'],
            'w_loss_multiplier': args['w_loss_multiplier'],
            'other_params': args,
            'mode': args['mode']
        }

        if args['simple_joint']:
            model = Joint(**combined_args)
        else:
            combined_args['rho'] = args['rho']
            model = ADMM(**combined_args)

    def save_model():
        fname = os.path.join(args['base_dir'], 'model-%d.pkl.gz' % model.k)
        sys.stdout.write('dumping model to %s' % fname)
        sys.stdout.flush()
        with gzip.open(fname, 'wb') as f:
            cPickle.dump(model, f)
        sys.stdout.write('\r')
        sys.stdout.flush()

    # save the initial state
    if not model_loaded:
Exemplo n.º 4
0
            semantic_args['n_hidden'] = args['semantic_tensor_n_hidden']

        _semantic_model = semantic_class(**semantic_args)

        combined_args = {
            'w_trainer': _syntactic_model,
            'v_trainer': _semantic_model,
            'vocab_size': vocab_size,
            'indices_in_intersection': list(indices_in_intersection),
            'dimensions': args['dimensions'],
            'w_loss_multiplier': args['w_loss_multiplier'],
            'other_params': args,
            'mode': args['mode']
        }
        if args['simple_joint']:
            model = Joint(**combined_args)
        else:
            combined_args['rho'] = args['rho']
            model = ADMM(**combined_args)

    def save_model(filename=None):
        if filename is None:
            filename = 'model-%d.pkl.gz' % model.k
        fname = os.path.join(args['base_dir'], filename)
        sys.stdout.write('dumping model to %s' % fname)
        sys.stdout.flush()
        with gzip.open(fname, 'wb') as f:
            cPickle.dump(model, f)
        sys.stdout.write('\r')
        sys.stdout.flush()