def train_nipscnn_ns(): params = copy.deepcopy(default_params) params['save_params']['dbname'] = 'deepretina' params['save_params']['collname'] = stim_type params['save_params']['exp_id'] = 'trainval0' base.get_params() base.train_from_params(**params)
'labelfunc': 'labels', 'train_q': None, 'test_q': None, 'split_by': 'labels', } res = compute_metric_base(layer_features, meta, category_eval_spec) res.pop('split_results') retval['imagenet_%s' % layer] = res return retval if __name__ == '__main__': """ Illustrates how to run the configured model using tfutils """ base.get_params() m = ImageNetClassificationExperiment() params = m.setup_params() base.test_from_params(**params) """ exp='exp_reg' batch=50 crop=224 for iteration in [10000, 20000, 40000]: print("Running imagenet model at step %s" % iteration) base.get_params() m = ImageNetClassificationExperiment('exp_reg', iteration, 32, 224) params = m.setup_params() base.test_from_params(**params) """