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)
"""
