ENH: model path as cmdline arg, verbose mode

configs/explore.json

@@ -7,8 +7,8 @@
  },
 
  "optimizer" : {
- "name": "adam",
- "epochs" : 100,
+ "name": "momentum",
+ "epochs" : 50,
  "learning_rate" : 1e-2,
  "momentum" : 0.95,
  "decay_rate" : 1.0,
@@ -19,21 +19,21 @@
  },
 
  "model" : {
- "dropout" : 0.0,
+ "dropout" : 0.5,
  "batch_size" : 32,
  "conv_layers" : [
- { "filter_size" : 512,
- "num_filters" : 512,
- "stride" : 2
- },
  { "filter_size" : 256,
- "num_filters" : 512,
- "stride" : 2
+ "num_filters" : 64, 
+ "stride" : 7
+ },
+ { "filter_size" : 128,
+ "num_filters" : 64, 
+ "stride" : 7
  }
  ]
  },
 
  "io" : {
- "output_save_path" : "/deep/group/sudnya/experiments/cnn2-size512_256-num512_512_adam"
+ "output_save_path" : "/deep/group/sudnya/repro/cnn2-size256_128-num64_64_dp0_5_str7"
  }
 }

score.py

@@ -21,17 +21,22 @@ def print_scores(labels, predictions, classes):
 
 def main():
  parser = argparse.ArgumentParser(description="Evaluater Script")
- parser.add_argument("model_path")
+ parser.add_argument("-m", "--model_path", default="~/")
+ parser.add_argument("-v", "--verbose", default=False, action="store_true")
 
- args = parser.parse_args()
+ parsed_arguments = parser.parse_args()
+ arguments = vars(parsed_arguments)
+
+ is_verbose = arguments['verbose']
+ model_path = arguments['model_path']
 
  batch_size = 8
- evaler = test.Evaler(args.model_path,
+ evaler = test.Evaler(model_path, is_verbose,
  batch_size=batch_size)
 
  # TODO, (awni), would be good to simplify loading and
  # not rely on random seed for validation set.
- config_file = os.path.join(args.model_path, "config.json")
+ config_file = os.path.join(model_path, "config.json")
  with open(config_file, 'r') as fid:
  config = json.load(fid)
  data_conf = config['data']

test.py

@@ -9,14 +9,14 @@
 
 class Evaler:
 
- def __init__(self, save_path, batch_size=1):
+ def __init__(self, save_path, is_verbose, batch_size=1):
  config_file = os.path.join(save_path, "config.json")
 
  with open(config_file, 'r') as fid:
  config = json.load(fid)
  config['model']['batch_size'] = batch_size
 
- self.model = network.Network()
+ self.model = network.Network(is_verbose)
  self.graph = tf.Graph()
  self.session = sess = tf.Session(graph=self.graph)
  with self.graph.as_default():