merge

configs/test.json

@@ -0,0 +1,63 @@
+{
+ "seed" : 4337,
+
+ "data" : {
+ "path" : "/deep/group/med/alivecor/training2017",
+ "seed" : 2016,
+ "augment" : true,
+ "random_noise": true,
+ "random_samples": 200
+ },
+
+ "optimizer" : {
+ "name" : "momentum",
+ "epochs" : 50,
+ "learning_rate" : 1e-3,
+ "momentum" : 0.95,
+ "decay_rate" : 1.0,
+ "decay_steps" : 2000
+ },
+
+ "model" : {
+ "dropout" : 0.0,
+ "batch_size" : 32,
+ "conv_layers" : [
+ { "filter_size" : 64,
+ "num_filters" : 32,
+ "stride" : 2
+ },
+ { "filter_size" : 64,
+ "num_filters" : 32,
+ "stride" : 2
+ },
+ { "filter_size" : 64,
+ "num_filters" : 32,
+ "stride" : 2
+ },
+ { "filter_size" : 64,
+ "num_filters" : 32,
+ "stride" : 2
+ },
+ { "filter_size" : 64,
+ "num_filters" : 32,
+ "stride" : 2
+ },
+ { "filter_size" : 64,
+ "num_filters" : 32,
+ "stride" : 2
+ },
+ { "filter_size" : 64,
+ "num_filters" : 32,
+ "stride" : 2
+ },
+ { "filter_size" : 64,
+ "num_filters" : 32,
+ "stride" : 2
+ }
+ ]
+ },
+
+ "io" : {
+ "output_save_path" : "/deep/group/sudnya/test_noise-200"
+ }
+}

configs/train.json

@@ -4,7 +4,9 @@
  "data" : {
  "path" : "/deep/group/med/alivecor/training2017",
  "seed" : 2016,
- "augment" : true
+ "augment" : true,
+ "random_noise": true,
+ "random_samples": 200
  },
 
  "optimizer" : {

loader.py

@@ -29,7 +29,7 @@ class Loader:
 
  def __init__(self, data_path, batch_size,
  val_frac=0.2, seed=None,
- augment=False):
+ augment=False, random_noise=False, random_samples=200):
  """
  :param data_path: path to the training and validation files
  :param batch_size: size of the minibatches to train on
@@ -48,7 +48,7 @@ def __init__(self, data_path, batch_size,
  self.batch_size = batch_size
  self.augment = augment
 
- self._train, self._val = load_all_data(data_path, val_frac)
+ self._train, self._val = load_all_data(data_path, val_frac, random_noise, random_samples)
  logger.debug("Training set has " + str(len(self._train)) + " samples")
  logger.debug("Validation set has " + str(len(self._val)) + " samples")
 
@@ -177,7 +177,20 @@ def transform(ecg):
 
  return ecg * flip * scale
 
-def load_all_data(data_path, val_frac):
+
+def add_random_noise_samples(sample_count):
+ retVal = []
+ for i in range(0, sample_count):
+ length_window = random.randint(3000, 18000)
+ logger.debug("Random window length " + str(length_window))
+ retVal.append((np.random.randint(low=-100, high=100, size=(length_window), dtype=np.int16), 'N'))
+ logger.debug("First entry of random samples" + str(retVal[0]))
+
+ return retVal
+
+
+
+def load_all_data(data_path, val_frac, train_noise, random_samples):
  """
  Returns tuple of training and validation sets. Each set
  will contain a list of pairs of raw ecg and the
@@ -193,12 +206,18 @@ def load_all_data(data_path, val_frac):
  for record, label in records:
  ecg_file = os.path.join(data_path, record + ".mat")
  ecg = load_ecg_mat(ecg_file)
+ #logger.info( "ecg " + str(ecg.size))
  all_records.append((ecg, label))
 
  # Shuffle before train/val split
  random.shuffle(all_records)
  cut = int(len(all_records) * val_frac)
  train, val = all_records[cut:], all_records[:cut]
+ logger.info(len(train))
+ logger.info(train[0])
+ if train_noise:
+ logger.info("Adding random noise samples to training set")
+ t = add_random_noise_samples(random_samples)
  return train, val
 
 def load_ecg_mat(ecg_file):
@@ -226,7 +245,7 @@ def main():
  logging.basicConfig(level=logging.INFO)
 
  random.seed(2016)
- ldr = Loader(data_path, batch_size)
+ ldr = Loader(data_path, batch_size, random_noise=True, random_samples=200)
  logger.info("Length of training set {}".format(len(list(ldr.train))))
  logger.info("Length of validation set {}".format(len(ldr.val)))
  logger.info("Output dimension {}".format(ldr.output_dim))

train.py

@@ -92,7 +92,9 @@ def main(argv=None):
  data_loader = loader.Loader(config['data']['path'],
  config['model']['batch_size'],
  seed=config['data']['seed'],
- augment=config['data'].get('augment', False))
+ augment=config['data'].get('augment', False),
+ random_noise=config['data'].get('random_noise', False),
+ random_samples=config['data'].get('random_samples', 0))
 
  model = network.Network(is_verbose)