SYMBOL INDEX (20 symbols across 3 files)

FILE: addnoise.py
  function main (line 13) | def main():

FILE: main.py
  function denoiser_train (line 20) | def denoiser_train(denoiser, lr):
  function denoiser_test (line 28) | def denoiser_test(denoiser):
  function main (line 41) | def main(_):
  function test (line 74) | def test():

FILE: model.py
  function dncnn (line 9) | def dncnn(input, is_training=True, output_channels=3):
  class denoiser (line 26) | class denoiser(object):
    method __init__ (line 27) | def __init__(self, sess, input_c_dim=3, batch_size=128):
    method evaluate (line 47) | def evaluate(self, iter_num, eval_files, noisy_files, summary_writer):
    method train (line 72) | def train(self, eval_files, noisy_files, batch_size, ckpt_dir, epoch, ...
    method save (line 136) | def save(self, iter_num, ckpt_dir, model_name='DnCNN-tensorflow'):
    method load (line 146) | def load(self, checkpoint_dir):
    method test (line 158) | def test(self, eval_files, noisy_files, ckpt_dir, save_dir, temporal):
  class dataset (line 195) | class dataset(object):
    method __init__ (line 196) | def __init__(self,sess):
    method get_batch (line 226) | def get_batch(self):
  function im_read (line 230) | def im_read(filename):
  function get_patches (line 238) | def get_patches(image, num_patches=128, patch_size=64):
  function cal_psnr (line 250) | def cal_psnr(im1, im2): # PSNR function for 0-255 values
  function psnr_scaled (line 255) | def psnr_scaled(im1, im2): # PSNR function for 0-1 values