SYMBOL INDEX (44 symbols across 13 files)

FILE: alexnet/finetune.py
  function main (line 25) | def main(_):

FILE: alexnet/model.py
  class AlexNetModel (line 8) | class AlexNetModel(object):
    method __init__ (line 10) | def __init__(self, num_classes=1000, dropout_keep_prob=0.5):
    method inference (line 14) | def inference(self, x, training=False):
    method loss (line 50) | def loss(self, batch_x, batch_y=None):
    method optimize (line 55) | def optimize(self, learning_rate, train_layers=[]):
    method load_original_weights (line 59) | def load_original_weights(self, session, skip_layers=[]):
  function conv (line 82) | def conv(x, filter_height, filter_width, num_filters, stride_y, stride_x...
  function fc (line 102) | def fc(x, num_in, num_out, name, relu=True):
  function max_pool (line 115) | def max_pool(x, filter_height, filter_width, stride_y, stride_x, name, p...
  function lrn (line 119) | def lrn(x, radius, alpha, beta, name, bias=1.0):
  function dropout (line 122) | def dropout(x, keep_prob):

FILE: alexnet/predict.py
  function main (line 17) | def main(_):

FILE: alexnet/test.py
  function main (line 18) | def main(_):

FILE: resnet/finetune.py
  function main (line 25) | def main(_):

FILE: resnet/model.py
  class ResNetModel (line 24) | class ResNetModel(object):
    method __init__ (line 26) | def __init__(self, is_training, depth=50, num_classes=1000):
    method inference (line 37) | def inference(self, x):
    method loss (line 69) | def loss(self, batch_x, batch_y=None):
    method optimize (line 78) | def optimize(self, learning_rate, train_layers=[]):
    method load_original_weights (line 93) | def load_original_weights(self, session, skip_layers=[]):
  function _get_variable (line 114) | def _get_variable(name, shape, initializer, weight_decay=0.0, dtype='flo...
  function conv (line 125) | def conv(x, ksize, stride, filters_out):
  function bn (line 133) | def bn(x, is_training):
  function stack (line 158) | def stack(x, is_training, num_blocks, stack_stride, block_filters_intern...
  function block (line 166) | def block(x, is_training, block_filters_internal, block_stride):
  function fc (line 195) | def fc(x, num_units_out):
  function contains (line 203) | def contains(target_str, search_arr):

FILE: resnet/predict.py
  function main (line 18) | def main(_):

FILE: resnet/test.py
  function main (line 19) | def main(_):

FILE: utils/preprocessor.py
  class BatchPreprocessor (line 8) | class BatchPreprocessor(object):
    method __init__ (line 10) | def __init__(self, dataset_file_path, num_classes, output_size=[227, 2...
    method shuffle_data (line 35) | def shuffle_data(self):
    method reset_pointer (line 46) | def reset_pointer(self):
    method next_batch (line 52) | def next_batch(self, batch_size):

FILE: vggnet/finetune.py
  function main (line 25) | def main(_):

FILE: vggnet/model.py
  class VggNetModel (line 8) | class VggNetModel(object):
    method __init__ (line 10) | def __init__(self, num_classes=1000, dropout_keep_prob=0.5):
    method inference (line 14) | def inference(self, x, training=False):
    method loss (line 164) | def loss(self, batch_x, batch_y=None):
    method optimize (line 169) | def optimize(self, learning_rate, train_layers=[]):
    method load_original_weights (line 173) | def load_original_weights(self, session, skip_layers=[]):

FILE: vggnet/predict.py
  function main (line 17) | def main(_):

FILE: vggnet/test.py
  function main (line 18) | def main(_):