SYMBOL INDEX (172 symbols across 44 files)

FILE: codes/cdcgan/net.py
  function transpose_conv (line 4) | def transpose_conv(in_channels, out_channels,
  function conv (line 21) | def conv(in_channels, out_channels,
  class Generator (line 38) | class Generator(nn.Module):
    method __init__ (line 39) | def __init__(self, num_class=10, z_dim=100):
    method forward (line 47) | def forward(self, y, z):
  class Discriminator (line 59) | class Discriminator(nn.Module):
    method __init__ (line 60) | def __init__(self, num_class=10):
    method forward (line 68) | def forward(self, x):

FILE: codes/cdcgan/solver.py
  class Solver (line 10) | class Solver():
    method __init__ (line 11) | def __init__(self, args):
    method fit (line 51) | def fit(self):
    method sample (line 113) | def sample(self, global_step=0):
    method save (line 137) | def save(self, ckpt_dir, global_step):
  function denormalize (line 147) | def denormalize(tensor):

FILE: codes/cdcgan/train.py
  function main (line 5) | def main():

FILE: codes/char_rnn/net.py
  class Net (line 4) | class Net(nn.Module):
    method __init__ (line 5) | def __init__(self,
    method forward (line 18) | def forward(self, x):
    method sample (line 29) | def sample(self, prime, length):

FILE: codes/char_rnn/solver.py
  class Solver (line 7) | class Solver():
    method __init__ (line 8) | def __init__(self, args):
    method fit (line 27) | def fit(self):
    method sample (line 50) | def sample(self, length, prime="The"):
    method save (line 71) | def save(self, ckpt_dir, ckpt_name, global_step):

FILE: codes/char_rnn/train.py
  function main (line 5) | def main():

FILE: codes/char_rnn/utils.py
  class Shakespeare (line 5) | class Shakespeare(datasets.LanguageModelingDataset):
    method splits (line 11) | def splits(cls,
    method iters (line 20) | def iters(cls,
  function load_shakespeare (line 34) | def load_shakespeare(batch_size, bptt_len):

FILE: codes/colorization/dataset.py
  class Dataset (line 11) | class Dataset(data.Dataset):
    method __init__ (line 12) | def __init__(self, train, **kwargs):
    method __getitem__ (line 25) | def __getitem__(self, index):
    method __len__ (line 46) | def __len__(self):
    method _prepare_dataset (line 49) | def _prepare_dataset(self, data_root):

FILE: codes/colorization/net.py
  class Net (line 5) | class Net(nn.Module):
    method __init__ (line 6) | def __init__(self):
    method forward (line 30) | def forward(self, x):

FILE: codes/colorization/solver.py
  class Solver (line 10) | class Solver():
    method __init__ (line 11) | def __init__(self, args):
    method fit (line 35) | def fit(self):
    method save (line 55) | def save(self, ckpt_dir, ckpt_name, global_step):

FILE: codes/colorization/train.py
  function main (line 5) | def main():

FILE: codes/colorization/utils.py
  function download_and_uncompress_tarball (line 11) | def download_and_uncompress_tarball(tarball_url, dataset_dir):
  function download_and_convert (line 19) | def download_and_convert(data_root):

FILE: codes/flower_cls/dataset.py
  class Dataset (line 12) | class Dataset(data.Dataset):
    method __init__ (line 17) | def __init__(self, train, **kwargs):
    method __getitem__ (line 37) | def __getitem__(self, index):
    method __len__ (line 49) | def __len__(self):
    method _prepare_dataset (line 52) | def _prepare_dataset(self, data_root):

FILE: codes/flower_cls/net.py
  class Net (line 4) | class Net(nn.Module):
    method __init__ (line 5) | def __init__(self):
    method _make_block (line 24) | def _make_block(self,
    method forward (line 38) | def forward(self, x):

FILE: codes/flower_cls/solver.py
  class Solver (line 8) | class Solver():
    method __init__ (line 9) | def __init__(self, args):
    method fit (line 34) | def fit(self):
    method evaluate (line 59) | def evaluate(self, data):
    method save (line 82) | def save(self, ckpt_dir, ckpt_name, global_step):

FILE: codes/flower_cls/train.py
  function main (line 5) | def main():

FILE: codes/flower_cls/utils.py
  function download_and_uncompress_tarball (line 12) | def download_and_uncompress_tarball(tarball_url, dataset_dir):
  function download_and_convert (line 20) | def download_and_convert(data_root):

FILE: codes/mnist/net.py
  class Net (line 4) | class Net(nn.Module):
    method __init__ (line 5) | def __init__(self):
    method forward (line 22) | def forward(self, x):

FILE: codes/mnist/train.py
  function evaluate (line 8) | def evaluate(net, loader, device):
  function train (line 27) | def train(args):
  function main (line 76) | def main():

FILE: codes/show_and_tell/dataset.py
  class CaptionDataset (line 9) | class CaptionDataset(torch.utils.data.Dataset):
    method __init__ (line 16) | def __init__(self, train, **kwargs):
    method __getitem__ (line 47) | def __getitem__(self, index):
    method __len__ (line 61) | def __len__(self):
    method indices_to_string (line 64) | def indices_to_string(self, indices, words=False):
  function collate_fn (line 92) | def collate_fn(data):
  function get_caption_dataset (line 119) | def get_caption_dataset(train,

FILE: codes/show_and_tell/net.py
  class Net (line 6) | class Net(nn.Module):
    method __init__ (line 7) | def __init__(self, TEXT,
    method forward (line 19) | def forward(self, image, caption, lengths):
    method sample (line 25) | def sample(self, image):
  class Encoder (line 32) | class Encoder(nn.Module):
    method __init__ (line 33) | def __init__(self, embed_dim):
    method forward (line 44) | def forward(self, x):
  class Decoder (line 48) | class Decoder(nn.Module):
    method __init__ (line 49) | def __init__(self, TEXT,
    method forward (line 63) | def forward(self, feature, caption, lengths):
    method sample (line 76) | def sample(self, feature):

FILE: codes/show_and_tell/solver.py
  class Solver (line 9) | class Solver():
    method __init__ (line 10) | def __init__(self, args):
    method fit (line 36) | def fit(self):
    method save (line 64) | def save(self, ckpt_dir, ckpt_name, global_step):

FILE: codes/show_and_tell/train.py
  function main (line 5) | def main():

FILE: codes/show_and_tell/visdomX.py
  class VisdomX (line 4) | class VisdomX:
    method __init__ (line 5) | def __init__(self):
    method add_scalars (line 9) | def add_scalars(self,
    method add_text (line 34) | def add_text(self, text):

FILE: codes/show_attend_and_tell/dataset.py
  class CaptionDataset (line 10) | class CaptionDataset(torch.utils.data.Dataset):
    method __init__ (line 17) | def __init__(self, train, **kwargs):
    method __getitem__ (line 65) | def __getitem__(self, index):
    method __len__ (line 79) | def __len__(self):
    method indices_to_string (line 82) | def indices_to_string(self, indices, words=False):
  function collate_fn (line 110) | def collate_fn(data):
  function get_caption_dataset (line 137) | def get_caption_dataset(train,

FILE: codes/show_attend_and_tell/net.py
  class Net (line 6) | class Net(nn.Module):
    method __init__ (line 7) | def __init__(self, TEXT,
    method forward (line 21) | def forward(self, image, caption, lengths):
    method sample (line 27) | def sample(self, image):
  class Encoder (line 34) | class Encoder(nn.Module):
    method __init__ (line 35) | def __init__(self, embed_dim):
    method forward (line 44) | def forward(self, x):
  class AttentionDecoder (line 48) | class AttentionDecoder(nn.Module):
    method __init__ (line 49) | def __init__(self, TEXT,
    method _attention (line 74) | def _attention(self, feature, feature_proj, hx):
    method forward (line 87) | def forward(self, feature, caption, lengths):
    method sample (line 124) | def sample(self, feature):

FILE: codes/show_attend_and_tell/solver.py
  class Solver (line 7) | class Solver():
    method __init__ (line 8) | def __init__(self, args):
    method fit (line 39) | def fit(self):
    method save (line 78) | def save(self, ckpt_dir, ckpt_name, global_step):

FILE: codes/show_attend_and_tell/train.py
  function main (line 5) | def main():

FILE: codes/show_attend_and_tell/visdomX.py
  class VisdomX (line 4) | class VisdomX:
    method __init__ (line 5) | def __init__(self):
    method add_scalars (line 9) | def add_scalars(self,
    method add_text (line 34) | def add_text(self, text):

FILE: codes/style_transfer/train.py
  function mse (line 7) | def mse(feat1, feat2):
  function gram_matrix (line 11) | def gram_matrix(matrix):
  function single_layer_style_loss (line 18) | def single_layer_style_loss(X_feat, style_feat):
  function single_layer_content_loss (line 27) | def single_layer_content_loss(X_feat, content_feat):
  function total_variance_loss (line 31) | def total_variance_loss(image):
  function fit (line 38) | def fit(X, content, style, device, args):
  function main (line 79) | def main(args):

FILE: codes/style_transfer/utils.py
  function prepare_images (line 8) | def prepare_images(content_path, style_path,
  function save_image (line 36) | def save_image(tensor, filename):

FILE: codes/style_transfer/vgg.py
  class VGGNet (line 5) | class VGGNet(nn.Module):
    method __init__ (line 11) | def __init__(self):
    method forward (line 15) | def forward(self, x, phase):

FILE: codes/super_resolution/dataset.py
  class Dataset (line 10) | class Dataset(data.Dataset):
    method __init__ (line 11) | def __init__(self, scale, train, **kwargs):
    method __getitem__ (line 28) | def __getitem__(self, index):
    method __len__ (line 41) | def __len__(self):
    method _prepare_dataset (line 44) | def _prepare_dataset(self, data_root):

FILE: codes/super_resolution/net.py
  class Net (line 6) | class Net(nn.Module):
    method __init__ (line 7) | def __init__(self, scale):
    method forward (line 24) | def forward(self, x):

FILE: codes/super_resolution/solver.py
  class Solver (line 8) | class Solver():
    method __init__ (line 9) | def __init__(self, args):
    method fit (line 35) | def fit(self):
    method evaluate (line 56) | def evaluate(self, global_step):
    method save (line 81) | def save(self, ckpt_dir, ckpt_name, global_step):
  function psnr (line 87) | def psnr(im1, im2):

FILE: codes/super_resolution/train.py
  function main (line 5) | def main():

FILE: codes/super_resolution/utils.py
  function download_and_uncompress_tarball (line 11) | def download_and_uncompress_tarball(tarball_url, dataset_dir):
  function download_and_convert (line 19) | def download_and_convert(data_root):

FILE: codes/text_cls/net.py
  class Net (line 3) | class Net(nn.Module):
    method __init__ (line 4) | def __init__(self, TEXT,
    method forward (line 25) | def forward(self, x):

FILE: codes/text_cls/solver.py
  class Solver (line 7) | class Solver():
    method __init__ (line 8) | def __init__(self, args):
    method fit (line 35) | def fit(self):
    method evaluate (line 59) | def evaluate(self, iters):
    method save (line 78) | def save(self, ckpt_dir, ckpt_name, global_step):

FILE: codes/text_cls/train.py
  function main (line 5) | def main():

FILE: codes/text_cls/utils.py
  function _iters (line 5) | def _iters(batch_size,
  function load_sst (line 28) | def load_sst(batch_size, max_vocab, fine_grained=True):

FILE: codes/utilities/net.py
  class Net (line 4) | class Net(nn.Module):
    method __init__ (line 5) | def __init__(self):
    method forward (line 22) | def forward(self, x):

FILE: codes/utilities/train.py
  function evaluate (line 12) | def evaluate(net, loader, device):
  function train (line 31) | def train(args):
  function main (line 96) | def main():

FILE: codes/utilities/visdomX.py
  class VisdomX (line 4) | class VisdomX:
    method __init__ (line 5) | def __init__(self):
    method add_scalars (line 9) | def add_scalars(self,
    method add_text (line 34) | def add_text(self, text):