SYMBOL INDEX (197 symbols across 24 files)

FILE: a2/sgd.py
  function load_saved_params (line 12) | def load_saved_params():
  function save_params (line 34) | def save_params(iter, params):
  function sgd (line 41) | def sgd(f, x0, step, iterations, postprocessing=None, useSaved=False,
  function sanity_check (line 114) | def sanity_check():

FILE: a2/utils/gradcheck.py
  function gradcheck_naive (line 8) | def gradcheck_naive(f, x, gradientText):
  function grad_tests_softmax (line 60) | def grad_tests_softmax(skipgram, dummy_tokens, dummy_vectors, dataset):
  function grad_tests_negsamp (line 137) | def grad_tests_negsamp(skipgram, dummy_tokens, dummy_vectors, dataset, n...

FILE: a2/utils/treebank.py
  class StanfordSentiment (line 9) | class StanfordSentiment:
    method __init__ (line 10) | def __init__(self, path=None, tablesize = 1000000):
    method tokens (line 17) | def tokens(self):
    method sentences (line 49) | def sentences(self):
    method numSentences (line 71) | def numSentences(self):
    method allSentences (line 78) | def allSentences(self):
    method getRandomContext (line 95) | def getRandomContext(self, C=5):
    method sent_labels (line 113) | def sent_labels(self):
    method dataset_split (line 150) | def dataset_split(self):
    method getRandomTrainSentence (line 168) | def getRandomTrainSentence(self):
    method categorify (line 173) | def categorify(self, label):
    method getDevSentences (line 185) | def getDevSentences(self):
    method getTestSentences (line 188) | def getTestSentences(self):
    method getTrainSentences (line 191) | def getTrainSentences(self):
    method getSplitSentences (line 194) | def getSplitSentences(self, split=0):
    method sampleTable (line 198) | def sampleTable(self):
    method rejectProb (line 230) | def rejectProb(self):
    method sampleTokenIdx (line 247) | def sampleTokenIdx(self):

FILE: a2/utils/utils.py
  function normalizeRows (line 5) | def normalizeRows(x):
  function softmax (line 15) | def softmax(x):

FILE: a2/word2vec.py
  function sigmoid (line 11) | def sigmoid(x):
  function naiveSoftmaxLossAndGradient (line 27) | def naiveSoftmaxLossAndGradient(
  function getNegativeSamples (line 99) | def getNegativeSamples(outsideWordIdx, dataset, K):
  function negSamplingLossAndGradient (line 111) | def negSamplingLossAndGradient(
  function skipgram (line 163) | def skipgram(currentCenterWord, windowSize, outsideWords, word2Ind,
  function word2vec_sgd_wrapper (line 230) | def word2vec_sgd_wrapper(word2vecModel, word2Ind, wordVectors, dataset,
  function test_sigmoid (line 253) | def test_sigmoid():
  function getDummyObjects (line 261) | def getDummyObjects():
  function test_naiveSoftmaxLossAndGradient (line 283) | def test_naiveSoftmaxLossAndGradient():
  function test_negSamplingLossAndGradient (line 299) | def test_negSamplingLossAndGradient():
  function test_skipgram (line 315) | def test_skipgram():
  function test_word2vec (line 331) | def test_word2vec():

FILE: a3/parser_model.py
  class ParserModel (line 16) | class ParserModel(nn.Module):
    method __init__ (line 33) | def __init__(self, embeddings, n_features=36,
    method embedding_lookup (line 84) | def embedding_lookup(self, w):
    method forward (line 122) | def forward(self, w):
  function check_embedding (line 172) | def check_embedding():
  function check_forward (line 178) | def check_forward():

FILE: a3/parser_transitions.py
  class PartialParse (line 12) | class PartialParse(object):
    method __init__ (line 13) | def __init__(self, sentence):
    method parse_step (line 43) | def parse_step(self, transition):
    method parse (line 72) | def parse(self, transitions):
  function minibatch_parse (line 86) | def minibatch_parse(sentences, model, batch_size):
  function test_step (line 149) | def test_step(name, transition, stack, buf, deps,
  function test_parse_step (line 166) | def test_parse_step():
  function test_parse (line 178) | def test_parse():
  class DummyModel (line 193) | class DummyModel(object):
    method __init__ (line 196) | def __init__(self, mode = "unidirectional"):
    method predict (line 199) | def predict(self, partial_parses):
    method unidirectional_predict (line 207) | def unidirectional_predict(self, partial_parses):
    method interleave_predict (line 214) | def interleave_predict(self, partial_parses):
  function test_dependencies (line 220) | def test_dependencies(name, deps, ex_deps):
  function test_minibatch_parse (line 227) | def test_minibatch_parse():

FILE: a3/run.py
  function train (line 30) | def train(parser, train_data, dev_data, output_path, batch_size=1024, n_...
  function train_for_epoch (line 71) | def train_for_epoch(parser, train_data, dev_data, optimizer, loss_func, ...

FILE: a3/utils/general_utils.py
  function get_minibatches (line 14) | def get_minibatches(data, minibatch_size, shuffle=True):
  function _minibatch (line 52) | def _minibatch(data, minibatch_idx):
  function test_all_close (line 56) | def test_all_close(name, actual, expected):

FILE: a3/utils/parser_utils.py
  class Config (line 27) | class Config(object):
  class Parser (line 42) | class Parser(object):
    method __init__ (line 45) | def __init__(self, dataset):
    method vectorize (line 97) | def vectorize(self, examples):
    method extract_features (line 111) | def extract_features(self, stack, buf, arcs, ex):
    method get_oracle (line 171) | def get_oracle(self, stack, buf, ex):
    method create_instances (line 199) | def create_instances(self, examples):
    method legal_labels (line 233) | def legal_labels(self, stack, buf):
    method parse (line 239) | def parse(self, dataset, eval_batch_size=5000):
  class ModelWrapper (line 269) | class ModelWrapper(object):
    method __init__ (line 270) | def __init__(self, parser, dataset, sentence_id_to_idx):
    method predict (line 275) | def predict(self, partial_parses):
  function read_conll (line 290) | def read_conll(in_file, lowercase=False, max_example=None):
  function build_dict (line 312) | def build_dict(keys, n_max=None, offset=0):
  function punct (line 322) | def punct(language, pos):
  function minibatches (line 342) | def minibatches(data, batch_size):
  function load_and_preprocess_data (line 350) | def load_and_preprocess_data(reduced=True):
  class AverageMeter (line 403) | class AverageMeter(object):
    method __init__ (line 405) | def __init__(self):
    method reset (line 408) | def reset(self):
    method update (line 414) | def update(self, val, n=1):

FILE: a4/model_embeddings.py
  class ModelEmbeddings (line 15) | class ModelEmbeddings(nn.Module):
    method __init__ (line 19) | def __init__(self, embed_size, vocab):

FILE: a4/nmt_model.py
  class NMT (line 24) | class NMT(nn.Module):
    method __init__ (line 30) | def __init__(self, embed_size, hidden_size, vocab, dropout_rate=0.2):
    method forward (line 92) | def forward(self, source: List[List[str]], target: List[List[str]]) ->...
    method encode (line 131) | def encode(self, source_padded: torch.Tensor, source_lengths: List[int...
    method decode (line 191) | def decode(self, enc_hiddens: torch.Tensor, enc_masks: torch.Tensor,
    method step (line 270) | def step(self, Ybar_t: torch.Tensor,
    method generate_sent_masks (line 371) | def generate_sent_masks(self, enc_hiddens: torch.Tensor, source_length...
    method beam_search (line 387) | def beam_search(self, src_sent: List[str], beam_size: int=5, max_decod...
    method device (line 479) | def device(self) -> torch.device:
    method load (line 485) | def load(model_path: str):
    method save (line 496) | def save(self, path: str):

FILE: a4/run.py
  function evaluate_ppl (line 64) | def evaluate_ppl(model, dev_data, batch_size=32):
  function compute_corpus_level_bleu_score (line 94) | def compute_corpus_level_bleu_score(references: List[List[str]], hypothe...
  function train (line 114) | def train(args: Dict):
  function decode (line 277) | def decode(args: Dict[str, str]):
  function beam_search (line 313) | def beam_search(model: NMT, test_data_src: List[List[str]], beam_size: i...
  function main (line 336) | def main():

FILE: a4/sanity_check.py
  function reinitialize_layers (line 44) | def reinitialize_layers(model):
  function generate_outputs (line 60) | def generate_outputs(model, source, target, vocab):
  function question_1d_sanity_check (line 104) | def question_1d_sanity_check(model, src_sents, tgt_sents, vocab):
  function question_1e_sanity_check (line 137) | def question_1e_sanity_check(model, src_sents, tgt_sents, vocab):
  function question_1f_sanity_check (line 175) | def question_1f_sanity_check(model, src_sents, tgt_sents, vocab):
  function sanity_read_corpus (line 214) | def sanity_read_corpus(file_path, source):
  function main (line 231) | def main():

FILE: a4/utils.py
  function pad_sents (line 24) | def pad_sents(sents, pad_token):
  function read_corpus (line 46) | def read_corpus(file_path, source, vocab_size=2500):
  function autograder_read_corpus (line 69) | def autograder_read_corpus(file_path, source):
  function batch_iter (line 86) | def batch_iter(data, batch_size, shuffle=False):

FILE: a4/vocab.py
  class VocabEntry (line 32) | class VocabEntry(object):
    method __init__ (line 36) | def __init__(self, word2id=None):
    method __getitem__ (line 51) | def __getitem__(self, word):
    method __contains__ (line 59) | def __contains__(self, word):
    method __setitem__ (line 66) | def __setitem__(self, key, value):
    method __len__ (line 71) | def __len__(self):
    method __repr__ (line 77) | def __repr__(self):
    method id2word (line 83) | def id2word(self, wid):
    method add (line 90) | def add(self, word):
    method words2indices (line 102) | def words2indices(self, sents):
    method indices2words (line 113) | def indices2words(self, word_ids):
    method to_input_tensor (line 120) | def to_input_tensor(self, sents: List[List[str]], device: torch.device...
    method from_corpus (line 135) | def from_corpus(corpus, size, freq_cutoff=2):
    method from_subword_list (line 153) | def from_subword_list(subword_list):
  class Vocab (line 160) | class Vocab(object):
    method __init__ (line 163) | def __init__(self, src_vocab: VocabEntry, tgt_vocab: VocabEntry):
    method build (line 172) | def build(src_sents, tgt_sents) -> 'Vocab':
    method save (line 186) | def save(self, file_path):
    method load (line 194) | def load(file_path):
    method __repr__ (line 205) | def __repr__(self):
  function get_vocab_list (line 212) | def get_vocab_list(file_path, source, vocab_size):

FILE: a5/mingpt-demo/mingpt/model.py
  class GPTConfig (line 19) | class GPTConfig:
    method __init__ (line 25) | def __init__(self, vocab_size, block_size, **kwargs):
  class GPT1Config (line 31) | class GPT1Config(GPTConfig):
  class CausalSelfAttention (line 37) | class CausalSelfAttention(nn.Module):
    method __init__ (line 44) | def __init__(self, config):
    method forward (line 61) | def forward(self, x, layer_past=None):
  class Block (line 81) | class Block(nn.Module):
    method __init__ (line 84) | def __init__(self, config):
    method forward (line 96) | def forward(self, x):
  class GPT (line 101) | class GPT(nn.Module):
    method __init__ (line 104) | def __init__(self, config):
    method get_block_size (line 122) | def get_block_size(self):
    method _init_weights (line 125) | def _init_weights(self, module):
    method configure_optimizers (line 134) | def configure_optimizers(self, train_config):
    method forward (line 180) | def forward(self, idx, targets=None):

FILE: a5/mingpt-demo/mingpt/trainer.py
  class TrainerConfig (line 19) | class TrainerConfig:
    method __init__ (line 35) | def __init__(self, **kwargs):
  class Trainer (line 39) | class Trainer:
    method __init__ (line 41) | def __init__(self, model, train_dataset, test_dataset, config):
    method save_checkpoint (line 53) | def save_checkpoint(self):
    method train (line 59) | def train(self):

FILE: a5/mingpt-demo/mingpt/utils.py
  function set_seed (line 7) | def set_seed(seed):
  function top_k_logits (line 13) | def top_k_logits(logits, k):
  function sample (line 20) | def sample(model, x, steps, temperature=1.0, sample=False, top_k=None):

FILE: a5/src/attention.py
  class CausalSelfAttention (line 10) | class CausalSelfAttention(nn.Module):
    method __init__ (line 17) | def __init__(self, config):
    method forward (line 34) | def forward(self, x, layer_past=None):
  class SynthesizerAttention (line 71) | class SynthesizerAttention(nn.Module):
    method __init__ (line 72) | def __init__(self, config):
    method forward (line 97) | def forward(self, x, layer_past=None):

FILE: a5/src/dataset.py
  class NameDataset (line 24) | class NameDataset(Dataset):
    method __init__ (line 25) | def __init__(self, pretraining_dataset, data):
    method __len__ (line 33) | def __len__(self):
    method __getitem__ (line 37) | def __getitem__(self, idx):
  class CharCorruptionDataset (line 144) | class CharCorruptionDataset(Dataset):
    method __init__ (line 145) | def __init__(self, data, block_size):
    method __len__ (line 165) | def __len__(self):
    method __getitem__ (line 169) | def __getitem__(self, idx):

FILE: a5/src/model.py
  class GPTConfig (line 20) | class GPTConfig:
    method __init__ (line 27) | def __init__(self, vocab_size, block_size, synthesizer=False, **kwargs):
  class GPT1Config (line 34) | class GPT1Config(GPTConfig):
  class Block (line 40) | class Block(nn.Module):
    method __init__ (line 43) | def __init__(self, config):
    method forward (line 60) | def forward(self, x):
  class GPT (line 65) | class GPT(nn.Module):
    method __init__ (line 68) | def __init__(self, config):
    method _init_weights (line 86) | def _init_weights(self, module):
    method get_block_size (line 95) | def get_block_size(self):
    method forward (line 98) | def forward(self, idx, targets=None):
  class CustomLayerNorm (line 117) | class CustomLayerNorm(nn.Module):

FILE: a5/src/trainer.py
  class TrainerConfig (line 21) | class TrainerConfig:
    method __init__ (line 37) | def __init__(self, **kwargs):
  class Trainer (line 41) | class Trainer:
    method __init__ (line 43) | def __init__(self, model, train_dataset, test_dataset, config):
    method save_checkpoint (line 55) | def save_checkpoint(self):
    method train (line 61) | def train(self):

FILE: a5/src/utils.py
  function set_seed (line 12) | def set_seed(seed):
  function top_k_logits (line 18) | def top_k_logits(logits, k):
  function sample (line 25) | def sample(model, x, steps, temperature=1.0, sample=False, top_k=None):
  function evaluate_places (line 55) | def evaluate_places(filepath, predicted_places):