SYMBOL INDEX (51 symbols across 6 files)

FILE: gcn/inits.py
  function uniform (line 5) | def uniform(shape, scale=0.05, name=None):
  function glorot (line 11) | def glorot(shape, name=None):
  function zeros (line 18) | def zeros(shape, name=None):
  function ones (line 24) | def ones(shape, name=None):

FILE: gcn/layers.py
  function get_layer_uid (line 11) | def get_layer_uid(layer_name=''):
  function sparse_dropout (line 21) | def sparse_dropout(x, keep_prob, noise_shape):
  function dot (line 30) | def dot(x, y, sparse=False):
  class Layer (line 39) | class Layer(object):
    method __init__ (line 54) | def __init__(self, **kwargs):
    method _call (line 68) | def _call(self, inputs):
    method __call__ (line 71) | def __call__(self, inputs):
    method _log_vars (line 80) | def _log_vars(self):
  class Dense (line 85) | class Dense(Layer):
    method __init__ (line 87) | def __init__(self, input_dim, output_dim, placeholders, dropout=0., sp...
    method _call (line 113) | def _call(self, inputs):
  class GraphConvolution (line 132) | class GraphConvolution(Layer):
    method __init__ (line 134) | def __init__(self, input_dim, output_dim, placeholders, dropout=0.,
    method _call (line 163) | def _call(self, inputs):

FILE: gcn/metrics.py
  function masked_softmax_cross_entropy (line 4) | def masked_softmax_cross_entropy(preds, labels, mask):
  function masked_accuracy (line 13) | def masked_accuracy(preds, labels, mask):

FILE: gcn/models.py
  class Model (line 8) | class Model(object):
    method __init__ (line 9) | def __init__(self, **kwargs):
    method _build (line 35) | def _build(self):
    method build (line 38) | def build(self):
    method predict (line 60) | def predict(self):
    method _loss (line 63) | def _loss(self):
    method _accuracy (line 66) | def _accuracy(self):
    method save (line 69) | def save(self, sess=None):
    method load (line 76) | def load(self, sess=None):
  class MLP (line 85) | class MLP(Model):
    method __init__ (line 86) | def __init__(self, placeholders, input_dim, **kwargs):
    method _loss (line 99) | def _loss(self):
    method _accuracy (line 108) | def _accuracy(self):
    method _build (line 112) | def _build(self):
    method predict (line 128) | def predict(self):
  class GCN (line 132) | class GCN(Model):
    method __init__ (line 133) | def __init__(self, placeholders, input_dim, **kwargs):
    method _loss (line 146) | def _loss(self):
    method _accuracy (line 155) | def _accuracy(self):
    method _build (line 159) | def _build(self):
    method predict (line 176) | def predict(self):

FILE: gcn/train.py
  function evaluate (line 66) | def evaluate(features, support, labels, mask, placeholders):

FILE: gcn/utils.py
  function parse_index_file (line 9) | def parse_index_file(filename):
  function sample_mask (line 17) | def sample_mask(idx, l):
  function load_data (line 24) | def load_data(dataset_str):
  function sparse_to_tuple (line 93) | def sparse_to_tuple(sparse_mx):
  function preprocess_features (line 112) | def preprocess_features(features):
  function normalize_adj (line 122) | def normalize_adj(adj):
  function preprocess_adj (line 132) | def preprocess_adj(adj):
  function construct_feed_dict (line 138) | def construct_feed_dict(features, support, labels, labels_mask, placehol...
  function chebyshev_polynomials (line 149) | def chebyshev_polynomials(adj, k):