SYMBOL INDEX (115 symbols across 28 files)

FILE: AFM/AFMLayer.py
  class AFMLayer (line 11) | class AFMLayer(Layer):
    method __init__ (line 37) | def __init__(self, attention_factor=4, l2_reg_w=0, dropout_rate=0, see...
    method build (line 44) | def build(self, input_shape):
    method call (line 86) | def call(self, inputs, training=None, **kwargs):
    method compute_output_shape (line 118) | def compute_output_shape(self, input_shape):
    method get_config (line 125) | def get_config(self, ):

FILE: AFM/AFM_Model.py
  function AFM (line 13) | def AFM(

FILE: AFM/data_generator.py
  function init_output (line 6) | def init_output():
  function file_generator (line 19) | def file_generator(input_path, batch_size):

FILE: AFM/preprocess.py
  function gen_data_set (line 6) | def gen_data_set(data, negsample=0):
  function gen_model_input (line 36) | def gen_model_input(train_set,user_profile,seq_max_len):

FILE: BST/bst_model.py
  function bst_model (line 14) | def bst_model(sparse_input_length = 1, \

FILE: BST/din.py
  function din_padding_mask (line 7) | def din_padding_mask(seq):
  class LocalActivationUnit (line 16) | class LocalActivationUnit(tf.keras.layers.Layer):
    method __init__ (line 18) | def __init__(self, d_model, middle_units, dropout_rate, **kwargs):
    method build (line 26) | def build(self, input_shape):
    method call (line 37) | def call(self, inputs, training=None, **kwargs):
    method compute_output_shape (line 50) | def compute_output_shape(self, input_shape):
    method get_config (line 54) | def get_config(self):
  class DinAttentionLayer (line 64) | class DinAttentionLayer(tf.keras.layers.Layer):
    method __init__ (line 65) | def __init__(self, d_model, middle_units, dropout_rate, **kwargs):
    method call (line 75) | def call(self, inputs, **kwargs):
    method compute_output_shape (line 90) | def compute_output_shape(self, input_shape):
    method get_config (line 94) | def get_config(self):

FILE: BST/transformer.py
  class Encoder (line 9) | class Encoder(tf.keras.layers.Layer):
    method __init__ (line 10) | def __init__(self, n_layers, d_model, num_heads, middle_units,
    method call (line 24) | def call(self, inputs, **kwargs):
  class EncoderLayer (line 37) | class EncoderLayer(tf.keras.layers.Layer):
    method __init__ (line 38) | def __init__(self, d_model, num_heads, middle_units, \
    method call (line 53) | def call(self, inputs, mask, **kwargs):
  class LayerNormalization (line 70) | class LayerNormalization(tf.keras.layers.Layer):
    method __init__ (line 71) | def __init__(self, epsilon=1e-6, **kwargs):
    method build (line 75) | def build(self, input_shape):
    method call (line 82) | def call(self, x):
    method compute_output_shape (line 87) | def compute_output_shape(self, input_shape):
  function point_wise_feed_forward_network (line 94) | def point_wise_feed_forward_network(d_model, middle_units):
  function scaled_dot_product_attention (line 103) | def scaled_dot_product_attention(q, k, v, mask):
  class MultiHeadAttention (line 120) | class MultiHeadAttention(tf.keras.layers.Layer):
    method __init__ (line 121) | def __init__(self, d_model, num_heads, **kwargs):
    method split_heads (line 140) | def split_heads(self, x, batch_size):
    method call (line 145) | def call(self, inputs, **kwargs):
  function padding_mask (line 177) | def padding_mask(seq):
  class PositionalEncoding (line 188) | class PositionalEncoding(tf.keras.layers.Layer):
    method __init__ (line 189) | def __init__(self, sequence_len=None, embedding_dim=None, **kwargs):
    method call (line 194) | def call(self, inputs):
    method compute_output_shape (line 210) | def compute_output_shape(self, input_shape):

FILE: BilinearFFM/BilinearFFM.py
  function BilinearFFM (line 13) | def BilinearFFM(

FILE: BilinearFFM/BilinearInteraction.py
  class BilinearInteraction (line 9) | class BilinearInteraction(Layer):
    method __init__ (line 23) | def __init__(self, bilinear_type="each", seed=1024, **kwargs):
    method build (line 29) | def build(self, input_shape):
    method call (line 51) | def call(self, inputs, **kwargs):
    method compute_output_shape (line 72) | def compute_output_shape(self, input_shape):
    method get_config (line 78) | def get_config(self, ):

FILE: BilinearFFM/data_generator.py
  function init_output (line 6) | def init_output():
  function file_generator (line 19) | def file_generator(input_path, batch_size):

FILE: BilinearFFM/preprocess.py
  function gen_data_set (line 6) | def gen_data_set(data, negsample=0):
  function gen_model_input (line 36) | def gen_model_input(train_set,user_profile,seq_max_len):

FILE: ESMM/split_train_val.py
  function split_train_val (line 5) | def split_train_val(sample_path, train_path, val_path, summary_path, tra...

FILE: ESMM/tar_model.py
  function tar (line 9) | def tar(input_paths, output_path):
  function md5_file (line 18) | def md5_file(name):

FILE: ESMM/train_esmm_finetune.py
  function parse_example (line 10) | def parse_example(proto):
  function get_tfrecord_dataset (line 28) | def get_tfrecord_dataset(tf_path, batch_size = None, num_parallel_calls ...
  function train_finetune (line 40) | def train_finetune(train_path, val_path, model_path, \
  function build_model (line 105) | def build_model():

FILE: ESMM/write_tfrecord.py
  function _parse_line (line 5) | def _parse_line(line):
  function _to_float_feature (line 15) | def _to_float_feature(value_list):
  function _to_int64_feature (line 19) | def _to_int64_feature(value):
  function serialize_example (line 23) | def serialize_example(pin_vec, sku_vec, ctr_label, cvr_label):
  function make_tfrecord (line 39) | def make_tfrecord(input_path, tf_path):

FILE: MIND/CapsuleLayer.py
  class SequencePoolingLayer (line 7) | class SequencePoolingLayer(Layer):
    method __init__ (line 26) | def __init__(self, mode='mean', supports_masking=False, **kwargs):
    method build (line 36) | def build(self, input_shape):
    method call (line 41) | def call(self, seq_value_len_list, mask=None, **kwargs):
    method compute_output_shape (line 73) | def compute_output_shape(self, input_shape):
    method compute_mask (line 79) | def compute_mask(self, inputs, mask):
    method get_config (line 82) | def get_config(self, ):
  class LabelAwareAttention (line 89) | class LabelAwareAttention(Layer):
    method __init__ (line 90) | def __init__(self, k_max, pow_p=1, **kwargs):
    method build (line 95) | def build(self, input_shape):
    method call (line 100) | def call(self, inputs, training=None, **kwargs):
    method compute_output_shape (line 124) | def compute_output_shape(self, input_shape):
    method get_config (line 127) | def get_config(self, ):
  class CapsuleLayer (line 134) | class CapsuleLayer(Layer):
    method __init__ (line 135) | def __init__(self, input_units, out_units, max_len, k_max, iteration_t...
    method build (line 145) | def build(self, input_shape):
    method call (line 154) | def call(self, inputs, **kwargs):
    method compute_output_shape (line 177) | def compute_output_shape(self, input_shape):
    method get_config (line 180) | def get_config(self, ):
  function squash (line 188) | def squash(inputs):

FILE: MIND/data_generator.py
  function init_output (line 6) | def init_output():
  function file_generator (line 22) | def file_generator(input_path, batch_size):

FILE: MIND/mind.py
  function tile_user_otherfeat (line 13) | def tile_user_otherfeat(user_other_feature, k_max):
  function mind (line 17) | def mind(

FILE: MIND/preprocess.py
  function gen_data_set (line 6) | def gen_data_set(data, negsample=0):
  function gen_model_input (line 37) | def gen_model_input(train_set,user_profile,seq_max_len):

FILE: Wide&Deep/wide_and_deep.py
  function wide_and_deep (line 9) | def wide_and_deep(

FILE: YouTubeNet/SequencePoolingLayer.py
  class SequencePoolingLayer (line 8) | class SequencePoolingLayer(Layer):
    method __init__ (line 10) | def __init__(self, mode="mean", support_mask=True, sequence_mask_lengt...
    method build (line 23) | def build(self, input_shape):
    method call (line 27) | def call(self, input_hist_seq_list, **kwargs):
    method compute_output_shape (line 78) | def compute_output_shape(self, input_shape):
    method config (line 82) | def config(self):

FILE: YouTubeNet/YouTubeNet.py
  function YouTubeNet (line 13) | def YouTubeNet(

FILE: YouTubeNet/data_generator.py
  function init_output (line 6) | def init_output():
  function file_generator (line 22) | def file_generator(input_path, batch_size):

FILE: YouTubeNet/preprocess.py
  function gen_data_set (line 6) | def gen_data_set(data, negsample=0):
  function gen_model_input (line 36) | def gen_model_input(train_set,user_profile,seq_max_len):

FILE: vgg16_figure_search_annoy/build_figure_ann.py
  function build_ann (line 9) | def build_ann(name_path=None, \

FILE: vgg16_figure_search_annoy/download_jd_figures.py
  function download_images (line 30) | def download_images(inx, key):
  function main (line 69) | def main():

FILE: vgg16_figure_search_annoy/search_topN_figure.py
  function load_ann (line 10) | def load_ann(ann_path=None, index_to_name_dict_path=None, dim=64):
  function query_ann (line 19) | def query_ann(ann=None, index_to_name_dict=None, query_vec=None, topN=No...

FILE: vgg16_figure_search_annoy/threadings_download_txt.py
  function download_image (line 9) | def download_image(url, filename):
  function execute_thread (line 16) | def execute_thread(i):
  function main (line 22) | def main():