SparseTensorsのテンソルをエバリュエーターにフィード

Question

TFを使い始めるには、試合の試合結果のプレディクタを知りたいと思っていました。チーム0の5人のヒーロー、チーム1の5人のヒーロー、そしてマップの3つの機能があります。勝者は0または1のラベルです。私はSparseTensorsとしてチームとマップを表現したいと思います。 71人の英雄のうち5人が選ばれます。マップの場合と同様に、可能な13のうち1つが選択されます。

import tensorflow as tf 
import packunpack as source 
import tempfile 
from collections import namedtuple 

GameRecord = namedtuple('GameRecord', 'team_0 team_1 game_map winner') 
def parse(line): 
    parts = line.rstrip().split("\t") 
    return GameRecord(
     game_map = parts[1], 
     team_0 = parts[2].split(","), 
     team_1 = parts[3].split(","), 
     winner = int(parts[4])) 

def conjugate(record): 
    return GameRecord(
     team_0 = record.team_1, 
     team_1 = record.team_0, 
     game_map = record.game_map, 
     winner = 0 if record.winner == 1 else 1) 

def sparse_team(team): 
    indices = list(map(lambda x: [x], map(source.encode_hero, team))) 
    return tf.SparseTensor(indices=indices, values = [1] * len(indices), dense_shape=[len(source.heroes_array)]) 

def sparse_map(map_name): 
    return tf.SparseTensor(indices=[[source.encode_hero(map_name)]], values = [1], dense_shape=[len(source.maps_array)]) 

def make_input_fn(filename, shuffle = True, add_conjugate_games = True): 
    def _fn(): 
     records = [] 
     with open(filename, "r") as raw: 
      i = 0 
      for line in raw: 
       record = parse(line) 
       records.append(record) 
       if add_conjugate_games: 
       # since 0 and 1 are arbitrary team labels, learn and test the conjugate game whenever 
       # learning the original inference 
        records.append(conjugate(record)) 

     print("Making team 0") 
     team_0s = tf.constant(list(map(lambda r: sparse_team(r.team_0), records))) 
     print("Making team 1") 
     team_1s = tf.constant(list(map(lambda r: sparse_team(r.team_1), records))) 
     print("making maps") 
     maps = tf.constant(list(map(lambda r: sparse_map(r.game_map), records))) 
     print("Making winners") 
     winners = tf.constant(list(map(lambda r: tf.constant([r.winner]), records))) 

     return { 
        "team_0": team_0s, 
        "team_1": team_1s, 
        "game_map": maps, 
       }, winners 
     #Please help me finish this function? 

    return _fn 

team_0 = tf.feature_column.embedding_column(
    tf.feature_column.categorical_column_with_vocabulary_list("team_0", source.heroes_array), len(source.heroes_array)) 
team_1 = tf.feature_column.embedding_column(
    tf.feature_column.categorical_column_with_vocabulary_list("team_1", source.heroes_array), len(source.heroes_array)) 
game_map = tf.feature_column.embedding_column(
    tf.feature_column.categorical_column_with_vocabulary_list("game_map", source.maps_array), len(source.maps_array)) 

model_dir = tempfile.mkdtemp() 
m = tf.estimator.DNNClassifier(
    model_dir=model_dir, 
    hidden_units = [1024, 512, 256], 
    feature_columns=[team_0, team_1, game_map]) 

def main(): 
    m.train(input_fn=make_input_fn("tiny.txt"), steps = 100) 

if __name__ == "__main__": 
    main() 

これは私がドキュメントで見つけることができる例はすべてが最終的にパンダまたはnumpyのヘルパーを呼び出すので、それは、TFは私が私のinput_fnに戻すために望んでいるかを理解することは非常に困難ですteam_0s = tf.constant(list(map(lambda r: sparse_team(r.team_0), records)))

に失敗します私はこれらのフレームワークに精通していません。私は、各辞書の値は、単一のフィーチャのすべての例を含むテンソルでなければならないと考えました。私の例はそれぞれSparseTensorであり、DNNClassifierのためにそれらを密集させたバージョンとして単純に埋め込みたいのです。

私の精神モデルは今はひどく壊れていると確信しています。

エラー出力：

python3 estimator.py 
Making team 0 
Traceback (most recent call last): 
    File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_util.py", line 468, in make_tensor_proto 
    str_values = [compat.as_bytes(x) for x in proto_values] 
    File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_util.py", line 468, in <listcomp> 
    str_values = [compat.as_bytes(x) for x in proto_values] 
    File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/compat.py", line 65, in as_bytes 
    (bytes_or_text,)) 
TypeError: Expected binary or unicode string, got <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7fe8 
b4d7aef0> 

During handling of the above exception, another exception occurred: 

Traceback (most recent call last): 
    File "estimator.py", line 79, in <module> 
    main() 
    File "estimator.py", line 76, in main 
    m.train(input_fn=make_input_fn("tiny.txt"), steps = 100) 
    File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py", line 302, in train 
    loss = self._train_model(input_fn, hooks, saving_listeners) 
    File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py", line 709, in _train_model 
    input_fn, model_fn_lib.ModeKeys.TRAIN) 
    File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py", line 577, in _get_features_and_l 
abels_from_input_fn 
    result = self._call_input_fn(input_fn, mode) 
    File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/estimator/estimator.py", line 663, in _call_input_fn 
    return input_fn(**kwargs) 
    File "estimator.py", line 44, in _fn 
    team_0s = tf.constant(list(map(lambda r: sparse_team(r.team_0), records))) 
    File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/constant_op.py", line 208, in constant 
    value, dtype=dtype, shape=shape, verify_shape=verify_shape)) 
    File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/tensor_util.py", line 472, in make_tensor_proto 
    "supported type." % (type(values), values)) 
TypeError: Failed to convert object of type <class 'list'> to Tensor. Contents: [<tensorflow.python.framework.sparse_tenso 
r.SparseTensor object at 0x7fe8b4d7aef0>, <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7fe8b4d7af28 
>, <tensorflow.python.framework.sparse_tensor.SparseTensor object at 0x7fe8b4d7af60>, <tensorflow.python.framework.sparse_ 
tensor.SparseTensor object at 0x7fe8b4d7aeb8> ... ] 

Answer 1

は、最終的に私のinput_fnでスパースベクトルに私のテキスト表現を変換する必要はありませんでした。代わりに、モデルに文字列の入力を期待する必要がありました。これは「単語の袋」またはnホットベクトルに変換する方法と、密なベクトルとして埋め込む方法を理解しています。

import tensorflow as tf 
import tempfile 
import os 
from collections import namedtuple 

GameRecord = namedtuple('GameRecord', 'team_0 team_1 game_map winner') 
def parse(line): 
    parts = line.rstrip().split("\t") 
    return GameRecord(
     game_map = parts[1], 
     team_0 = parts[2].split(","), 
     team_1 = parts[3].split(","), 
     winner = int(parts[4])) 

def conjugate(record): 
    return GameRecord(
     team_0 = record.team_1, 
     team_1 = record.team_0, 
     game_map = record.game_map, 
     winner = 0 if record.winner == 1 else 1) 

def make_input_fn(filename, batch_size=128, shuffle = True, add_conjugate_games = True, epochs=1): 
    def _fn(): 
     records = [] 
     with open(filename, "r") as raw: 
      i = 0 
      for line in raw: 
       record = parse(line) 
       records.append(record) 
       if add_conjugate_games: 
        records.append(conjugate(record)) 

     team_0s = tf.constant(list(map(lambda r: r.team_0, records))) 
     team_1s = tf.constant(list(map(lambda r: r.team_1, records))) 
     maps = tf.constant(list(map(lambda r: r.game_map, records))) 
     winners = tf.constant(list(map(lambda r: [r.winner], 

     return { 
        "team_0": team_0s, 
        "team_1": team_1s, 
        "game_map": maps, 
       }, winners 

    return _fn 

team_0 = tf.feature_column.embedding_column(
    tf.feature_column.categorical_column_with_vocabulary_list("team_0", source.heroes_array), dimension=len(source.heroes_array)) 
team_1 = tf.feature_column.embedding_column(
    tf.feature_column.categorical_column_with_vocabulary_list("team_1", source.heroes_array), dimension=len(source.heroes_array)) 
game_map = tf.feature_column.embedding_column(
    tf.feature_column.categorical_column_with_vocabulary_list("game_map", source.maps_array), dimension=len(source.maps_array)) 

model_dir = "DNNClassifierModel_00" 
os.mkdir(model_dir) 
m = tf.estimator.DNNClassifier(
    model_dir=model_dir, 
    hidden_units = [1024, 512, 256], 
    feature_columns=[team_0, team_1, game_map]) 

def main(): 
     m.train(input_fn=make_input_fn("training.txt")) 
     results = m.evaluate(input_fn=make_input_fn("validation.txt")) 
     print("model directory = %s" % model_dir) 
     for key in sorted(results): 
      print("%s: %s" % (key, results[key])) 

if __name__ == "__main__": 
    main() 

このコードはまだ完全ではありません。バッチ処理で追加する必要があります。