Tensorflow：グローバルなステップは、損失と同じグラフでなければなりません

Question

Tensorflowを使用してtf.contrib.layersパッケージでいくつかの分類を実行しようとしています。 。例からわかるように（例：thisおよびそれはtutorial）、グラフのすべてがAPIによって処理されます。私はダウンロードして、私の環境で完全に同じコードを実行することができます。私は自分のコードを実行したときに

はしかし、私は私の世界のステップは奇妙なようで、私の損失、同じグラフからではないエラーが表示されます。エラーがtrain_op

の建設ここだ時に発生ValueError: Tensor("global_step:0", shape=(), dtype=int64_ref) must be from the same graph as Tensor("softmax_cross_entropy_loss/value:0", shape=(), dtype=float32).テンソルフローコード（私はデータのロードを扱うためのいくつかのコードを持っていますが、テンソルフローからは何も使用しません）。申し訳ありませんが、今のコードは面倒です。私は、このエラーを解明しようとしています。ここで

import numpy as np 
import tensorflow as tf 
from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib 

import data # my data loading module 


def train(training_file, vocab_path, hidden_units=[10, 20, 10], estimator=tf.contrib.learn.DNNClassifier): 
    """ 
    Given a training CSV file, train a Tensorflow neural network 
    """ 

    training_set = data.load(training_file) 

    vocab = tf.contrib.learn.preprocessing.VocabularyProcessor(data.DOC_LENGTH) 
    vocab = vocab.restore(vocab_path) 
    training_data = tf.one_hot(training_set.data, len(vocab.vocabulary_._mapping), dtype=tf.float32) 
    training_targets = tf.constant(np.array(training_set.targets, dtype=np.int32)) 

    classifier = tf.contrib.learn.Estimator(model_fn=lambda features, targets, mode, params: model_fn(features, targets, mode, params, hidden_units)) 

    classifier.fit(input_fn=lambda: (training_data, training_targets), steps=2000) 

    return classifier 


def model_fn(features, targets, mode, params, hidden_units): 
    if len(hidden_units) <= 0: 
     raise ValueError("Hidden units must be a iterable of ints of length >= 1") 

    # Define the network 
    network = tf.contrib.layers.relu(features, hidden_units[0]) 
    for i in range(1, len(hidden_units)): 
     network = tf.contrib.layers.relu(network, hidden_units[i]) 

    # Flatten the network 
    network = tf.reshape(network, [-1, hidden_units[-1] * data.DOC_LENGTH]) 

    # Add dropout to enhance feature use 
    network = tf.layers.dropout(inputs=network, rate=0.5, training=mode == tf.contrib.learn.ModeKeys.TRAIN) 

    # Calculate the logits 
    logits = tf.contrib.layers.fully_connected(network, 15) 

    loss = None 
    train_op = None 

    if mode != tf.contrib.learn.ModeKeys.INFER: 
     targets = tf.cast(tf.one_hot(targets, 15, 1, 0), dtype=tf.float32) 
     loss = tf.losses.softmax_cross_entropy(logits=logits, onehot_labels=targets) 

    if mode == tf.contrib.learn.ModeKeys.TRAIN: 
     # This train_op causes the error 
     train_op = tf.contrib.layers.optimize_loss(
      loss=loss, 
      global_step=tf.train.get_global_step(), 
      optimizer='Adam', 
      learning_rate=0.01) 

    predictions = { 
      "classes": tf.argmax(input=logits, axis=1), 
      "probabilities": tf.nn.softmax(logits, name="softmax_tensor") 
    } 

    return model_fn_lib.ModelFnOps(mode=mode, predictions=predictions, loss=loss, train_op=train_op) 


def main(unusedargv): 

    # ... parses arguments 

    classifier = train(args.train_data, args.vocab) 
    print(evaluate(classifier, args.train_data)) 
    print(evaluate(classifier, args.test_data)) 


if __name__ == "__main__": 
    tf.app.run() 

は完全なスタックトレースです：

File "categorize.py", line 126, in main 
    classifier = train(args.train_data, args.vocab) 
File "categorize.py", line 39, in train 
    classifier.fit(input_fn=lambda: (training_data, training_targets), steps=2000) 
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/util/deprecation.py", line 280, in new_func 
    return func(*args, **kwargs) 
File "/usr/local/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 426, in fit 
    loss = self._train_model(input_fn=input_fn, hooks=hooks) 
File "/usr/local/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 934, in _train_model 
    model_fn_ops = self._call_legacy_get_train_ops(features, labels) 
File "/usr/local/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1003, in _call_legacy_get_train_ops 
    train_ops = self._get_train_ops(features, labels) 
File "/usr/local/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1162, in _get_train_ops 
    return self._call_model_fn(features, labels, model_fn_lib.ModeKeys.TRAIN) 
File "/usr/local/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 1133, in _call_model_fn 
    model_fn_results = self._model_fn(features, labels, **kwargs) 
File "categorize.py", line 37, in <lambda> 
    classifier = tf.contrib.learn.Estimator(model_fn=lambda features, targets, mode, params: model_fn(features, targets, mode, params, hidden_units)) 
File "categorize.py", line 73, in model_fn 
    learning_rate=0.01) 
File "/usr/local/lib/python3.6/site-packages/tensorflow/contrib/layers/python/layers/optimizers.py", line 152, in optimize_loss 
    with vs.variable_scope(name, "OptimizeLoss", [loss, global_step]): 
File "/usr/local/Cellar/python3/3.6.0_1/Frameworks/Python.framework/Versions/3.6/lib/python3.6/contextlib.py", line 82, in __enter__ 
    return next(self.gen) 
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/ops/variable_scope.py", line 1410, in variable_scope 
    g = ops._get_graph_from_inputs(values) # pylint: disable=protected-access 
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3968, in _get_graph_from_inputs 
    _assert_same_graph(original_graph_element, graph_element) 
File "/usr/local/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3907, in _assert_same_graph 
    "%s must be from the same graph as %s." % (item, original_item)) 
ValueError: Tensor("global_step:0", shape=(), dtype=int64_ref) must be from the same graph as Tensor("softmax_cross_entropy_loss/value:0", shape=(), dtype=float32). 

は、ここに私のコードです：

Answer 1

問題を見つけました。これは、Estimatorインターフェイスと関係がありますが、基本的にテンソルフロー変数定義をEstimatorに移動する必要がありました。私はこれを行うための方法を作ってしまったが、私はラムダで変数を定義したとき、それはまた働いた：

def train(training_file, vocab_path, hidden_units=[10, 20, 10]): 
    """ 
    Given a training CSV file, train a Tensorflow neural network 
    """ 

    training_set = data.load(training_file) 

    vocab = tf.contrib.learn.preprocessing.VocabularyProcessor(data.DOC_LENGTH) 
    vocab = vocab.restore(vocab_path) 
    # Note not defining the variables here 
    training_data = training_set.data 
    training_targets = np.array(training_set.targets, dtype=np.int32) 

    classifier = tf.contrib.learn.Estimator(model_fn=lambda features, targets, mode, params: model_fn(features, targets, mode, params, hidden_units)) 

    # Note the variable definition here 
    classifier.fit(
     input_fn=lambda: 
      (tf.one_hot(training_data, len(vocab.vocabulary_._mapping), dtype=tf.float32) 
      tf.constant(training_targets)), 
     steps=2000)) 

    return classifier 

Answer 2

2つの関数のコンテキストが異なっているので、あなたが設定して呼び出し元の関数にtf.Graph()を使用する必要がありますデフォルトのグラフは次のようになります。

def train(...): 
    with tf.Graph().as_default(): 
     ... 
     ... 
     training_data = tf.one_hot(training_set.data, len(vocab.vocabulary_._mapping), dtype=tf.float32) 
     training_targets = tf.constant(np.array(training_set.targets, dtype=np.int32)) 

     classifier = tf.contrib.learn.Estimator(model_fn=lambda features, targets, mode, params: model_fn(features, targets, mode, params, hidden_units)) 

     classifier.fit(input_fn=lambda: (training_data, training_targets), steps=2000) 

     return classifier