TensorFlowの例を実行すると、何故警告メッセージが表示されるのですか？

Question

私はチュートリアルに従っています：https://www.tensorflow.org/get_started/get_started

なぜ以下のようなエラーが発生しましたか？また、最終的な損失スコアは異なります。ドキュメントは言う：

{'global_step': 1000, 'loss': 1.9650059e-11} 

私の損失であるのに対し：{'loss': 6.3995182e-09, 'global_step': 1000}

import tensorflow as tf 
# NumPy is often used to load, manipulate and preprocess data. 
import numpy as np 

# Declare list of features. We only have one real-valued feature. There are many 
# other types of columns that are more complicated and useful. 
features = [tf.contrib.layers.real_valued_column("x", dimension=1)] 

# An estimator is the front end to invoke training (fitting) and evaluation 
# (inference). There are many predefined types like linear regression, 
# logistic regression, linear classification, logistic classification, and 
# many neural network classifiers and regressors. The following code 
# provides an estimator that does linear regression. 
estimator = tf.contrib.learn.LinearRegressor(feature_columns=features) 

# TensorFlow provides many helper methods to read and set up data sets. 
# Here we use `numpy_input_fn`. We have to tell the function how many batches 
# of data (num_epochs) we want and how big each batch should be. 
x = np.array([1., 2., 3., 4.]) 
y = np.array([0., -1., -2., -3.]) 
input_fn = tf.contrib.learn.io.numpy_input_fn({"x":x}, y, batch_size=4, 
               num_epochs=1000) 

# We can invoke 1000 training steps by invoking the `fit` method and passing the 
# training data set. 
estimator.fit(input_fn=input_fn, steps=1000) 

# Here we evaluate how well our model did. In a real example, we would want 
# to use a separate validation and testing data set to avoid overfitting. 
print(estimator.evaluate(input_fn=input_fn)) 

INFO:tensorflow:Using default config. 
INFO:tensorflow:Using config: {'_task_type': None, '_task_id': 0, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7f1555e351d0>, '_master': '', '_num_ps_replicas': 0, '_num_worker_replicas': 0, '_environment': 'local', '_is_chief': True, '_evaluation_master': '', '_tf_config': gpu_options { 
    per_process_gpu_memory_fraction: 1.0 
} 
, '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_secs': 600, '_save_checkpoints_steps': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_model_dir': None} 
WARNING:tensorflow:Using temporary folder as model directory: /tmp/tmpol66d18y 
WARNING:tensorflow:Rank of input Tensor (1) should be the same as output_rank (2) for column. Will attempt to expand dims. It is highly recommended that you resize your input, as this behavior may change. 
WARNING:tensorflow:From /home/abigail/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:615: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30. 
Instructions for updating: 
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported. 
INFO:tensorflow:Create CheckpointSaverHook. 
INFO:tensorflow:Saving checkpoints for 1 into /tmp/tmpol66d18y/model.ckpt. 
INFO:tensorflow:loss = 2.25, step = 1 
INFO:tensorflow:global_step/sec: 2197.95 
INFO:tensorflow:loss = 0.0537609, step = 101 (0.047 sec) 
INFO:tensorflow:global_step/sec: 2106.83 
INFO:tensorflow:loss = 0.0114769, step = 201 (0.047 sec) 
INFO:tensorflow:global_step/sec: 2184.51 
INFO:tensorflow:loss = 0.00149274, step = 301 (0.046 sec) 
INFO:tensorflow:global_step/sec: 2126.71 
INFO:tensorflow:loss = 0.000284785, step = 401 (0.047 sec) 
INFO:tensorflow:global_step/sec: 2112.6 
INFO:tensorflow:loss = 3.2641e-05, step = 501 (0.048 sec) 
INFO:tensorflow:global_step/sec: 2048.21 
INFO:tensorflow:loss = 3.71825e-06, step = 601 (0.048 sec) 
INFO:tensorflow:global_step/sec: 2154.48 
INFO:tensorflow:loss = 1.1719e-06, step = 701 (0.047 sec) 
INFO:tensorflow:global_step/sec: 2287.71 
INFO:tensorflow:loss = 1.42258e-07, step = 801 (0.043 sec) 
INFO:tensorflow:global_step/sec: 3059.53 
INFO:tensorflow:loss = 7.27343e-08, step = 901 (0.033 sec) 
INFO:tensorflow:Saving checkpoints for 1000 into /tmp/tmpol66d18y/model.ckpt. 
INFO:tensorflow:Loss for final step: 6.50745e-09. 
WARNING:tensorflow:Rank of input Tensor (1) should be the same as output_rank (2) for column. Will attempt to expand dims. It is highly recommended that you resize your input, as this behavior may change. 
WARNING:tensorflow:From /home/abigail/anaconda3/lib/python3.6/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:615: scalar_summary (from tensorflow.python.ops.logging_ops) is deprecated and will be removed after 2016-11-30. 
Instructions for updating: 
Please switch to tf.summary.scalar. Note that tf.summary.scalar uses the node name instead of the tag. This means that TensorFlow will automatically de-duplicate summary names based on the scope they are created in. Also, passing a tensor or list of tags to a scalar summary op is no longer supported. 
INFO:tensorflow:Starting evaluation at 2017-05-08-06:39:50 
INFO:tensorflow:Restoring parameters from /tmp/tmpol66d18y/model.ckpt-1000 
INFO:tensorflow:Finished evaluation at 2017-05-08-06:39:51 
INFO:tensorflow:Saving dict for global step 1000: global_step = 1000, loss = 6.39952e-09 
WARNING:tensorflow:Skipping summary for global_step, must be a float or np.float32. 
{'loss': 6.3995182e-09, 'global_step': 1000} 

Answer 1

これは期待通りに働いています。

ランダム初期化のような問題のため、プログラムを実行するたびに正確に同じ数値の損失を得ることは期待できません。 （決定的な出力が必要な場合は、Tensorflowグラフのランダムシードを固定値に設定してみてください）

警告メッセージと情報メッセージは問題ありません。私は彼らが少し怖い見ていることに同意します。情報メッセージはほとんど無視することができます。ただ今のところ警告メッセージを無視してください。私はチュートリアルの著者に、それを更新するように頼んだ。

希望に役立ちます！

Answer 2

非常に迷惑です。

tf.logging.set_verbosity(tf.logging.ERROR) 

Answer 3

モデルを保存するために一時フォルダを使用していることは明らかです。この問題を解決するには、見積もり文を変更するだけです。変更

estimator=tf.estimator.LinearRegressor(feature_columns = feature_columns) 

estimator=tf.estimator.LinearRegressor(feature_columns = feature_columns, model_dir="D:\test") #Or any other directory as per your wish 

へとトレーニングモデルの私たちはあなたから取得違いを心配しないでくださいTraining.Thereforeの処理中に受けるのランタイムエラーを準備しているので、答えは正確ではありません。そのモデルは、損失がほぼゼロであることに焦点を当てています。