チェックに失敗しました：NDIMS ==暗くなる（）（2対1）私は、SVMモデルを構築するとき、私はtf.learnとSVMモデルを構築する際

Question

が、それはこのようなエラーが出る： を私がLinearClassifier実行したとそれは成功！ エラーを修正する方法がわかりません。だれでも助けてくれますか？ここで

は、エラーを再現するためのコードです：

import tensorflow as tf 
import pandas as pd 
from tensorflow.contrib.learn.python.learn.estimators import svm 


detailed_occupation_recode = tf.contrib.layers.sparse_column_with_hash_bucket(
    column_name='detailed_occupation_recode', 
    hash_bucket_size = 1000 
) 
education = tf.contrib.layers.sparse_column_with_hash_bucket(
    column_name='education', 
    hash_bucket_size=1000 
) 
# Continuous base columns 
age = tf.contrib.layers.real_valued_column('age') 
wage_per_hour = tf.contrib.layers.real_valued_column('wage_per_hour') 



columns = ['age', 'detailed_occupation_recode', 'education', 'wage_per_hour','label'] 
FEATURE_COLUMNS = [ 
    # age, age_buckets, class_of_worker, detailed_industry_recode, 
    age, detailed_occupation_recode, education, wage_per_hour 

] 


LABEL_COLUMN = 'label' 

CONTINUOUS_COLUMNS = ['age', 'wage_per_hour'] 

CATEGORICAL_COLUMNS = ['detailed_occupation_recode','education'] 


df_train = pd.DataFrame([[12,'12','7th and 8th grade',40,'- 50000'], 
       [40,'45','7th and 8th grade',40, '50000+'], 
       [50,'50','10th grade',40,'50000+'], 
       [60,'30','7th and 8th grade',40,'- 50000']], 
       columns=['age', 'detailed_occupation_recode', 'education', 'wage_per_hour', 'label']) 


df_test = pd.DataFrame([[12,'12','7th and 8th grade',40,'- 50000'], 
       [40,'45','7th and 8th grade',40, '50000+'], 
       [50,'50','10th grade',40,'50000+'], 
       [60,'30','7th and 8th grade',40,'- 50000']], 
       columns=['age', 'detailed_occupation_recode', 'education', 'wage_per_hour', 'label']) 
df_train[LABEL_COLUMN] = (df_train[LABEL_COLUMN].apply(lambda x: '+' in x)).astype(int) 
df_test[LABEL_COLUMN] = (df_test[LABEL_COLUMN].apply(lambda x: '+' in x)).astype(int) 
dtypess = df_train.dtypes 


def input_fn(df): 
    continuous_cols = {k: tf.constant(df[k].values) for k in CONTINUOUS_COLUMNS} 
    categorical_cols = {k: tf.SparseTensor(
     indices=[[i, 0] for i in range(df[k].size)], 
     values=df[k].values, 
     dense_shape=[df[k].size, 1]) for k in CATEGORICAL_COLUMNS}  
    feature_cols = dict(continuous_cols.items() + categorical_cols.items()) 
    feature_cols['example_id'] = tf.constant([str(i+1) for i in range(df['age'].size)]) 
    label = tf.constant(df[LABEL_COLUMN].values) 
    return feature_cols, label 

def train_input_fn(): 
    return input_fn(df_train) 

def eval_input_fn(): 
    return input_fn(df_test) 

model_dir = '../svm_model_dir' 

model = svm.SVM(example_id_column='example_id', feature_columns=FEATURE_COLUMNS, model_dir=model_dir) 
model.fit(input_fn=train_input_fn, steps=10) 
results = model.evaluate(input_fn=eval_input_fn, steps=1) 
for key in sorted(results): 
    print("%s: %s" % (key, results[key])) 

と、すべての出力テキスト結果：

WARNING:tensorflow:The default value of combiner will change from "sum" to 

"sqrtn" after 2016/11/01. 
WARNING:tensorflow:The default value of combiner will change from "sum" to "sqrtn" after 2016/11/01. 
WARNING:tensorflow:tf.variable_op_scope(values, name, default_name) is deprecated, use tf.variable_scope(name, default_name, values) 
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: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 /Users/burness/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:882: hinge_loss (from t 
ensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30. 
Instructions for updating: 
Use tf.losses.hinge_loss instead. 
WARNING:tensorflow:From /Users/burness/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:1362: scalar_summary (f 
rom 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-dupl 
icate 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. 
WARNING:tensorflow:From /Users/burness/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/head.py:882: hinge_loss (from t 
ensorflow.contrib.losses.python.losses.loss_ops) is deprecated and will be removed after 2016-12-30. 
Instructions for updating: 
Use tf.losses.hinge_loss instead. 
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machi 
ne and could speed up CPU computations. 
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machi 
ne and could speed up CPU computations. 
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine 
and could speed up CPU computations. 
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine 
and could speed up CPU computations. 
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine 
and could speed up CPU computations. 
F tensorflow/core/framework/tensor_shape.cc:36] Check failed: NDIMS == dims() (2 vs. 1)Asking for tensor of 2 dimensions from a tensor of 1 dimensions 
[1] 66225 abort  python simple-tf-svm.py 

Answer 1

ちょうど他の場合、誰かにフォローアップすることは、このに動作します：real_valued_columnsは現在ベクトルでなければなりませんバッチディメンションでは、あなたの例ではスカラーですが。ここでは、より良いエラーメッセージの作成を追跡するgithub issueがあります。