title: how to create tf.feature_columns with data have no header(csv file)?
body: <p>I am dealing with multi-class_classification_of_handwritten_digits in the following link <a href="https://colab.research.google.com/notebooks/mlcc/multi-class_classification_of_handwritten_digits.ipynb?utm_source=mlcc&amp;utm_campaign=colab-external&amp;utm_medium=referral&amp;utm_content=multiclass-colab&amp;hl=en#scrollTo=6sfw3LH0Oycm" rel="nofollow noreferrer">google colab</a></p>
  
  <p>Then I tried to put the code in my way to re write, feed and train the DNN.
  Due to the csv file has no header I am not able to create my feature columns, so I cannot train my model.</p>
  
  <p>Can you please help me to figure out how it has been done in the link or how it need to be for my code? Thanks in advance.</p>
  
  <pre><code>import pandas as pd
  import seaborn as sns
  import tensorflow as tf
  
  mnist_df = pd.read_csv("https://download.mlcc.google.com/mledu-datasets/mnist_train_small.csv",header=None)
  
  mnist_df.columns
  
  hand_df = mnist_df[0]
  hand_df.head()
  
  matrix_df = mnist_df.drop([0],axis=1)
  matrix_df.head()
  
  mnist_df = mnist_df.head(10000)
  
  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(matrix_df, hand_df, test_size=0.3, random_state=101)
  
  from sklearn.preprocessing import MinMaxScaler
  scaler = MinMaxScaler()
  
  matrix_df = pd.DataFrame(data=scaler.fit_transform(matrix_df),
                           columns=matrix_df.columns,
                           index=matrix_df.index)
  
  input_func = tf.estimator.inputs.pandas_input_fn(x=X_train,y=y_train,
                                                   batch_size=10,
                                                   num_epochs=1000,
                                                   shuffle=True)
  
  my_optimizer = tf.train.AdagradOptimizer(learning_rate=0.03)
  
  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)
  
  model = tf.estimator.LinearClassifier(feature_columns=feat_cols,
                                        n_classes=10,
                                        optimizer=my_optimizer,
                        config=tf.estimator.RunConfig(keep_checkpoint_max=1))
  
  model.train(input_fn=input_func,steps=1000)
  </code></pre>