title: ValueError: Cannot convert a partially known TensorShape to a Tensor: (?,)
body: <p>I am getting error which is specify "<strong>ValueError: Cannot convert a partially known TensorShape to a Tensor: (?,)</strong>"
  I just want to get prediction results bu I can not run fit method.(<code>estimator.fit(input_fn=get_input_fn_train(), steps=10000</code>) I get the error while trying to run this code. I have 10000 rows and 8 int columns.I dropped string columns for now . <code>(x_train.shape)</code> -->(8000, 8).<code>y_train.shape</code>--> (8000,).What kind of shape is talking about? I am googling bu I can not find useful things. What shoul I do? What am I missing? All codes below. Thanks.</p>
  
  <pre><code>import pandas as pd
  import tensorflow as tf
  import numpy as np
  import tempfile
  from sklearn.model_selection import train_test_split
  
  def split_data(data, rate, label):
      data = data.dropna()
  
      train_data, test_data = train_test_split(data, test_size=rate)
  
      train_label = train_data[label]
      train_data = train_data.drop(label, 1)
  
      test_label = test_data[label]
      test_data = test_data.drop(label, 1)
      return train_data, train_label, test_data, test_label
  
  
  LABEL="Exited"
  
  
  data = pd.read_csv("Churn_Modelling.csv", skipinitialspace=True, header=0)
  
  data.drop("Surname", axis=1, inplace=True)
  data.drop("RowNumber", axis=1, inplace=True)
  data.drop("CustomerId", axis=1, inplace=True)
  data.drop("Geography", axis=1, inplace=True)
  data.drop("Gender", axis=1, inplace=True)
  
  x_train, y_train, x_test, y_test = split_data(data, 0.20, LABEL)
  
  
  
  def get_input_fn_train():
          input_fn = tf.estimator.inputs.pandas_input_fn(
              x=x_train.astype('float64'),
              y=y_train.astype('float32'),
              shuffle=False
          )
          return input_fn
  
  def get_input_fn_test():
          input_fn = tf.estimator.inputs.pandas_input_fn(
              x=x_test.astype('float64'),
              y=y_test.astype('float32'),
              shuffle=False
          )
          return input_fn
  
  
  feature_columns = tf.contrib.learn.infer_real_valued_columns_from_input_fn(
                                                         get_input_fn_train())
  
  
  model_dir = tempfile.mkdtemp()
  estimator = tf.contrib.learn.SVM(
  example_id_column=tf.constant(np.arange(len(y_train))),
  feature_columns=feature_columns, l2_regularization=10.0,model_dir=model_dir)                                                       
  
  estimator.fit(input_fn=get_input_fn_train(), steps=10000) 
  #(I am getting  error this line)
  
  
  results=estimator.evaluate(input_fn=get_input_fn_test(), steps=1)
  
  
  for key in sorted(results):
    print("%s: %s" % (key, results[key]))
  
  
  pred=list(estimator.predict(input_fn=get_input_fn_test()))
  </code></pre>