title: Tensorflow error: "Tensor must be from the same graph as Tensor..."
body: <p>I am trying to train a simple binary logistic regression classifier using Tensorflow (version 0.9.0) in a very similar way to the <a href="https://www.tensorflow.org/versions/master/tutorials/wide/index.html" rel="noreferrer">beginner's tutorial</a> and am encountering the following error when fitting the model:</p>
  
  <pre><code>ValueError: Tensor("centered_bias_weight:0", shape=(1,), dtype=float32_ref) must be from the same graph as Tensor("linear_14/BiasAdd:0", shape=(?, 1), dtype=float32).
  </code></pre>
  
  <p>Here is my code:</p>
  
  <pre><code>import tempfile
  import tensorflow as tf
  import pandas as pd
  
  # Customized training data parsing
  train_data = read_train_data()
  feature_names = get_feature_names(train_data)
  labels = get_labels(train_data)
  
  # Construct dataframe from training data features
  x_train = pd.DataFrame(train_data , columns=feature_names)
  x_train["label"] = labels
  
  y_train = tf.constant(labels)
  
  # Create SparseColumn for each feature (assume all feature values are integers and either 0 or 1)
  feature_cols = [ tf.contrib.layers.sparse_column_with_integerized_feature(f,2) for f in feature_names ]
  
  # Create SparseTensor for each feature based on data
  categorical_cols = { f: tf.SparseTensor(indices=[[i,0] for i in range(x_train[f].size)],
                 values=x_train[f].values,
                 shape=[x_train[f].size,1]) for f in feature_names }
  
  # Initialize logistic regression model
  model_dir = tempfile.mkdtemp()
  model = tf.contrib.learn.LinearClassifier(feature_columns=feature_cols, model_dir=model_dir)
  
  def eval_input_fun():
      return categorical_cols, y_train
  
  # Fit the model - similarly to the tutorial
  model.fit(input_fn=eval_input_fun, steps=200)
  </code></pre>
  
  <p>I feel like I'm missing something critical... maybe something that was assumed in the tutorial but wasn't explicitly mentioned?</p>
  
  <p>Also, I get the following warning every time I call fit():</p>
  
  <pre><code>WARNING:tensorflow:create_partitioned_variables is deprecated.  Use tf.get_variable with a partitioner set, or tf.get_partitioned_variable_list, instead.
  </code></pre>