title: tf.contrib.learn Quickstart: Fix float64 Warning
body: <p>I'm getting myself started with TensorFlow by working through the posted tutorials.</p>
  
  <p>I have the Linux CPU python2.7 version 0.10.0 running on Fedora 23 (twenty three).</p>
  
  <p>I am trying the tf.contrib.learn Quickstart tutorial as per the following code.</p>
  
  <blockquote>
    <p><a href="https://www.tensorflow.org/versions/r0.10/tutorials/tflearn/index.html#tf-contrib-learn-quickstart" rel="nofollow">https://www.tensorflow.org/versions/r0.10/tutorials/tflearn/index.html#tf-contrib-learn-quickstart</a></p>
  </blockquote>
  
  <pre><code>from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import tensorflow as tf
  import numpy as np
  
  # Data sets
  IRIS_TRAINING = "IRIS_data/iris_training.csv"
  IRIS_TEST = "IRIS_data/iris_test.csv"
  
  # Load datasets.
  training_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TRAINING,
                                                     target_dtype=np.int)
  test_set = tf.contrib.learn.datasets.base.load_csv(filename=IRIS_TEST,
                                                 target_dtype=np.int)
  
  # Specify that all features have real-value data
  feature_columns = [tf.contrib.layers.real_valued_column("", dimension=4)]
  
  # Build 3 layer DNN with 10, 20, 10 units respectively.
  classifier = tf.contrib.learn.DNNClassifier(feature_columns=feature_columns,
                                          hidden_units=[10, 20, 10],
                                          n_classes=3,
                                          model_dir="/tmp/iris_model")
  
  # Fit model.
  classifier.fit(x=training_set.data, 
             y=training_set.target, 
             steps=2000)
  
  # Evaluate accuracy.
  accuracy_score = classifier.evaluate(x=test_set.data,
                                   y=test_set.target)["accuracy"]
  print('Accuracy: {0:f}'.format(accuracy_score))
  
  # Classify two new flower samples.
  new_samples = np.array(
  [[6.4, 3.2, 4.5, 1.5], [5.8, 3.1, 5.0, 1.7]], dtype=float)
  y = classifier.predict(new_samples)
  print('Predictions: {}'.format(str(y)))
  </code></pre>
  
  <p>The Code Executes, but gives float64 warnings. As Such:</p>
  
  <pre><code>$ python confErr.py
  WARNING:tensorflow:load_csv (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed after 2016-09-15.
  Instructions for updating:
  Please use load_csv_{with|without}_header instead.
  WARNING:tensorflow:load_csv (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed after 2016-09-15.
  Instructions for updating:
  Please use load_csv_{with|without}_header instead.
  WARNING:tensorflow:Using default config.
  WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
  WARNING:tensorflow:Setting feature info to TensorSignature(dtype=tf.float64, shape=TensorShape([Dimension(None), Dimension(4)]), is_sparse=False)
  WARNING:tensorflow:Setting targets info to TensorSignature(dtype=tf.int64, shape=TensorShape([Dimension(None)]), is_sparse=False)
  WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
  WARNING:tensorflow:Given features: Tensor("input:0", shape=(?, 4), dtype=float64), required signatures: TensorSignature(dtype=tf.float64, shape=TensorShape([Dimension(None), Dimension(4)]), is_sparse=False).
  WARNING:tensorflow:Given targets: Tensor("output:0", shape=(?,), dtype=int64), required signatures: TensorSignature(dtype=tf.int64, shape=TensorShape([Dimension(None)]), is_sparse=False).
  Accuracy: 0.966667
  WARNING:tensorflow:float64 is not supported by many models, consider casting to float32.
  Predictions: [1 1]
  </code></pre>
  
  <p>Note: replace 'load_csv()' with 'load_csv_with_header()' produces the correct Prediction. but float64 warnings remain.</p>
  
  <p>I have tried explicitly listing dtype (np.int32 ; np.float32; tf.int32; tf.float32) for training_set, test_set and new_samples.</p>
  
  <p>I also tried 'casting' feature_columns as:</p>
  
  <pre><code>feature_columns = tf.cast(feature_columns, tf.float32)
  </code></pre>
  
  <p>The problems with float64 are known development issue, but I'm wondering if there is some workaround?</p>