title: Tensorflow evaluation frequency
body: <p>I am using the <code>train_and_evaluate</code> function in tensorflow and want to make the eval step happen more frequently (either by the global step or time elapsed). This is my code (model function is not shown).</p>
  
  <pre><code>def get_classifier(batch_size):
      config = tf.estimator.RunConfig(
          model_dir="models/shape_model_cnn_3",
          save_checkpoints_secs=300,
          save_summary_steps=100)
  
      params = tf.contrib.training.HParams(
          batch_size=batch_size,
          num_conv=[48,64,96], # Sizes of each convolutional layer
          conv_len=[2,3,4], # Kernel size of each convolutional layer
          num_nodes=128, # Number of LSTM nodes for each LSTM layer
          num_layers=3, # Number of LSTM layers
          num_classes=7, # Number of classes in final layer
          learning_rate=0.0001,
          gradient_clipping_norm=9.0,
          dropout=0.3)
  
      classifier = tf.estimator.Estimator(
          model_fn=my_model,
          config=config,
          params=params
      )
  
      return classifier
  
  classifier = get_classifier(8)
  
  train_spec = tf.estimator.TrainSpec(
      input_fn=lambda:input.batch_dataset("dataset/shape-train-???.tfrecords", tf.estimator.ModeKeys.TRAIN, 8),
      max_steps=100000
  )
  
  eval_spec = tf.estimator.EvalSpec(
      input_fn=lambda:input.batch_dataset("dataset/shape-eval-???.tfrecords", tf.estimator.ModeKeys.EVAL, 8)
  )
  
  tf.estimator.train_and_evaluate(classifier, train_spec, eval_spec)
  </code></pre>
  
  <p>I have tried using the <code>start_delay_secs</code> parameter in my <code>EvalSpec</code>, im not sure if this is what it is for but it doesn't seem to have any effect anyway</p>