title: h2o deeplearning checkpoint model
body: <p>Folks,</p>
  
  <p>I have some problem when try resuming h2o deep learning in R from a checkpointed model <strong>with validation frame provided</strong>. It says "Validation dataset must be the same as for the check pointed model", which I believe I do have the same validation datasets. If I leave validation_frame blank, checkpointing model works fine. I attach my code below:</p>
  
  <pre><code>localh2o &lt;- h2o.init(nthreads = -1)
  train_image.hex &lt;- read.csv("mnist_train.csv",header=FALSE)
  train_image.hex[,785] &lt;- factor(train_image.hex[,785])
  train_image.hex &lt;- as.h2o(train_image.hex)
  test_image.hex &lt;- read.csv("mnist_test.csv",header=FALSE)
  test_image.hex[,785] &lt;- factor(test_image.hex[,785])
  test_image.hex &lt;- as.h2o(test_image.hex)
  
  
  mnist_model &lt;- h2o.deeplearning(x=1:784, y = 785,
  training_frame= train_image.hex, 
  validation_frame = test_image.hex,
  activation = "RectifierWithDropout", hidden = c(500,1000),
  input_dropout_ratio = 0.2,
  hidden_dropout_ratios = c(0.5,0.5), adaptive_rate=TRUE,
  rho=0.98, epsilon = 1e-7,
  l1 = 1e-8, l2 = 1e-7, max_w2 = 10, 
  epochs = 10, export_weights_and_biases = TRUE,
  variable_importances = FALSE
  )
  h2o.saveModel(mnist_model, path="/tmp",force=TRUE)
  </code></pre>
  
  <p>Then I shut down the h2o, quit R and restart h2o in R to resume training, where h2o errors out:</p>
  
  <pre><code>localh2o &lt;- h2o.init(nthreads = -1)
  train_image.hex &lt;- read.csv("mnist_train.csv",header=FALSE)
  train_image.hex[,785] &lt;- factor(train_image.hex[,785])
  train_image.hex &lt;- as.h2o(train_image.hex)
  test_image.hex &lt;- read.csv("mnist_test.csv",header=FALSE)
  test_image.hex[,785] &lt;- factor(test_image.hex[,785])
  test_image.hex &lt;- as.h2o(test_image.hex)
  startmodel &lt;- h2o.loadModel("/tmp/DeepLearning_model_R_1443812402059_20", localh2o)
  
  mnist_model &lt;- h2o.deeplearning(x=1:784, y = 785,
  checkpoint = startmodel@model_id,
  training_frame= train_image.hex, 
  validation_frame = test_image.hex,
  activation = "RectifierWithDropout", hidden = c(500,1000),
  input_dropout_ratio = 0.2,
  hidden_dropout_ratios = c(0.5,0.5), adaptive_rate=TRUE,
  rho=0.98, epsilon = 1e-7,
  l1 = 1e-8, l2 = 1e-7, max_w2 = 10, 
  epochs = 10, export_weights_and_biases = TRUE,
  variable_importances = FALSE
  )
  </code></pre>