public class DeepLearningModel extends hex.SupervisedModel<DeepLearningModel,DeepLearningModel.DeepLearningParameters,DeepLearningModel.DeepLearningOutput>
| Modifier and Type | Class and Description |
|---|---|
static class |
DeepLearningModel.DeepLearningModelInfo |
static class |
DeepLearningModel.DeepLearningOutput |
static class |
DeepLearningModel.DeepLearningParameters |
static class |
DeepLearningModel.Errors |
| Modifier and Type | Field and Description |
|---|---|
long |
_timeLastScoreEnter |
water.Key |
actual_best_model_key |
long |
actual_train_samples_per_iteration |
double |
epoch_counter |
double |
time_for_communication_us |
long |
training_rows |
long |
validation_rows |
| Constructor and Description |
|---|
DeepLearningModel(water.Key destKey,
DeepLearningModel.DeepLearningParameters parms,
DeepLearningModel.DeepLearningOutput output,
water.fvec.Frame train,
water.fvec.Frame valid) |
DeepLearningModel(water.Key destKey,
DeepLearningModel cp,
boolean store_best_model,
water.fvec.Frame train,
FrameTask.DataInfo dataInfo)
Constructor to restart from a checkpointed model
|
| Modifier and Type | Method and Description |
|---|---|
hex.ConfusionMatrix2 |
cm()
for grid search error reporting
|
int |
compareTo(DeepLearningModel o) |
void |
delete_best_model() |
void |
delete_xval_models() |
float |
error() |
DeepLearningModel.DeepLearningParameters |
get_params() |
protected double |
missingColumnsType() |
DeepLearningModel.DeepLearningModelInfo |
model_info() |
double |
mse() |
water.api.ModelSchema |
schema() |
water.fvec.Frame |
score(water.fvec.Frame frame)
This is an overridden version of Model.score().
|
float[] |
score0(double[] data,
float[] preds)
Predict from raw double values representing the data
|
water.fvec.Frame |
scoreAutoEncoder(water.fvec.Frame frame)
Score auto-encoded reconstruction (on-the-fly, without allocating the reconstruction as done in Frame score(Frame fr))
|
DeepLearningModel.Errors[] |
scoring_history() |
java.lang.String |
toString() |
java.lang.String |
toStringAll() |
hex.VarImp |
varimp() |
adapt, adapt, adapt, addWarning, checksum, computeModelMetrics, getDomainMapping, getDomainMapping, remove_impl, score, score, score, score, score, start_training, start_training, stop_trainingdelete_and_lock, delete, delete, delete, read_lock, read_lock, unlock_all, unlock, update, write_lockpublic long actual_train_samples_per_iteration
public double time_for_communication_us
public double epoch_counter
public long training_rows
public long validation_rows
public water.Key actual_best_model_key
public long _timeLastScoreEnter
public DeepLearningModel(water.Key destKey,
DeepLearningModel cp,
boolean store_best_model,
water.fvec.Frame train,
FrameTask.DataInfo dataInfo)
cp - Checkpoint to restart fromdestKey - New destination key for the modelstore_best_model - Store only the best model instead of the latest onepublic DeepLearningModel(water.Key destKey,
DeepLearningModel.DeepLearningParameters parms,
DeepLearningModel.DeepLearningOutput output,
water.fvec.Frame train,
water.fvec.Frame valid)
public water.api.ModelSchema schema()
schema in class hex.Model<DeepLearningModel,DeepLearningModel.DeepLearningParameters,DeepLearningModel.DeepLearningOutput>public final DeepLearningModel.DeepLearningModelInfo model_info()
public DeepLearningModel.Errors[] scoring_history()
public final DeepLearningModel.DeepLearningParameters get_params()
protected double missingColumnsType()
missingColumnsType in class hex.Model<DeepLearningModel,DeepLearningModel.DeepLearningParameters,DeepLearningModel.DeepLearningOutput>public float error()
public int compareTo(DeepLearningModel o)
public hex.ConfusionMatrix2 cm()
public double mse()
public hex.VarImp varimp()
public java.lang.String toString()
toString in class java.lang.Objectpublic java.lang.String toStringAll()
public water.fvec.Frame score(water.fvec.Frame frame)
score in class hex.Model<DeepLearningModel,DeepLearningModel.DeepLearningParameters,DeepLearningModel.DeepLearningOutput>frame - Test datasetpublic float[] score0(double[] data,
float[] preds)
score0 in class hex.Model<DeepLearningModel,DeepLearningModel.DeepLearningParameters,DeepLearningModel.DeepLearningOutput>data - raw array containing categorical values (horizontalized to 1,0,0,1,0,0 etc.) and numerical values (0.35,1.24,5.3234,etc), both can contain NaNspreds - predicted label and per-class probabilities (for classification), predicted target (regression), can contain NaNspublic water.fvec.Frame scoreAutoEncoder(water.fvec.Frame frame)
frame - Original data (can contain response, will be ignored)public void delete_best_model()
public void delete_xval_models()