DeepLearningModelInfo (h2o-algos 3.0.0 API)

java.lang.Object
- water.Iced
- - hex.deeplearning.DeepLearningModelInfo

All Implemented Interfaces:

java.io.Externalizable, java.io.Serializable, java.lang.Cloneable, water.Freezable
```
public class DeepLearningModelInfo
extends water.Iced
```
This class contains the state of the Deep Learning model This will be shared: one per node

See Also:
Serialized Form

Field Summary

Fields
Modifier and Type Field and Description

DataInfo data_info

float[] mean_a

DeepLearningParameters parameters

float[] rms_weight

water.util.TwoDimTable summaryTable

Fields
Modifier and Type	Field and Description
`DataInfo`	`data_info`
`float[]`	`mean_a`
`DeepLearningParameters`	`parameters`
`float[]`	`rms_weight`
`water.util.TwoDimTable`	`summaryTable`

Constructor Summary

Constructors
Constructor and Description
`DeepLearningModelInfo(DeepLearningParameters params, DataInfo dinfo, boolean classification, water.fvec.Frame train, water.fvec.Frame valid)` Main constructor

Method Summary

Methods
Modifier and Type	Method and Description
`void`	`add_processed_global(long p)`
`void`	`add_processed_local(long p)`
`void`	`add(DeepLearningModelInfo other)` Add another model info into this This will add the weights/biases/learning rate helpers, and the number of processed training samples Note: It will NOT add the elastic averaging helpers, which are always kept constant (they already are the result of a reduction)
`protected long`	`checksum_impl()` Unique identifier for this model's state, based on raw numbers
`void`	`computeStats()` Compute statistics about this model on all nodes
`float[]`	`computeVariableImportances()` Compute Variable Importance, based on GEDEON: DATA MINING OF INPUTS: ANALYSING MAGNITUDE AND FUNCTIONAL MEASURES
`DataInfo`	`data_info()`
`protected void`	`div(float N)` Divide all weights/biases by a real-valued number
`water.Key`	`elasticAverageModelInfoKey()`
`Storage.Matrix`	`get_ada_dx_g(int i)`
`Storage.DenseVector`	`get_avg_activations(int i)`
`Storage.DenseVector`	`get_biases_ada_dx_g(int i)`
`Storage.DenseVector`	`get_biases_momenta(int i)`
`Storage.DenseVector`	`get_biases(int i)`
`DeepLearningParameters`	`get_params()`
`long`	`get_processed_global()`
`long`	`get_processed_local()`
`long`	`get_processed_total()`
`Storage.Matrix`	`get_weights_momenta(int i)`
`Storage.Matrix`	`get_weights(int i)`
`water.Key`	`localModelInfoKey(water.H2ONode node)`
`protected void`	`mult(float N)` Multiply all weights/biases by a real-valued number
`void`	`set_params(DeepLearningParameters p)`
`void`	`set_processed_global(long p)`
`void`	`set_processed_local(long p)`
`void`	`set_unstable()`
`long`	`size()`
`static DeepLearningModelInfo`	`timeAverage(DeepLearningModelInfo nodeAverageModel)` TimeAveraging as part of Elastic Averaging Algorithm Cf.
`java.lang.String`	`toString()` Print a summary table
`java.lang.String`	`toStringAll()` Debugging printout
`boolean`	`unstable()`

Methods inherited from class water.Iced
clone, frozenType, read_impl, read, readExternal, readJSON_impl, readJSON, toJsonString, write_impl, write, writeExternal, writeHTML_impl, writeHTML, writeJSON_impl, writeJSON

Methods inherited from class java.lang.Object
equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - summaryTable
```
public water.util.TwoDimTable summaryTable
```
  - data_info
```
public DataInfo data_info
```
  - parameters
```
public DeepLearningParameters parameters
```
  - rms_weight
```
public float[] rms_weight
```
  - mean_a
```
public float[] mean_a
```
- Constructor Detail
  - DeepLearningModelInfo
```
public DeepLearningModelInfo(DeepLearningParameters params,
                     DataInfo dinfo,
                     boolean classification,
                     water.fvec.Frame train,
                     water.fvec.Frame valid)
```
    Main constructor
    
    Parameters:
    params - Model parameters
    dinfo - Data Info
    classification - Whether we do classification or not
    train - User-given training data frame, prepared by AdaptTestTrain
    valid - User-specified validation data frame, prepared by AdaptTestTrain
- Method Detail
  - data_info
```
public DataInfo data_info()
```
  - size
```
public long size()
```
  - get_weights
```
public final Storage.Matrix get_weights(int i)
```
  - get_biases
```
public final Storage.DenseVector get_biases(int i)
```
  - get_weights_momenta
```
public final Storage.Matrix get_weights_momenta(int i)
```
  - get_biases_momenta
```
public final Storage.DenseVector get_biases_momenta(int i)
```
  - get_ada_dx_g
```
public final Storage.Matrix get_ada_dx_g(int i)
```
  - get_biases_ada_dx_g
```
public final Storage.DenseVector get_biases_ada_dx_g(int i)
```
  - get_avg_activations
```
public final Storage.DenseVector get_avg_activations(int i)
```
  - get_params
```
public final DeepLearningParameters get_params()
```
  - set_params
```
public final void set_params(DeepLearningParameters p)
```
  - unstable
```
public boolean unstable()
```
  - set_unstable
```
public void set_unstable()
```
  - get_processed_global
```
public long get_processed_global()
```
  - set_processed_global
```
public void set_processed_global(long p)
```
  - add_processed_global
```
public void add_processed_global(long p)
```
  - get_processed_local
```
public long get_processed_local()
```
  - set_processed_local
```
public void set_processed_local(long p)
```
  - add_processed_local
```
public void add_processed_local(long p)
```
  - get_processed_total
```
public long get_processed_total()
```
  - toString
```
public java.lang.String toString()
```
    Print a summary table
    
    Overrides:
    
    toString in class java.lang.Object
    
    Returns:
    String containing ASCII version of summary table
  - toStringAll
```
public java.lang.String toStringAll()
```
    Debugging printout
    
    Returns:
    String with useful info
  - add
```
public void add(DeepLearningModelInfo other)
```
    Add another model info into this This will add the weights/biases/learning rate helpers, and the number of processed training samples Note: It will NOT add the elastic averaging helpers, which are always kept constant (they already are the result of a reduction)
    
    Parameters:
    other -
  - mult
```
protected void mult(float N)
```
    Multiply all weights/biases by a real-valued number
    
    Parameters:
    N -
  - div
```
protected void div(float N)
```
    Divide all weights/biases by a real-valued number
    
    Parameters:
    N -
  - computeVariableImportances
```
public float[] computeVariableImportances()
```
    Compute Variable Importance, based on GEDEON: DATA MINING OF INPUTS: ANALYSING MAGNITUDE AND FUNCTIONAL MEASURES
    
    Returns:
    variable importances for input features
  - computeStats
```
public void computeStats()
```
    Compute statistics about this model on all nodes
  - checksum_impl
```
protected long checksum_impl()
```
    Unique identifier for this model's state, based on raw numbers
  - timeAverage
```
public static DeepLearningModelInfo timeAverage(DeepLearningModelInfo nodeAverageModel)
```
    TimeAveraging as part of Elastic Averaging Algorithm Cf. equation 6 of arXiv:1412.6651v5
    
    Parameters:
    nodeAverageModel - current average of per-node models
    
    Returns:
    Time-average of node-averages (consensus model, "the" model)
  - localModelInfoKey
```
public water.Key localModelInfoKey(water.H2ONode node)
```
  - elasticAverageModelInfoKey
```
public water.Key elasticAverageModelInfoKey()
```

Class DeepLearningModelInfo

Field Summary

Constructor Summary

Method Summary

Methods inherited from class water.Iced

Methods inherited from class java.lang.Object

Field Detail

summaryTable

data_info

parameters

rms_weight

mean_a

Constructor Detail

DeepLearningModelInfo

Method Detail

data_info

size

get_weights

get_biases

get_weights_momenta

get_biases_momenta

get_ada_dx_g

get_biases_ada_dx_g

get_avg_activations

get_params

set_params

unstable

set_unstable

get_processed_global

set_processed_global

add_processed_global

get_processed_local

set_processed_local

add_processed_local

get_processed_total

toString

toStringAll

add

mult

div

computeVariableImportances

computeStats

checksum_impl

timeAverage

localModelInfoKey

elasticAverageModelInfoKey