Experiment tabs

An experiment contains the following tabs during and after model training:

• Charts
• Summary
• Metrics
• Train Data Insights
• Validation Prediction Insights
• Validation Interpretation Insights
• Logs
• Config

Access experiment tabs

To access the tabs of a completed or running experiment, consider the following instructions:

1. In the H2O Hydrogen Torch navigation menu, click View experiments.
2. Click the name of the experiment.

Charts

H2O Hydrogen Torch offers four different graphs to highlight performance metrics during an experiment, and such graphs can be located on the Charts tab of an experiment. The graphs are as follows:

• Learning Rate
• Train Batch Loss
• Validation Batch Loss
• Validation {Metric}

Learning Rate

The Learning Rate graph displays the current learning rate throughout the training process visualized after each training batch.

Train Batch Loss

The Train Batch Loss graph displays the current training loss throughout the training process visualized after each training batch.

Validation Batch Loss

The Validation Batch Loss graph displays the current validation loss throughout the training process. Loss is calculated as the average of all validation batches. By default, it is calculated and visualized after each training epoch, but this frequency can be adjusted via the Evaluation Epochs setting when creating an experiment.

Note

The Evaluation Epochs setting is only available when the Experience Level is set to Expert or Master. To learn more, see Experience levels.

Validation {Metric}

The Validation {Metric} graph displays the current validation metric throughout the training process. In this case, the {Metric} refers to the selected metric for the problem type (e.g., MAE for an image regression experiment). {Metric} is calculated on full validation set predictions. By default, it is calculated and visualized after each training epoch, but this frequency can be adjusted via the Evaluation Epochs setting when creating an experiment.

Note

The Evaluation Epochs setting is only available when the Experience Level is set to Expert or Master. To learn more, see Experience levels.

Summary

On the Summary tab, H2O Hydrogen Torch displays the following table containing summary metrics about the selected experiment:

Name	Description
Name	Name of the experiment.
Dataset	Dataset used for the experiment.
Problem Type	Problem type of the experiment.
Fold	The validation fold value.
Seed	The random seed value that you selected for model training.
GPU List	List of GPUs that H2O Hydrogen Torch used during model training.
Loss	Loss refers to the objective function optimized directly during model training.
Metric	Metric refers to the selected metric to evaluate model performance.
Epoch	The number of epochs used to train the model.
Train Metric	Train metric refers to evaluation metric computed on train data. A value for Train metric is available when Calculate Train Metric is turned on when defining the experiment settings.
Val Metric	Val Metric refers to the evaluation metric computed on the validation data.
Test Metric	Test metric refers to the evaluation metric computed on the test data.

Metrics

On the Metrics tab, H2O Hydrogen Torch displays the following metrics:

• An array of scorer values (metrics) applicable to the problem type of the experiment (e.g., Accuracy, MAE, MSE, MAPE, etc.), where the scorer values refer to the validation dataset
  • If you turned the Calculate Train Metric setting (for the experiment), H2O Hydrogen Torch also provides scorer values for the training dataset
  • If you provided a test dataset, H2O Hydrogen Torch also provides scorer values for the training dataset
• Best thresholds and their corresponding metric values are shown for threshold-dependant metrics
• For classification experiments, H2O Hydrogen Torch displays confusion matrices where an interactive threshold slider is available in the case of binary or multi-label classification. Moving the slider changes the metric values for threshold-dependant metrics along with the confusion matrices

Train data insights

On the Train data insight tab, H2O Hydrogen Torch displays random train visual samples after augmentations.

Note

The Train data insights tab is available for image problem-type experiments.

Validation prediction insights

On the Validation Prediction Insights tab, H2O Hydrogen Torch visualizes the validation samples of an experiment while providing the loss value of each sample. In particular, isights are divided as follows:

• Random validation samples
  • In the Random validation samples tab, H2O Hydrogen Torch visualizes an experiment's best and worst validation samples while proving the loss value for each sample.
• Best validation samples
  • In the Best validation samples tab, H2O Hydrogen Torch visualizes the best validation samples of an experiment while providing the loss value of each sample. Samples in this tab are regarded as the model's smallest significant errors.
• Worst validation samples
  • In the Worst validation samples tab, H2O Hydrogen Torch visualizes the worst validation samples of an experiment while providing the loss value of each sample. Samples in this tab are regarded as the model's most significant errors. Samples in this tab can help identify issues the model might have.

Note

Evaluating the best and the worst performance differs for each problem type and might vary from the selected loss and the selected metric to assess the model.

Validation interpretation insights

The Validation interpretation insights tab is only available for image, text, and audio classification and regression experiments.

• Image
  • For an image regression and classification experiment, on the Validation interpretation insights tab, H2O Hydrogen Torch highlights with Gradient-weighted Class Activation Mapping (Grad-CAM) the areas of an image the model considered the most when generating a prediction for the image.
• Text
  • For a regression and classification text experiment, on the Validation interpretation insights tab, H2O Hydrogen Torch highlights with Integrated Gradients the words the model considered the most when generating a prediction for the text.
    • Highlighted green text: The greener the highlight, the higher the positive contribution to the prediction the text had
    • Highlighted red text: The redder the highlight, the higher the negative contribution to the prediction the text had
• Audio
  • For an audio regression and classification experiment, on the Validation Interpretation Insights tab, H2O Hydrogen Torch highlights with Gradient-weighted Class Activation Mapping (Grad-CAM) the areas of a spectrogram the model considered the most when generating a prediction for the image.
    Note

    H2O Hydrogen Torch only presents the first five seconds of an audio (spectrogram) if the audio is longer than five seconds.

Insights are divided as follows:

• Random validation samples
  • In the Random validation samples tab, H2O Hydrogen Torch visualizes random samples.
• Best validation samples
  • In the Best validation samples tab, H2O Hydrogen Torch visualizes the best validation samples. Samples in this tab are regarded as the model's smallest significant errors.
• Worst validation samples
  • In the Worst validation samples tab, H2O Hydrogen Torch visualizes the worst validation samples. Samples in this tab are regarded as the model's most significant errors. Samples in this tab can help identify issues the model might have.

Note

For image classification experiments, in the Random validation samples, Best validation samples, and Worst validation samples tab, H2O Hydrogen Torch for each class separately highlights with Gradient-weighted Class Activation Mapping (Grad-CAM) the areas of an image the model considered the most when generating a prediction for the image.

Logs

On the Logs tab, you can view an experiment's logs.

Config

On the Config tab, you can view the configurations (setting values) used during the experiment.

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