Evaluate / Train Model
Learn how to evaluate and train models
Last updated
Learn how to evaluate and train models
Last updated
Once the model, dataset, loss, and optimizer are set, we can perform operations that fetch the data throughout the model. These operations are Evaluate Model and Train Model.
The difference between these two are that the Evaluate Model operation does not change the state of the model (its weights and parameters), while the Train Model operation does.
The Evaluate Model operation inferences the dataset throughout the model, and collects performance metrics and metadata for each sample.
This operation is performed on models that have a state (post train weights), and used to collect metrics on the performance and metadata of the dataset. After it is finished, the collected data is presented on the Dashboard.
More than one evaluation can be performed on a model. For example, different data slices (training/validation/test) on different datasets. In this case, the metrics are added to the Dashboard.
After the model is all set, follow this procedure to evaluate the current dataset:
Click the button at the top to open the Evaluate Model dialog. More info about the properties below.
Select a source model from the right panel.
Set the Evaluated Epoch and Subset.
Set the Batch Size.
Click .
Selected Subset - Training / Validation / Test
Batch Size - the amount of samples to be fetched per iteration
Add to Dashboard - make the model visible on the current dashboard
Evaluated Network - the Revision Name of the network version. This is based on the model selected for evaluation (see below).
Evaluated Model - the Model selected for evaluation (set from the right panel)
Evaluated Epoch - shows the current epoch of the selected model
Tensorleap can import trained and untrained models. More info at Import Model.
If created from scratch, a model needs to be trained.
For more information on importing trained and untrained models into Tensorleap, see Import Model.
To train a model:
Select the Training Model.
Set the Batch Size and Number of Epochs.
(Optional) Check the Early Stop with Main KPI metric and Patience Period.
Enter a New Model Name.
Training Model - Train from Scratch / Copy Initial Weights / Continue Training.
Batch Size - the amount of samples to be fetched per iteration
Number of Epochs - the number of complete passes through the training data
Early Stop - Stops the training when the main KPI stops improving
Main KPI - Loss / Accuracy / Mean
Patience Period -
Add to Dashboard - make the model visible on the current dashboard . Checked by default.
Source Network - the Revision Name of the network version (set from the right panel)
New Model Name - the resulting Model's Name (set from the right panel)
Click the button at the top to open the Train Model dialog. More info about the properties below.
Click .
