Training
Here we will train our model with multiple GPU's or CPU's device.
- Training a model simply means learning (determining) good values for all the weights and the bias from labeled examples.
- Deep learning models are built using neural networks. A neural network takes in inputs, which are then processed in hidden layers using weights that are adjusted during training. Then the model spits out a prediction.
- Deep Learning Studio supports multiple GPU's devices which accelerates the training.
- In the training tab, you can change the default “Run Name”.
- Also, you can “Save Weights” in three ways :
- “End of epoch”
- “Best Accuracy”
- “Lowest Loss"
- Select CPU or GPU whichever you have by clicking on the drop-down button of “Select Devices”.
- You can choose multiple devices (“CPU’s” or “GPUs”) at the same time.
- You can also start new training by “Loading the previous weight”.
- While training is going on you will see the graph of Accuracy and Loss.
- After completion of every batch, you will see the changes in Accuracy and Loss graph.
- While training is in progress, you can stop the training in between if you wish.
- Note : Early stopping will stop the model from training before the number of epochs is reached if the model stops improving.
- suppose you are stop the training after 3 epoch. This means that after 3 epochs in a row in which the model doesn’t improve, training will stop.
- On the training screen you can able to see the :
- “Processing Speed”
- “Epoch Speed”
- "Loss"
- "Accuracy"
- “Load Memory” for CPU and GPU
- “Free Memory” for CPU and GPU
- “Elapsed Time” for training
- “Epochs” .
Last modified 2yr ago