Training

Here we will train our model with multiple GPU's or CPU's device.

Overview:

  • 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” .

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