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How to reduce training time in TensorFlow tutorial?
If you use TPUs, you might consider taking a deeper look at the official Tensorflow tutorial from documentation on training distribution. All those steps lead to a massive reduction of your model training time. This graph traces the 5 epochs training time after each improvement of the training pipeline.
How to save and load weights in TensorFlow?
To demonstrate how to save and load weights, you’ll use the MNIST dataset. To speed up these runs, use the first 1000 examples: You can use a trained model without having to retrain it, or pick-up training where you left off in case the training process was interrupted.
How to train binary classifier in TensorFlow core?
You’ll train a binary classifier to perform sentiment analysis on an IMDB dataset. At the end of the notebook, there is an exercise for you to try, in which you’ll train a multi-class classifier to predict the tag for a programming question on Stack Overflow.
How is text classification done in TensorFlow hub?
Text classification can be done in two different ways: manual and automatic classification. For manual classification, a human annotator interprets the content of text and categorizes it accordingly. This method usually can provide quality results but is very time-consuming and expensive.
How to do multi GPU training in TensorFlow?
The easiest way to perform multi-GPU training is to use the MirroredStrategy. It instantiates your model on each GPU. At each step, different batches are sent to the GPUs which run the backward pass. Then, gradients are aggregated to perform weights update, and the updated values are propagated to each model instantiated.
How are weights initialized in a TensorFlow model?
Each weight in the model is backend by a Variable object. In TensorFlow.js, a Variable is a floating-point Tensor with one additional method assign () used for updating its values. The Layers API automatically initializes the weights using best practices.
Which is the summary method in TensorFlow.js?
model.summary () is a useful method if you want to get an overview of your model and see the total number of parameters: Each weight in the model is backend by a Variable object. In TensorFlow.js, a Variable is a floating-point Tensor with one additional method assign () used for updating its values.