Difference between revisions of "Loss"
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There are many options for loss in Tensorflow (Keras). The actual optimized objective is the mean of the output array across all datapoints. A loss function gives a distance between a model's predictions to the ground truth labels. This is the distance (loss value) that the network aims to minimize; the lower this value, the better the current model describes our training data set. [http://keras.io/losses/ Click here For a list of Keras loss functions.] Loss is one of the two parameters required to compile a model... | There are many options for loss in Tensorflow (Keras). The actual optimized objective is the mean of the output array across all datapoints. A loss function gives a distance between a model's predictions to the ground truth labels. This is the distance (loss value) that the network aims to minimize; the lower this value, the better the current model describes our training data set. [http://keras.io/losses/ Click here For a list of Keras loss functions.] Loss is one of the two parameters required to compile a model... | ||
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Revision as of 09:24, 31 August 2019
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- Optimizer Functions
- Objective vs. Cost vs. Loss vs. Error Function
- Common Loss functions in machine learning | Ravindra Parmar - Towards data Science
- Loss Functions Explained | Siraj Raval
- Loss Functions | ML Cheatsheet
There are many options for loss in Tensorflow (Keras). The actual optimized objective is the mean of the output array across all datapoints. A loss function gives a distance between a model's predictions to the ground truth labels. This is the distance (loss value) that the network aims to minimize; the lower this value, the better the current model describes our training data set. Click here For a list of Keras loss functions. Loss is one of the two parameters required to compile a model...
