Improve plotting in notebook gpflux_with_keras_layers #41
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…lux into vincent/notebooks/hybrid
sebastianober
approved these changes
Aug 24, 2021
docs/notebooks/deep_cde.ipynb
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| @@ -234,7 +234,8 @@ | |||
| "source": [ | |||
| "## Deep Gaussian process with latent variables\n", | |||
| "\n", | |||
| "We suggest a Deep Gaussian process with a latent variable in the first layer to improve the error bars on the given dataset. The latent variable allows to model the heteroscedasticity, while an extra layer makes the model more expressive to catch sharp transitions.\n", | |||
| "To tackle the problem we suggest a Deep Gaussian process with a latent variable in the first layer. The latent variable will be able to capture the \n", | |||
| "heteroscedasticity, while the two layered deep GP is able to model the sharp transitions. \n", | |||
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Grammatically should be "two-layered" with hyphen.
docs/notebooks/deep_cde.ipynb
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| @@ -380,7 +381,7 @@ | |||
| "source": [ | |||
| "### Fit\n", | |||
| "\n", | |||
| "We can now fit the model. Because of the `DirectlyParameterizedEncoder`, which stores a sorted array of means and std. dev. for each point in the dataset, it is important to set the `batch_size` to the number of datapoints and set `shuffle` to `False`." | |||
| "We can now fit the model. Because of the `DirectlyParameterizedEncoder` it is important to set the batch size to the number of datapoints and turn off shuffle. This is so that we use the associated latent variable for each datapoint. If we would use an Amortized Encoder network this would not be necessary." | |||
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Minor but probably "Amortized Encoder" shouldn't have caps.
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