### Resource retrieval

Get the pre-trained network:

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### Basic usage

Predict the next character of a given sequence:

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Get the top 15 probabilities:

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Plot the top 15 probabilities:

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### Generation

Generate text efficiently with NetStateObject. A built-in option for temperature sampling is available in Wolfram Language 12.0, while it has to be implemented explicitly in earlier versions.

Generate for 100 steps using “\begin” as an initial string:

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The third optional argument is a “temperature” parameter that scales the input to the final softmax. A high temperature flattens the distribution from which characters are sampled, increasing the probability of extracting less likely characters:

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Decreasing the temperature sharpens the peaks of the sampling distribution, further decreasing the probability of extracting less likely characters:

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Very high temperature settings are equivalent to random sampling:

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Very low temperature settings are equivalent to always picking the character with maximum probability. It is typical for sampling to “get stuck in a loop”:

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### Inspection of predictions

Define a function that takes a string and guesses the next character as it reads, showing the predictions in a grid. The input string is shown on top, while the top 5 predictions are aligned below each character, starting from more likely guesses. For each prediction, the intensity of the color is proportional to the probability:

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### Net information

Inspect the sizes of all arrays in the net:

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Obtain the total number of parameters:

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Obtain the layer type counts:

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Display the summary graphic:

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### Export to MXNet

Export the net into a format that can be opened in MXNet:

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Export also creates a net.params file containing parameters:

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Get the size of the parameter file:

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The size is similar to the byte count of the resource object:

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