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+ using TensorBoardLogger # import the TensorBoardLogger package
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+ using Logging # import Logging package
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+ using Random # Exports randn
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+
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+ # Run 10 experiments to see a plot
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+ for j in 1 : 10
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+ logger = TBLogger (" random_walks/run$j " , tb_append)
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+
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+ sigma = 0.05
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+ epochs = 50
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+ bias = (rand ()* 2 - 1 ) # create a random bias
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+ use_seed = false
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+ # Add in the a dummy loss metric
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+ with_logger (logger) do
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+ x = 0.0
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+ for i in 1 : epochs
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+ x += sigma * randn () + bias
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+ @info " scalar" loss = x
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+ end
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+ end
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+
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+ # Hyperparameter is a dictionary of parameter names to their values. This
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+ # supports numerical types, bools and strings. Non-bool numerical types
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+ # are converted to Float64 to be displayed.
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+ hparams_config = Dict {String, Any} (
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+ " sigma" => sigma,
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+ " epochs" => epochs,
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+ " bias" => bias,
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+ " use_seed" => use_seed,
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+ " method" => " MC"
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+ )
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+ # Specify a list of tags that you want to show up in the hyperparameter
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+ # comparison
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+ metrics = [" scalar/loss" ]
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+
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+ # Write the hyperparameters and metrics config to the logger.
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+ write_hparams! (logger, hparams_config, metrics)
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+ end
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