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ADTypes = " 47edcb42-4c32-4615-8424-f2b9edc5f35b"
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BenchmarkTools = " 6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
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Bijectors = " 76274a88-744f-5084-9051-94815aaf08c4"
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- ChangesOfVariables = " 9e997f8a-9a97-42d5-a9f1-ce6bfc15e2c0"
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DiffResults = " 163ba53b-c6d8-5494-b064-1a9d43ac40c5"
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DifferentiationInterface = " a0c0ee7d-e4b9-4e03-894e-1c5f64a51d63"
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Distributions = " 31c24e10-a181-5473-b8eb-7969acd0382f"
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- Flux = " 587475ba-b771-5e3f-ad9e-33799f191a9c"
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Functors = " d9f16b24-f501-4c13-a1f2-28368ffc5196"
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IrrationalConstants = " 92d709cd-6900-40b7-9082-c6be49f344b6"
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LinearAlgebra = " 37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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NormalizingFlows = " 50e4474d-9f12-44b7-af7a-91ab30ff6256"
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Optimisers = " 3bd65402-5787-11e9-1adc-39752487f4e2"
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using Random, Distributions, LinearAlgebra
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using Bijectors: transformed
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- using Flux
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-
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- """
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- A simple wrapper for a 3 layer dense MLP
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- """
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- function mlp3 (input_dim:: Int , hidden_dims:: Int , output_dim:: Int ; activation= Flux. leakyrelu)
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- return Chain (
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- Flux. Dense (input_dim, hidden_dims, activation),
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- Flux. Dense (hidden_dims, hidden_dims, activation),
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- Flux. Dense (hidden_dims, output_dim),
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- )
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- end
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function compare_trained_and_untrained_flow (
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flow_trained:: Bijectors.MultivariateTransformed ,
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