65 lines
1.5 KiB
Julia
65 lines
1.5 KiB
Julia
using CSV, Tables, Glob
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const TEST_DATA_DIR = joinpath(@__DIR__, "..", "data")
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function csv_to_matrix(file_path, types=Float32)
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return CSV.read(file_path, Tables.matrix, header=0, delim=',', types=types)
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end
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function load_test_data()
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inputs_path = joinpath(TEST_DATA_DIR, "inputs.csv")
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inputs = vec(csv_to_matrix(inputs_path))
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weight_matrices, bias_vectors = Matrix{Float32}[], Vector{Float32}[]
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for file_path in glob("weights*.csv", TEST_DATA_DIR)
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push!(weight_matrices, csv_to_matrix(file_path))
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end
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for file_path in glob("biases*.csv", TEST_DATA_DIR)
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push!(bias_vectors, vec(csv_to_matrix(file_path)))
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end
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return inputs, [zip(weight_matrices, bias_vectors)...]
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end
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function sigmoid(x)
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return 1 ./ (1 .+ exp.(-x))
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end
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function layer_func(input_vector, weight_matrix, bias_vector)
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return sigmoid(weight_matrix * input_vector + bias_vector)
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end
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function feed_forward(x, layers...)
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for (w, b) in layers
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x = layer_func(x, w, b)
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end
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return x
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end
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function main()
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n = parse(Int32, ARGS[1])
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x, weights_biases = load_test_data()
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# # To get info about memory allocations etc.:
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# @time feed_forward(x, weights_biases...)
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# Call once to compile, then again to time
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@elapsed feed_forward(x, weights_biases...)
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t = @elapsed begin
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for _ in 1:n
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feed_forward(x, weights_biases...)
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end
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end
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println(round(t, digits=5))
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end
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if abspath(PROGRAM_FILE) == @__FILE__
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main()
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end
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