Move to separate repo

jl/feed_forward.jl

@@ -0,0 +1,64 @@
+using CSV, Tables, Glob
+
+
+const TEST_DATA_DIR = joinpath(@__DIR__, "..", "data")
+
+
+function csv_to_matrix(file_path, types=Float32)
+    return CSV.read(file_path, Tables.matrix, header=0, delim=',', types=types)
+end
+
+
+function load_test_data()
+    inputs_path = joinpath(TEST_DATA_DIR, "inputs.csv")
+    inputs = vec(csv_to_matrix(inputs_path))
+    weight_matrices, bias_vectors = Matrix{Float32}[], Vector{Float32}[]
+    for file_path in glob("weights*.csv", TEST_DATA_DIR)
+        push!(weight_matrices, csv_to_matrix(file_path))
+    end
+    for file_path in glob("biases*.csv", TEST_DATA_DIR)
+        push!(bias_vectors, vec(csv_to_matrix(file_path)))
+    end
+    return inputs, [zip(weight_matrices, bias_vectors)...]
+end
+
+
+function sigmoid(x)
+    return 1 ./ (1 .+ exp.(-x))
+end
+
+
+function layer_func(input_vector, weight_matrix, bias_vector)
+    return sigmoid(weight_matrix * input_vector + bias_vector)
+end
+
+
+function feed_forward(x, layers...)
+    for (w, b) in layers
+        x = layer_func(x, w, b)
+    end
+    return x
+end
+
+
+function main()
+    n = parse(Int32, ARGS[1])
+    x, weights_biases = load_test_data()
+    
+    # # To get info about memory allocations etc.:
+    # @time feed_forward(x, weights_biases...)
+
+    # Call once to compile, then again to time
+    @elapsed feed_forward(x, weights_biases...)
+    t = @elapsed begin
+        for _ in 1:n
+            feed_forward(x, weights_biases...)
+        end
+    end
+    println(round(t, digits=5))
+end
+
+
+if abspath(PROGRAM_FILE) == @__FILE__
+    main()
+end