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const std = @import("std"); const assert = std.debug.assert; pub fn Layer(comptime InputType: type, comptime input_size: usize, comptime layer_size_: usize) type { return struct { const SelfType = @This(); layer_size: usize = layer_size_, weights: [layer_size_][input_size]InputType = undefined, biases: [input_size]InputType = undefined, pub fn evaluate(self: *const SelfType, inputs: [*]InputType, outputs: [*]InputType) void { comptime var neuron_index: usize = 0; inline while (neuron_index < layer_size_) : (neuron_index += 1) { var input_index: usize = 0; var neuron_result: InputType = 0; while (input_index < input_size) : (input_index += 1) { neuron_result += self.weights[neuron_index][input_index] * inputs[input_index] + self.biases[input_index]; } outputs[neuron_index] = neuron_result; } } }; } pub fn Network(comptime InputType: type, comptime layer_list: anytype) type { return struct { const SelfType = @This(); layers: @TypeOf(layer_list) = layer_list, pub fn feedForward(self: *const SelfType, inputs: [*]InputType, outputs: [*]InputType) void { comptime assert(self.layers.len > 0); var layer_inputs = inputs; comptime var layer_index: usize = 0; inline while (layer_index < self.layers.len - 1) : (layer_index += 1) { var output: [self.layers[layer_index].layer_size]InputType = undefined; self.layers[layer_index].evaluate(layer_inputs, &output); layer_inputs = &output; } self.layers[layer_index].evaluate(layer_inputs, outputs); } }; } pub fn main() anyerror!void { var inputs = [2]i16{ 2, 3 }; const layer1 = Layer(i16, 2, 2){ .weights = [2][2]i16{ [2]i16{ 2, 4 }, [2]i16{ 3, 4 }, }, .biases = [2]i16{ 10, 50 }, }; const layer2 = Layer(i16, 2, 1){ .weights = [1][2]i16{ [2]i16{ 3, 5 }, }, .biases = [2]i16{1, 2}, }; var outputs: [1]i16 = undefined; const network = Network(i16, .{ layer1, layer2 }){}; network.feedForward(&inputs, &outputs); assert(outputs[0] == 621); }
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