| import torch | |
| import torch.nn as nn | |
| class ResNetBlock(nn.Module): | |
| def __init__(self, input_channel_count, internal_channel_count, downsampler = None, stride=1, layers = 'all'): | |
| # input_channel_count: An image tensor is a h*w*k tensor where k is represents | |
| # how many channels, naively, groups of features discovered by the ml model. | |
| # the shape of the input tensor is h*w*input_channel. | |
| # internal_channel_count: like above, but the shape of the output tensor is | |
| # h*w*internal_channel_count. In a typical convnet strategy, you will want the |
This is inspired by https://fasterthanli.me/blog/2020/a-half-hour-to-learn-rust/
the command zig run my_code.zig will compile and immediately run your Zig
program. Each of these cells contains a zig program that you can try to run
(some of them contain compile-time errors that you can comment out to play
with)
| const std = @import("std"); | |
| pub fn Algebra(comptime fields: []const type) type { | |
| return struct{ | |
| pub fn @"<+>"(a: anytype, b: anytype) FieldFor(@TypeOf(a), @TypeOf(b)).Type { | |
| const F: type = FieldFor(@TypeOf(a), @TypeOf(b)); | |
| return F.@"<+>"(a, b); | |
| } | |
| pub fn FieldFor(comptime AType: type, comptime BType: type) type { |
| const std = @import("std"); | |
| const print = std.debug.print; | |
| var yielded = false; | |
| fn yield() void { | |
| yielded = true; | |
| suspend; | |
| } |
| defmodule Lisp do | |
| @type ast :: [String.t | number | ast] | |
| @type token :: :lp | :rp | number | String.t | |
| @letters Enum.concat(?a..?z, ?A..?Z) | |
| @numbers ?0..?9 | |
| @operators [?+] | |
| @doc """ |
sudo apt-get update && sudo apt-get install -y build-essential libssl-dev uuid-dev \
libgpgme11-dev squashfs-tools libseccomp-dev wget pkg-config git cryptsetup
| using JSON | |
| #make sure we have what we're looking for. | |
| (length(ARGS) == 0) && throw(ErrorException("needs a file name")) | |
| ################################### | |
| #rows iterator | |
| type rows; tgt::Matrix; end | |
| Base.start(r::rows) = 1 | |
| Base.next(r::rows, idx) = (r.tgt[idx, :], idx + 1) | |
| Base.done(r::rows, idx) = idx > size(r.tgt, 1) |
| //! The Computer Language Benchmarks Game | |
| //! https://salsa.debian.org/benchmarksgame-team/benchmarksgame/ | |
| //! | |
| //! This implementation is written in simple and idiomatic Zig. | |
| //! The only optimization is using SIMD vector intrinsics. | |
| const std = @import("std"); | |
| ///////////////////////////////////////////////////////////////////////// | |
| // important constants. ALLCAPS constants are not idiomatic Zig, but it's |
| { | |
| "Inspect": { | |
| "prefix": "ins", | |
| "body": "|> IO.inspect(label: \"$0$TM_LINE_NUMBER\")", | |
| "description": "Adds a pipeline with a labelled `IO.inspect`", | |
| } | |
| } |
| #!/bin/sh | |
| IMAGE=<your image id> | |
| CMD=<your cmd> | |
| podman run --net host -v $HOME:$HOME -e HOME="$HOME" -w$(PWD) -it $IMAGE "$CMD $@" |