karpathy

"""
This is a batched LSTM forward and backward pass
"""
import numpy as np
import code

class LSTM:
  
  @staticmethod
  def init(input_size, hidden_size, fancy_forget_bias_init = 3):

karpathy

--[[ 
Efficient LSTM in Torch using nngraph library. This code was optimized 
by Justin Johnson (@jcjohnson) based on the trick of batching up the 
LSTM GEMMs, as also seen in my efficient Python LSTM gist.
--]]

function LSTM.fast_lstm(input_size, rnn_size)
  local x = nn.Identity()()
  local prev_c = nn.Identity()()
  local prev_h = nn.Identity()()

karpathy

--[[ 
   This layer expects an [n x d] Tensor and normalizes each
   row to have unit L2 norm.
]]--
local L2Normalize, parent = torch.class('nn.L2Normalize', 'nn.Module')
function L2Normalize:__init()
   parent.__init(self)
end
function L2Normalize:updateOutput(input)

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

karpathy

.punch-viewer-speakernotes-side-panel {
width: 400px !important;
}
.punch-viewer-speakernotes-text-body-scrollable {
left: 435px !important;
}
.punch-viewer-speakernotes-page,
.punch-viewer-speakernotes-page svg {
width:400px !important;
height:300px !important;

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

karpathy

"""
A bare bones examples of optimizing a black-box function (f) using
Natural Evolution Strategies (NES), where the parameter distribution is a 
gaussian of fixed standard deviation.
"""

import numpy as np
np.random.seed(0)

# the function we want to optimize

karpathy

"""
stable diffusion dreaming
creates hypnotic moving videos by smoothly walking randomly through the sample space

example way to run this script:

$ python stablediffusionwalk.py --prompt "blueberry spaghetti" --name blueberry

to stitch together the images, e.g.:
$ ffmpeg -r 10 -f image2 -s 512x512 -i blueberry/frame%06d.jpg -vcodec libx264 -crf 10 -pix_fmt yuv420p blueberry.mp4

karpathy

import torch
import torch.nn as nn

torch.manual_seed(42)
x = torch.randn(2, 768)

# matrix multiply "ignores" the second row when calculating the first row
w = torch.randn(768, 768)
z1 = x[0] @ w
z2 = (x @ w)[0]

karpathy

# -----------------------------------------------------------------------------
# AI-powered Git Commit Function
# Copy paste this gist into your ~/.bashrc or ~/.zshrc to gain the `gcm` command. It:
# 1) gets the current staged changed diff
# 2) sends them to an LLM to write the git commit message
# 3) allows you to easily accept, edit, regenerate, cancel
# But - just read and edit the code however you like
# the `llm` CLI util is awesome, can get it here: https://llm.datasette.io/en/stable/

gcm() {