Algomancer

import torch
import torch.nn as nn
import torch.nn.functional as F


class SubspaceLinear(nn.Linear):
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        Forward pass for the BaseSubspaceLinear layer. Calls `subspace_weights` to sample from the subspace
        and uses the corresponding weight and bias.

Algomancer

learning_rate = 4e-4
warmup_steps = 2000
log_step_interval = 1
eval_iters = 100
save_step_interval = 1000
eval_step_interval = 1000

weight_decay = 1e-1
beta1 = 0.9
beta2 = 0.95

Algomancer

import torch
from torchdiffeq import odeint
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import tqdm
import imageio
import os

# Load target image and preprocess it

Algomancer

# Author Adam Hibble @algomancer
import torch 
import torch.nn.functional as F
import torch.nn as nn
import tqdm

def get_padding(padding_type, kernel_size):
    assert padding_type in ['SAME', 'VALID']
    if padding_type == 'SAME':
        return tuple((k - 1) // 2 for k in kernel_size)

Algomancer

# Install useful stuff
! apt install --yes ssh screen nano htop ranger git > /dev/null
# SSH setting
! echo "root:carbonara" | chpasswd
! echo "PasswordAuthentication yes" > /etc/ssh/sshd_config
! echo "PermitUserEnvironment yes" >> /etc/ssh/sshd_config
! echo "PermitRootLogin yes" >> /etc/ssh/sshd_config
! service ssh restart > /dev/null
# Download ngrok
! wget -q -c -nc https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip

Algomancer

#include <stdio.h>
#include <stdlib.h>

#define N 16 /* buffer size */

int main(void) {
  char name[N]; /* buffer */
  
  /* prompt user for name */
  printf("What's your name? ");

Algomancer

from torch.autograd import Variable

from torch.nn import functional as F



class SELU(nn.Module):

    def __init__(self):

Algomancer

import logging
from logging.handlers import RotatingFileHandler

from flask import Flask

app = Flask(__name__)

@app.route('/')
def foo():
    app.logger.warning('A warning occurred (%d apples)', 42)

Algomancer

from IPython.display import Image, display, HTML

def strip_consts(graph_def, max_const_size=32):
    """Strip large constant values from graph_def."""
    strip_def = tf.GraphDef()
    for n0 in graph_def.node:
        n = strip_def.node.add() 
        n.MergeFrom(n0)
        if n.op == 'Const':

Algomancer

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "s3:PutObject"
            ],
            "Resource": [
                "arn:aws:s3:::YOUR_S3_BUCKET_NAME/*"