got most of the new interfaces/abstractions written, and they appear to be working. Have the basics of a NN outlined. Have added a document with infomration about neural networks and PyTorch in particular. Wrote my own Upscaling module.
youainti
5 years ago
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# Things I have learned about PyTorch and Neural networks.
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## Building models
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All model building in Pytorch is based on the following three steps
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1. start by creating an object that extends the nn.Module base class
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1. define layers as class attributes (sequential wrapper for ease of use)
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2. implement the `.forward()` method
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Each layer is just a predefined 'function'.
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Really, they are objects that extend the nn.Module base class.
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Thus each NN can act as a layer in another NN.
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For example, I reimplemented an upscaling layer in BasicNeuralNet2.
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(I picked up a lot of this info here.)[https://deeplizard.com/learn/video/k4jY9L8H89U]
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Also, neural networks can return more than just a single output as long as the
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loss function that is used for optimization can consume both of them.
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Thus I could write two separate neural networks (such as for launch and partials),
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and then write a third NN that binds the two together.
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@ -0,0 +1,239 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "standing-catch",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"from torch.autograd.functional import jacobian\n",
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"import itertools\n",
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"import math"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "going-accident",
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"metadata": {},
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"outputs": [],
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"source": [
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"import combined as c"
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]
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},
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{
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"cell_type": "markdown",
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"id": "severe-employment",
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"metadata": {},
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"source": [
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"So, this contains a bunch of initial tests of my abstractions. I eventually need to change these to assertions and package them."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "ranking-family",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Instantiate some objects\n",
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"pm = c.PhysicalModel(1.0,1e-6,0.01,2.0,1e-8)\n",
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"s = c.States(torch.tensor([1.0,2,3]), torch.tensor([0.0]))\n",
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"lp = c.LinearProfit(0,0.95,torch.tensor([1.0,0,0]), 5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "premium-brisbane",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(tensor([-7.5676e+05, 2.8893e+05, 4.6783e+05, 1.5236e+00]),\n",
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" <combined.States at 0x7f31f0146c10>)"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"c.single_transition(pm,lp,s,est_int)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "raised-worthy",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([-718920.5625, 274490.1562, 444444.6250])"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"minimand, iterated_partials, iterated_state = c.optimality(pm,lp,s,est_int)\n",
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"minimand"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "horizontal-insight",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([-2285563., -2285557., -2285557.])"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"est_int2 = c.EstimandInterface(iterated_partials,torch.ones(3))\n",
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"\n",
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"minimand2, iterated_partials2, iterated_state2 = c.optimality(pm,lp,iterated_state,est_int2)\n",
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"minimand2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "automatic-builder",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([-2405858.5000, -2405852.5000, -2405852.5000])"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"est_int3 = c.EstimandInterface(iterated_partials2,torch.ones(3))\n",
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"\n",
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"minimand3, iterated_partials3, iterated_state3 = c.optimality(pm,lp,iterated_state2,est_int3)\n",
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"minimand3"
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]
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},
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{
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"cell_type": "markdown",
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"id": "changing-mainland",
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"metadata": {},
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"source": [
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"So, this succesfylly let me link the two. I'm going to move to another notebook, clean up, and start integrating the system"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "assumed-midwest",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = DoubleNetwork(input_size = 5, output_size=5, layers_size=15)\n",
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"\n",
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"data_in = torch.tensor([1.5,2,3,4,5])\n",
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"\n",
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"data_in\n",
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"\n",
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"target = torch.zeros(5)\n",
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"\n",
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"def loss_fn2(output,target):\n",
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" return sum((output[1] +output[0] - target)**2)\n",
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" #could add a simplicity assumption i.e. l1 on parameters.\n",
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"\n",
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"#Prep Optimizer\n",
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"optimizer = torch.optim.SGD(model.parameters(),lr=0.01)\n",
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"\n",
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"for i in range(20):\n",
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" #training loop\n",
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" optimizer.zero_grad()\n",
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"\n",
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" output = model.forward(data_in)\n",
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" output\n",
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"\n",
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" l = loss_fn2(output, target)\n",
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"\n",
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" l.backward()\n",
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"\n",
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" optimizer.step()\n",
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"\n",
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" print(\"\\n\",l)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "surprising-fundamentals",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "parliamentary-delta",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "emotional-castle",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "substantial-exhibit",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.8"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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@ -0,0 +1,383 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "pleasant-equation",
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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"from torch.autograd.functional import jacobian\n",
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"import itertools\n",
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"import math"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "moved-christian",
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"metadata": {},
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"outputs": [],
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"source": [
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"import combined as c"
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]
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},
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{
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"cell_type": "markdown",
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"id": "pressed-slope",
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"metadata": {},
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"source": [
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"So, this contains a bunch of initial tests of my abstractions. I eventually need to change these to assertions and package them."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "capable-equality",
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"metadata": {},
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"outputs": [],
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"source": [
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"#Instantiate some objects\n",
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"pm = c.PhysicalModel(1.0,1e-6,0.01,2.0,1e-8)\n",
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"s = c.States(torch.tensor([1.0,2,3]), torch.tensor([0.0]))\n",
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"lp = c.LinearProfit(0,0.95,torch.tensor([1.0,0,0]), 5)\n",
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"est_int = c.EstimandInterface(torch.tensor([[1.0,2,3,2]\n",
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" ,[4,5,6,2]\n",
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" ,[7,8,9,2]\n",
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" ,[1,3,5,7]]\n",
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" ),torch.ones(3))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "written-experience",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"stocks\ttensor([1., 2., 3.]) \n",
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"debris\t tensor([0.])\n",
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"3\n",
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"1\n"
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]
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}
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],
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"source": [
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"#test State object \n",
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"print(s)\n",
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"print(s.number_constellations)\n",
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"print(s.number_debris_trackers)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "twelve-arthur",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Launch Decisions and Partial Derivativs of value function with\n",
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"\tlaunches\n",
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"\t\t tensor([1., 1., 1.])\n",
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"\tPartials\n",
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"\t\ttensor([[1., 2., 3., 2.],\n",
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" [4., 5., 6., 2.],\n",
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" [7., 8., 9., 2.],\n",
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" [1., 3., 5., 7.]])\n",
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"tensor([1., 1., 1.]) tensor([[1., 2., 3., 2.],\n",
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" [4., 5., 6., 2.],\n",
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" [7., 8., 9., 2.],\n",
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" [1., 3., 5., 7.]])\n",
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"tensor(1.)\n",
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"tensor([0., 1., 0.])\n",
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"tensor([2., 5., 8., 3.])\n",
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"tensor([[0., 2., 0., 0.],\n",
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" [0., 5., 0., 0.],\n",
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" [0., 8., 0., 0.],\n",
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" [0., 3., 0., 0.]])\n"
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]
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}
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],
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"source": [
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"#Test estimand interface\n",
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"print(est_int)\n",
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"print(est_int.launches,est_int.partials)\n",
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"\n",
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"print(est_int.launch_single(1))\n",
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"print(est_int.launch_vector(1))\n",
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"print(est_int.partial_vector(1)) \n",
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"print(est_int.partial_matrix(1)) #TODO: double check orientation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "impressive-tribe",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"1.0\n",
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"1e-06\n",
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"0.01\n",
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"2.0\n",
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"1e-08\n",
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"tensor([1.0133e-06, 2.0266e-06, 2.9802e-06])\n",
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"tensor([1., 2., 3.]) tensor([0.])\n",
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"tensor([1.0000, 1.0000, 1.0000]) tensor([12.0000])\n"
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]
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}
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],
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"source": [
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"#Test physical model methods\n",
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"print(pm)\n",
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"print(pm.survival(s))\n",
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"s2 = pm.transition(s,est_int)\n",
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"print(s.stocks,s.debris)\n",
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"print(s2.stocks,s2.debris)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "stretch-reward",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"LinearProfit\n",
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" Benefit weights:\ttensor([1., 0., 0.])\n",
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" launch cost:\t5\n",
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" Deorbit cost:\t0\n",
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"tensor(-4.)\n"
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]
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}
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],
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"source": [
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"#test linear profit object\n",
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"print(lp)\n",
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"print(lp.period_benefit(s,est_int))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "advance-folder",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([1., 0., 0., 0.])"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"lp._period_benefit_jacobian_wrt_states( s.stocks, s.debris, est_int.launches)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "posted-subscriber",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([-5., 0., 0.])"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"lp._period_benefit_jacobian_wrt_launches( s.stocks, s.debris, est_int.launches)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "divine-agenda",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([1., 0., 0., 0.])"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"lp.period_benefit_jacobian_wrt_states( s, est_int)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
|
||||
"id": "surgical-direction",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"tensor([[1., 2., 3., 2.],\n",
|
||||
" [4., 5., 6., 2.],\n",
|
||||
" [7., 8., 9., 2.],\n",
|
||||
" [1., 3., 5., 7.]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"est_int.partials"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "mounted-roots",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(tensor([-7.5676e+05, 2.8893e+05, 4.6783e+05, 1.5236e+00]),\n",
|
||||
" <combined.States at 0x7f8c3c9c54f0>)"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"c.single_transition(pm,lp,s,est_int)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"id": "pediatric-iceland",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"tensor([-718920.5625, 274490.1562, 444444.6250])"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"minimand, iterated_partials, iterated_state = c.optimality(pm,lp,s,est_int)\n",
|
||||
"minimand"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"id": "isolated-cleveland",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"tensor([-2285563., -2285557., -2285557.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"est_int2 = c.EstimandInterface(iterated_partials,torch.ones(3))\n",
|
||||
"\n",
|
||||
"minimand2, iterated_partials2, iterated_state2 = c.optimality(pm,lp,iterated_state,est_int2)\n",
|
||||
"minimand2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"id": "relevant-romance",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"tensor([-2405858.5000, -2405852.5000, -2405852.5000])"
|
||||
]
|
||||
},
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"est_int3 = c.EstimandInterface(iterated_partials2,torch.ones(3))\n",
|
||||
"\n",
|
||||
"minimand3, iterated_partials3, iterated_state3 = c.optimality(pm,lp,iterated_state2,est_int3)\n",
|
||||
"minimand3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "israeli-oracle",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"So, this succesfylly let me link the two. I'm going to move to another notebook, clean up, and start integrating the system"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -1,65 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"id": "victorian-produce",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"a = [1,2,3]\n",
|
||||
"b = [\"a\",\"b\",\"c\"]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "sought-beginning",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[(0, (1, 'a')), (1, (2, 'b')), (2, (3, 'c'))]"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"[x for x in enumerate(zip(a,b))]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "recent-lingerie",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Loading…
Reference in New Issue