From c5d087dadae5c1e5202bf7c08d5ad87cd8d4e8bc Mon Sep 17 00:00:00 2001 From: jlevine18 Date: Sun, 22 Sep 2019 23:23:29 -0500 Subject: [PATCH] don't need the testing notebook up here anymore --- data analysis/cudaRegressTesting.ipynb | 248 ------------------------- 1 file changed, 248 deletions(-) delete mode 100644 data analysis/cudaRegressTesting.ipynb diff --git a/data analysis/cudaRegressTesting.ipynb b/data analysis/cudaRegressTesting.ipynb deleted file mode 100644 index a684148f..00000000 --- a/data analysis/cudaRegressTesting.ipynb +++ /dev/null @@ -1,248 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "device='cuda:0' if torch.cuda.is_available() else 'cpu'" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "def factorial(n):\n", - " if n==0:\n", - " return 1\n", - " else:\n", - " return n*factorial(n-1)\n", - "\n", - "def take_all_pwrs(vec,pwr):\n", - " #todo: vectorize (kinda)\n", - " combins=torch.combinations(vec, r=pwr, with_replacement=True)\n", - " out=torch.ones(combins.size()[0])\n", - " for i in torch.t(combins):\n", - " out *= i\n", - " return out\n", - "\n", - "def set_device(new_device):\n", - " device=new_device\n", - "\n", - "class LinearRegKernel():\n", - " parameters= []\n", - " weights=None\n", - " bias=None\n", - " def __init__(self, num_vars):\n", - " self.weights=torch.rand(num_vars, requires_grad=True, device=device)\n", - " self.bias=torch.rand(1, requires_grad=True, device=device)\n", - " self.parameters=[self.weights,self.bias]\n", - " def forward(self,mtx):\n", - " long_bias=self.bias.repeat([1,mtx.size()[1]])\n", - " return torch.matmul(self.weights,mtx)+long_bias\n", - " \n", - "class SigmoidalRegKernel():\n", - " parameters= []\n", - " weights=None\n", - " bias=None\n", - " sigmoid=torch.nn.Sigmoid()\n", - " def __init__(self, num_vars):\n", - " self.weights=torch.rand(num_vars, requires_grad=True, device=device)\n", - " self.bias=torch.rand(1, requires_grad=True, device=device)\n", - " self.parameters=[self.weights,self.bias]\n", - " def forward(self,mtx):\n", - " long_bias=self.bias.repeat([1,mtx.size()[1]])\n", - " return self.sigmoid(torch.matmul(self.weights,mtx)+long_bias)\n", - "\n", - "class LogRegKernel():\n", - " parameters= []\n", - " weights=None\n", - " bias=None\n", - " def __init__(self, num_vars):\n", - " self.weights=torch.rand(num_vars, requires_grad=True, device=device)\n", - " self.bias=torch.rand(1, requires_grad=True, device=device)\n", - " self.parameters=[self.weights,self.bias]\n", - " def forward(self,mtx):\n", - " long_bias=self.bias.repeat([1,mtx.size()[1]])\n", - " return torch.log(torch.matmul(self.weights,mtx)+long_bias)\n", - "\n", - "class ExpRegKernel():\n", - " parameters= []\n", - " weights=None\n", - " bias=None\n", - " def __init__(self, num_vars):\n", - " self.weights=torch.rand(num_vars, requires_grad=True, device=device)\n", - " self.bias=torch.rand(1, requires_grad=True, device=device)\n", - " self.parameters=[self.weights,self.bias]\n", - " def forward(self,mtx):\n", - " long_bias=self.bias.repeat([1,mtx.size()[1]])\n", - " return torch.exp(torch.matmul(self.weights,mtx)+long_bias)\n", - "\n", - "class PolyRegKernel():\n", - " parameters= []\n", - " weights=None\n", - " bias=None\n", - " power=None\n", - " def __init__(self, num_vars, power):\n", - " self.power=power\n", - " num_terms=int(factorial(num_vars+power-1) / factorial(power) / factorial(num_vars-1))\n", - " self.weights=torch.rand(num_terms, requires_grad=True, device=device)\n", - " self.bias=torch.rand(1, requires_grad=True, device=device)\n", - " self.parameters=[self.weights,self.bias]\n", - " def forward(self,mtx):\n", - " #TODO: Vectorize the last part\n", - " cols=[]\n", - " for i in torch.t(mtx):\n", - " cols.append(take_all_pwrs(i,self.power))\n", - " new_mtx=torch.t(torch.stack(cols))\n", - " long_bias=self.bias.repeat([1,mtx.size()[1]])\n", - " return torch.matmul(self.weights,new_mtx)+long_bias\n", - "\n", - "def SGDTrain(kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False):\n", - " optim=torch.optim.SGD(kernel.parameters, lr=learning_rate)\n", - " data_cuda=data.to(device)\n", - " ground_cuda=ground.to(device)\n", - " if (return_losses):\n", - " losses=[]\n", - " for i in range(iterations):\n", - " with torch.set_grad_enabled(True):\n", - " optim.zero_grad()\n", - " pred=kernel.forward(data_cuda)\n", - " ls=loss(pred,ground_cuda)\n", - " losses.append(ls.item())\n", - " ls.backward()\n", - " optim.step()\n", - " return [kernel,losses]\n", - " else:\n", - " for i in range(iterations):\n", - " with torch.set_grad_enabled(True):\n", - " optim.zero_grad()\n", - " pred=kernel.forward(data_cuda)\n", - " ls=loss(pred,ground_cuda)\n", - " ls.backward()\n", - " optim.step() \n", - " return kernel\n", - "\n", - "def CustomTrain(kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False):\n", - " data_cuda=data.to(device)\n", - " ground_cuda=ground.to(device)\n", - " if (return_losses):\n", - " losses=[]\n", - " for i in range(iterations):\n", - " with torch.set_grad_enabled(True):\n", - " optim.zero_grad()\n", - " pred=kernel.forward(data)\n", - " ls=loss(pred,ground)\n", - " losses.append(ls.item())\n", - " ls.backward()\n", - " optim.step()\n", - " return [kernel,losses]\n", - " else:\n", - " for i in range(iterations):\n", - " with torch.set_grad_enabled(True):\n", - " optim.zero_grad()\n", - " pred=kernel.forward(data_cuda)\n", - " ls=loss(pred,ground_cuda)\n", - " ls.backward()\n", - " optim.step() \n", - " return kernel" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "tensor([[1.0000, 2.0000]], device='cuda:0', grad_fn=)" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model=SGDTrain(LinearRegKernel(3),torch.tensor([[1,2],[3,4],[5,6]]).to(torch.float).cuda(),torch.tensor([[1,2]]).to(torch.float).cuda(),iterations=10000, learning_rate=.01, return_losses=True)\n", - "model[0].forward(torch.tensor([[1,2],[3,4],[5,6]]).to(torch.float).cuda())" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[tensor([0.2347, 0.4494, 0.3156], device='cuda:0', requires_grad=True),\n", - " tensor([0.9541], device='cuda:0', requires_grad=True)]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "kernel=LinearRegKernel(3)\n", - "kernel.parameters\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "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": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -}