mirror of
https://github.com/titanscouting/tra-analysis.git
synced 2024-12-26 17:49:09 +00:00
don't need the testing notebook up here anymore
This commit is contained in:
jlevine18
GitHub
parent
06a07bb983
commit
d76075fbfa
@ -1,248 +0,0 @@
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import torch\n",
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"device='cuda:0' if torch.cuda.is_available() else 'cpu'"
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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": 29,
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"metadata": {},
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"outputs": [],
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"source": [
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"def factorial(n):\n",
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" if n==0:\n",
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" return 1\n",
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" else:\n",
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" return n*factorial(n-1)\n",
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"\n",
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"def take_all_pwrs(vec,pwr):\n",
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" #todo: vectorize (kinda)\n",
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" combins=torch.combinations(vec, r=pwr, with_replacement=True)\n",
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" out=torch.ones(combins.size()[0])\n",
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" for i in torch.t(combins):\n",
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" out *= i\n",
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" return out\n",
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"\n",
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"def set_device(new_device):\n",
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" device=new_device\n",
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"\n",
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"class LinearRegKernel():\n",
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" parameters= []\n",
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" weights=None\n",
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" bias=None\n",
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" def __init__(self, num_vars):\n",
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" self.weights=torch.rand(num_vars, requires_grad=True, device=device)\n",
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" self.bias=torch.rand(1, requires_grad=True, device=device)\n",
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" self.parameters=[self.weights,self.bias]\n",
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" def forward(self,mtx):\n",
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" long_bias=self.bias.repeat([1,mtx.size()[1]])\n",
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" return torch.matmul(self.weights,mtx)+long_bias\n",
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" \n",
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"class SigmoidalRegKernel():\n",
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" parameters= []\n",
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" weights=None\n",
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" bias=None\n",
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" sigmoid=torch.nn.Sigmoid()\n",
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" def __init__(self, num_vars):\n",
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" self.weights=torch.rand(num_vars, requires_grad=True, device=device)\n",
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" self.bias=torch.rand(1, requires_grad=True, device=device)\n",
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" self.parameters=[self.weights,self.bias]\n",
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" def forward(self,mtx):\n",
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" long_bias=self.bias.repeat([1,mtx.size()[1]])\n",
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" return self.sigmoid(torch.matmul(self.weights,mtx)+long_bias)\n",
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"\n",
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"class LogRegKernel():\n",
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" parameters= []\n",
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" weights=None\n",
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" bias=None\n",
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" def __init__(self, num_vars):\n",
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" self.weights=torch.rand(num_vars, requires_grad=True, device=device)\n",
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" self.bias=torch.rand(1, requires_grad=True, device=device)\n",
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" self.parameters=[self.weights,self.bias]\n",
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" def forward(self,mtx):\n",
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" long_bias=self.bias.repeat([1,mtx.size()[1]])\n",
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" return torch.log(torch.matmul(self.weights,mtx)+long_bias)\n",
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"\n",
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"class ExpRegKernel():\n",
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" parameters= []\n",
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" weights=None\n",
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" bias=None\n",
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" def __init__(self, num_vars):\n",
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" self.weights=torch.rand(num_vars, requires_grad=True, device=device)\n",
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" self.bias=torch.rand(1, requires_grad=True, device=device)\n",
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" self.parameters=[self.weights,self.bias]\n",
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" def forward(self,mtx):\n",
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" long_bias=self.bias.repeat([1,mtx.size()[1]])\n",
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" return torch.exp(torch.matmul(self.weights,mtx)+long_bias)\n",
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"\n",
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"class PolyRegKernel():\n",
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" parameters= []\n",
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" weights=None\n",
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" bias=None\n",
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" power=None\n",
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" def __init__(self, num_vars, power):\n",
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" self.power=power\n",
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" num_terms=int(factorial(num_vars+power-1) / factorial(power) / factorial(num_vars-1))\n",
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" self.weights=torch.rand(num_terms, requires_grad=True, device=device)\n",
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" self.bias=torch.rand(1, requires_grad=True, device=device)\n",
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" self.parameters=[self.weights,self.bias]\n",
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" def forward(self,mtx):\n",
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" #TODO: Vectorize the last part\n",
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" cols=[]\n",
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" for i in torch.t(mtx):\n",
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" cols.append(take_all_pwrs(i,self.power))\n",
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" new_mtx=torch.t(torch.stack(cols))\n",
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" long_bias=self.bias.repeat([1,mtx.size()[1]])\n",
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" return torch.matmul(self.weights,new_mtx)+long_bias\n",
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"\n",
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"def SGDTrain(kernel, data, ground, loss=torch.nn.MSELoss(), iterations=1000, learning_rate=.1, return_losses=False):\n",
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" optim=torch.optim.SGD(kernel.parameters, lr=learning_rate)\n",
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" data_cuda=data.to(device)\n",
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" ground_cuda=ground.to(device)\n",
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" if (return_losses):\n",
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" losses=[]\n",
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" for i in range(iterations):\n",
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" with torch.set_grad_enabled(True):\n",
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" optim.zero_grad()\n",
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" pred=kernel.forward(data_cuda)\n",
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" ls=loss(pred,ground_cuda)\n",
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" losses.append(ls.item())\n",
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" ls.backward()\n",
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" optim.step()\n",
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" return [kernel,losses]\n",
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" else:\n",
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" for i in range(iterations):\n",
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" with torch.set_grad_enabled(True):\n",
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" optim.zero_grad()\n",
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" pred=kernel.forward(data_cuda)\n",
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" ls=loss(pred,ground_cuda)\n",
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" ls.backward()\n",
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" optim.step() \n",
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" return kernel\n",
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"\n",
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"def CustomTrain(kernel, optim, data, ground, loss=torch.nn.MSELoss(), iterations=1000, return_losses=False):\n",
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" data_cuda=data.to(device)\n",
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" ground_cuda=ground.to(device)\n",
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" if (return_losses):\n",
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" losses=[]\n",
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" for i in range(iterations):\n",
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" with torch.set_grad_enabled(True):\n",
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" optim.zero_grad()\n",
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" pred=kernel.forward(data)\n",
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" ls=loss(pred,ground)\n",
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" losses.append(ls.item())\n",
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" ls.backward()\n",
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" optim.step()\n",
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" return [kernel,losses]\n",
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" else:\n",
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" for i in range(iterations):\n",
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" with torch.set_grad_enabled(True):\n",
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" optim.zero_grad()\n",
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" pred=kernel.forward(data_cuda)\n",
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" ls=loss(pred,ground_cuda)\n",
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" ls.backward()\n",
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" optim.step() \n",
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" return kernel"
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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": 31,
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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([[1.0000, 2.0000]], device='cuda:0', grad_fn=<AddBackward0>)"
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]
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},
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"execution_count": 31,
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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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"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",
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"model[0].forward(torch.tensor([[1,2],[3,4],[5,6]]).to(torch.float).cuda())"
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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": 30,
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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([0.2347, 0.4494, 0.3156], device='cuda:0', requires_grad=True),\n",
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" tensor([0.9541], device='cuda:0', requires_grad=True)]"
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]
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},
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"execution_count": 30,
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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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"kernel=LinearRegKernel(3)\n",
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"kernel.parameters\n"
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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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"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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"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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"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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"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": "3.7.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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