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tl.py
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jlevine18
GitHub
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@ -1,199 +1,201 @@
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#Titan Robotics Team 2022: ML Module
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#Written by Arthur Lu & Jacob Levine
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#Notes:
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# this should be imported as a python module using 'import titanlearn'
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# this should be included in the local directory or environment variable
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# this module has not been optimized for multhreaded computing
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# this module learns from its mistakes far faster than 2022's captains
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#setup:
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__version__ = "1.0.0.001"
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#changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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1.0.0.xxx:
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-added generation of ANNS, basic SGD training"""
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__author__ = (
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"Arthur Lu <arthurlu@ttic.edu>, "
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"Jacob Levine <jlevine@ttic.edu>,"
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)
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__all__ = [
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'linear_nn',
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'train_sgd_minibatch',
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'train_sgd_simple'
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]
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#imports
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import torch
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import warnings
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from collections import OrderedDict
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from sklearn import metrics
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import numpy as np
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import matplotlib.pyplot as plt
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import math
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from sklearn import datasets
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#enable CUDA if possible
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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#linear_nn: creates a fully connected network given params
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def linear_nn(in_dim, hidden_dim, out_dim, num_hidden, act_fn="tanh", end="none"):
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if act_fn.lower()=="tanh":
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k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('tanh0', torch.nn.Tanh())])
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for i in range(num_hidden):
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k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "tanh"+str(i+1):torch.nn.Tanh()})
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elif act_fn.lower()=="sigmoid":
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k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('sig0', torch.nn.Sigmoid())])
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for i in range(num_hidden):
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k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "sig"+str(i+1):torch.nn.Sigmoid()})
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elif act_fn.lower()=="relu":
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k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('relu0', torch.nn.ReLU())])
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for i in range(num_hidden):
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k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "relu"+str(i+1):torch.nn.ReLU()})
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elif act_fn.lower()=="leaky relu":
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k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('lre0', torch.nn.LeakyReLU())])
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for i in range(num_hidden):
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k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "lre"+str(i+1):torch.nn.LeakyReLU()})
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else:
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warnings.warn("Did not specify a valid inner activation function. Returning nothing.")
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return None
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if end.lower()=="softmax":
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k.update({"out": torch.nn.Linear(hidden_dim,out_dim), "softmax": torch.nn.Softmax()})
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elif end.lower()=="none":
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k.update({"out": torch.nn.Linear(hidden_dim,out_dim)})
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elif end.lower()=="sigmoid":
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k.update({"out": torch.nn.Linear(hidden_dim,out_dim), "sigmoid": torch.nn.Sigmoid()})
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else:
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warnings.warn("Did not specify a valid final activation function. Returning nothing.")
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return None
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return torch.nn.Sequential(k)
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#train_sgd_simple: trains network using SGD
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def train_sgd_simple(net, evalType, data, ground, dev=None, devg=None, iters=1000, learnrate=1e-4, testevery=1, graphsaveloc=None, modelsaveloc=None, loss="mse"):
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model=net.to(device)
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data=data.to(device)
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ground=ground.to(device)
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if dev != None:
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dev=dev.to(device)
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losses=[]
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dev_losses=[]
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if loss.lower()=="mse":
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loss_fn = torch.nn.MSELoss()
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elif loss.lower()=="cross entropy":
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loss_fn = torch.nn.CrossEntropyLoss()
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elif loss.lower()=="nll":
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loss_fn = torch.nn.NLLLoss()
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elif loss.lower()=="poisson nll":
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loss_fn = torch.nn.PoissonNLLLoss()
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else:
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warnings.warn("Did not specify a valid loss function. Returning nothing.")
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return None
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optimizer=torch.optim.SGD(model.parameters(), lr=learnrate)
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for i in range(iters):
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if i%testevery==0:
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with torch.no_grad():
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output = model(data)
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if evalType == "ap":
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ap = metrics.average_precision_score(ground.cpu().numpy(), output.cpu().numpy())
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if evalType == "regression":
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ap = metrics.explained_variance_score(ground.cpu().numpy(), output.cpu().numpy())
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losses.append(ap)
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print(str(i)+": "+str(ap))
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plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="train AP")
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if dev != None:
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output = model(dev)
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print(evalType)
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if evalType == "ap":
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ap = metrics.average_precision_score(devg.numpy(), output.numpy())
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dev_losses.append(ap)
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plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
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elif evalType == "regression":
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ap = metrics.explained_variance_score(devg.numpy(), output.numpy())
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dev_losses.append(ap)
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plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev EV")
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if graphsaveloc != None:
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plt.savefig(graphsaveloc+".pdf")
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with torch.enable_grad():
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optimizer.zero_grad()
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output = model(data)
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loss = loss_fn(output, ground)
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print(loss.item())
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loss.backward()
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optimizer.step()
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if modelsaveloc != None:
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torch.save(model, modelsaveloc)
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plt.show()
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return model
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#train_sgd_minibatch: same as above, but with minibatches
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def train_sgd_minibatch(net, data, ground, dev=None, devg=None, epoch=100, batchsize=20, learnrate=1e-4, testevery=20, graphsaveloc=None, modelsaveloc=None, loss="mse"):
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model=net.to(device)
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data=data.to(device)
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ground=ground.to(device)
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if dev != None:
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dev=dev.to(device)
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losses=[]
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dev_losses=[]
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if loss.lower()=="mse":
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loss_fn = torch.nn.MSELoss()
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elif loss.lower()=="cross entropy":
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loss_fn = torch.nn.CrossEntropyLoss()
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elif loss.lower()=="nll":
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loss_fn = torch.nn.NLLLoss()
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elif loss.lower()=="poisson nll":
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loss_fn = torch.nn.PoissonNLLLoss()
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else:
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warnings.warn("Did not specify a valid loss function. Returning nothing.")
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return None
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optimizer=torch.optim.LBFGS(model.parameters(), lr=learnrate)
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itercount=0
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for i in range(epoch):
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print("EPOCH "+str(i)+" OF "+str(epoch-1))
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batches=math.ceil(data.size()[0].item()/batchsize)
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for j in range(batches):
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batchdata=[]
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batchground=[]
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for k in range(j*batchsize, min((j+1)*batchsize, data.size()[0].item()),1):
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batchdata.append(data[k])
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batchground.append(ground[k])
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batchdata=torch.stack(batchdata)
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batchground=torch.stack(batchground)
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if itercount%testevery==0:
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with torch.no_grad():
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output = model(data)
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ap = metrics.average_precision_score(ground.numpy(), output.numpy())
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losses.append(ap)
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print(str(i)+": "+str(ap))
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plt.plot(np.array(range(0,i+1,testevery)),np.array(losses))
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if dev != None:
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output = model(dev)
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ap = metrics.average_precision_score(devg.numpy(), output.numpy())
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dev_losses.append(ap)
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plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
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if graphsaveloc != None:
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plt.savefig(graphsaveloc+".pdf")
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with torch.enable_grad():
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optimizer.zero_grad()
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output = model(batchdata)
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loss = loss_fn(output, ground)
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loss.backward()
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optimizer.step()
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itercount +=1
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if modelsaveloc != None:
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torch.save(model, modelsaveloc)
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plt.show()
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return model
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data = datasets.load_diabetes()
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print(data["data"], data["target"])
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ground = torch.tensor(data["target"]).to(torch.float)
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data = torch.tensor(data["data"]).to(torch.float)
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model = linear_nn(10, 100, 1, 20, act_fn = "tanh")
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model = train_sgd_simple(model,"regression", data, ground, learnrate=1e-4)
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#Titan Robotics Team 2022: ML Module
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#Written by Arthur Lu & Jacob Levine
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#Notes:
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# this should be imported as a python module using 'import titanlearn'
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# this should be included in the local directory or environment variable
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# this module has not been optimized for multhreaded computing
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# this module learns from its mistakes far faster than 2022's captains
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#setup:
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__version__ = "1.0.0.001"
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#changelog should be viewed using print(analysis.__changelog__)
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__changelog__ = """changelog:
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1.0.0.xxx:
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-added generation of ANNS, basic SGD training"""
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__author__ = (
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"Arthur Lu <arthurlu@ttic.edu>, "
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"Jacob Levine <jlevine@ttic.edu>,"
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)
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__all__ = [
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'linear_nn',
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'train_sgd_minibatch',
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'train_sgd_simple'
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]
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#imports
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import torch
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import warnings
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from collections import OrderedDict
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from sklearn import metrics, datasets
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import numpy as np
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import matplotlib.pyplot as plt
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import math
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#enable CUDA if possible
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
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#linear_nn: creates a fully connected network given params
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def linear_nn(in_dim, hidden_dim, out_dim, num_hidden, act_fn="tanh", end="none"):
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if act_fn.lower()=="tanh":
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k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
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for i in range(num_hidden):
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k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "tanh"+str(i+1):torch.nn.Tanh()})
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elif act_fn.lower()=="sigmoid":
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k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
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for i in range(num_hidden):
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k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "sig"+str(i+1):torch.nn.Sigmoid()})
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elif act_fn.lower()=="relu":
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k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
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for i in range(num_hidden):
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k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "relu"+str(i+1):torch.nn.ReLU()})
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elif act_fn.lower()=="leaky relu":
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k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
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for i in range(num_hidden):
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k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "lre"+str(i+1):torch.nn.LeakyReLU()})
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else:
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warnings.warn("Did not specify a valid inner activation function. Returning nothing.")
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return None
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if end.lower()=="softmax":
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k.update({"out": torch.nn.Linear(hidden_dim,out_dim), "softmax": torch.nn.Softmax()})
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elif end.lower()=="none":
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k.update({"out": torch.nn.Linear(hidden_dim,out_dim)})
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elif end.lower()=="sigmoid":
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k.update({"out": torch.nn.Linear(hidden_dim,out_dim), "sigmoid": torch.nn.Sigmoid()})
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else:
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warnings.warn("Did not specify a valid final activation function. Returning nothing.")
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return None
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return torch.nn.Sequential(k)
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#train_sgd_simple: trains network using SGD
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def train_sgd_simple(net, evalType, data, ground, dev=None, devg=None, iters=1000, learnrate=1e-4, testevery=1, graphsaveloc=None, modelsaveloc=None, loss="mse"):
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model=net.to(device)
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data=data.to(device)
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ground=ground.to(device)
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if dev != None:
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dev=dev.to(device)
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losses=[]
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dev_losses=[]
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if loss.lower()=="mse":
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loss_fn = torch.nn.MSELoss()
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elif loss.lower()=="cross entropy":
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loss_fn = torch.nn.CrossEntropyLoss()
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elif loss.lower()=="nll":
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loss_fn = torch.nn.NLLLoss()
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elif loss.lower()=="poisson nll":
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loss_fn = torch.nn.PoissonNLLLoss()
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else:
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warnings.warn("Did not specify a valid loss function. Returning nothing.")
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return None
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optimizer=torch.optim.SGD(model.parameters(), lr=learnrate)
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for i in range(iters):
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if i%testevery==0:
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with torch.no_grad():
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output = model(data)
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if evalType == "ap":
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ap = metrics.average_precision_score(ground.cpu().numpy(), output.cpu().numpy())
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if evalType == "regression":
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ap = metrics.explained_variance_score(ground.cpu().numpy(), output.cpu().numpy())
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losses.append(ap)
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print(str(i)+": "+str(ap))
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plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="train AP")
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if dev != None:
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output = model(dev)
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print(evalType)
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if evalType == "ap":
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ap = metrics.average_precision_score(devg.numpy(), output.numpy())
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dev_losses.append(ap)
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plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
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elif evalType == "regression":
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ev = metrics.explained_variance_score(devg.numpy(), output.numpy())
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dev_losses.append(ev)
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plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev EV")
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if graphsaveloc != None:
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plt.savefig(graphsaveloc+".pdf")
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with torch.enable_grad():
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optimizer.zero_grad()
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output = model(data)
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loss = loss_fn(output, ground)
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print(loss.item())
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loss.backward()
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optimizer.step()
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if modelsaveloc != None:
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torch.save(model, modelsaveloc)
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plt.show()
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return model
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#train_sgd_minibatch: same as above, but with minibatches
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def train_sgd_minibatch(net, data, ground, dev=None, devg=None, epoch=100, batchsize=20, learnrate=1e-4, testevery=20, graphsaveloc=None, modelsaveloc=None, loss="mse"):
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model=net.to(device)
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data=data.to(device)
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ground=ground.to(device)
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if dev != None:
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dev=dev.to(device)
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losses=[]
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dev_losses=[]
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if loss.lower()=="mse":
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loss_fn = torch.nn.MSELoss()
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elif loss.lower()=="cross entropy":
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loss_fn = torch.nn.CrossEntropyLoss()
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elif loss.lower()=="nll":
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loss_fn = torch.nn.NLLLoss()
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elif loss.lower()=="poisson nll":
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loss_fn = torch.nn.PoissonNLLLoss()
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else:
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warnings.warn("Did not specify a valid loss function. Returning nothing.")
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return None
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optimizer=torch.optim.LBFGS(model.parameters(), lr=learnrate)
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itercount=0
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for i in range(epoch):
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print("EPOCH "+str(i)+" OF "+str(epoch-1))
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batches=math.ceil(data.size()[0].item()/batchsize)
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for j in range(batches):
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batchdata=[]
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batchground=[]
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for k in range(j*batchsize, min((j+1)*batchsize, data.size()[0].item()),1):
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batchdata.append(data[k])
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batchground.append(ground[k])
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batchdata=torch.stack(batchdata)
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batchground=torch.stack(batchground)
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if itercount%testevery==0:
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with torch.no_grad():
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output = model(data)
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ap = metrics.average_precision_score(ground.numpy(), output.numpy())
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losses.append(ap)
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print(str(i)+": "+str(ap))
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plt.plot(np.array(range(0,i+1,testevery)),np.array(losses))
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if dev != None:
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output = model(dev)
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ap = metrics.average_precision_score(devg.numpy(), output.numpy())
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dev_losses.append(ap)
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plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
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if graphsaveloc != None:
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plt.savefig(graphsaveloc+".pdf")
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with torch.enable_grad():
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optimizer.zero_grad()
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output = model(batchdata)
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loss = loss_fn(output, ground)
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loss.backward()
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optimizer.step()
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itercount +=1
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if modelsaveloc != None:
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torch.save(model, modelsaveloc)
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plt.show()
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return model
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def retyuoipufdyu():
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data = torch.tensor(datasets.fetch_california_housing()['data']).to(torch.float)
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ground = datasets.fetch_california_housing()['target']
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ground=torch.tensor(ground).to(torch.float)
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model = linear_nn(8, 100, 1, 20, act_fn = "relu")
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print(model)
|
||||
return train_sgd_simple(model,"regression", data, ground, learnrate=1e-4, iters=1000)
|
||||
retyuoipufdyu()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user