alu/tra-analysis
1
0
Fork 0
mirror of https://github.com/titanscouting/tra-analysis.git synced 2024-11-10 06:54:44 +00:00
Code Issues Packages Projects Releases Wiki Activity

Update titanlearn.py

Browse Source
This commit is contained in:
ltcptgeneral 2019-03-16 13:12:59 -05:00
parent 69ef08bfd4
commit 9fb53f4297
1 changed files with 8 additions and 3 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

11
data analysis/titanlearn.py
View File

@ -30,9 +30,10 @@ from sklearn import metrics, datasets
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import math import math
import time
#enable CUDA if possible #enable CUDA if possible
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") device = torch.device("cpu")
#linear_nn: creates a fully connected network given params #linear_nn: creates a fully connected network given params
def linear_nn(in_dim, hidden_dim, out_dim, num_hidden, act_fn="tanh", end="none"): def linear_nn(in_dim, hidden_dim, out_dim, num_hidden, act_fn="tanh", end="none"):
@ -194,8 +195,12 @@ def retyuoipufdyu():
data = torch.tensor(datasets.fetch_california_housing()['data']).to(torch.float) data = torch.tensor(datasets.fetch_california_housing()['data']).to(torch.float)
ground = datasets.fetch_california_housing()['target'] ground = datasets.fetch_california_housing()['target']
ground=torch.tensor(ground).to(torch.float) ground = torch.tensor(ground).to(torch.float)
model = linear_nn(8, 100, 1, 20, act_fn = "relu") model = linear_nn(8, 100, 1, 20, act_fn = "relu")
print(model) print(model)
return train_sgd_simple(model,"regression", data, ground, learnrate=1e-4, iters=1000) return train_sgd_simple(model,"regression", data, ground, learnrate=1e-4, iters=1000)
#retyuoipufdyu()
start = time.time()
retyuoipufdyu()
end = time.time()
print(end-start)