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tl.py

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jlevine18 2019-03-02 08:18:28 -06:00 committed by GitHub
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data analysis/titanlearn.py
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@ -26,11 +26,10 @@ __all__ = [
import torch
import warnings
from collections import OrderedDict
from sklearn import metrics
from sklearn import metrics, datasets
import numpy as np
import matplotlib.pyplot as plt
import math
from sklearn import datasets
#enable CUDA if possible
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
@ -38,22 +37,22 @@ device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#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"):
if act_fn.lower()=="tanh":
k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('tanh0', torch.nn.Tanh())])
k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
for i in range(num_hidden):
k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "tanh"+str(i+1):torch.nn.Tanh()})
elif act_fn.lower()=="sigmoid":
k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('sig0', torch.nn.Sigmoid())])
k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
for i in range(num_hidden):
k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "sig"+str(i+1):torch.nn.Sigmoid()})
elif act_fn.lower()=="relu":
k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('relu0', torch.nn.ReLU())])
k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
for i in range(num_hidden):
k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "relu"+str(i+1):torch.nn.ReLU()})
elif act_fn.lower()=="leaky relu":
k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim)), ('lre0', torch.nn.LeakyReLU())])
k=OrderedDict([("in", torch.nn.Linear(in_dim,hidden_dim))])
for i in range(num_hidden):
k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "lre"+str(i+1):torch.nn.LeakyReLU()})
else:
@ -113,8 +112,8 @@ def train_sgd_simple(net, evalType, data, ground, dev=None, devg=None, iters=100
dev_losses.append(ap)
plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
elif evalType == "regression":
ap = metrics.explained_variance_score(devg.numpy(), output.numpy())
dev_losses.append(ap)
ev = metrics.explained_variance_score(devg.numpy(), output.numpy())
dev_losses.append(ev)
plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev EV")
@ -191,9 +190,12 @@ def train_sgd_minibatch(net, data, ground, dev=None, devg=None, epoch=100, batch
plt.show()
return model
data = datasets.load_diabetes()
print(data["data"], data["target"])
ground = torch.tensor(data["target"]).to(torch.float)
data = torch.tensor(data["data"]).to(torch.float)
model = linear_nn(10, 100, 1, 20, act_fn = "tanh")
model = train_sgd_simple(model,"regression", data, ground, learnrate=1e-4)
def retyuoipufdyu():
data = torch.tensor(datasets.fetch_california_housing()['data']).to(torch.float)
ground = datasets.fetch_california_housing()['target']
ground=torch.tensor(ground).to(torch.float)
model = linear_nn(8, 100, 1, 20, act_fn = "relu")
print(model)
return train_sgd_simple(model,"regression", data, ground, learnrate=1e-4, iters=1000)
retyuoipufdyu()