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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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commit d3b8329164
1 changed files with 201 additions and 199 deletions
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data analysis/titanlearn.py
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@ -26,11 +26,10 @@ __all__ = [
import torch import torch
import warnings import warnings
from collections import OrderedDict from collections import OrderedDict
from sklearn import metrics 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
from sklearn import datasets
#enable CUDA if possible #enable CUDA if possible
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") 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 #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"):
if act_fn.lower()=="tanh": 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): 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()}) 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": 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): 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()}) 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": 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): 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()}) 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": 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): 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()}) k.update({"lin"+str(i+1): torch.nn.Linear(hidden_dim,hidden_dim), "lre"+str(i+1):torch.nn.LeakyReLU()})
else: else:
@ -113,8 +112,8 @@ def train_sgd_simple(net, evalType, data, ground, dev=None, devg=None, iters=100
dev_losses.append(ap) dev_losses.append(ap)
plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP") plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev AP")
elif evalType == "regression": elif evalType == "regression":
ap = metrics.explained_variance_score(devg.numpy(), output.numpy()) ev = metrics.explained_variance_score(devg.numpy(), output.numpy())
dev_losses.append(ap) dev_losses.append(ev)
plt.plot(np.array(range(0,i+1,testevery)),np.array(losses), label="dev 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() plt.show()
return model return model
data = datasets.load_diabetes() def retyuoipufdyu():
print(data["data"], data["target"])
ground = torch.tensor(data["target"]).to(torch.float) data = torch.tensor(datasets.fetch_california_housing()['data']).to(torch.float)
data = torch.tensor(data["data"]).to(torch.float) ground = datasets.fetch_california_housing()['target']
model = linear_nn(10, 100, 1, 20, act_fn = "tanh") ground=torch.tensor(ground).to(torch.float)
model = train_sgd_simple(model,"regression", data, ground, learnrate=1e-4) 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()