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test2

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ksanu 2019-12-04 00:12:28 +01:00
parent 8b4b2a5232
commit 9fdfb598ae
10 changed files with 33317 additions and 33175 deletions
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2
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dev-0/out_float.tsv
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results.txt Normal file
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@ -0,0 +1,2 @@
loss = 0.0010254832450300455
f1: 0.963087248322

53
s.py
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@ -16,7 +16,7 @@ model = nn.Sequential(
nn.Linear(16,1),
nn.Sigmoid())
criterion = nn.MSELoss()
optimizer = optim.SGD(model.parameters(), lr=0.000001, momentum=0.8)
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
#optimizer = optim.Adam(model.parameters())
@ -132,27 +132,27 @@ testA_x = torch.cat([testA_x_temp.transpose(1,0), testA_x3], 1)
dataset_train = TrainDataset(x, y)
trainloader=DataLoader(dataset=dataset_train,batch_size=5)
def train_loop(i = 4200): #~7h
for i in range(i):
for xb, yb_expected in trainloader: # for each iteration a bach of samples is taken from loader(currently batch_size=5)
yp = model(xb)
for i in range(2):
for xb, yb_expected in trainloader: # for each iteration a bach of samples is taken from loader(currently batch_size=5)
yp = model(xb)
# debug
"""
debug_xb = pandas.DataFrame(xb.numpy())
debug_yb_expected = pandas.DataFrame(yb_expected.numpy())
debug_yp = pandas.DataFrame(yp.detach().numpy())
"""
loss = criterion(yp, yb_expected)
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(loss)
# debug
"""
debug_xb = pandas.DataFrame(xb.numpy())
debug_yb_expected = pandas.DataFrame(yb_expected.numpy())
debug_yp = pandas.DataFrame(yp.detach().numpy())
"""
#print(torch.squeeze(yp))
#print(yb_expected)
loss = criterion(torch.squeeze(yp), yb_expected)
optimizer.zero_grad()
loss.backward()
optimizer.step()
#print(loss)
#4 200
elapsed_time = timeit.timeit(train_loop, number=1)
print("Training time: ", elapsed_time, "seconds")
#elapsed_time = timeit.timeit(train_loop, number=1)
#print("Training time: ", elapsed_time, "seconds")
#saving results:
#dev0:
@ -179,14 +179,21 @@ print("f1_score_dev0 after training: ", score,"\nAcc: ", accuracy_score(dev_y_te
#@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
#testA:
testA_y = model(testA_x)
file=open("test-A/out.tsv","w")
file=open("test-A/out.tsv","w+")
file2=open("test-A/out_float.tsv","w")
for i in range(0,11061):
file2.write(str(testA_y[i].data.item()) + "\n")
if testA_y[i].data.item() < 0.5:
file.write("0" + "\n")
file.write("0\n")
else:
file.write("1" + "\n")
file.write("1\n")
file.close()
file2.close()
file2.close()
file=open("results.txt","w")
file.write("loss = " + str(loss.item())+ "\n")
file.write("f1: " + str(score) + "\n" )
file.close()

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test-A/out.tsv
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test-A/out_float.tsv
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