added train.py and test.py
This commit is contained in:
Jakub Henyk
parent
be5eee8e9c
commit
a317c615ea
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__pycache__/train.cpython-38.pyc
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__pycache__/zadanie1.cpython-38.pyc
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cifar_net.pth
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cifar_net.pth
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test.py
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test.py
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import pandas as pd
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import numpy as np
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import zadanie1 as z
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import train as tr
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import torch
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import torchvision
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import torchvision.transforms as transforms
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.optim as optim
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class Net(nn.Module):
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def __init__(self):
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super().__init__()
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#self.conv1 = nn.Conv2d(3, 6, 5)
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#self.pool = nn.MaxPool2d(2, 2)
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#self.conv2 = nn.Conv2d(6, 16, 5)
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#self.fc1 = nn.Linear(16 * 5 * 5, 120)
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#self.fc2 = nn.Linear(20, 6)
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self.fc3 = nn.Linear(6, 6)
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def forward(self, x):
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#x = self.pool(F.relu(self.conv1(x)))
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#x = self.pool(F.relu(self.conv2(x)))
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#x = torch.flatten(x, 1)
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#x = F.relu(self.fc1(x))
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#x = F.relu(self.fc2(x))
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x = self.fc3(x)
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return x
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testdata = []
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def testNet(testloader):
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PATH = './cifar_net.pth'
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net = Net()
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net.load_state_dict(torch.load(PATH))
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correct = 0
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total = 0
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with torch.no_grad():
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for data in testloader:
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input, labels = data
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labelsX = torch.Tensor([x for x in labels])
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labels = labelsX.type(torch.LongTensor)
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outputs = net(input)
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_, predicted = torch.max(outputs.data, 1)
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testdata.append([input, labels, predicted])
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total += labels.size(0)
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correct += (predicted == labels).sum().item()
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print(f'Accuracy of the network: {100 * correct // total} %')
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if __name__ == '__main__':
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train, dev, test = z.prepareData()
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batch_size = 4
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trainlist = train.values.tolist()
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testlist = test.values.tolist()
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trainset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in trainlist]
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trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
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shuffle=True, num_workers=2)
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testset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in testlist]
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testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
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shuffle=False, num_workers=2)
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classes = ('male', 'female')
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testNet(testloader)
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with open('testresults.txt', 'w') as the_file:
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for item in testdata:
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for i in range(len(item)):
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the_file.write(f'data: {item[0][i]} \n true value: {item[1][i]} \n prediction: {item[2][i]}\n')
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testresults.txt
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testresults.txt
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data: tensor([0.8081, 0.4909, 0.5100, 0.5556, 0.2353, 0.5000])
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true value: 0
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prediction: 1
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data: tensor([0.2020, 0.9983, 0.6000, 0.3333, 0.4706, 0.3750])
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true value: 0
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prediction: 1
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data: tensor([0.8990, 0.6046, 0.5300, 0.5556, 0.5882, 0.0000])
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true value: 1
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prediction: 1
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data: tensor([0.7576, 0.7115, 0.1200, 0.2222, 0.4118, 0.6250])
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true value: 1
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prediction: 1
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data: tensor([0.0505, 0.6646, 0.5500, 1.0000, 0.3529, 0.2500])
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true value: 1
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prediction: 1
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true value: 1
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|
||||
data: tensor([0.7071, 0.4048, 0.6400, 0.6667, 0.2941, 0.1250])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.5859, 0.4611, 0.2500, 0.6667, 0.2941, 0.2500])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.7273, 0.9597, 0.4600, 0.1111, 0.4706, 0.6250])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.2323, 0.3153, 0.7900, 0.0000, 0.4118, 0.5000])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.8788, 0.6746, 0.4200, 0.5556, 0.4118, 0.6250])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.6465, 0.7507, 0.5800, 0.7778, 0.2353, 0.6250])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.1414, 0.3580, 0.1700, 0.5556, 0.5294, 0.2500])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.5455, 0.8748, 0.6500, 0.5556, 0.4118, 0.6250])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.7677, 0.6977, 0.4400, 0.7778, 0.2941, 0.2500])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.5253, 0.3340, 0.3800, 0.5556, 0.2941, 0.5000])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.9697, 0.5818, 0.0600, 0.5556, 0.5294, 0.3750])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.5556, 0.3512, 0.5000, 0.1111, 0.5882, 0.5000])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.1919, 0.6824, 1.0000, 0.5556, 0.3529, 0.3750])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.2929, 0.7032, 0.7400, 1.0000, 0.2941, 0.2500])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.9697, 0.6644, 0.4700, 0.2222, 0.4118, 0.3750])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.9192, 0.7506, 0.6400, 0.3333, 0.4118, 0.6250])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.3737, 0.5391, 0.3800, 0.1111, 0.2941, 0.5000])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.4242, 0.3620, 0.6900, 0.4444, 0.2353, 0.0000])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.5859, 0.7856, 0.8000, 0.4444, 0.4706, 0.6250])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.4242, 0.9730, 0.2400, 0.4444, 0.3529, 0.0000])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.4848, 0.6930, 0.4500, 0.3333, 0.5294, 0.6250])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.5960, 0.5353, 0.8700, 0.5556, 0.5294, 0.2500])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.4545, 0.8031, 0.3200, 0.4444, 0.5294, 0.1250])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.7374, 0.7501, 0.9700, 0.7778, 0.2941, 0.0000])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.7071, 0.5714, 0.4900, 0.4444, 0.2353, 0.5000])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.5253, 0.5608, 0.4600, 0.2222, 0.4118, 0.1250])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.7071, 0.6546, 0.5100, 0.8889, 0.4118, 0.7500])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.6162, 0.9590, 0.9600, 0.5556, 0.5294, 0.5000])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.4646, 0.6024, 0.3400, 0.5556, 0.4706, 0.2500])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.6566, 0.7065, 0.3200, 0.8889, 0.4706, 0.3750])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.7677, 0.6529, 0.8800, 0.6667, 0.5882, 0.1250])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.3131, 0.3574, 0.4200, 0.7778, 0.5294, 0.0000])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.5556, 0.5638, 0.8900, 0.6667, 0.2353, 0.6250])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.9899, 0.5488, 0.1200, 0.2222, 0.2941, 0.1250])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.4040, 0.8887, 0.3600, 0.6667, 0.2353, 0.5000])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.1414, 0.8061, 0.5900, 0.1111, 0.4706, 0.6250])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.6061, 0.6708, 0.8200, 0.2222, 0.4118, 0.1250])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.6061, 0.6631, 1.0000, 0.3333, 0.4706, 0.1250])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.8485, 0.5505, 0.8500, 0.5556, 0.5882, 0.1250])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.1010, 0.4576, 0.7600, 0.5556, 0.4118, 0.1250])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.6263, 0.7885, 0.1900, 0.5556, 0.2941, 0.6250])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.3333, 0.7216, 0.6800, 0.3333, 0.2353, 0.0000])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.0202, 0.8086, 0.5100, 0.3333, 0.3529, 0.6250])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.2727, 0.3898, 0.4400, 0.4444, 0.4706, 0.0000])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.0404, 0.3584, 0.6100, 0.7778, 0.2353, 0.7500])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.5455, 0.6261, 0.7700, 0.5556, 0.2353, 0.3750])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.4747, 0.3963, 0.5500, 0.7778, 0.3529, 0.7500])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.3030, 0.8790, 0.6900, 0.5556, 0.4118, 0.2500])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.9495, 0.9537, 0.2400, 1.0000, 0.5294, 0.2500])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.6465, 0.9225, 1.0000, 0.5556, 0.5294, 0.5000])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.1919, 0.2849, 0.8900, 0.2222, 0.3529, 0.2500])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.9192, 0.3851, 0.3200, 0.7778, 0.4118, 0.7500])
|
||||
true value: 1
|
||||
prediction: 1
|
||||
data: tensor([0.8788, 0.4788, 0.1400, 0.1111, 0.5294, 0.1250])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
data: tensor([0.7778, 0.9586, 0.0400, 0.6667, 0.4118, 0.1250])
|
||||
true value: 0
|
||||
prediction: 1
|
||||
81
train.py
Normal file
81
train.py
Normal file
@ -0,0 +1,81 @@
|
||||
#!/usr/bin/python
|
||||
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import zadanie1 as z
|
||||
import torch
|
||||
import torchvision
|
||||
import torchvision.transforms as transforms
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch.optim as optim
|
||||
|
||||
|
||||
class Net(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
#self.conv1 = nn.Conv2d(3, 6, 5)
|
||||
#self.pool = nn.MaxPool2d(2, 2)
|
||||
#self.conv2 = nn.Conv2d(6, 16, 5)
|
||||
#self.fc1 = nn.Linear(16 * 5 * 5, 120)
|
||||
#self.fc2 = nn.Linear(20, 6)
|
||||
self.fc3 = nn.Linear(6, 6)
|
||||
|
||||
def forward(self, x):
|
||||
#x = self.pool(F.relu(self.conv1(x)))
|
||||
#x = self.pool(F.relu(self.conv2(x)))
|
||||
#x = torch.flatten(x, 1)
|
||||
#x = F.relu(self.fc1(x))
|
||||
#x = F.relu(self.fc2(x))
|
||||
x = self.fc3(x)
|
||||
return x
|
||||
|
||||
|
||||
def trainNet(trainloader, criterion, optimizer):
|
||||
for epoch in range(20):
|
||||
|
||||
for i, data in enumerate(trainloader, 0):
|
||||
inputs, labels = data
|
||||
|
||||
labelsX = torch.Tensor([x for x in labels])
|
||||
labels = labelsX.type(torch.LongTensor)
|
||||
|
||||
optimizer.zero_grad()
|
||||
|
||||
outputs = net(inputs)
|
||||
|
||||
loss = criterion(outputs, labels)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
print('Finished Training')
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
train, dev, test = z.prepareData()
|
||||
|
||||
batch_size = 4
|
||||
|
||||
trainlist = train.values.tolist()
|
||||
testlist = test.values.tolist()
|
||||
|
||||
trainset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in trainlist]
|
||||
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
|
||||
shuffle=True, num_workers=2)
|
||||
|
||||
testset = [[torch.Tensor(x[1:]), torch.Tensor([x[0]])] for x in testlist]
|
||||
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
|
||||
shuffle=False, num_workers=2)
|
||||
|
||||
classes = ('male', 'female')
|
||||
|
||||
net = Net()
|
||||
|
||||
criterion = nn.CrossEntropyLoss()
|
||||
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
|
||||
|
||||
trainNet(trainloader, criterion, optimizer)
|
||||
|
||||
PATH = './cifar_net.pth'
|
||||
torch.save(net.state_dict(), PATH)
|
||||
|
||||
44
zadanie1.py
44
zadanie1.py
@ -3,34 +3,38 @@
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
|
||||
data = pd.read_csv("Customers.csv")
|
||||
print(data[:10])
|
||||
|
||||
dataF = data
|
||||
def prepareData():
|
||||
data = pd.read_csv("Customers.csv")
|
||||
#print(data[:10])
|
||||
|
||||
mapping = {'NaN' : 0, 'Healthcare' : 1, 'Engineer' : 2, 'Lawyer' : 3, 'Entertainment' : 4, 'Artist' : 5, 'Executive' : 6,
|
||||
'Doctor' : 7, 'Homemaker' : 8, 'Marketing' : 9}
|
||||
dataF = data
|
||||
|
||||
mapping2 = {'Male' : 0, 'Female' : 1}
|
||||
mapping = {'NaN' : 0, 'Healthcare' : 1, 'Engineer' : 2, 'Lawyer' : 3, 'Entertainment' : 4, 'Artist' : 5, 'Executive' : 6,
|
||||
'Doctor' : 7, 'Homemaker' : 8, 'Marketing' : 9}
|
||||
|
||||
dataF = dataF.replace({'Profession': mapping})
|
||||
dataF = dataF.replace({'Gender': mapping2})
|
||||
mapping2 = {'Male' : 0, 'Female' : 1}
|
||||
|
||||
dataF = dataF.drop(columns=['CustomerID'])
|
||||
dataF = dataF.replace({'Profession': mapping})
|
||||
dataF = dataF.replace({'Gender': mapping2})
|
||||
|
||||
dataF['Profession'] = dataF['Profession'].fillna(0)
|
||||
dataF = dataF.drop(columns=['CustomerID'])
|
||||
|
||||
normalized_dataF = (dataF - dataF.min())/(dataF.max() - dataF.min())
|
||||
dataF['Profession'] = dataF['Profession'].fillna(0)
|
||||
|
||||
print(normalized_dataF[:10])
|
||||
normalized_dataF = (dataF - dataF.min())/(dataF.max() - dataF.min())
|
||||
|
||||
train_data = normalized_dataF[0:1600]
|
||||
dev_data = normalized_dataF[1600:1800]
|
||||
test_data = normalized_dataF[1800:]
|
||||
#print(normalized_dataF[:10])
|
||||
|
||||
print(f"Wielkość zbioru Customers: {len(data)} elementów")
|
||||
print(f"Wielkość zbioru trenującego: {len(train_data)} elementów")
|
||||
print(f"Wielkość zbioru walidującego: {len(dev_data)} elementów")
|
||||
print(f"Wielkość zbioru testującego: {len(test_data)} elementów")
|
||||
train_data = normalized_dataF[0:1600]
|
||||
dev_data = normalized_dataF[1600:1800]
|
||||
test_data = normalized_dataF[1800:]
|
||||
|
||||
print(f" \nDane i wartości na temat zbioru: \n \n {normalized_dataF.describe()}")
|
||||
#print(f"Wielkość zbioru Customers: {len(data)} elementów")
|
||||
#print(f"Wielkość zbioru trenującego: {len(train_data)} elementów")
|
||||
#print(f"Wielkość zbioru walidującego: {len(dev_data)} elementów")
|
||||
#print(f"Wielkość zbioru testującego: {len(test_data)} elementów")
|
||||
|
||||
#print(f" \nDane i wartości na temat zbioru: \n \n {normalized_dataF.describe()}")
|
||||
|
||||
return train_data, dev_data, test_data
|
||||
|
||||
Loading…
Reference in New Issue
Block a user