fix_kox
This commit is contained in:
parent
63d63f0522
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5
my_runs/5/config.json
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5
my_runs/5/config.json
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{
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"epochs": 10,
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"learning_rate": 0.001,
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"seed": 102385107
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}
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800
my_runs/5/cout.txt
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800
my_runs/5/cout.txt
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tensor(1.9501, grad_fn=<NllLossBackward>)
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2410
my_runs/5/metrics.json
Normal file
2410
my_runs/5/metrics.json
Normal file
File diff suppressed because it is too large
Load Diff
88
my_runs/5/run.json
Normal file
88
my_runs/5/run.json
Normal file
@ -0,0 +1,88 @@
|
||||
{
|
||||
"artifacts": [],
|
||||
"command": "my_main",
|
||||
"experiment": {
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||||
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|
||||
"dependencies": [
|
||||
"numpy==1.20.0",
|
||||
"pandas==1.4.1",
|
||||
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|
||||
"torch==1.8.1+cu102",
|
||||
"torchvision==0.9.1+cu102"
|
||||
],
|
||||
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|
||||
"name": "s452627",
|
||||
"repositories": [],
|
||||
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|
||||
[
|
||||
"sacred_train.py",
|
||||
"_sources\\sacred_train_58880e146636573a7d2893b734269763.py"
|
||||
],
|
||||
[
|
||||
"zadanie1.py",
|
||||
"_sources\\zadanie1_214ad86c108ac00197ed071c54ee3658.py"
|
||||
]
|
||||
]
|
||||
},
|
||||
"heartbeat": "2023-05-11T19:47:40.525506",
|
||||
"host": {
|
||||
"ENV": {},
|
||||
"cpu": "Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz",
|
||||
"gpus": {
|
||||
"driver_version": "472.12",
|
||||
"gpus": [
|
||||
{
|
||||
"model": "NVIDIA GeForce GTX 1070",
|
||||
"persistence_mode": false,
|
||||
"total_memory": 8192
|
||||
}
|
||||
]
|
||||
},
|
||||
"hostname": "JAKUB-HENYK",
|
||||
"os": [
|
||||
"Windows",
|
||||
"Windows-10-10.0.19041-SP0"
|
||||
],
|
||||
"python_version": "3.8.3"
|
||||
},
|
||||
"meta": {
|
||||
"command": "my_main",
|
||||
"config_updates": {},
|
||||
"named_configs": [],
|
||||
"options": {
|
||||
"--beat-interval": null,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"resources": [
|
||||
[
|
||||
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|
||||
"my_runs\\_resources\\Customers_6514be2808e61a30190fa6265e2352da.csv"
|
||||
]
|
||||
],
|
||||
"result": null,
|
||||
"start_time": "2023-05-11T19:47:00.196563",
|
||||
"status": "RUNNING"
|
||||
}
|
||||
5
my_runs/6/config.json
Normal file
5
my_runs/6/config.json
Normal file
@ -0,0 +1,5 @@
|
||||
{
|
||||
"epochs": 10,
|
||||
"learning_rate": 0.001,
|
||||
"seed": 562570933
|
||||
}
|
||||
0
my_runs/6/cout.txt
Normal file
0
my_runs/6/cout.txt
Normal file
1
my_runs/6/metrics.json
Normal file
1
my_runs/6/metrics.json
Normal file
@ -0,0 +1 @@
|
||||
{}
|
||||
96
my_runs/6/run.json
Normal file
96
my_runs/6/run.json
Normal file
@ -0,0 +1,96 @@
|
||||
{
|
||||
"artifacts": [],
|
||||
"command": "my_main",
|
||||
"experiment": {
|
||||
"base_dir": "e:\\Pyton\\IUM\\ium_452627",
|
||||
"dependencies": [
|
||||
"numpy==1.20.0",
|
||||
"pandas==1.4.1",
|
||||
"sacred==0.8.4",
|
||||
"torch==1.8.1+cu102",
|
||||
"torchvision==0.9.1+cu102"
|
||||
],
|
||||
"mainfile": "sacred_train.py",
|
||||
"name": "s452627",
|
||||
"repositories": [],
|
||||
"sources": [
|
||||
[
|
||||
"sacred_train.py",
|
||||
"_sources\\sacred_train_ff46cdc09c67889917b2588f7c9f993f.py"
|
||||
],
|
||||
[
|
||||
"zadanie1.py",
|
||||
"_sources\\zadanie1_214ad86c108ac00197ed071c54ee3658.py"
|
||||
]
|
||||
]
|
||||
},
|
||||
"fail_trace": [
|
||||
"Traceback (most recent call last):\n",
|
||||
" File \"C:\\Users\\kubak\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\sacred\\config\\captured_function.py\", line 42, in captured_function\n result = wrapped(*args, **kwargs)\n",
|
||||
" File \"e:/Pyton/IUM/ium_452627/sacred_train.py\", line 94, in my_main\n trainNet(_run, trainloader, criterion, optimizer, net, int(float(epochs)))\n",
|
||||
" File \"e:/Pyton/IUM/ium_452627/sacred_train.py\", line 50, in trainNet\n print(loss[0])\n",
|
||||
"IndexError: invalid index of a 0-dim tensor. Use `tensor.item()` in Python or `tensor.item<T>()` in C++ to convert a 0-dim tensor to a number\n"
|
||||
],
|
||||
"heartbeat": "2023-05-11T19:48:10.063748",
|
||||
"host": {
|
||||
"ENV": {},
|
||||
"cpu": "Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz",
|
||||
"gpus": {
|
||||
"driver_version": "472.12",
|
||||
"gpus": [
|
||||
{
|
||||
"model": "NVIDIA GeForce GTX 1070",
|
||||
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||||
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||||
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||||
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||||
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|
||||
"hostname": "JAKUB-HENYK",
|
||||
"os": [
|
||||
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|
||||
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|
||||
],
|
||||
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|
||||
},
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||||
"meta": {
|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
},
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||||
"resources": [
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||||
[
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||||
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|
||||
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"start_time": "2023-05-11T19:48:03.769748",
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||||
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||||
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|
||||
}
|
||||
5
my_runs/7/config.json
Normal file
5
my_runs/7/config.json
Normal file
@ -0,0 +1,5 @@
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||||
{
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||||
"epochs": 10,
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||||
"learning_rate": 0.001,
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||||
}
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||||
400
my_runs/7/cout.txt
Normal file
400
my_runs/7/cout.txt
Normal file
@ -0,0 +1,400 @@
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1.6230660676956177
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1210
my_runs/7/metrics.json
Normal file
1210
my_runs/7/metrics.json
Normal file
File diff suppressed because it is too large
Load Diff
88
my_runs/7/run.json
Normal file
88
my_runs/7/run.json
Normal file
@ -0,0 +1,88 @@
|
||||
{
|
||||
"artifacts": [],
|
||||
"command": "my_main",
|
||||
"experiment": {
|
||||
"base_dir": "e:\\Pyton\\IUM\\ium_452627",
|
||||
"dependencies": [
|
||||
"numpy==1.20.0",
|
||||
"pandas==1.4.1",
|
||||
"sacred==0.8.4",
|
||||
"torch==1.8.1+cu102",
|
||||
"torchvision==0.9.1+cu102"
|
||||
],
|
||||
"mainfile": "sacred_train.py",
|
||||
"name": "s452627",
|
||||
"repositories": [],
|
||||
"sources": [
|
||||
[
|
||||
"sacred_train.py",
|
||||
"_sources\\sacred_train_544504897ad356dd64cd4527a9914747.py"
|
||||
],
|
||||
[
|
||||
"zadanie1.py",
|
||||
"_sources\\zadanie1_214ad86c108ac00197ed071c54ee3658.py"
|
||||
]
|
||||
]
|
||||
},
|
||||
"heartbeat": "2023-05-11T19:49:13.645610",
|
||||
"host": {
|
||||
"ENV": {},
|
||||
"cpu": "Intel(R) Core(TM) i7-6700K CPU @ 4.00GHz",
|
||||
"gpus": {
|
||||
"driver_version": "472.12",
|
||||
"gpus": [
|
||||
{
|
||||
"model": "NVIDIA GeForce GTX 1070",
|
||||
"persistence_mode": false,
|
||||
"total_memory": 8192
|
||||
}
|
||||
]
|
||||
},
|
||||
"hostname": "JAKUB-HENYK",
|
||||
"os": [
|
||||
"Windows",
|
||||
"Windows-10-10.0.19041-SP0"
|
||||
],
|
||||
"python_version": "3.8.3"
|
||||
},
|
||||
"meta": {
|
||||
"command": "my_main",
|
||||
"config_updates": {},
|
||||
"named_configs": [],
|
||||
"options": {
|
||||
"--beat-interval": null,
|
||||
"--capture": null,
|
||||
"--comment": null,
|
||||
"--debug": false,
|
||||
"--enforce_clean": false,
|
||||
"--file_storage": null,
|
||||
"--force": false,
|
||||
"--help": false,
|
||||
"--id": null,
|
||||
"--loglevel": null,
|
||||
"--mongo_db": null,
|
||||
"--name": null,
|
||||
"--pdb": false,
|
||||
"--print-config": false,
|
||||
"--priority": null,
|
||||
"--queue": false,
|
||||
"--s3": null,
|
||||
"--sql": null,
|
||||
"--tiny_db": null,
|
||||
"--unobserved": false,
|
||||
"COMMAND": null,
|
||||
"UPDATE": [],
|
||||
"help": false,
|
||||
"with": false
|
||||
}
|
||||
},
|
||||
"resources": [
|
||||
[
|
||||
"E:\\Pyton\\IUM\\ium_452627\\Customers.csv",
|
||||
"my_runs\\_resources\\Customers_6514be2808e61a30190fa6265e2352da.csv"
|
||||
]
|
||||
],
|
||||
"result": null,
|
||||
"start_time": "2023-05-11T19:48:43.353238",
|
||||
"status": "RUNNING"
|
||||
}
|
||||
@ -0,0 +1,104 @@
|
||||
#!/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
|
||||
from sacred import Experiment
|
||||
from sacred.observers import FileStorageObserver
|
||||
from sacred.observers import MongoObserver
|
||||
|
||||
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(_run, trainloader, criterion, optimizer, net, epochs=20):
|
||||
for epoch in range(epochs):
|
||||
|
||||
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)
|
||||
print(loss.item())
|
||||
|
||||
_run.log_scalar("training.loss", loss)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
print('Finished Training')
|
||||
|
||||
ex = Experiment("s452627", interactive=True, save_git_info=False)
|
||||
ex.observers.append(FileStorageObserver('my_runs'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
|
||||
@ex.config
|
||||
def my_config():
|
||||
epochs = 10
|
||||
learning_rate = 0.001
|
||||
|
||||
@ex.automain
|
||||
def my_main(epochs, learning_rate, _run):
|
||||
|
||||
ex.open_resource("Customers.csv", "r")
|
||||
|
||||
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=learning_rate, momentum=0.9)
|
||||
|
||||
trainNet(_run, trainloader, criterion, optimizer, net, int(float(epochs)))
|
||||
|
||||
PATH = './cifar_net.pth'
|
||||
torch.save(net.state_dict(), PATH)
|
||||
|
||||
ex.add_artifact("cifar_net.pth")
|
||||
|
||||
#if __name__ == '__main__':
|
||||
|
||||
#ex.run()
|
||||
|
||||
@ -0,0 +1,104 @@
|
||||
#!/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
|
||||
from sacred import Experiment
|
||||
from sacred.observers import FileStorageObserver
|
||||
from sacred.observers import MongoObserver
|
||||
|
||||
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(_run, trainloader, criterion, optimizer, net, epochs=20):
|
||||
for epoch in range(epochs):
|
||||
|
||||
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)
|
||||
print(loss)
|
||||
|
||||
_run.log_scalar("training.loss", loss)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
print('Finished Training')
|
||||
|
||||
ex = Experiment("s452627", interactive=True, save_git_info=False)
|
||||
ex.observers.append(FileStorageObserver('my_runs'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
|
||||
@ex.config
|
||||
def my_config():
|
||||
epochs = 10
|
||||
learning_rate = 0.001
|
||||
|
||||
@ex.automain
|
||||
def my_main(epochs, learning_rate, _run):
|
||||
|
||||
ex.open_resource("Customers.csv", "r")
|
||||
|
||||
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=learning_rate, momentum=0.9)
|
||||
|
||||
trainNet(_run, trainloader, criterion, optimizer, net, int(float(epochs)))
|
||||
|
||||
PATH = './cifar_net.pth'
|
||||
torch.save(net.state_dict(), PATH)
|
||||
|
||||
ex.add_artifact("cifar_net.pth")
|
||||
|
||||
#if __name__ == '__main__':
|
||||
|
||||
#ex.run()
|
||||
|
||||
@ -0,0 +1,104 @@
|
||||
#!/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
|
||||
from sacred import Experiment
|
||||
from sacred.observers import FileStorageObserver
|
||||
from sacred.observers import MongoObserver
|
||||
|
||||
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(_run, trainloader, criterion, optimizer, net, epochs=20):
|
||||
for epoch in range(epochs):
|
||||
|
||||
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)
|
||||
print(loss[0])
|
||||
|
||||
_run.log_scalar("training.loss", loss)
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
|
||||
print('Finished Training')
|
||||
|
||||
ex = Experiment("s452627", interactive=True, save_git_info=False)
|
||||
ex.observers.append(FileStorageObserver('my_runs'))
|
||||
#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
|
||||
|
||||
@ex.config
|
||||
def my_config():
|
||||
epochs = 10
|
||||
learning_rate = 0.001
|
||||
|
||||
@ex.automain
|
||||
def my_main(epochs, learning_rate, _run):
|
||||
|
||||
ex.open_resource("Customers.csv", "r")
|
||||
|
||||
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=learning_rate, momentum=0.9)
|
||||
|
||||
trainNet(_run, trainloader, criterion, optimizer, net, int(float(epochs)))
|
||||
|
||||
PATH = './cifar_net.pth'
|
||||
torch.save(net.state_dict(), PATH)
|
||||
|
||||
ex.add_artifact("cifar_net.pth")
|
||||
|
||||
#if __name__ == '__main__':
|
||||
|
||||
#ex.run()
|
||||
|
||||
@ -47,6 +47,7 @@ def trainNet(_run, trainloader, criterion, optimizer, net, epochs=20):
|
||||
outputs = net(inputs)
|
||||
|
||||
loss = criterion(outputs, labels)
|
||||
print(loss.item())
|
||||
|
||||
_run.log_scalar("training.loss", loss)
|
||||
loss.backward()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user