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Implemented batching for TicTacToe AI

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Clemens Dautermann 2020-01-28 14:45:00 +01:00
parent 55cff9b18f
commit 56ee2635b5
96 changed files with 8426 additions and 7 deletions
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64
TicTacToe_AI/Net/pytorch_ai.py
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@ -4,6 +4,11 @@ import torch.optim as optim
from torch import nn from torch import nn
import torch.nn.functional as F import torch.nn.functional as F
from tqdm import tqdm from tqdm import tqdm
import wandb
wandb.init(project="tictactoe")
BATCH_SIZE = 3
def to_set(raw_list): def to_set(raw_list):
@ -35,6 +40,40 @@ def to_set(raw_list):
return out_set return out_set
def to_batched_set(raw_list):
counter = 0
out_set = []
boardtensor = torch.zeros((BATCH_SIZE, 9))
labeltensor = torch.zeros(BATCH_SIZE, dtype=torch.long)
for line in tqdm(raw_list):
line = line.replace('\n', '')
raw_board, raw_label = line.split('|')[0], line.split('|')[1]
if not (int(raw_label) is -1):
labeltensor[counter] = int(raw_label)
else:
labeltensor[counter] = 9
raw_board = raw_board.split(',')
for n, block in enumerate(raw_board):
if int(block) is -1:
boardtensor[counter][n] = 0
elif int(block) is 0:
boardtensor[counter][n] = 0.5
elif int(block) is 1:
boardtensor[counter][n] = 1
if counter == (BATCH_SIZE - 1):
out_set.append([boardtensor, labeltensor])
boardtensor = torch.zeros((BATCH_SIZE, 9))
labeltensor = torch.zeros(BATCH_SIZE, dtype=torch.long)
counter = 0
else:
counter += 1
return out_set
def buildsets(): def buildsets():
with open('boards.bds', 'r') as infile: with open('boards.bds', 'r') as infile:
print('Loading file...') print('Loading file...')
@ -43,10 +82,10 @@ def buildsets():
random.shuffle(alllines) random.shuffle(alllines)
print('Generating testset...') print('Generating testset...')
testset = to_set(alllines[0:10000]) testset = to_batched_set(alllines[0:10000])
print('Generating trainset...') print('Generating trainset...')
trainset = to_set(alllines[10001:200000]) trainset = to_batched_set(alllines[10001:20000])
return trainset, testset return trainset, testset
@ -60,6 +99,7 @@ def testnet(net, testset):
if torch.argmax(output) == label[0]: if torch.argmax(output) == label[0]:
correct += 1 correct += 1
total += 1 total += 1
wandb.log({'test_accuracy': correct / total})
print("Accuracy: ", round(correct / total, 3)) print("Accuracy: ", round(correct / total, 3))
@ -79,21 +119,33 @@ class Net(torch.nn.Module):
return F.log_softmax(x, dim=1) return F.log_softmax(x, dim=1)
net = torch.load('./nets/net_3.pt') device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print('running on %s' % device)
# net = torch.load('./nets/net_3.pt')
net = Net()
wandb.watch(net)
net.to(device)
optimizer = optim.Adam(net.parameters(), lr=0.001) optimizer = optim.Adam(net.parameters(), lr=0.001)
loss_function = nn.CrossEntropyLoss()
trainset, testset = buildsets() trainset, testset = buildsets()
for epoch in range(100): for epoch in range(100):
print('Epoch: ' + str(epoch)) print('Epoch: ' + str(epoch))
wandb.log({'epoch': epoch})
for X, label in tqdm(trainset): for X, label in tqdm(trainset):
net.zero_grad() net.zero_grad()
X.to(device)
output = net(X) output = net(X)
loss = F.nll_loss(output.view(1, 10), label[0]) output.cpu()
loss = loss_function(output.view(-1, 10), label)
loss.backward() loss.backward()
optimizer.step() optimizer.step()
wandb.log({'loss': loss})
print(loss) torch.save(net, './nets/gpunets/net_' + str(epoch) + '.pt')
torch.save(net, './nets/net_' + str(epoch + 3) + '.pt')
testnet(net, testset) testnet(net, testset)

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TicTacToe_AI/Net/wandb/debug.log Normal file
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@ -0,0 +1,18 @@
2020-01-28 14:43:14,846 DEBUG MainThread:32731 [wandb_config.py:_load_defaults():111] no defaults not found in config-defaults.yaml
2020-01-28 14:43:14,864 DEBUG MainThread:32731 [cmd.py:execute():728] Popen(['git', 'cat-file', '--batch-check'], cwd=/home/clemens/repositorys/pytorch-ai, universal_newlines=False, shell=None, istream=<valid stream>)
2020-01-28 14:43:14,877 DEBUG MainThread:32731 [cmd.py:execute():728] Popen(['git', 'rev-parse', '--show-toplevel'], cwd=/home/clemens/repositorys/pytorch-ai, universal_newlines=False, shell=None, istream=None)
2020-01-28 14:43:14,887 DEBUG MainThread:32731 [cmd.py:execute():728] Popen(['git', 'status', '--porcelain', '--untracked-files'], cwd=/home/clemens/repositorys/pytorch-ai, universal_newlines=False, shell=None, istream=None)
2020-01-28 14:43:14,906 DEBUG MainThread:32731 [run_manager.py:__init__():535] Initialized sync for tictactoe/mvx4evw0
2020-01-28 14:43:14,912 INFO MainThread:32731 [run_manager.py:wrap_existing_process():1133] wrapping existing process 32725
2020-01-28 14:43:14,913 WARNING MainThread:32731 [io_wrap.py:register():104] SIGWINCH handler was not None: <Handlers.SIG_DFL: 0>
2020-01-28 14:43:14,919 DEBUG MainThread:32731 [connectionpool.py:_new_conn():815] Starting new HTTPS connection (1): pypi.org:443
2020-01-28 14:43:15,060 DEBUG MainThread:32731 [connectionpool.py:_make_request():393] https://pypi.org:443 "GET /pypi/wandb/json HTTP/1.1" 200 39767
2020-01-28 14:43:15,179 INFO MainThread:32731 [run_manager.py:init_run():918] system metrics and metadata threads started
2020-01-28 14:43:15,181 INFO MainThread:32731 [run_manager.py:wrap_existing_process():1150] informing user process we are ready to proceed
2020-01-28 14:43:15,183 INFO MainThread:32731 [run_manager.py:_sync_etc():1257] entering loop for messages from user process
2020-01-28 14:43:15,862 INFO Thread-3 :32731 [run_manager.py:_on_file_modified():682] file/dir modified: /home/clemens/repositorys/pytorch-ai/TicTacToe_AI/Net/wandb/dryrun-20200128_134313-mvx4evw0/config.yaml
2020-01-28 14:43:32,850 INFO MainThread:32731 [run_manager.py:_sync_etc():1313] process received interrupt signal, shutting down
2020-01-28 14:43:32,850 INFO MainThread:32731 [run_manager.py:_sync_etc():1366] closing log streams and sending exitcode to W&B
2020-01-28 14:43:32,851 INFO MainThread:32731 [run_manager.py:shutdown():1057] shutting down system stats and metadata service
MainThread:32731 [mvx4evw0:run_manager.py:_sync_etc():1366] closing log streams and sending exitcode to W&B
2020-01-28 14:43:32,851 INFO MainThread:32731 [mvx4evw0:run_manager.py:shutdown():1057] shutting down system stats and metadata service

9
TicTacToe_AI/Net/wandb/dryrun-20200128_133612-8qzzaedg/config.yaml Normal file
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@ -0,0 +1,9 @@
wandb_version: 1
_wandb:
desc: null
value:
cli_version: 0.8.22
framework: torch
is_jupyter_run: false
python_version: 3.7.5

13
TicTacToe_AI/Net/wandb/dryrun-20200128_133612-8qzzaedg/output.log Normal file
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@ -0,0 +1,13 @@
running on cpu
Loading file...
986410
Generating testset...
0%| | 0/10000 [00:00<?, ?it/s] 0%| | 0/10000 [00:00<?, ?it/s]
Traceback (most recent call last):
File "pytorch_ai.py", line 135, in <module>
trainset, testset = buildsets()
File "pytorch_ai.py", line 85, in buildsets
testset = to_batched_set(alllines[0:10000])
File "pytorch_ai.py", line 55, in to_batched_set
labeltensor[counter][9] = 1
IndexError: dimension specified as 0 but tensor has no dimensions

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TicTacToe_AI/Net/wandb/dryrun-20200128_133612-8qzzaedg/wandb-events.jsonl Normal file
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@ -0,0 +1 @@
{"system.cpu": 46.3, "system.memory": 47.33, "system.disk": 8.1, "system.proc.memory.availableMB": 4055.41, "system.proc.memory.rssMB": 168.55, "system.proc.memory.percent": 2.19, "system.proc.cpu.threads": 1.0, "system.network.sent": 5487, "system.network.recv": 52086, "_wandb": true, "_timestamp": 1580218575, "_runtime": 3}

0
TicTacToe_AI/Net/wandb/dryrun-20200128_133612-8qzzaedg/wandb-history.jsonl Normal file
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TicTacToe_AI/Net/wandb/dryrun-20200128_133612-8qzzaedg/wandb-metadata.json Normal file
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@ -0,0 +1,23 @@
{
"root": "/home/clemens/repositorys/pytorch-ai",
"program": "pytorch_ai.py",
"git": {
"remote": "git@github.com:Clemens-Dautermann/pytorch-ai.git",
"commit": "55cff9b18f8558ae7a9170e56a3d5c6f6665d9ab"
},
"email": "clemens.dautermann@gmail.com",
"startedAt": "2020-01-28T13:36:12.626726",
"host": "ubuntu-laptop",
"username": "clemens",
"executable": "/usr/bin/python3",
"os": "Linux-5.3.0-26-generic-x86_64-with-Ubuntu-19.10-eoan",
"python": "3.7.5",
"cpu_count": 2,
"args": [],
"state": "failed",
"jobType": null,
"mode": "dryrun",
"project": "tictactoe",
"heartbeatAt": "2020-01-28T13:36:16.645560",
"exitcode": 1
}

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TicTacToe_AI/Net/wandb/dryrun-20200128_133739-4u7vedo7/config.yaml Normal file
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@ -0,0 +1,9 @@
wandb_version: 1
_wandb:
desc: null
value:
cli_version: 0.8.22
framework: torch
is_jupyter_run: false
python_version: 3.7.5

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TicTacToe_AI/Net/wandb/dryrun-20200128_133739-4u7vedo7/output.log Normal file
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@ -0,0 +1,32 @@
running on cpu
Loading file...
986410
Generating testset...
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Epoch: 0
0%| | 0/3333 [00:00<?, ?it/s]tensor([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
tensor([[[0, 1, 0, 0, 0, 0, 0, 0, 0, 0]],
[[0, 0, 0, 0, 0, 0, 0, 0, 0, 1]],
[[0, 0, 0, 0, 1, 0, 0, 0, 0, 0]]])
0%| | 0/3333 [00:00<?, ?it/s]
Traceback (most recent call last):
File "pytorch_ai.py", line 147, in <module>
loss = loss_function(output.view(-1, 10), label)
File "/home/clemens/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in __call__
result = self.forward(*input, **kwargs)
File "/home/clemens/.local/lib/python3.7/site-packages/torch/nn/modules/loss.py", line 916, in forward
ignore_index=self.ignore_index, reduction=self.reduction)
File "/home/clemens/.local/lib/python3.7/site-packages/torch/nn/functional.py", line 2009, in cross_entropy
return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
File "/home/clemens/.local/lib/python3.7/site-packages/torch/nn/functional.py", line 1838, in nll_loss
ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: multi-target not supported at /pytorch/aten/src/THNN/generic/ClassNLLCriterion.c:22

1
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@ -0,0 +1 @@
{"system.cpu": 52.85, "system.memory": 47.72, "system.disk": 8.1, "system.proc.memory.availableMB": 4028.75, "system.proc.memory.rssMB": 173.29, "system.proc.memory.percent": 2.25, "system.proc.cpu.threads": 1.67, "system.network.sent": 5779, "system.network.recv": 53955, "_wandb": true, "_timestamp": 1580218669, "_runtime": 9}

1
TicTacToe_AI/Net/wandb/dryrun-20200128_133739-4u7vedo7/wandb-history.jsonl Normal file
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@ -0,0 +1 @@
{"epoch": 0, "_runtime": 10.099808931350708, "_timestamp": 1580218669.1546183, "_step": 0}

23
TicTacToe_AI/Net/wandb/dryrun-20200128_133739-4u7vedo7/wandb-metadata.json Normal file
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@ -0,0 +1,23 @@
{
"root": "/home/clemens/repositorys/pytorch-ai",
"program": "pytorch_ai.py",
"git": {
"remote": "git@github.com:Clemens-Dautermann/pytorch-ai.git",
"commit": "55cff9b18f8558ae7a9170e56a3d5c6f6665d9ab"
},
"email": "clemens.dautermann@gmail.com",
"startedAt": "2020-01-28T13:37:39.809337",
"host": "ubuntu-laptop",
"username": "clemens",
"executable": "/usr/bin/python3",
"os": "Linux-5.3.0-26-generic-x86_64-with-Ubuntu-19.10-eoan",
"python": "3.7.5",
"cpu_count": 2,
"args": [],
"state": "failed",
"jobType": null,
"mode": "dryrun",
"project": "tictactoe",
"heartbeatAt": "2020-01-28T13:37:49.833434",
"exitcode": 1
}

1
TicTacToe_AI/Net/wandb/dryrun-20200128_133739-4u7vedo7/wandb-summary.json Normal file
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@ -0,0 +1 @@
{"_runtime": 10.099808931350708, "_step": 0, "_timestamp": 1580218669.1546183, "epoch": 0}

9
TicTacToe_AI/Net/wandb/dryrun-20200128_134136-5prsp2w0/config.yaml Normal file
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@ -0,0 +1,9 @@
wandb_version: 1
_wandb:
desc: null
value:
cli_version: 0.8.22
framework: torch
is_jupyter_run: false
python_version: 3.7.5

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TicTacToe_AI/Net/wandb/dryrun-20200128_134136-5prsp2w0/output.log Normal file
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@ -0,0 +1,39 @@
running on cpu
Loading file...
986410
Generating testset...
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Generating trainset...
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Epoch: 0
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[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
tensor([9, 8, 4])
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[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
tensor([[[9, 9, 9, 9, 9, 9, 9, 9, 9, 9]],
[[2, 2, 2, 2, 2, 2, 2, 2, 2, 2]],
[[9, 9, 9, 9, 9, 9, 9, 9, 9, 9]]])
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Traceback (most recent call last):
File "pytorch_ai.py", line 147, in <module>
loss = loss_function(output.view(-1, 10), label)
File "/home/clemens/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in __call__
result = self.forward(*input, **kwargs)
File "/home/clemens/.local/lib/python3.7/site-packages/torch/nn/modules/loss.py", line 916, in forward
ignore_index=self.ignore_index, reduction=self.reduction)
File "/home/clemens/.local/lib/python3.7/site-packages/torch/nn/functional.py", line 2009, in cross_entropy
return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
File "/home/clemens/.local/lib/python3.7/site-packages/torch/nn/functional.py", line 1838, in nll_loss
ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: multi-target not supported at /pytorch/aten/src/THNN/generic/ClassNLLCriterion.c:22

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{"system.cpu": 53.07, "system.memory": 47.88, "system.disk": 8.1, "system.proc.memory.availableMB": 4014.31, "system.proc.memory.rssMB": 175.12, "system.proc.memory.percent": 2.27, "system.proc.cpu.threads": 2.17, "system.network.sent": 6514, "system.network.recv": 54693, "_wandb": true, "_timestamp": 1580218907, "_runtime": 11}

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{"epoch": 0, "_runtime": 10.460375547409058, "_timestamp": 1580218906.140515, "_step": 0}
{"loss": 2.3025851249694824, "_runtime": 10.78924560546875, "_timestamp": 1580218906.4693851, "_step": 1}

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{
"root": "/home/clemens/repositorys/pytorch-ai",
"program": "pytorch_ai.py",
"git": {
"remote": "git@github.com:Clemens-Dautermann/pytorch-ai.git",
"commit": "55cff9b18f8558ae7a9170e56a3d5c6f6665d9ab"
},
"email": "clemens.dautermann@gmail.com",
"startedAt": "2020-01-28T13:41:36.538539",
"host": "ubuntu-laptop",
"username": "clemens",
"executable": "/usr/bin/python3",
"os": "Linux-5.3.0-26-generic-x86_64-with-Ubuntu-19.10-eoan",
"python": "3.7.5",
"cpu_count": 2,
"args": [],
"state": "failed",
"jobType": null,
"mode": "dryrun",
"project": "tictactoe",
"heartbeatAt": "2020-01-28T13:41:48.527435",
"exitcode": 1
}

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{"_step": 1, "epoch": 0, "_timestamp": 1580218906.4693851, "_runtime": 10.78924560546875, "graph_0": {"_type": "graph", "format": "torch", "nodes": [{"name": "fc1", "id": 139691765707856, "class_name": "Linear(in_features=9, out_features=9, bias=True)", "parameters": [["weight", [9, 9]], ["bias", [9]]], "output_shape": [[3, 1, 9]], "num_parameters": [81, 9]}, {"name": "fc2", "id": 139691765708240, "class_name": "Linear(in_features=9, out_features=20, bias=True)", "parameters": [["weight", [20, 9]], ["bias", [20]]], "output_shape": [[3, 1, 20]], "num_parameters": [180, 20]}, {"name": "fc3", "id": 139691765708304, "class_name": "Linear(in_features=20, out_features=50, bias=True)", "parameters": [["weight", [50, 20]], ["bias", [50]]], "output_shape": [[3, 1, 50]], "num_parameters": [1000, 50]}, {"name": "fc4", "id": 139691765708048, "class_name": "Linear(in_features=50, out_features=10, bias=True)", "parameters": [["weight", [10, 50]], ["bias", [10]]], "output_shape": [[3, 1, 10]], "num_parameters": [500, 10]}], "edges": []}, "loss": 2.3025851249694824}

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wandb_version: 1
_wandb:
desc: null
value:
cli_version: 0.8.22
framework: torch
is_jupyter_run: false
python_version: 3.7.5

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running on cpu
Loading file...
986410
Generating testset...
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[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
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[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
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[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
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wandb_version: 1
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running on cpu
Loading file...
986410
Generating testset...
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Generating trainset...
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Epoch: 0
0%| | 0/3333 [00:00<?, ?it/s]tensor([[-2.2606, -2.3016, -2.3324, -2.1078, -2.4331, -2.3292, -2.4555, -2.1577,
-2.3959, -2.3086],
[-2.2527, -2.3219, -2.3240, -2.1096, -2.4330, -2.3107, -2.4580, -2.1627,
-2.3925, -2.3163],
[-2.3171, -2.3093, -2.3601, -2.0109, -2.4205, -2.4251, -2.4450, -2.1626,
-2.3624, -2.2971]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 9, 9])
0%| | 1/3333 [00:00<17:50, 3.11it/s]tensor([[-2.2756, -2.3164, -2.3221, -2.0758, -2.4144, -2.3594, -2.4396, -2.1908,
-2.3865, -2.3002],
[-2.2879, -2.3256, -2.3406, -2.0495, -2.4257, -2.3662, -2.4585, -2.1805,
-2.3813, -2.2777],
[-2.2534, -2.3107, -2.3269, -2.1052, -2.4295, -2.3340, -2.4431, -2.1795,
-2.3939, -2.3015]], grad_fn=<LogSoftmaxBackward>)
tensor([4, 9, 9])
0%| | 2/3333 [00:00<14:31, 3.82it/s]tensor([[-2.2714, -2.3200, -2.3438, -2.0782, -2.4236, -2.3574, -2.4690, -2.1722,
-2.3955, -2.2591],
[-2.3185, -2.3122, -2.3693, -2.0265, -2.4150, -2.4169, -2.4660, -2.1623,
-2.3694, -2.2528],
[-2.2624, -2.3141, -2.3460, -2.0996, -2.4266, -2.3471, -2.4651, -2.1675,
-2.3957, -2.2612]], grad_fn=<LogSoftmaxBackward>)
tensor([2, 9, 5])
0%| | 3/3333 [00:00<12:11, 4.55it/s]tensor([[-2.2861, -2.3200, -2.3502, -2.0903, -2.4236, -2.3607, -2.4754, -2.1649,
-2.3871, -2.2320],
[-2.3026, -2.3210, -2.3485, -2.0647, -2.4148, -2.3664, -2.4793, -2.1702,
-2.3897, -2.2383],
[-2.2406, -2.3362, -2.3302, -2.1319, -2.4198, -2.2811, -2.4863, -2.1779,
-2.4037, -2.2727]], grad_fn=<LogSoftmaxBackward>)
tensor([6, 3, 9])
0%| | 4/3333 [00:00<10:33, 5.25it/s]tensor([[-2.2478, -2.3374, -2.3269, -2.1172, -2.4215, -2.3116, -2.4681, -2.1904,
-2.4018, -2.2560],
[-2.2880, -2.3389, -2.3378, -2.0648, -2.4154, -2.3548, -2.4689, -2.1920,
-2.3918, -2.2385],
[-2.2581, -2.3288, -2.3277, -2.1122, -2.4221, -2.3202, -2.4582, -2.1883,
-2.3994, -2.2625]], grad_fn=<LogSoftmaxBackward>)
tensor([7, 4, 9])
0%| | 5/3333 [00:00<09:25, 5.88it/s]tensor([[-2.3107, -2.3244, -2.3572, -2.0726, -2.4097, -2.3645, -2.4784, -2.1731,
-2.3932, -2.2114],
[-2.3267, -2.3186, -2.3651, -2.0596, -2.4089, -2.3640, -2.4796, -2.1745,
-2.3893, -2.2124],
[-2.2623, -2.3268, -2.3336, -2.1178, -2.4183, -2.3215, -2.4645, -2.1790,
-2.4061, -2.2494]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 9, 9])
0%| | 6/3333 [00:00<08:37, 6.43it/s]tensor([[-2.3081, -2.3159, -2.3551, -2.0756, -2.4174, -2.3747, -2.4694, -2.1609,
-2.4064, -2.2136],
[-2.2806, -2.3176, -2.3370, -2.1004, -2.4165, -2.3380, -2.4641, -2.1725,
-2.4148, -2.2431],
[-2.3250, -2.3180, -2.3603, -2.0373, -2.4065, -2.4279, -2.4522, -2.1708,
-2.3891, -2.2189]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 2, 8])
0%| | 7/3333 [00:01<08:04, 6.87it/s]tensor([[-2.2817, -2.3275, -2.3342, -2.1107, -2.4127, -2.3317, -2.4715, -2.1759,
-2.4121, -2.2256],
[-2.3241, -2.3238, -2.3538, -2.0402, -2.4024, -2.4277, -2.4504, -2.1772,
-2.3881, -2.2160],
[-2.3170, -2.3439, -2.3584, -2.0741, -2.4081, -2.3626, -2.4867, -2.1711,
-2.3946, -2.1838]], grad_fn=<LogSoftmaxBackward>)
tensor([2, 5, 5])
0%| | 8/3333 [00:01<07:29, 7.39it/s]tensor([[-2.2864, -2.3414, -2.3356, -2.1119, -2.4130, -2.3283, -2.4789, -2.1768,
-2.4127, -2.2019],
[-2.3035, -2.3849, -2.3451, -2.0847, -2.4183, -2.3249, -2.5108, -2.1680,
-2.4071, -2.1607],
[-2.3567, -2.3474, -2.3607, -2.0231, -2.3987, -2.4192, -2.4883, -2.1724,
-2.3933, -2.1634]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 9, 3])
tensor([[-2.3214, -2.3341, -2.3440, -2.0693, -2.4097, -2.3730, -2.4662, -2.1748,
-2.4060, -2.1984],
[-2.2913, -2.3438, -2.3343, -2.1234, -2.4194, -2.3207, -2.4808, -2.1723,
-2.4118, -2.1895],
[-2.3261, -2.3620, -2.3193, -2.0527, -2.3749, -2.3780, -2.4794, -2.2136,
-2.3985, -2.1923]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 9, 9])
0%|▏ | 10/3333 [00:01<06:54, 8.02it/s]tensor([[-2.3560, -2.3741, -2.3582, -2.0290, -2.4012, -2.3669, -2.5343, -2.1787,
-2.3985, -2.1345],
[-2.3232, -2.3676, -2.3245, -2.0907, -2.3900, -2.3326, -2.4869, -2.2045,
-2.4054, -2.1670],
[-2.3370, -2.3664, -2.3384, -2.0707, -2.4053, -2.3475, -2.4946, -2.1856,
-2.4100, -2.1508]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 9, 4])
tensor([[-2.2873, -2.3531, -2.3239, -2.1248, -2.4094, -2.3020, -2.4832, -2.1868,
-2.4149, -2.1986],
[-2.3729, -2.3631, -2.3615, -2.0280, -2.3982, -2.4023, -2.5033, -2.1804,
-2.3984, -2.1224],
[-2.3551, -2.3759, -2.3400, -2.0557, -2.3883, -2.3579, -2.5027, -2.1997,
-2.4041, -2.1335]], grad_fn=<LogSoftmaxBackward>)
tensor([1, 4, 3])
0%|▏ | 12/3333 [00:01<06:35, 8.41it/s]tensor([[-2.3229, -2.3882, -2.3401, -2.0903, -2.4072, -2.3122, -2.5321, -2.1808,
-2.4281, -2.1161],
[-2.3191, -2.3677, -2.3229, -2.1030, -2.3919, -2.3170, -2.4828, -2.2059,
-2.4081, -2.1700],
[-2.3181, -2.3723, -2.3336, -2.0893, -2.3976, -2.3284, -2.5013, -2.1994,
-2.4202, -2.1421]], grad_fn=<LogSoftmaxBackward>)
tensor([8, 4, 2])
0%|▏ | 13/3333 [00:01<06:16, 8.83it/s]tensor([[-2.3643, -2.3715, -2.3515, -2.0656, -2.3907, -2.3614, -2.5088, -2.1961,
-2.4012, -2.1070],
[-2.3424, -2.3575, -2.3301, -2.0730, -2.3820, -2.3563, -2.4875, -2.2074,
-2.4067, -2.1559],
[-2.3225, -2.3725, -2.3349, -2.1063, -2.4044, -2.3132, -2.5183, -2.1845,
-2.4226, -2.1275]], grad_fn=<LogSoftmaxBackward>)
tensor([1, 0, 3])
tensor([[-2.3413, -2.4002, -2.3512, -2.0830, -2.3920, -2.3197, -2.5432, -2.2075,
-2.4043, -2.0825],
[-2.3238, -2.3654, -2.3269, -2.1013, -2.3910, -2.3167, -2.5043, -2.2039,
-2.4172, -2.1464],
[-2.2936, -2.3716, -2.3201, -2.1313, -2.4004, -2.2874, -2.4984, -2.1999,
-2.4144, -2.1702]], grad_fn=<LogSoftmaxBackward>)
tensor([5, 9, 2])
0%|▏ | 15/3333 [00:01<06:02, 9.15it/s]tensor([[-2.3649, -2.3669, -2.3403, -2.0573, -2.3789, -2.3549, -2.5068, -2.2148,
-2.4065, -2.1220],
[-2.3131, -2.3718, -2.3193, -2.1177, -2.3995, -2.2970, -2.5006, -2.2019,
-2.4146, -2.1565],
[-2.3689, -2.3832, -2.3237, -2.0500, -2.3535, -2.3604, -2.5303, -2.2353,
-2.4074, -2.1083]], grad_fn=<LogSoftmaxBackward>)
tensor([6, 0, 9])
0%|▏ | 16/3333 [00:01<05:53, 9.39it/s]tensor([[-2.3848, -2.3738, -2.3524, -2.0436, -2.3752, -2.3689, -2.5305, -2.2147,
-2.4059, -2.0834],
[-2.3562, -2.3779, -2.3441, -2.0836, -2.3828, -2.3315, -2.5272, -2.2160,
-2.4068, -2.0908],
[-2.3750, -2.3813, -2.3533, -2.0481, -2.3854, -2.3705, -2.5488, -2.2051,
-2.4106, -2.0646]], grad_fn=<LogSoftmaxBackward>)
tensor([1, 3, 3])
tensor([[-2.3394, -2.3727, -2.3114, -2.0750, -2.3653, -2.3333, -2.5086, -2.2442,
-2.4177, -2.1346],
[-2.3788, -2.3742, -2.3518, -2.0418, -2.3774, -2.3731, -2.5431, -2.2177,
-2.4114, -2.0706],
[-2.3775, -2.3678, -2.3574, -2.0595, -2.3785, -2.3520, -2.5314, -2.2192,
-2.4014, -2.0824]], grad_fn=<LogSoftmaxBackward>)
tensor([0, 9, 3])
1%|▎ | 18/3333 [00:02<05:47, 9.55it/s]tensor([[-2.3366, -2.3906, -2.3306, -2.0914, -2.3870, -2.3065, -2.5437, -2.2276,
-2.4294, -2.0791],
[-2.3471, -2.3639, -2.3331, -2.0711, -2.3817, -2.3334, -2.5249, -2.2218,
-2.4212, -2.1154],
[-2.3776, -2.3806, -2.3331, -2.0165, -2.3443, -2.4071, -2.5192, -2.2579,
-2.3914, -2.1035]], grad_fn=<LogSoftmaxBackward>)
tensor([6, 9, 0])
1%|▎ | 19/3333 [00:02<05:53, 9.37it/s]tensor([[-2.3218, -2.3660, -2.3282, -2.1020, -2.3896, -2.3077, -2.5298, -2.2237,
-2.4309, -2.1090],
[-2.3337, -2.4280, -2.3441, -2.0773, -2.3949, -2.2880, -2.5762, -2.2384,
-2.4255, -2.0413],
[-2.3494, -2.3619, -2.3375, -2.0671, -2.3861, -2.3456, -2.5414, -2.2209,
-2.4291, -2.0871]], grad_fn=<LogSoftmaxBackward>)
tensor([0, 9, 9])
tensor([[-2.3692, -2.3648, -2.3461, -2.0246, -2.3710, -2.3894, -2.5360, -2.2400,
-2.4223, -2.0774],
[-2.3524, -2.3868, -2.3187, -2.0489, -2.3477, -2.3573, -2.5255, -2.2748,
-2.4129, -2.0955],
[-2.3995, -2.3848, -2.3778, -2.0116, -2.3871, -2.3974, -2.5629, -2.2311,
-2.4127, -2.0123]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 6, 9])
1%|▎ | 21/3333 [00:02<05:46, 9.55it/s]tensor([[-2.3640, -2.3936, -2.3584, -2.0486, -2.3946, -2.3617, -2.5563, -2.2384,
-2.4259, -2.0168],
[-2.3231, -2.3716, -2.3352, -2.0939, -2.3952, -2.3205, -2.5348, -2.2323,
-2.4352, -2.0781],
[-2.3513, -2.4259, -2.3660, -2.0467, -2.3882, -2.3166, -2.5870, -2.2476,
-2.4190, -2.0172]], grad_fn=<LogSoftmaxBackward>)
tensor([0, 0, 8])
tensor([[-2.3869, -2.3927, -2.3850, -2.0029, -2.3957, -2.4121, -2.5707, -2.2404,
-2.4190, -1.9876],
[-2.3162, -2.3832, -2.3211, -2.0673, -2.3795, -2.3325, -2.5156, -2.2643,
-2.4304, -2.1035],
[-2.3074, -2.3662, -2.3317, -2.0963, -2.3955, -2.3188, -2.5247, -2.2369,
-2.4339, -2.0995]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 9, 5])
1%|▎ | 23/3333 [00:02<05:41, 9.68it/s]tensor([[-2.3135, -2.3787, -2.3322, -2.0889, -2.3872, -2.3268, -2.5215, -2.2581,
-2.4293, -2.0792],
[-2.3497, -2.3871, -2.3547, -2.0359, -2.3827, -2.3729, -2.5389, -2.2635,
-2.4254, -2.0377],
[-2.3680, -2.3759, -2.3737, -2.0220, -2.3827, -2.3948, -2.5359, -2.2566,
-2.4155, -2.0313]], grad_fn=<LogSoftmaxBackward>)
tensor([0, 6, 7])
1%|▎ | 24/3333 [00:02<05:38, 9.77it/s]tensor([[-2.3508, -2.4103, -2.3546, -2.0191, -2.3758, -2.3772, -2.5503, -2.2845,
-2.4254, -2.0164],
[-2.2979, -2.4187, -2.3499, -2.0935, -2.4057, -2.3145, -2.5536, -2.2610,
-2.4383, -2.0138],
[-2.3423, -2.4031, -2.3423, -2.0337, -2.3575, -2.3819, -2.5286, -2.2958,
-2.4104, -2.0462]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 1, 9])
1%|▍ | 25/3333 [00:02<05:36, 9.84it/s]tensor([[-2.3313, -2.3699, -2.3699, -2.0525, -2.3986, -2.3809, -2.5286, -2.2496,
-2.4244, -2.0373],
[-2.3241, -2.4048, -2.3655, -2.0695, -2.4170, -2.3568, -2.5489, -2.2503,
-2.4312, -1.9929],
[-2.3310, -2.4001, -2.3705, -2.0666, -2.4004, -2.3513, -2.5412, -2.2659,
-2.4182, -2.0060]], grad_fn=<LogSoftmaxBackward>)
tensor([6, 4, 3])
tensor([[-2.3339, -2.3793, -2.3625, -2.0312, -2.3812, -2.4055, -2.5053, -2.2739,
-2.4142, -2.0523],
[-2.2823, -2.4205, -2.3584, -2.0941, -2.4100, -2.3169, -2.5584, -2.2697,
-2.4449, -1.9994],
[-2.2781, -2.3708, -2.3387, -2.1102, -2.4048, -2.3206, -2.5083, -2.2551,
-2.4396, -2.0836]], grad_fn=<LogSoftmaxBackward>)
tensor([3, 9, 0])
1%|▍ | 27/3333 [00:03<05:39, 9.73it/s]tensor([[-2.3139, -2.4264, -2.3952, -2.0653, -2.4072, -2.3370, -2.5566, -2.2748,
-2.4087, -1.9818],
[-2.2635, -2.4030, -2.3448, -2.1148, -2.4121, -2.3047, -2.5232, -2.2591,
-2.4359, -2.0600],
[-2.3185, -2.4049, -2.3740, -2.0536, -2.3979, -2.3629, -2.5329, -2.2772,
-2.4241, -2.0076]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 3, 2])
1%|▍ | 28/3333 [00:03<05:58, 9.21it/s]tensor([[-2.2879, -2.3890, -2.3416, -2.0846, -2.3928, -2.3432, -2.5075, -2.2819,
-2.4363, -2.0570],
[-2.2857, -2.3832, -2.3505, -2.0962, -2.4034, -2.3384, -2.5163, -2.2669,
-2.4372, -2.0471],
[-2.2979, -2.3874, -2.3391, -2.0623, -2.3832, -2.3669, -2.4995, -2.2890,
-2.4350, -2.0638]], grad_fn=<LogSoftmaxBackward>)
tensor([5, 1, 3])
1%|▍ | 29/3333 [00:03<06:12, 8.88it/s]tensor([[-2.3017, -2.4018, -2.3528, -2.0636, -2.3897, -2.3568, -2.5303, -2.2920,
-2.4412, -2.0165],
[-2.3201, -2.4089, -2.3590, -2.0330, -2.3650, -2.4046, -2.5084, -2.3165,
-2.4115, -2.0218],
[-2.2972, -2.4291, -2.3774, -2.0674, -2.4205, -2.3478, -2.5466, -2.2720,
-2.4288, -1.9805]], grad_fn=<LogSoftmaxBackward>)
tensor([2, 5, 7])
1%|▍ | 30/3333 [00:03<06:21, 8.66it/s]tensor([[-2.3346, -2.4107, -2.4052, -2.0234, -2.4195, -2.4090, -2.5537, -2.2785,
-2.4352, -1.9361],
[-2.2963, -2.3953, -2.3665, -2.0670, -2.4135, -2.3531, -2.5269, -2.2718,
-2.4463, -2.0126],
[-2.3295, -2.3910, -2.3792, -2.0173, -2.3893, -2.4168, -2.5127, -2.2906,
-2.4263, -2.0103]], grad_fn=<LogSoftmaxBackward>)
tensor([4, 7, 9])
1%|▍ | 31/3333 [00:03<06:27, 8.51it/s]tensor([[-2.3296, -2.4346, -2.3938, -2.0315, -2.4148, -2.3866, -2.5514, -2.2957,
-2.4434, -1.9257],
[-2.3095, -2.3789, -2.3784, -2.0580, -2.4105, -2.3804, -2.5164, -2.2697,
-2.4402, -2.0090],
[-2.2732, -2.4413, -2.3781, -2.0893, -2.4294, -2.3287, -2.5659, -2.2794,
-2.4566, -1.9447]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 8, 3])
1%|▍ | 32/3333 [00:03<06:32, 8.41it/s]tensor([[-2.3149, -2.4493, -2.4028, -2.0365, -2.4206, -2.3716, -2.5821, -2.2923,
-2.4474, -1.9068],
[-2.3046, -2.4133, -2.3605, -2.0619, -2.3862, -2.3583, -2.5299, -2.3073,
-2.4401, -1.9937],
[-2.2838, -2.4111, -2.3769, -2.0874, -2.4297, -2.3517, -2.5468, -2.2698,
-2.4509, -1.9633]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 9, 9])
1%|▌ | 33/3333 [00:03<06:35, 8.34it/s]tensor([[-2.3187, -2.3893, -2.3897, -2.0319, -2.4086, -2.4010, -2.5262, -2.2756,
-2.4428, -1.9879],
[-2.3647, -2.4236, -2.4449, -1.9853, -2.4164, -2.4604, -2.5669, -2.2978,
-2.4125, -1.8848],
[-2.2934, -2.4445, -2.3868, -2.0596, -2.4206, -2.3522, -2.5557, -2.2940,
-2.4501, -1.9375]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 2, 6])
1%|▌ | 34/3333 [00:03<06:37, 8.30it/s]tensor([[-2.3490, -2.4703, -2.4302, -1.9863, -2.4241, -2.4139, -2.6156, -2.3080,
-2.4360, -1.8538],
[-2.2673, -2.4744, -2.3827, -2.0785, -2.4358, -2.3061, -2.5752, -2.2983,
-2.4545, -1.9301],
[-2.2742, -2.4024, -2.3638, -2.0991, -2.4237, -2.3339, -2.5231, -2.2723,
-2.4508, -2.0026]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 1, 3])
1%|▌ | 35/3333 [00:04<06:28, 8.50it/s]tensor([[-2.3067, -2.3893, -2.3721, -2.0541, -2.4112, -2.3771, -2.5094, -2.2842,
-2.4509, -2.0002],
[-2.3246, -2.4008, -2.3864, -2.0172, -2.4022, -2.4242, -2.5345, -2.2938,
-2.4467, -1.9610],
[-2.3098, -2.4089, -2.3908, -2.0480, -2.4268, -2.3905, -2.5527, -2.2783,
-2.4534, -1.9378]], grad_fn=<LogSoftmaxBackward>)
tensor([4, 9, 3])
tensor([[-2.3407, -2.4508, -2.3991, -2.0006, -2.4063, -2.4069, -2.5594, -2.3272,
-2.4502, -1.8969],
[-2.3328, -2.4345, -2.3793, -2.0141, -2.3794, -2.4118, -2.5405, -2.3351,
-2.4406, -1.9361],
[-2.3076, -2.4432, -2.4002, -2.0549, -2.4401, -2.3767, -2.5664, -2.2883,
-2.4611, -1.8890]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 9, 3])
1%|▌ | 37/3333 [00:04<06:10, 8.90it/s]tensor([[-2.3614, -2.4425, -2.4385, -2.0024, -2.4282, -2.4162, -2.5828, -2.3179,
-2.4404, -1.8462],
[-2.2751, -2.3994, -2.3599, -2.0840, -2.4184, -2.3368, -2.5218, -2.2805,
-2.4582, -2.0118],
[-2.3266, -2.4445, -2.4089, -2.0303, -2.4328, -2.4001, -2.5679, -2.3018,
-2.4617, -1.8718]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 0, 1])
1%|▌ | 38/3333 [00:04<05:58, 9.20it/s]tensor([[-2.2898, -2.4147, -2.3862, -2.0711, -2.4340, -2.3604, -2.5525, -2.2860,
-2.4683, -1.9315],
[-2.3077, -2.3964, -2.3851, -2.0431, -2.4228, -2.3875, -2.5454, -2.2827,
-2.4655, -1.9533],
[-2.3304, -2.4316, -2.3908, -2.0184, -2.3995, -2.4133, -2.5579, -2.3267,
-2.4560, -1.9019]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 4, 4])
tensor([[-2.3798, -2.4426, -2.4536, -1.9705, -2.4204, -2.4696, -2.5834, -2.3325,
-2.4400, -1.8208],
[-2.4080, -2.4654, -2.4854, -1.9233, -2.4217, -2.5535, -2.6041, -2.3390,
-2.4338, -1.7665],
[-2.3451, -2.4138, -2.4015, -1.9943, -2.3869, -2.4550, -2.5343, -2.3270,
-2.4426, -1.9215]], grad_fn=<LogSoftmaxBackward>)
tensor([6, 9, 1])
1%|▌ | 40/3333 [00:04<05:49, 9.43it/s]tensor([[-2.3346, -2.4312, -2.3799, -2.0035, -2.3727, -2.4330, -2.5473, -2.3511,
-2.4586, -1.9127],
[-2.2813, -2.4675, -2.4033, -2.0664, -2.4450, -2.3502, -2.5803, -2.3127,
-2.4850, -1.8589],
[-2.3012, -2.4814, -2.4153, -2.0463, -2.4472, -2.3689, -2.5892, -2.3173,
-2.4767, -1.8330]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 0, 9])
1%|▋ | 41/3333 [00:04<05:43, 9.59it/s]tensor([[-2.3254, -2.4641, -2.4346, -2.0341, -2.4464, -2.4068, -2.5995, -2.3183,
-2.4883, -1.7943],
[-2.3477, -2.4431, -2.4040, -1.9984, -2.3798, -2.4409, -2.5597, -2.3611,
-2.4603, -1.8650],
[-2.2995, -2.4107, -2.3789, -2.0531, -2.4070, -2.3770, -2.5402, -2.3185,
-2.4785, -1.9317]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 9, 9])
1%|▋ | 42/3333 [00:04<05:38, 9.71it/s]tensor([[-2.3239, -2.4547, -2.4348, -2.0264, -2.4372, -2.4148, -2.5947, -2.3317,
-2.4920, -1.7989],
[-2.3603, -2.4626, -2.4338, -1.9831, -2.4088, -2.4474, -2.5837, -2.3609,
-2.4769, -1.8032],
[-2.3990, -2.4736, -2.4844, -1.9458, -2.4272, -2.5287, -2.6186, -2.3561,
-2.4741, -1.7222]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 9, 9])
tensor([[-2.3918, -2.4713, -2.4809, -1.9667, -2.4206, -2.5071, -2.6118, -2.3666,
-2.4759, -1.7214],
[-2.3318, -2.4543, -2.4428, -2.0268, -2.4310, -2.4116, -2.5888, -2.3306,
-2.4740, -1.8076],
[-2.3272, -2.4084, -2.4252, -2.0357, -2.4284, -2.4245, -2.5599, -2.3094,
-2.4812, -1.8550]], grad_fn=<LogSoftmaxBackward>)
tensor([0, 0, 0])
1%|▋ | 44/3333 [00:04<05:35, 9.79it/s]tensor([[-2.2970, -2.4606, -2.4179, -2.0474, -2.4392, -2.3894, -2.5738, -2.3220,
-2.4822, -1.8384],
[-2.3575, -2.4162, -2.4651, -1.9924, -2.4253, -2.4962, -2.5773, -2.3284,
-2.4717, -1.7957],
[-2.3281, -2.4065, -2.4344, -2.0251, -2.4244, -2.4483, -2.5633, -2.3174,
-2.4805, -1.8420]], grad_fn=<LogSoftmaxBackward>)
tensor([7, 1, 0])
tensor([[-2.3114, -2.4068, -2.4138, -2.0316, -2.4093, -2.4195, -2.5352, -2.3240,
-2.4754, -1.8966],
[-2.3023, -2.5068, -2.4694, -2.0455, -2.4597, -2.4097, -2.6337, -2.3571,
-2.5000, -1.7115],
[-2.3087, -2.4395, -2.4305, -2.0337, -2.4276, -2.4250, -2.5792, -2.3396,
-2.5012, -1.8100]], grad_fn=<LogSoftmaxBackward>)
tensor([1, 9, 7])
1%|▋ | 46/3333 [00:05<05:33, 9.86it/s]tensor([[-2.3410, -2.4354, -2.4856, -2.0133, -2.4479, -2.4983, -2.6035, -2.3312,
-2.4901, -1.7340],
[-2.2617, -2.4473, -2.4258, -2.0788, -2.4447, -2.3873, -2.5824, -2.3233,
-2.5020, -1.8219],
[-2.2896, -2.4200, -2.4209, -2.0665, -2.4225, -2.4179, -2.5725, -2.3327,
-2.4952, -1.8286]], grad_fn=<LogSoftmaxBackward>)
tensor([7, 5, 3])
1%|▋ | 47/3333 [00:05<05:44, 9.55it/s]tensor([[-2.2920, -2.4299, -2.4500, -2.0620, -2.4479, -2.4303, -2.5874, -2.3195,
-2.5057, -1.7852],
[-2.3059, -2.4386, -2.4168, -2.0344, -2.4006, -2.4390, -2.5768, -2.3672,
-2.5016, -1.8113],
[-2.3174, -2.4141, -2.4592, -2.0284, -2.4346, -2.4739, -2.5879, -2.3242,
-2.4973, -1.7853]], grad_fn=<LogSoftmaxBackward>)
tensor([1, 8, 1])
1%|▋ | 48/3333 [00:05<05:39, 9.68it/s]tensor([[-2.3217, -2.4487, -2.4809, -2.0146, -2.4458, -2.4818, -2.6382, -2.3392,
-2.5159, -1.7175],
[-2.3108, -2.4528, -2.4586, -2.0427, -2.4300, -2.4679, -2.6167, -2.3603,
-2.5087, -1.7263],
[-2.3443, -2.4744, -2.5279, -2.0176, -2.4586, -2.4859, -2.6572, -2.3687,
-2.4921, -1.6514]], grad_fn=<LogSoftmaxBackward>)
tensor([7, 3, 5])
tensor([[-2.3364, -2.4279, -2.4982, -2.0058, -2.4475, -2.5179, -2.6179, -2.3375,
-2.5085, -1.7134],
[-2.2649, -2.4602, -2.4742, -2.0795, -2.4687, -2.4180, -2.6215, -2.3346,
-2.5272, -1.7243],
[-2.3129, -2.4374, -2.4619, -2.0397, -2.4263, -2.4674, -2.6099, -2.3558,
-2.5064, -1.7419]], grad_fn=<LogSoftmaxBackward>)
tensor([3, 8, 1])
2%|▊ | 50/3333 [00:05<05:35, 9.77it/s]tensor([[-2.2549, -2.4156, -2.4481, -2.0850, -2.4507, -2.3995, -2.5904, -2.3162,
-2.5129, -1.8118],
[-2.2836, -2.4151, -2.4677, -2.0680, -2.4580, -2.4425, -2.6101, -2.3129,
-2.5137, -1.7633],
[-2.2631, -2.4276, -2.4585, -2.0763, -2.4512, -2.3978, -2.6021, -2.3266,
-2.5101, -1.7918]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 9, 2])
2%|▊ | 51/3333 [00:05<05:33, 9.84it/s]tensor([[-2.3640, -2.4467, -2.5526, -1.9956, -2.4743, -2.5504, -2.6538, -2.3535,
-2.5135, -1.6251],
[-2.3429, -2.4072, -2.5186, -2.0011, -2.4543, -2.5468, -2.6211, -2.3286,
-2.5016, -1.7050],
[-2.3138, -2.4230, -2.5021, -2.0327, -2.4696, -2.4853, -2.6349, -2.3216,
-2.5194, -1.7077]], grad_fn=<LogSoftmaxBackward>)
tensor([1, 9, 1])
tensor([[-2.3304, -2.4357, -2.5053, -2.0240, -2.4491, -2.5146, -2.6526, -2.3635,
-2.5161, -1.6669],
[-2.3111, -2.4158, -2.4665, -2.0463, -2.4262, -2.4626, -2.6118, -2.3604,
-2.5052, -1.7462],
[-2.3348, -2.4346, -2.5180, -2.0182, -2.4637, -2.4957, -2.6339, -2.3481,
-2.5103, -1.6827]], grad_fn=<LogSoftmaxBackward>)
tensor([2, 5, 6])
2%|▊ | 53/3333 [00:05<05:31, 9.89it/s]tensor([[-2.3605, -2.4211, -2.5263, -1.9878, -2.4426, -2.5504, -2.6344, -2.3729,
-2.5070, -1.6693],
[-2.3225, -2.4013, -2.5061, -2.0390, -2.4710, -2.4916, -2.6252, -2.3278,
-2.5165, -1.7055],
[-2.3540, -2.4459, -2.5712, -2.0160, -2.5056, -2.5547, -2.6872, -2.3488,
-2.5307, -1.5787]], grad_fn=<LogSoftmaxBackward>)
tensor([0, 9, 9])
tensor([[-2.2792, -2.3949, -2.4899, -2.0906, -2.4870, -2.4422, -2.6184, -2.3110,
-2.5238, -1.7285],
[-2.3742, -2.4223, -2.5583, -1.9861, -2.4614, -2.5969, -2.6587, -2.3775,
-2.5080, -1.6121],
[-2.3286, -2.3824, -2.5165, -2.0243, -2.4749, -2.5137, -2.6286, -2.3205,
-2.5143, -1.7095]], grad_fn=<LogSoftmaxBackward>)
tensor([6, 7, 9])
2%|▊ | 55/3333 [00:06<05:35, 9.76it/s]tensor([[-2.2444, -2.4150, -2.4537, -2.0959, -2.4790, -2.3749, -2.6013, -2.3084,
-2.5116, -1.8072],
[-2.3573, -2.4411, -2.5813, -2.0151, -2.5211, -2.5704, -2.7044, -2.3498,
-2.5440, -1.5533],
[-2.3249, -2.4656, -2.5575, -2.0341, -2.5141, -2.4952, -2.7048, -2.3504,
-2.5381, -1.5882]], grad_fn=<LogSoftmaxBackward>)
tensor([8, 9, 2])
2%|▊ | 56/3333 [00:06<05:55, 9.23it/s]tensor([[-2.2963, -2.4706, -2.5473, -2.0726, -2.5316, -2.4626, -2.6903, -2.3530,
-2.5398, -1.5890],
[-2.2605, -2.3736, -2.4605, -2.1005, -2.4770, -2.4160, -2.5944, -2.3089,
-2.5197, -1.7901],
[-2.3786, -2.4433, -2.5826, -1.9862, -2.5031, -2.6136, -2.7098, -2.3753,
-2.5411, -1.5407]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 5, 9])
2%|▊ | 57/3333 [00:06<06:08, 8.89it/s]tensor([[-2.3172, -2.3770, -2.5108, -2.0645, -2.4970, -2.4976, -2.6242, -2.3165,
-2.5213, -1.6904],
[-2.3005, -2.3843, -2.4802, -2.0677, -2.4772, -2.4652, -2.6233, -2.3274,
-2.5247, -1.7245],
[-2.3558, -2.4179, -2.5340, -2.0178, -2.4770, -2.5681, -2.6626, -2.3776,
-2.5254, -1.6067]], grad_fn=<LogSoftmaxBackward>)
tensor([2, 0, 0])
2%|▉ | 58/3333 [00:06<06:17, 8.67it/s]tensor([[-2.2985, -2.3756, -2.4931, -2.0709, -2.4924, -2.4611, -2.6147, -2.3099,
-2.5118, -1.7357],
[-2.2778, -2.3913, -2.4976, -2.1083, -2.5165, -2.4545, -2.6270, -2.3130,
-2.5327, -1.6880],
[-2.2611, -2.3952, -2.4794, -2.1135, -2.5065, -2.4251, -2.6144, -2.3149,
-2.5259, -1.7259]], grad_fn=<LogSoftmaxBackward>)
tensor([8, 6, 4])
2%|▉ | 59/3333 [00:06<06:24, 8.52it/s]tensor([[-2.3457, -2.4646, -2.5807, -2.0556, -2.5438, -2.5287, -2.7415, -2.3915,
-2.5300, -1.5074],
[-2.2683, -2.3827, -2.4765, -2.1078, -2.5036, -2.4356, -2.6161, -2.3134,
-2.5256, -1.7299],
[-2.3695, -2.4242, -2.5793, -2.0206, -2.5335, -2.6020, -2.6878, -2.3627,
-2.5384, -1.5379]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 4, 9])
2%|▉ | 60/3333 [00:06<06:29, 8.41it/s]tensor([[-2.3842, -2.4542, -2.6009, -2.0180, -2.5486, -2.6474, -2.7232, -2.3900,
-2.5510, -1.4676],
[-2.3729, -2.4441, -2.5869, -2.0234, -2.5320, -2.6198, -2.7090, -2.3858,
-2.5413, -1.5013],
[-2.2787, -2.3894, -2.4645, -2.0945, -2.4827, -2.4480, -2.6098, -2.3406,
-2.5252, -1.7267]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 9, 9])
2%|▉ | 61/3333 [00:06<06:32, 8.34it/s]tensor([[-2.3799, -2.4402, -2.6095, -2.0388, -2.5459, -2.6446, -2.7169, -2.3967,
-2.5197, -1.4717],
[-2.3215, -2.4407, -2.5614, -2.0939, -2.5531, -2.5344, -2.6898, -2.3635,
-2.5460, -1.5286],
[-2.3096, -2.3752, -2.5082, -2.0763, -2.5089, -2.5154, -2.6286, -2.3256,
-2.5225, -1.6684]], grad_fn=<LogSoftmaxBackward>)
tensor([3, 9, 0])
2%|▉ | 62/3333 [00:06<06:34, 8.30it/s]tensor([[-2.3461, -2.4392, -2.5792, -2.0690, -2.5522, -2.5815, -2.6978, -2.3746,
-2.5418, -1.5042],
[-2.3075, -2.4121, -2.4686, -2.0778, -2.4550, -2.4935, -2.6227, -2.3971,
-2.5177, -1.6701],
[-2.2561, -2.4252, -2.4809, -2.1209, -2.5134, -2.4152, -2.6321, -2.3338,
-2.5129, -1.6985]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 3, 9])
2%|▉ | 63/3333 [00:07<06:24, 8.49it/s]tensor([[-2.3663, -2.4473, -2.5793, -2.0407, -2.5281, -2.6162, -2.6942, -2.4112,
-2.5395, -1.4927],
[-2.3210, -2.4621, -2.5462, -2.0849, -2.5428, -2.5383, -2.6962, -2.3937,
-2.5592, -1.5137],
[-2.3019, -2.4403, -2.5233, -2.1033, -2.5333, -2.5200, -2.6794, -2.3746,
-2.5502, -1.5560]], grad_fn=<LogSoftmaxBackward>)
tensor([8, 2, 7])
tensor([[-2.3817, -2.4637, -2.6069, -2.0340, -2.5551, -2.6650, -2.7228, -2.4188,
-2.5471, -1.4371],
[-2.3334, -2.4273, -2.5267, -2.0555, -2.5047, -2.5926, -2.6714, -2.3980,
-2.5300, -1.5580],
[-2.2930, -2.4320, -2.5336, -2.1177, -2.5570, -2.5242, -2.6858, -2.3503,
-2.5485, -1.5505]], grad_fn=<LogSoftmaxBackward>)
tensor([9, 2, 9])
2%|▉ | 65/3333 [00:07<06:07, 8.90it/s]tensor([[-2.3214, -2.4840, -2.5838, -2.1162, -2.5970, -2.5768, -2.7298, -2.3831,
-2.5564, -1.4401],
[-2.3130, -2.4602, -2.5357, -2.1007, -2.5527, -2.5419, -2.6930, -2.3836,
-2.5494, -1.5167],
[-2.3326, -2.4942, -2.5973, -2.1029, -2.5973, -2.5881, -2.7465, -2.3880,
-2.5440, -1.4285]], grad_fn=<LogSoftmaxBackward>)
tensor([5, 8, 9])
2%|█ | 66/3333 [00:07<05:55, 9.20it/s]tensor([[-2.2865, -2.4138, -2.5047, -2.1230, -2.5434, -2.5163, -2.6637, -2.3459,
-2.5307, -1.5940],
[-2.3179, -2.4412, -2.5159, -2.0943, -2.5253, -2.5574, -2.6833, -2.3961,
-2.5281, -1.5430],
[-2.3036, -2.4738, -2.5399, -2.1147, -2.5618, -2.5221, -2.6996, -2.3896,
-2.5375, -1.5102]], grad_fn=<LogSoftmaxBackward>)
tensor([6, 9, 8])
tensor([[-2.3064, -2.5133, -2.5707, -2.1424, -2.5957, -2.5224, -2.7460, -2.4092,
-2.5321, -1.4392],
[-2.3168, -2.4775, -2.5657, -2.1311, -2.6002, -2.5688, -2.7224, -2.3841,
-2.5494, -1.4483],
[-2.3607, -2.4710, -2.5676, -2.0652, -2.5551, -2.6773, -2.7271, -2.4298,
-2.5284, -1.4357]], grad_fn=<LogSoftmaxBackward>)
tensor([2, 2, 0])
2%|█ | 68/3333 [00:07<05:46, 9.43it/s]tensor([[-2.3638, -2.5032, -2.5969, -2.0946, -2.6084, -2.6587, -2.7651, -2.4258,
-2.5400, -1.3762],
[-2.3533, -2.5096, -2.6064, -2.1141, -2.6290, -2.6608, -2.7805, -2.4173,
-2.5474, -1.3563],
[-2.2815, -2.4469, -2.5022, -2.1465, -2.5579, -2.5154, -2.6767, -2.3726,
-2.5268, -1.5499]], grad_fn=<LogSoftmaxBackward>)
tensor([3, 2, 9])
2%|█ | 69/3333 [00:07<05:40, 9.59it/s]tensor([[-2.3399, -2.5824, -2.6122, -2.1381, -2.6441, -2.6037, -2.8404, -2.4772,
-2.5246, -1.3120],
[-2.3366, -2.4803, -2.5481, -2.1032, -2.5893, -2.6064, -2.7263, -2.4117,
-2.5288, -1.4457],
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{
"root": "/home/clemens/repositorys/pytorch-ai",
"program": "pytorch_ai.py",
"git": {
"remote": "git@github.com:Clemens-Dautermann/pytorch-ai.git",
"commit": "55cff9b18f8558ae7a9170e56a3d5c6f6665d9ab"
},
"email": "clemens.dautermann@gmail.com",
"startedAt": "2020-01-28T13:43:14.042432",
"host": "ubuntu-laptop",
"username": "clemens",
"executable": "/usr/bin/python3",
"os": "Linux-5.3.0-26-generic-x86_64-with-Ubuntu-19.10-eoan",
"python": "3.7.5",
"cpu_count": 2,
"args": [],
"state": "killed",
"jobType": null,
"mode": "dryrun",
"project": "tictactoe",
"heartbeatAt": "2020-01-28T13:43:33.198554",
"exitcode": 255
}

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@ -0,0 +1 @@
{"_runtime": 19.902369260787964, "epoch": 0, "_timestamp": 1580219012.7107317, "_step": 90, "graph_0": {"_type": "graph", "format": "torch", "nodes": [{"name": "fc1", "id": 140306656036880, "class_name": "Linear(in_features=9, out_features=9, bias=True)", "parameters": [["weight", [9, 9]], ["bias", [9]]], "output_shape": [[3, 9]], "num_parameters": [81, 9]}, {"name": "fc2", "id": 140306656037072, "class_name": "Linear(in_features=9, out_features=20, bias=True)", "parameters": [["weight", [20, 9]], ["bias", [20]]], "output_shape": [[3, 20]], "num_parameters": [180, 20]}, {"name": "fc3", "id": 140306656037008, "class_name": "Linear(in_features=20, out_features=50, bias=True)", "parameters": [["weight", [50, 20]], ["bias", [50]]], "output_shape": [[3, 50]], "num_parameters": [1000, 50]}, {"name": "fc4", "id": 140306656036688, "class_name": "Linear(in_features=50, out_features=10, bias=True)", "parameters": [["weight", [10, 50]], ["bias", [10]]], "output_shape": [[3, 10]], "num_parameters": [500, 10]}], "edges": []}, "loss": 1.7429243326187134}

9
TicTacToe_AI/Net/wandb/run-20200128_092953-yloo6l66/config.yaml Normal file
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@ -0,0 +1,9 @@
wandb_version: 1
_wandb:
desc: null
value:
cli_version: 0.8.22
framework: torch
is_jupyter_run: false
python_version: 3.7.5

67
TicTacToe_AI/Net/wandb/run-20200128_092953-yloo6l66/diff.patch Normal file
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@ -0,0 +1,67 @@
diff --git a/TicTacToe_AI/Net/pytorch_ai.py b/TicTacToe_AI/Net/pytorch_ai.py
index efea5ae..701918f 100644
--- a/TicTacToe_AI/Net/pytorch_ai.py
+++ b/TicTacToe_AI/Net/pytorch_ai.py
@@ -4,6 +4,9 @@ import torch.optim as optim
from torch import nn
import torch.nn.functional as F
from tqdm import tqdm
+import wandb
+
+wandb.init(project="tictactoe")
def to_set(raw_list):
@@ -46,7 +49,7 @@ def buildsets():
testset = to_set(alllines[0:10000])
print('Generating trainset...')
- trainset = to_set(alllines[10001:200000])
+ trainset = to_set(alllines[10001:])
return trainset, testset
@@ -60,6 +63,7 @@ def testnet(net, testset):
if torch.argmax(output) == label[0]:
correct += 1
total += 1
+ wandb.log({'test_accuracy': correct / total})
print("Accuracy: ", round(correct / total, 3))
@@ -79,7 +83,15 @@ class Net(torch.nn.Module):
return F.log_softmax(x, dim=1)
-net = torch.load('./nets/net_3.pt')
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('running on %s' % device)
+
+# net = torch.load('./nets/net_3.pt')
+
+net = Net()
+wandb.watch(net)
+
+net.to(device)
optimizer = optim.Adam(net.parameters(), lr=0.001)
@@ -87,13 +99,16 @@ trainset, testset = buildsets()
for epoch in range(100):
print('Epoch: ' + str(epoch))
+ wandb.log({'epoch': epoch})
for X, label in tqdm(trainset):
net.zero_grad()
+ X.to(device)
output = net(X)
+ output.cpu()
loss = F.nll_loss(output.view(1, 10), label[0])
loss.backward()
optimizer.step()
+ wandb.log({'loss': loss})
- print(loss)
- torch.save(net, './nets/net_' + str(epoch + 3) + '.pt')
+ torch.save(net, './nets/gpunets/net_' + str(epoch) + '.pt')
testnet(net, testset)

9
TicTacToe_AI/Net/wandb/run-20200128_092953-yloo6l66/output.log Normal file
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109
TicTacToe_AI/Net/wandb/run-20200128_092953-yloo6l66/requirements.txt Normal file
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@ -0,0 +1,109 @@
apturl==0.5.2
argh==0.26.2
asn1crypto==0.24.0
bcrypt==3.1.6
binwalk==2.1.2
blinker==1.4
brlapi==0.6.7
certifi==2018.8.24
chardet==3.0.4
click==7.0
command-not-found==0.3
configparser==4.0.2
cryptography==2.6.1
cupshelpers==1.0
cycler==0.10.0
dbus-python==1.2.12
decorator==4.3.0
defer==1.0.6
distro-info==0.21ubuntu4
distro==1.3.0
docker-pycreds==0.4.0
duplicity==0.8.4
entrypoints==0.3
fasteners==0.12.0
future==0.16.0
gitdb2==2.0.6
gitpython==3.0.5
gql==0.2.0
graphql-core==1.1
httplib2==0.11.3
idna==2.6
keyring==18.0.1
keyrings.alt==3.1.1
kiwisolver==1.0.1
language-selector==0.1
launchpadlib==1.10.7
lazr.restfulclient==0.14.2
lazr.uri==1.0.3
lockfile==0.12.2
louis==3.10.0
macaroonbakery==1.2.3
mako==1.0.7
markupsafe==1.1.0
matplotlib==3.0.2
monotonic==1.5
netifaces==0.10.4
numpy==1.16.2
nvidia-ml-py3==7.352.0
oauth==1.0.1
oauthlib==2.1.0
olefile==0.46
paramiko==2.6.0
pathtools==0.1.2
pexpect==4.6.0
pillow==6.1.0
pip==18.1
promise==2.3
protobuf==3.6.1
psutil==5.6.7
pycairo==1.16.2
pycrypto==2.6.1
pycups==1.9.73
pygments==2.3.1
pygobject==3.34.0
pyjwt==1.7.0
pymacaroons==0.13.0
pynacl==1.3.0
pyopengl==3.1.0
pyparsing==2.2.0
pyqt5==5.12.3
pyqtgraph==0.11.0.dev0
pyrfc3339==1.1
python-apt==1.9.0+ubuntu1.3
python-dateutil==2.7.3
python-debian==0.1.36
pytz==2019.2
pyxdg==0.25
pyyaml==5.1.2
reportlab==3.5.23
requests-unixsocket==0.1.5
requests==2.21.0
scipy==1.2.2
secretstorage==2.3.1
sentry-sdk==0.14.0
setuptools==41.1.0
shortuuid==0.5.0
simplejson==3.16.0
sip==4.19.18
six==1.12.0
smmap2==2.0.5
subprocess32==3.5.4
system-service==0.3
systemd-python==234
torch==1.3.1+cpu
torchvision==0.4.2+cpu
tqdm==4.41.0
ubuntu-advantage-tools==19.5
ubuntu-drivers-common==0.0.0
ufw==0.36
unattended-upgrades==0.1
urllib3==1.24.1
usb-creator==0.3.7
virtualenv==15.1.0
wadllib==1.3.3
wandb==0.8.22
watchdog==0.9.0
wheel==0.32.3
xkit==0.0.0
zope.interface==4.3.2

15
TicTacToe_AI/Net/wandb/run-20200128_092953-yloo6l66/wandb-events.jsonl Normal file
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{"system.cpu": 75.24, "system.memory": 46.9, "system.disk": 8.1, "system.proc.memory.availableMB": 4091.78, "system.proc.memory.rssMB": 383.32, "system.proc.memory.percent": 4.98, "system.proc.cpu.threads": 2.0, "system.network.sent": 228132, "system.network.recv": 342421, "_wandb": true, "_timestamp": 1580203853, "_runtime": 59}
{"system.cpu": 74.24, "system.memory": 48.78, "system.disk": 8.1, "system.proc.memory.availableMB": 3945.44, "system.proc.memory.rssMB": 527.97, "system.proc.memory.percent": 6.85, "system.proc.cpu.threads": 2.0, "system.network.sent": 315327, "system.network.recv": 447180, "_wandb": true, "_timestamp": 1580203883, "_runtime": 90}
{"system.cpu": 74.62, "system.memory": 50.76, "system.disk": 8.1, "system.proc.memory.availableMB": 3794.17, "system.proc.memory.rssMB": 675.81, "system.proc.memory.percent": 8.77, "system.proc.cpu.threads": 2.0, "system.network.sent": 400754, "system.network.recv": 547558, "_wandb": true, "_timestamp": 1580203913, "_runtime": 120}
{"system.cpu": 74.14, "system.memory": 52.67, "system.disk": 8.1, "system.proc.memory.availableMB": 3645.53, "system.proc.memory.rssMB": 824.89, "system.proc.memory.percent": 10.71, "system.proc.cpu.threads": 2.0, "system.network.sent": 481243, "system.network.recv": 648614, "_wandb": true, "_timestamp": 1580203944, "_runtime": 150}
{"system.cpu": 76.31, "system.memory": 54.89, "system.disk": 8.1, "system.proc.memory.availableMB": 3475.83, "system.proc.memory.rssMB": 970.73, "system.proc.memory.percent": 12.6, "system.proc.cpu.threads": 2.0, "system.network.sent": 605526, "system.network.recv": 781492, "_wandb": true, "_timestamp": 1580203974, "_runtime": 180}
{"system.cpu": 74.81, "system.memory": 56.53, "system.disk": 8.1, "system.proc.memory.availableMB": 3349.2, "system.proc.memory.rssMB": 1120.59, "system.proc.memory.percent": 14.55, "system.proc.cpu.threads": 2.0, "system.network.sent": 686844, "system.network.recv": 879116, "_wandb": true, "_timestamp": 1580204004, "_runtime": 210}
{"system.cpu": 83.13, "system.memory": 58.46, "system.disk": 8.1, "system.proc.memory.availableMB": 3199.77, "system.proc.memory.rssMB": 1266.78, "system.proc.memory.percent": 16.44, "system.proc.cpu.threads": 2.0, "system.network.sent": 779460, "system.network.recv": 991476, "_wandb": true, "_timestamp": 1580204034, "_runtime": 241}
{"system.cpu": 83.2, "system.memory": 60.0, "system.disk": 8.1, "system.proc.memory.availableMB": 3080.83, "system.proc.memory.rssMB": 1385.21, "system.proc.memory.percent": 17.98, "system.proc.cpu.threads": 2.0, "system.network.sent": 855445, "system.network.recv": 1086470, "_wandb": true, "_timestamp": 1580204064, "_runtime": 271}
{"system.cpu": 78.99, "system.memory": 62.04, "system.disk": 8.1, "system.proc.memory.availableMB": 2925.1, "system.proc.memory.rssMB": 1528.88, "system.proc.memory.percent": 19.84, "system.proc.cpu.threads": 2.0, "system.network.sent": 947645, "system.network.recv": 1198317, "_wandb": true, "_timestamp": 1580204095, "_runtime": 301}
{"system.cpu": 59.51, "system.memory": 62.79, "system.disk": 8.1, "system.proc.memory.availableMB": 2866.57, "system.proc.memory.rssMB": 1589.07, "system.proc.memory.percent": 20.63, "system.proc.cpu.threads": 3.33, "system.network.sent": 1087153, "system.network.recv": 1327874, "_wandb": true, "_timestamp": 1580204125, "_runtime": 331}
{"system.cpu": 48.16, "system.memory": 62.83, "system.disk": 8.1, "system.proc.memory.availableMB": 2863.29, "system.proc.memory.rssMB": 1569.54, "system.proc.memory.percent": 20.37, "system.proc.cpu.threads": 4.0, "system.network.sent": 1378272, "system.network.recv": 1859485, "_wandb": true, "_timestamp": 1580204155, "_runtime": 361}
{"system.cpu": 42.49, "system.memory": 62.97, "system.disk": 8.1, "system.proc.memory.availableMB": 2853.56, "system.proc.memory.rssMB": 1569.54, "system.proc.memory.percent": 20.37, "system.proc.cpu.threads": 4.0, "system.network.sent": 1672192, "system.network.recv": 2556472, "_wandb": true, "_timestamp": 1580204185, "_runtime": 392}
{"system.cpu": 33.74, "system.memory": 63.01, "system.disk": 8.1, "system.proc.memory.availableMB": 2849.28, "system.proc.memory.rssMB": 1569.54, "system.proc.memory.percent": 20.37, "system.proc.cpu.threads": 4.0, "system.network.sent": 1997480, "system.network.recv": 3432976, "_wandb": true, "_timestamp": 1580204215, "_runtime": 422}
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TicTacToe_AI/Net/wandb/run-20200128_092953-yloo6l66/wandb-metadata.json Normal file
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@ -0,0 +1,23 @@
{
"root": "/home/clemens/repositorys/pytorch-ai",
"program": "pytorch_ai.py",
"git": {
"remote": "git@github.com:Clemens-Dautermann/pytorch-ai.git",
"commit": "55cff9b18f8558ae7a9170e56a3d5c6f6665d9ab"
},
"email": "clemens.dautermann@gmail.com",
"startedAt": "2020-01-28T09:29:53.524416",
"host": "ubuntu-laptop",
"username": "clemens",
"executable": "/usr/bin/python3",
"os": "Linux-5.3.0-26-generic-x86_64-with-Ubuntu-19.10-eoan",
"python": "3.7.5",
"cpu_count": 2,
"args": [],
"state": "killed",
"jobType": null,
"mode": "run",
"project": "tictactoe",
"heartbeatAt": "2020-01-28T09:37:08.580840",
"exitcode": 255
}

1
TicTacToe_AI/Net/wandb/run-20200128_092953-yloo6l66/wandb-summary.json Normal file
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9
TicTacToe_AI/Net/wandb/run-20200128_100231-umum9656/config.yaml Normal file
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@ -0,0 +1,9 @@
wandb_version: 1
_wandb:
desc: null
value:
cli_version: 0.8.22
framework: torch
is_jupyter_run: false
python_version: 3.7.5

127
TicTacToe_AI/Net/wandb/run-20200128_100231-umum9656/diff.patch Normal file
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@ -0,0 +1,127 @@
diff --git a/TicTacToe_AI/Net/pytorch_ai.py b/TicTacToe_AI/Net/pytorch_ai.py
index efea5ae..dd5e54d 100644
--- a/TicTacToe_AI/Net/pytorch_ai.py
+++ b/TicTacToe_AI/Net/pytorch_ai.py
@@ -4,6 +4,11 @@ import torch.optim as optim
from torch import nn
import torch.nn.functional as F
from tqdm import tqdm
+import wandb
+
+wandb.init(project="tictactoe")
+
+BATCH_SIZE = 15
def to_set(raw_list):
@@ -35,6 +40,39 @@ def to_set(raw_list):
return out_set
+def to_batched_set(raw_list):
+ counter = 0
+ out_set = []
+ boardtensor = torch.zeros((BATCH_SIZE, 1, 9))
+ labeltensor = torch.zeros(BATCH_SIZE)
+ for line in tqdm(raw_list):
+ line = line.replace('\n', '')
+ raw_board, raw_label = line.split('|')[0], line.split('|')[1]
+
+ if not (int(raw_label) is -1):
+ labeltensor[counter] = int(raw_label)
+ else:
+ labeltensor[counter] = 9
+
+ for n, block in enumerate(raw_board):
+ if int(block) is -1:
+ boardtensor[counter][0][n] = 0
+ elif int(block) is 0:
+ boardtensor[counter][0][n] = 0.5
+ elif int(block) is 1:
+ boardtensor[counter][0][n] = 1
+
+ if counter == (BATCH_SIZE - 1):
+ out_set.append([boardtensor, labeltensor])
+ boardtensor = torch.zeros((BATCH_SIZE, 1, 9))
+ labeltensor = torch.zeros(BATCH_SIZE)
+ counter = 0
+ else:
+ counter += 1
+
+ return out_set
+
+
def buildsets():
with open('boards.bds', 'r') as infile:
print('Loading file...')
@@ -46,7 +84,7 @@ def buildsets():
testset = to_set(alllines[0:10000])
print('Generating trainset...')
- trainset = to_set(alllines[10001:200000])
+ trainset = to_set(alllines[10001:100000])
return trainset, testset
@@ -60,6 +98,7 @@ def testnet(net, testset):
if torch.argmax(output) == label[0]:
correct += 1
total += 1
+ wandb.log({'test_accuracy': correct / total})
print("Accuracy: ", round(correct / total, 3))
@@ -79,7 +118,15 @@ class Net(torch.nn.Module):
return F.log_softmax(x, dim=1)
-net = torch.load('./nets/net_3.pt')
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('running on %s' % device)
+
+# net = torch.load('./nets/net_3.pt')
+
+net = Net()
+wandb.watch(net)
+
+net.to(device)
optimizer = optim.Adam(net.parameters(), lr=0.001)
@@ -87,13 +134,16 @@ trainset, testset = buildsets()
for epoch in range(100):
print('Epoch: ' + str(epoch))
+ wandb.log({'epoch': epoch})
for X, label in tqdm(trainset):
net.zero_grad()
+ X.to(device)
output = net(X)
+ output.cpu()
loss = F.nll_loss(output.view(1, 10), label[0])
loss.backward()
optimizer.step()
+ wandb.log({'loss': loss})
- print(loss)
- torch.save(net, './nets/net_' + str(epoch + 3) + '.pt')
+ torch.save(net, './nets/gpunets/net_' + str(epoch) + '.pt')
testnet(net, testset)
diff --git a/other_scripts/setcounter.py b/other_scripts/setcounter.py
index 9735f20..e9eb00c 100644
--- a/other_scripts/setcounter.py
+++ b/other_scripts/setcounter.py
@@ -7,9 +7,12 @@ data = datasets.MNIST('../datasets', train=True, download=True,
transforms.ToTensor()
]))
-loader = torch.utils.data.DataLoader(data, batch_size=1, shuffle=False)
+loader = torch.utils.data.DataLoader(data, batch_size=15, shuffle=False)
set = {'0': 0, '1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
+for data in loader:
+ print(data[1].shape)
+
for _, label in tqdm(loader):
set[str(label[0].item())] += 1

19
TicTacToe_AI/Net/wandb/run-20200128_100231-umum9656/output.log Normal file
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109
TicTacToe_AI/Net/wandb/run-20200128_100231-umum9656/requirements.txt Normal file
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@ -0,0 +1,109 @@
apturl==0.5.2
argh==0.26.2
asn1crypto==0.24.0
bcrypt==3.1.6
binwalk==2.1.2
blinker==1.4
brlapi==0.6.7
certifi==2018.8.24
chardet==3.0.4
click==7.0
command-not-found==0.3
configparser==4.0.2
cryptography==2.6.1
cupshelpers==1.0
cycler==0.10.0
dbus-python==1.2.12
decorator==4.3.0
defer==1.0.6
distro-info==0.21ubuntu4
distro==1.3.0
docker-pycreds==0.4.0
duplicity==0.8.4
entrypoints==0.3
fasteners==0.12.0
future==0.16.0
gitdb2==2.0.6
gitpython==3.0.5
gql==0.2.0
graphql-core==1.1
httplib2==0.11.3
idna==2.6
keyring==18.0.1
keyrings.alt==3.1.1
kiwisolver==1.0.1
language-selector==0.1
launchpadlib==1.10.7
lazr.restfulclient==0.14.2
lazr.uri==1.0.3
lockfile==0.12.2
louis==3.10.0
macaroonbakery==1.2.3
mako==1.0.7
markupsafe==1.1.0
matplotlib==3.0.2
monotonic==1.5
netifaces==0.10.4
numpy==1.16.2
nvidia-ml-py3==7.352.0
oauth==1.0.1
oauthlib==2.1.0
olefile==0.46
paramiko==2.6.0
pathtools==0.1.2
pexpect==4.6.0
pillow==6.1.0
pip==18.1
promise==2.3
protobuf==3.6.1
psutil==5.6.7
pycairo==1.16.2
pycrypto==2.6.1
pycups==1.9.73
pygments==2.3.1
pygobject==3.34.0
pyjwt==1.7.0
pymacaroons==0.13.0
pynacl==1.3.0
pyopengl==3.1.0
pyparsing==2.2.0
pyqt5==5.12.3
pyqtgraph==0.11.0.dev0
pyrfc3339==1.1
python-apt==1.9.0+ubuntu1.3
python-dateutil==2.7.3
python-debian==0.1.36
pytz==2019.2
pyxdg==0.25
pyyaml==5.1.2
reportlab==3.5.23
requests-unixsocket==0.1.5
requests==2.21.0
scipy==1.2.2
secretstorage==2.3.1
sentry-sdk==0.14.0
setuptools==41.1.0
shortuuid==0.5.0
simplejson==3.16.0
sip==4.19.18
six==1.12.0
smmap2==2.0.5
subprocess32==3.5.4
system-service==0.3
systemd-python==234
torch==1.3.1+cpu
torchvision==0.4.2+cpu
tqdm==4.41.0
ubuntu-advantage-tools==19.5
ubuntu-drivers-common==0.0.0
ufw==0.36
unattended-upgrades==0.1
urllib3==1.24.1
usb-creator==0.3.7
virtualenv==15.1.0
wadllib==1.3.3
wandb==0.8.22
watchdog==0.9.0
wheel==0.32.3
xkit==0.0.0
zope.interface==4.3.2

3
TicTacToe_AI/Net/wandb/run-20200128_100231-umum9656/wandb-events.jsonl Normal file
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@ -0,0 +1,3 @@
{"system.cpu": 74.29, "system.memory": 46.04, "system.disk": 8.1, "system.proc.memory.availableMB": 4156.89, "system.proc.memory.rssMB": 241.6, "system.proc.memory.percent": 3.14, "system.proc.cpu.threads": 1.87, "system.network.sent": 101434, "system.network.recv": 214391, "_wandb": true, "_timestamp": 1580205780, "_runtime": 29}
{"system.cpu": 37.87, "system.memory": 46.41, "system.disk": 8.1, "system.proc.memory.availableMB": 4128.12, "system.proc.memory.rssMB": 273.49, "system.proc.memory.percent": 3.55, "system.proc.cpu.threads": 3.6, "system.network.sent": 423172, "system.network.recv": 492021, "_wandb": true, "_timestamp": 1580205811, "_runtime": 59}
{"system.cpu": 40.1, "system.memory": 46.27, "system.disk": 8.1, "system.proc.memory.availableMB": 4137.31, "system.proc.memory.rssMB": 260.55, "system.proc.memory.percent": 3.38, "system.proc.cpu.threads": 3.67, "system.network.sent": 444763, "system.network.recv": 561533, "_wandb": true, "_timestamp": 1580205815, "_runtime": 64}

292
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23
TicTacToe_AI/Net/wandb/run-20200128_100231-umum9656/wandb-metadata.json Normal file
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@ -0,0 +1,23 @@
{
"root": "/home/clemens/repositorys/pytorch-ai",
"program": "pytorch_ai.py",
"git": {
"remote": "git@github.com:Clemens-Dautermann/pytorch-ai.git",
"commit": "55cff9b18f8558ae7a9170e56a3d5c6f6665d9ab"
},
"email": "clemens.dautermann@gmail.com",
"startedAt": "2020-01-28T10:02:31.733621",
"host": "ubuntu-laptop",
"username": "clemens",
"executable": "/usr/bin/python3",
"os": "Linux-5.3.0-26-generic-x86_64-with-Ubuntu-19.10-eoan",
"python": "3.7.5",
"cpu_count": 2,
"args": [],
"state": "killed",
"jobType": null,
"mode": "run",
"project": "tictactoe",
"heartbeatAt": "2020-01-28T10:03:36.821974",
"exitcode": 255
}

1
TicTacToe_AI/Net/wandb/run-20200128_100231-umum9656/wandb-summary.json Normal file
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9
TicTacToe_AI/Net/wandb/run-20200128_100418-glkym27z/config.yaml Normal file
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@ -0,0 +1,9 @@
wandb_version: 1
_wandb:
desc: null
value:
cli_version: 0.8.22
framework: torch
is_jupyter_run: false
python_version: 3.7.5

131
TicTacToe_AI/Net/wandb/run-20200128_100418-glkym27z/diff.patch Normal file
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@ -0,0 +1,131 @@
diff --git a/TicTacToe_AI/Net/pytorch_ai.py b/TicTacToe_AI/Net/pytorch_ai.py
index efea5ae..05da941 100644
--- a/TicTacToe_AI/Net/pytorch_ai.py
+++ b/TicTacToe_AI/Net/pytorch_ai.py
@@ -4,6 +4,11 @@ import torch.optim as optim
from torch import nn
import torch.nn.functional as F
from tqdm import tqdm
+import wandb
+
+wandb.init(project="tictactoe")
+
+BATCH_SIZE = 15
def to_set(raw_list):
@@ -35,6 +40,39 @@ def to_set(raw_list):
return out_set
+def to_batched_set(raw_list):
+ counter = 0
+ out_set = []
+ boardtensor = torch.zeros((BATCH_SIZE, 1, 9))
+ labeltensor = torch.zeros(BATCH_SIZE)
+ for line in tqdm(raw_list):
+ line = line.replace('\n', '')
+ raw_board, raw_label = line.split('|')[0], line.split('|')[1]
+
+ if not (int(raw_label) is -1):
+ labeltensor[counter] = int(raw_label)
+ else:
+ labeltensor[counter] = 9
+
+ for n, block in enumerate(raw_board):
+ if int(block) is -1:
+ boardtensor[counter][0][n] = 0
+ elif int(block) is 0:
+ boardtensor[counter][0][n] = 0.5
+ elif int(block) is 1:
+ boardtensor[counter][0][n] = 1
+
+ if counter == (BATCH_SIZE - 1):
+ out_set.append([boardtensor, labeltensor])
+ boardtensor = torch.zeros((BATCH_SIZE, 1, 9))
+ labeltensor = torch.zeros(BATCH_SIZE)
+ counter = 0
+ else:
+ counter += 1
+
+ return out_set
+
+
def buildsets():
with open('boards.bds', 'r') as infile:
print('Loading file...')
@@ -44,9 +82,11 @@ def buildsets():
print('Generating testset...')
testset = to_set(alllines[0:10000])
+ print(testset[0])
+ exit(0)
print('Generating trainset...')
- trainset = to_set(alllines[10001:200000])
+ trainset = to_set(alllines[10001:100000])
return trainset, testset
@@ -60,6 +100,7 @@ def testnet(net, testset):
if torch.argmax(output) == label[0]:
correct += 1
total += 1
+ wandb.log({'test_accuracy': correct / total})
print("Accuracy: ", round(correct / total, 3))
@@ -79,7 +120,15 @@ class Net(torch.nn.Module):
return F.log_softmax(x, dim=1)
-net = torch.load('./nets/net_3.pt')
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('running on %s' % device)
+
+# net = torch.load('./nets/net_3.pt')
+
+net = Net()
+wandb.watch(net)
+
+net.to(device)
optimizer = optim.Adam(net.parameters(), lr=0.001)
@@ -87,13 +136,16 @@ trainset, testset = buildsets()
for epoch in range(100):
print('Epoch: ' + str(epoch))
+ wandb.log({'epoch': epoch})
for X, label in tqdm(trainset):
net.zero_grad()
+ X.to(device)
output = net(X)
+ output.cpu()
loss = F.nll_loss(output.view(1, 10), label[0])
loss.backward()
optimizer.step()
+ wandb.log({'loss': loss})
- print(loss)
- torch.save(net, './nets/net_' + str(epoch + 3) + '.pt')
+ torch.save(net, './nets/gpunets/net_' + str(epoch) + '.pt')
testnet(net, testset)
diff --git a/other_scripts/setcounter.py b/other_scripts/setcounter.py
index 9735f20..e9eb00c 100644
--- a/other_scripts/setcounter.py
+++ b/other_scripts/setcounter.py
@@ -7,9 +7,12 @@ data = datasets.MNIST('../datasets', train=True, download=True,
transforms.ToTensor()
]))
-loader = torch.utils.data.DataLoader(data, batch_size=1, shuffle=False)
+loader = torch.utils.data.DataLoader(data, batch_size=15, shuffle=False)
set = {'0': 0, '1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
+for data in loader:
+ print(data[1].shape)
+
for _, label in tqdm(loader):
set[str(label[0].item())] += 1

6
TicTacToe_AI/Net/wandb/run-20200128_100418-glkym27z/output.log Normal file
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@ -0,0 +1,6 @@
running on cpu
Loading file...
986410
Generating testset...
0%| | 0/10000 [00:00<?, ?it/s] 3%|█▏ | 329/10000 [00:00<00:02, 3284.96it/s] 7%|██▌ | 702/10000 [00:00<00:02, 3406.48it/s] 11%|███▊ | 1071/10000 [00:00<00:02, 3484.39it/s] 14%|█████▏ | 1438/10000 [00:00<00:02, 3535.76it/s] 18%|██████▌ | 1806/10000 [00:00<00:02, 3576.26it/s] 21%|███████▌ | 2110/10000 [00:00<00:04, 1883.99it/s] 25%|████████▉ | 2483/10000 [00:00<00:03, 2211.89it/s] 29%|██████████▎ | 2855/10000 [00:01<00:02, 2517.61it/s] 32%|███████████▌ | 3225/10000 [00:01<00:02, 2783.04it/s] 36%|████████████▉ | 3592/10000 [00:01<00:02, 3000.32it/s] 40%|██████████████▎ | 3964/10000 [00:01<00:01, 3183.21it/s] 43%|███████████████▌ | 4339/10000 [00:01<00:01, 3333.87it/s] 47%|████████████████▉ | 4698/10000 [00:01<00:01, 3403.56it/s] 51%|██████████████████▎ | 5072/10000 [00:01<00:01, 3497.17it/s] 54%|███████████████████▌ | 5442/10000 [00:01<00:01, 3554.89it/s] 58%|████████████████████▉ | 5815/10000 [00:01<00:01, 3605.52it/s] 62%|██████████████████████▎ | 6191/10000 [00:01<00:01, 3648.30it/s] 66%|███████████████████████▋ | 6565/10000 [00:02<00:00, 3674.90it/s] 69%|████████████████████████▉ | 6936/10000 [00:02<00:00, 3649.80it/s] 73%|██████████████████████████▎ | 7307/10000 [00:02<00:00, 3667.44it/s] 77%|███████████████████████████▋ | 7676/10000 [00:02<00:00, 3637.19it/s] 80%|████████████████████████████▉ | 8041/10000 [00:02<00:00, 3634.91it/s] 84%|██████████████████████████████▎ | 8413/10000 [00:02<00:00, 3658.31it/s] 88%|███████████████████████████████▋ | 8785/10000 [00:02<00:00, 3676.50it/s] 92%|████████████████████████████████▉ | 9154/10000 [00:02<00:00, 3640.13it/s] 95%|██████████████████████████████████▎ | 9527/10000 [00:02<00:00, 3666.31it/s] 99%|███████████████████████████████████▋| 9900/10000 [00:02<00:00, 3683.42it/s] 100%|███████████████████████████████████| 10000/10000 [00:02<00:00, 3354.62it/s]
(tensor([[0., 0., 1., 0., 1., 1., 1., 1., 0.]]), tensor([[9]]))

109
TicTacToe_AI/Net/wandb/run-20200128_100418-glkym27z/requirements.txt Normal file
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@ -0,0 +1,109 @@
apturl==0.5.2
argh==0.26.2
asn1crypto==0.24.0
bcrypt==3.1.6
binwalk==2.1.2
blinker==1.4
brlapi==0.6.7
certifi==2018.8.24
chardet==3.0.4
click==7.0
command-not-found==0.3
configparser==4.0.2
cryptography==2.6.1
cupshelpers==1.0
cycler==0.10.0
dbus-python==1.2.12
decorator==4.3.0
defer==1.0.6
distro-info==0.21ubuntu4
distro==1.3.0
docker-pycreds==0.4.0
duplicity==0.8.4
entrypoints==0.3
fasteners==0.12.0
future==0.16.0
gitdb2==2.0.6
gitpython==3.0.5
gql==0.2.0
graphql-core==1.1
httplib2==0.11.3
idna==2.6
keyring==18.0.1
keyrings.alt==3.1.1
kiwisolver==1.0.1
language-selector==0.1
launchpadlib==1.10.7
lazr.restfulclient==0.14.2
lazr.uri==1.0.3
lockfile==0.12.2
louis==3.10.0
macaroonbakery==1.2.3
mako==1.0.7
markupsafe==1.1.0
matplotlib==3.0.2
monotonic==1.5
netifaces==0.10.4
numpy==1.16.2
nvidia-ml-py3==7.352.0
oauth==1.0.1
oauthlib==2.1.0
olefile==0.46
paramiko==2.6.0
pathtools==0.1.2
pexpect==4.6.0
pillow==6.1.0
pip==18.1
promise==2.3
protobuf==3.6.1
psutil==5.6.7
pycairo==1.16.2
pycrypto==2.6.1
pycups==1.9.73
pygments==2.3.1
pygobject==3.34.0
pyjwt==1.7.0
pymacaroons==0.13.0
pynacl==1.3.0
pyopengl==3.1.0
pyparsing==2.2.0
pyqt5==5.12.3
pyqtgraph==0.11.0.dev0
pyrfc3339==1.1
python-apt==1.9.0+ubuntu1.3
python-dateutil==2.7.3
python-debian==0.1.36
pytz==2019.2
pyxdg==0.25
pyyaml==5.1.2
reportlab==3.5.23
requests-unixsocket==0.1.5
requests==2.21.0
scipy==1.2.2
secretstorage==2.3.1
sentry-sdk==0.14.0
setuptools==41.1.0
shortuuid==0.5.0
simplejson==3.16.0
sip==4.19.18
six==1.12.0
smmap2==2.0.5
subprocess32==3.5.4
system-service==0.3
systemd-python==234
torch==1.3.1+cpu
torchvision==0.4.2+cpu
tqdm==4.41.0
ubuntu-advantage-tools==19.5
ubuntu-drivers-common==0.0.0
ufw==0.36
unattended-upgrades==0.1
urllib3==1.24.1
usb-creator==0.3.7
virtualenv==15.1.0
wadllib==1.3.3
wandb==0.8.22
watchdog==0.9.0
wheel==0.32.3
xkit==0.0.0
zope.interface==4.3.2

1
TicTacToe_AI/Net/wandb/run-20200128_100418-glkym27z/wandb-events.jsonl Normal file
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@ -0,0 +1 @@
{"system.cpu": 51.8, "system.memory": 44.84, "system.disk": 8.1, "system.proc.memory.availableMB": 4248.14, "system.proc.memory.rssMB": 152.26, "system.proc.memory.percent": 1.98, "system.proc.cpu.threads": 1.2, "system.network.sent": 28306, "system.network.recv": 87546, "_wandb": true, "_timestamp": 1580205867, "_runtime": 7}

0
TicTacToe_AI/Net/wandb/run-20200128_100418-glkym27z/wandb-history.jsonl Normal file
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23
TicTacToe_AI/Net/wandb/run-20200128_100418-glkym27z/wandb-metadata.json Normal file
View file

@ -0,0 +1,23 @@
{
"root": "/home/clemens/repositorys/pytorch-ai",
"program": "pytorch_ai.py",
"git": {
"remote": "git@github.com:Clemens-Dautermann/pytorch-ai.git",
"commit": "55cff9b18f8558ae7a9170e56a3d5c6f6665d9ab"
},
"email": "clemens.dautermann@gmail.com",
"startedAt": "2020-01-28T10:04:19.609416",
"host": "ubuntu-laptop",
"username": "clemens",
"executable": "/usr/bin/python3",
"os": "Linux-5.3.0-26-generic-x86_64-with-Ubuntu-19.10-eoan",
"python": "3.7.5",
"cpu_count": 2,
"args": [],
"state": "finished",
"jobType": null,
"mode": "run",
"project": "tictactoe",
"heartbeatAt": "2020-01-28T10:04:28.075857",
"exitcode": 0
}

9
TicTacToe_AI/Net/wandb/run-20200128_101238-9s87yjpe/config.yaml Normal file
View file

@ -0,0 +1,9 @@
wandb_version: 1
_wandb:
desc: null
value:
cli_version: 0.8.22
framework: torch
is_jupyter_run: false
python_version: 3.7.5

132
TicTacToe_AI/Net/wandb/run-20200128_101238-9s87yjpe/diff.patch Normal file
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@ -0,0 +1,132 @@
diff --git a/TicTacToe_AI/Net/pytorch_ai.py b/TicTacToe_AI/Net/pytorch_ai.py
index efea5ae..8990206 100644
--- a/TicTacToe_AI/Net/pytorch_ai.py
+++ b/TicTacToe_AI/Net/pytorch_ai.py
@@ -4,6 +4,11 @@ import torch.optim as optim
from torch import nn
import torch.nn.functional as F
from tqdm import tqdm
+import wandb
+
+wandb.init(project="tictactoe")
+
+BATCH_SIZE = 150
def to_set(raw_list):
@@ -35,6 +40,40 @@ def to_set(raw_list):
return out_set
+def to_batched_set(raw_list):
+ counter = 0
+ out_set = []
+ boardtensor = torch.zeros((BATCH_SIZE, 1, 9))
+ labeltensor = torch.zeros(BATCH_SIZE)
+ for line in tqdm(raw_list):
+ line = line.replace('\n', '')
+ raw_board, raw_label = line.split('|')[0], line.split('|')[1]
+
+ if not (int(raw_label) is -1):
+ labeltensor[counter] = int(raw_label)
+ else:
+ labeltensor[counter] = 9
+
+ raw_board = raw_board.split(',')
+ for n, block in enumerate(raw_board):
+ if int(block) is -1:
+ boardtensor[counter][0][n] = 0
+ elif int(block) is 0:
+ boardtensor[counter][0][n] = 0.5
+ elif int(block) is 1:
+ boardtensor[counter][0][n] = 1
+
+ if counter == (BATCH_SIZE - 1):
+ out_set.append([boardtensor, labeltensor])
+ boardtensor = torch.zeros((BATCH_SIZE, 1, 9))
+ labeltensor = torch.zeros(BATCH_SIZE)
+ counter = 0
+ else:
+ counter += 1
+
+ return out_set
+
+
def buildsets():
with open('boards.bds', 'r') as infile:
print('Loading file...')
@@ -43,10 +82,10 @@ def buildsets():
random.shuffle(alllines)
print('Generating testset...')
- testset = to_set(alllines[0:10000])
+ testset = to_batched_set(alllines[0:10000])
print('Generating trainset...')
- trainset = to_set(alllines[10001:200000])
+ trainset = to_batched_set(alllines[10001:100000])
return trainset, testset
@@ -60,6 +99,7 @@ def testnet(net, testset):
if torch.argmax(output) == label[0]:
correct += 1
total += 1
+ wandb.log({'test_accuracy': correct / total})
print("Accuracy: ", round(correct / total, 3))
@@ -79,7 +119,15 @@ class Net(torch.nn.Module):
return F.log_softmax(x, dim=1)
-net = torch.load('./nets/net_3.pt')
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('running on %s' % device)
+
+# net = torch.load('./nets/net_3.pt')
+
+net = Net()
+wandb.watch(net)
+
+net.to(device)
optimizer = optim.Adam(net.parameters(), lr=0.001)
@@ -87,13 +135,16 @@ trainset, testset = buildsets()
for epoch in range(100):
print('Epoch: ' + str(epoch))
+ wandb.log({'epoch': epoch})
for X, label in tqdm(trainset):
net.zero_grad()
+ X.to(device)
output = net(X)
+ output.cpu()
loss = F.nll_loss(output.view(1, 10), label[0])
loss.backward()
optimizer.step()
+ wandb.log({'loss': loss})
- print(loss)
- torch.save(net, './nets/net_' + str(epoch + 3) + '.pt')
+ torch.save(net, './nets/gpunets/net_' + str(epoch) + '.pt')
testnet(net, testset)
diff --git a/other_scripts/setcounter.py b/other_scripts/setcounter.py
index 9735f20..e9eb00c 100644
--- a/other_scripts/setcounter.py
+++ b/other_scripts/setcounter.py
@@ -7,9 +7,12 @@ data = datasets.MNIST('../datasets', train=True, download=True,
transforms.ToTensor()
]))
-loader = torch.utils.data.DataLoader(data, batch_size=1, shuffle=False)
+loader = torch.utils.data.DataLoader(data, batch_size=15, shuffle=False)
set = {'0': 0, '1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
+for data in loader:
+ print(data[1].shape)
+
for _, label in tqdm(loader):
set[str(label[0].item())] += 1

13
TicTacToe_AI/Net/wandb/run-20200128_101238-9s87yjpe/output.log Normal file
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109
TicTacToe_AI/Net/wandb/run-20200128_101238-9s87yjpe/requirements.txt Normal file
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@ -0,0 +1,109 @@
apturl==0.5.2
argh==0.26.2
asn1crypto==0.24.0
bcrypt==3.1.6
binwalk==2.1.2
blinker==1.4
brlapi==0.6.7
certifi==2018.8.24
chardet==3.0.4
click==7.0
command-not-found==0.3
configparser==4.0.2
cryptography==2.6.1
cupshelpers==1.0
cycler==0.10.0
dbus-python==1.2.12
decorator==4.3.0
defer==1.0.6
distro-info==0.21ubuntu4
distro==1.3.0
docker-pycreds==0.4.0
duplicity==0.8.4
entrypoints==0.3
fasteners==0.12.0
future==0.16.0
gitdb2==2.0.6
gitpython==3.0.5
gql==0.2.0
graphql-core==1.1
httplib2==0.11.3
idna==2.6
keyring==18.0.1
keyrings.alt==3.1.1
kiwisolver==1.0.1
language-selector==0.1
launchpadlib==1.10.7
lazr.restfulclient==0.14.2
lazr.uri==1.0.3
lockfile==0.12.2
louis==3.10.0
macaroonbakery==1.2.3
mako==1.0.7
markupsafe==1.1.0
matplotlib==3.0.2
monotonic==1.5
netifaces==0.10.4
numpy==1.16.2
nvidia-ml-py3==7.352.0
oauth==1.0.1
oauthlib==2.1.0
olefile==0.46
paramiko==2.6.0
pathtools==0.1.2
pexpect==4.6.0
pillow==6.1.0
pip==18.1
promise==2.3
protobuf==3.6.1
psutil==5.6.7
pycairo==1.16.2
pycrypto==2.6.1
pycups==1.9.73
pygments==2.3.1
pygobject==3.34.0
pyjwt==1.7.0
pymacaroons==0.13.0
pynacl==1.3.0
pyopengl==3.1.0
pyparsing==2.2.0
pyqt5==5.12.3
pyqtgraph==0.11.0.dev0
pyrfc3339==1.1
python-apt==1.9.0+ubuntu1.3
python-dateutil==2.7.3
python-debian==0.1.36
pytz==2019.2
pyxdg==0.25
pyyaml==5.1.2
reportlab==3.5.23
requests-unixsocket==0.1.5
requests==2.21.0
scipy==1.2.2
secretstorage==2.3.1
sentry-sdk==0.14.0
setuptools==41.1.0
shortuuid==0.5.0
simplejson==3.16.0
sip==4.19.18
six==1.12.0
smmap2==2.0.5
subprocess32==3.5.4
system-service==0.3
systemd-python==234
torch==1.3.1+cpu
torchvision==0.4.2+cpu
tqdm==4.41.0
ubuntu-advantage-tools==19.5
ubuntu-drivers-common==0.0.0
ufw==0.36
unattended-upgrades==0.1
urllib3==1.24.1
usb-creator==0.3.7
virtualenv==15.1.0
wadllib==1.3.3
wandb==0.8.22
watchdog==0.9.0
wheel==0.32.3
xkit==0.0.0
zope.interface==4.3.2

2
TicTacToe_AI/Net/wandb/run-20200128_101238-9s87yjpe/wandb-events.jsonl Normal file
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@ -0,0 +1,2 @@
{"system.cpu": 84.36, "system.memory": 49.44, "system.disk": 8.1, "system.proc.memory.availableMB": 3895.05, "system.proc.memory.rssMB": 191.12, "system.proc.memory.percent": 2.48, "system.proc.cpu.threads": 1.87, "system.network.sent": 109502, "system.network.recv": 249550, "_wandb": true, "_timestamp": 1580206387, "_runtime": 29}
{"system.cpu": 67.12, "system.memory": 48.28, "system.disk": 8.1, "system.proc.memory.availableMB": 3982.67, "system.proc.memory.rssMB": 166.54, "system.proc.memory.percent": 2.16, "system.proc.cpu.threads": 1.8, "system.network.sent": 132366, "system.network.recv": 276842, "_wandb": true, "_timestamp": 1580206395, "_runtime": 37}

1
TicTacToe_AI/Net/wandb/run-20200128_101238-9s87yjpe/wandb-history.jsonl Normal file
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@ -0,0 +1 @@
{"epoch": 0, "_runtime": 38.271220684051514, "_timestamp": 1580206395.6194339, "_step": 0}

23
TicTacToe_AI/Net/wandb/run-20200128_101238-9s87yjpe/wandb-metadata.json Normal file
View file

@ -0,0 +1,23 @@
{
"root": "/home/clemens/repositorys/pytorch-ai",
"program": "pytorch_ai.py",
"git": {
"remote": "git@github.com:Clemens-Dautermann/pytorch-ai.git",
"commit": "55cff9b18f8558ae7a9170e56a3d5c6f6665d9ab"
},
"email": "clemens.dautermann@gmail.com",
"startedAt": "2020-01-28T10:12:38.459566",
"host": "ubuntu-laptop",
"username": "clemens",
"executable": "/usr/bin/python3",
"os": "Linux-5.3.0-26-generic-x86_64-with-Ubuntu-19.10-eoan",
"python": "3.7.5",
"cpu_count": 2,
"args": [],
"state": "failed",
"jobType": null,
"mode": "run",
"project": "tictactoe",
"heartbeatAt": "2020-01-28T10:13:16.492404",
"exitcode": 1
}

1
TicTacToe_AI/Net/wandb/run-20200128_101238-9s87yjpe/wandb-summary.json Normal file
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@ -0,0 +1 @@
{"_runtime": 38.271220684051514, "_timestamp": 1580206395.6194339, "epoch": 0, "_step": 0}

9
TicTacToe_AI/Net/wandb/run-20200128_102106-bxrufgzi/config.yaml Normal file
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@ -0,0 +1,9 @@
wandb_version: 1
_wandb:
desc: null
value:
cli_version: 0.8.22
framework: torch
is_jupyter_run: false
python_version: 3.7.5

135
TicTacToe_AI/Net/wandb/run-20200128_102106-bxrufgzi/diff.patch Normal file
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@ -0,0 +1,135 @@
diff --git a/TicTacToe_AI/Net/pytorch_ai.py b/TicTacToe_AI/Net/pytorch_ai.py
index efea5ae..8334765 100644
--- a/TicTacToe_AI/Net/pytorch_ai.py
+++ b/TicTacToe_AI/Net/pytorch_ai.py
@@ -4,6 +4,11 @@ import torch.optim as optim
from torch import nn
import torch.nn.functional as F
from tqdm import tqdm
+import wandb
+
+wandb.init(project="tictactoe")
+
+BATCH_SIZE = 15
def to_set(raw_list):
@@ -35,6 +40,40 @@ def to_set(raw_list):
return out_set
+def to_batched_set(raw_list):
+ counter = 0
+ out_set = []
+ boardtensor = torch.zeros((BATCH_SIZE, 1, 9))
+ labeltensor = torch.zeros(BATCH_SIZE, dtype=torch.long)
+ for line in tqdm(raw_list):
+ line = line.replace('\n', '')
+ raw_board, raw_label = line.split('|')[0], line.split('|')[1]
+
+ if not (int(raw_label) is -1):
+ labeltensor[counter] = int(raw_label)
+ else:
+ labeltensor[counter] = 9
+
+ raw_board = raw_board.split(',')
+ for n, block in enumerate(raw_board):
+ if int(block) is -1:
+ boardtensor[counter][0][n] = 0
+ elif int(block) is 0:
+ boardtensor[counter][0][n] = 0.5
+ elif int(block) is 1:
+ boardtensor[counter][0][n] = 1
+
+ if counter == (BATCH_SIZE - 1):
+ out_set.append([boardtensor, labeltensor])
+ boardtensor = torch.zeros((BATCH_SIZE, 1, 9))
+ labeltensor = torch.zeros(BATCH_SIZE, dtype=torch.long)
+ counter = 0
+ else:
+ counter += 1
+
+ return out_set
+
+
def buildsets():
with open('boards.bds', 'r') as infile:
print('Loading file...')
@@ -43,10 +82,10 @@ def buildsets():
random.shuffle(alllines)
print('Generating testset...')
- testset = to_set(alllines[0:10000])
+ testset = to_batched_set(alllines[0:10000])
print('Generating trainset...')
- trainset = to_set(alllines[10001:200000])
+ trainset = to_batched_set(alllines[10001:20000])
return trainset, testset
@@ -60,6 +99,7 @@ def testnet(net, testset):
if torch.argmax(output) == label[0]:
correct += 1
total += 1
+ wandb.log({'test_accuracy': correct / total})
print("Accuracy: ", round(correct / total, 3))
@@ -79,7 +119,15 @@ class Net(torch.nn.Module):
return F.log_softmax(x, dim=1)
-net = torch.load('./nets/net_3.pt')
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('running on %s' % device)
+
+# net = torch.load('./nets/net_3.pt')
+
+net = Net()
+wandb.watch(net)
+
+net.to(device)
optimizer = optim.Adam(net.parameters(), lr=0.001)
@@ -87,13 +135,18 @@ trainset, testset = buildsets()
for epoch in range(100):
print('Epoch: ' + str(epoch))
+ wandb.log({'epoch': epoch})
for X, label in tqdm(trainset):
+ print(X.shape)
+ print(label.shape)
net.zero_grad()
+ X.to(device)
output = net(X)
- loss = F.nll_loss(output.view(1, 10), label[0])
+ output.cpu()
+ loss = F.nll_loss(output.view(-1, 10), label)
loss.backward()
optimizer.step()
+ wandb.log({'loss': loss})
- print(loss)
- torch.save(net, './nets/net_' + str(epoch + 3) + '.pt')
+ torch.save(net, './nets/gpunets/net_' + str(epoch) + '.pt')
testnet(net, testset)
diff --git a/other_scripts/setcounter.py b/other_scripts/setcounter.py
index 9735f20..e9eb00c 100644
--- a/other_scripts/setcounter.py
+++ b/other_scripts/setcounter.py
@@ -7,9 +7,12 @@ data = datasets.MNIST('../datasets', train=True, download=True,
transforms.ToTensor()
]))
-loader = torch.utils.data.DataLoader(data, batch_size=1, shuffle=False)
+loader = torch.utils.data.DataLoader(data, batch_size=15, shuffle=False)
set = {'0': 0, '1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
+for data in loader:
+ print(data[1].shape)
+
for _, label in tqdm(loader):
set[str(label[0].item())] += 1

238
TicTacToe_AI/Net/wandb/run-20200128_102106-bxrufgzi/output.log Normal file
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@ -0,0 +1,238 @@
running on cpu
Loading file...
986410
Generating testset...
0%| | 0/10000 [00:00<?, ?it/s] 3%|█▎ | 347/10000 [00:00<00:02, 3461.95it/s] 6%|██▏ | 580/10000 [00:00<00:03, 3018.65it/s] 9%|███▍ | 938/10000 [00:00<00:02, 3166.71it/s] 13%|████▋ | 1300/10000 [00:00<00:02, 3289.18it/s] 16%|█████▊ | 1622/10000 [00:00<00:02, 3266.47it/s] 20%|███████▏ | 1995/10000 [00:00<00:02, 3392.21it/s] 23%|████████▍ | 2343/10000 [00:00<00:02, 3416.22it/s] 27%|█████████▌ | 2660/10000 [00:00<00:02, 3314.88it/s] 30%|██████████▋ | 2981/10000 [00:00<00:02, 3280.43it/s] 33%|███████████▉ | 3326/10000 [00:01<00:02, 3327.29it/s] 37%|█████████████▎ | 3696/10000 [00:01<00:01, 3429.18it/s] 40%|██████████████▌ | 4038/10000 [00:01<00:01, 3423.89it/s] 44%|███████████████▊ | 4378/10000 [00:01<00:01, 3236.97it/s] 47%|████████████████▉ | 4702/10000 [00:01<00:01, 3117.76it/s] 50%|██████████████████ | 5015/10000 [00:01<00:02, 2461.36it/s] 53%|███████████████████ | 5284/10000 [00:01<00:01, 2479.04it/s] 55%|███████████████████▉ | 5548/10000 [00:01<00:02, 2187.80it/s] 58%|████████████████████▊ | 5784/10000 [00:02<00:02, 2036.79it/s] 60%|█████████████████████▌ | 6002/10000 [00:02<00:01, 2004.67it/s] 62%|██████████████████████▎ | 6213/10000 [00:02<00:02, 1505.19it/s] 64%|███████████████████████▏ | 6449/10000 [00:02<00:02, 1686.85it/s] 68%|████████████████████████▌ | 6808/10000 [00:02<00:01, 2005.53it/s] 71%|█████████████████████████▍ | 7073/10000 [00:02<00:01, 2163.20it/s] 74%|██████████████████████████▌ | 7384/10000 [00:02<00:01, 2379.31it/s] 77%|███████████████████████████▊ | 7719/10000 [00:02<00:00, 2605.07it/s] 80%|████████████████████████████▊ | 8015/10000 [00:02<00:00, 2692.98it/s] 83%|█████████████████████████████▉ | 8305/10000 [00:03<00:00, 2388.68it/s] 87%|███████████████████████████████▏ | 8663/10000 [00:03<00:00, 2652.11it/s] 90%|████████████████████████████████▍ | 9001/10000 [00:03<00:00, 2834.74it/s] 94%|█████████████████████████████████▋ | 9374/10000 [00:03<00:00, 3053.79it/s] 97%|███████████████████████████████████ | 9742/10000 [00:03<00:00, 3217.94it/s]Generating trainset...
100%|███████████████████████████████████| 10000/10000 [00:03<00:00, 2781.54it/s]
0%| | 0/9999 [00:00<?, ?it/s] 3%|█▏ | 323/9999 [00:00<00:03, 3224.48it/s] 6%|██▏ | 591/9999 [00:00<00:03, 3038.62it/s] 8%|███▏ | 845/9999 [00:00<00:03, 2867.57it/s] 10%|███▊ | 1033/9999 [00:00<00:03, 2474.68it/s] 13%|████▋ | 1277/9999 [00:00<00:03, 2439.35it/s] 15%|█████▍ | 1476/9999 [00:00<00:03, 2257.23it/s] 18%|██████▌ | 1769/9999 [00:00<00:03, 2424.05it/s] 21%|███████▊ | 2095/9999 [00:00<00:03, 2625.03it/s] 24%|████████▊ | 2374/9999 [00:00<00:02, 2670.86it/s] 26%|█████████▊ | 2637/9999 [00:01<00:02, 2590.42it/s] 29%|██████████▊ | 2923/9999 [00:01<00:02, 2665.49it/s] 32%|███████████▊ | 3189/9999 [00:01<00:02, 2491.89it/s] 34%|████████████▋ | 3440/9999 [00:01<00:03, 1682.45it/s] 36%|█████████████▍ | 3645/9999 [00:01<00:04, 1561.25it/s] 38%|██████████████▏ | 3828/9999 [00:01<00:03, 1575.58it/s] 40%|██████████████▉ | 4031/9999 [00:01<00:03, 1682.11it/s] 42%|███████████████▌ | 4215/9999 [00:02<00:03, 1578.92it/s] 44%|████████████████▏ | 4385/9999 [00:02<00:03, 1503.72it/s] 46%|████████████████▉ | 4584/9999 [00:02<00:03, 1621.80it/s] 48%|█████████████████▉ | 4837/9999 [00:02<00:02, 1817.52it/s] 51%|██████████████████▊ | 5071/9999 [00:02<00:02, 1927.99it/s] 54%|███████████████████▊ | 5356/9999 [00:02<00:02, 2134.97it/s] 57%|█████████████████████▏ | 5710/9999 [00:02<00:01, 2422.42it/s] 61%|██████████████████████▍ | 6078/9999 [00:02<00:01, 2698.46it/s] 64%|███████████████████████▊ | 6446/9999 [00:02<00:01, 2932.54it/s] 68%|█████████████████████████▏ | 6817/9999 [00:02<00:01, 3127.88it/s] 72%|██████████████████████████▌ | 7179/9999 [00:03<00:00, 3260.02it/s] 75%|███████████████████████████▊ | 7521/9999 [00:03<00:00, 3059.46it/s] 79%|█████████████████████████████ | 7867/9999 [00:03<00:00, 3168.65it/s] 82%|██████████████████████████████▍ | 8219/9999 [00:03<00:00, 3261.91it/s] 86%|███████████████████████████████▋ | 8554/9999 [00:03<00:00, 3164.81it/s] 89%|████████████████████████████████▊ | 8877/9999 [00:03<00:00, 3029.40it/s] 92%|█████████████████████████████████▉ | 9186/9999 [00:03<00:00, 3027.90it/s] 95%|███████████████████████████████████▏ | 9504/9999 [00:03<00:00, 3070.60it/s] 98%|████████████████████████████████████▎| 9814/9999 [00:03<00:00, 2988.52it/s] 100%|█████████████████████████████████████| 9999/9999 [00:03<00:00, 2520.75it/s]
Epoch: 0
torch.Size([15, 1, 9])
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109
TicTacToe_AI/Net/wandb/run-20200128_102106-bxrufgzi/requirements.txt Normal file
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@ -0,0 +1,109 @@
apturl==0.5.2
argh==0.26.2
asn1crypto==0.24.0
bcrypt==3.1.6
binwalk==2.1.2
blinker==1.4
brlapi==0.6.7
certifi==2018.8.24
chardet==3.0.4
click==7.0
command-not-found==0.3
configparser==4.0.2
cryptography==2.6.1
cupshelpers==1.0
cycler==0.10.0
dbus-python==1.2.12
decorator==4.3.0
defer==1.0.6
distro-info==0.21ubuntu4
distro==1.3.0
docker-pycreds==0.4.0
duplicity==0.8.4
entrypoints==0.3
fasteners==0.12.0
future==0.16.0
gitdb2==2.0.6
gitpython==3.0.5
gql==0.2.0
graphql-core==1.1
httplib2==0.11.3
idna==2.6
keyring==18.0.1
keyrings.alt==3.1.1
kiwisolver==1.0.1
language-selector==0.1
launchpadlib==1.10.7
lazr.restfulclient==0.14.2
lazr.uri==1.0.3
lockfile==0.12.2
louis==3.10.0
macaroonbakery==1.2.3
mako==1.0.7
markupsafe==1.1.0
matplotlib==3.0.2
monotonic==1.5
netifaces==0.10.4
numpy==1.16.2
nvidia-ml-py3==7.352.0
oauth==1.0.1
oauthlib==2.1.0
olefile==0.46
paramiko==2.6.0
pathtools==0.1.2
pexpect==4.6.0
pillow==6.1.0
pip==18.1
promise==2.3
protobuf==3.6.1
psutil==5.6.7
pycairo==1.16.2
pycrypto==2.6.1
pycups==1.9.73
pygments==2.3.1
pygobject==3.34.0
pyjwt==1.7.0
pymacaroons==0.13.0
pynacl==1.3.0
pyopengl==3.1.0
pyparsing==2.2.0
pyqt5==5.12.3
pyqtgraph==0.11.0.dev0
pyrfc3339==1.1
python-apt==1.9.0+ubuntu1.3
python-dateutil==2.7.3
python-debian==0.1.36
pytz==2019.2
pyxdg==0.25
pyyaml==5.1.2
reportlab==3.5.23
requests-unixsocket==0.1.5
requests==2.21.0
scipy==1.2.2
secretstorage==2.3.1
sentry-sdk==0.14.0
setuptools==41.1.0
shortuuid==0.5.0
simplejson==3.16.0
sip==4.19.18
six==1.12.0
smmap2==2.0.5
subprocess32==3.5.4
system-service==0.3
systemd-python==234
torch==1.3.1+cpu
torchvision==0.4.2+cpu
tqdm==4.41.0
ubuntu-advantage-tools==19.5
ubuntu-drivers-common==0.0.0
ufw==0.36
unattended-upgrades==0.1
urllib3==1.24.1
usb-creator==0.3.7
virtualenv==15.1.0
wadllib==1.3.3
wandb==0.8.22
watchdog==0.9.0
wheel==0.32.3
xkit==0.0.0
zope.interface==4.3.2

1
TicTacToe_AI/Net/wandb/run-20200128_102106-bxrufgzi/wandb-events.jsonl Normal file
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@ -0,0 +1 @@
{"system.cpu": 79.26, "system.memory": 48.27, "system.disk": 8.1, "system.proc.memory.availableMB": 3985.83, "system.proc.memory.rssMB": 148.22, "system.proc.memory.percent": 1.92, "system.proc.cpu.threads": 3.92, "system.network.sent": 163319, "system.network.recv": 297606, "_wandb": true, "_timestamp": 1580206890, "_runtime": 23}

116
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23
TicTacToe_AI/Net/wandb/run-20200128_102106-bxrufgzi/wandb-metadata.json Normal file
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@ -0,0 +1,23 @@
{
"root": "/home/clemens/repositorys/pytorch-ai",
"program": "pytorch_ai.py",
"git": {
"remote": "git@github.com:Clemens-Dautermann/pytorch-ai.git",
"commit": "55cff9b18f8558ae7a9170e56a3d5c6f6665d9ab"
},
"email": "clemens.dautermann@gmail.com",
"startedAt": "2020-01-28T10:21:06.610593",
"host": "ubuntu-laptop",
"username": "clemens",
"executable": "/usr/bin/python3",
"os": "Linux-5.3.0-26-generic-x86_64-with-Ubuntu-19.10-eoan",
"python": "3.7.5",
"cpu_count": 2,
"args": [],
"state": "killed",
"jobType": null,
"mode": "run",
"project": "tictactoe",
"heartbeatAt": "2020-01-28T10:21:30.645370",
"exitcode": 255
}

1
TicTacToe_AI/Net/wandb/run-20200128_102106-bxrufgzi/wandb-summary.json Normal file
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9
TicTacToe_AI/Net/wandb/run-20200128_102136-giubv24l/config.yaml Normal file
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@ -0,0 +1,9 @@
wandb_version: 1
_wandb:
desc: null
value:
cli_version: 0.8.22
framework: torch
is_jupyter_run: false
python_version: 3.7.5

133
TicTacToe_AI/Net/wandb/run-20200128_102136-giubv24l/diff.patch Normal file
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@ -0,0 +1,133 @@
diff --git a/TicTacToe_AI/Net/pytorch_ai.py b/TicTacToe_AI/Net/pytorch_ai.py
index efea5ae..2a5f8c7 100644
--- a/TicTacToe_AI/Net/pytorch_ai.py
+++ b/TicTacToe_AI/Net/pytorch_ai.py
@@ -4,6 +4,11 @@ import torch.optim as optim
from torch import nn
import torch.nn.functional as F
from tqdm import tqdm
+import wandb
+
+wandb.init(project="tictactoe")
+
+BATCH_SIZE = 15
def to_set(raw_list):
@@ -35,6 +40,40 @@ def to_set(raw_list):
return out_set
+def to_batched_set(raw_list):
+ counter = 0
+ out_set = []
+ boardtensor = torch.zeros((BATCH_SIZE, 1, 9))
+ labeltensor = torch.zeros(BATCH_SIZE, dtype=torch.long)
+ for line in tqdm(raw_list):
+ line = line.replace('\n', '')
+ raw_board, raw_label = line.split('|')[0], line.split('|')[1]
+
+ if not (int(raw_label) is -1):
+ labeltensor[counter] = int(raw_label)
+ else:
+ labeltensor[counter] = 9
+
+ raw_board = raw_board.split(',')
+ for n, block in enumerate(raw_board):
+ if int(block) is -1:
+ boardtensor[counter][0][n] = 0
+ elif int(block) is 0:
+ boardtensor[counter][0][n] = 0.5
+ elif int(block) is 1:
+ boardtensor[counter][0][n] = 1
+
+ if counter == (BATCH_SIZE - 1):
+ out_set.append([boardtensor, labeltensor])
+ boardtensor = torch.zeros((BATCH_SIZE, 1, 9))
+ labeltensor = torch.zeros(BATCH_SIZE, dtype=torch.long)
+ counter = 0
+ else:
+ counter += 1
+
+ return out_set
+
+
def buildsets():
with open('boards.bds', 'r') as infile:
print('Loading file...')
@@ -43,10 +82,10 @@ def buildsets():
random.shuffle(alllines)
print('Generating testset...')
- testset = to_set(alllines[0:10000])
+ testset = to_batched_set(alllines[0:10000])
print('Generating trainset...')
- trainset = to_set(alllines[10001:200000])
+ trainset = to_batched_set(alllines[10001:20000])
return trainset, testset
@@ -60,6 +99,7 @@ def testnet(net, testset):
if torch.argmax(output) == label[0]:
correct += 1
total += 1
+ wandb.log({'test_accuracy': correct / total})
print("Accuracy: ", round(correct / total, 3))
@@ -79,7 +119,15 @@ class Net(torch.nn.Module):
return F.log_softmax(x, dim=1)
-net = torch.load('./nets/net_3.pt')
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('running on %s' % device)
+
+# net = torch.load('./nets/net_3.pt')
+
+net = Net()
+wandb.watch(net)
+
+net.to(device)
optimizer = optim.Adam(net.parameters(), lr=0.001)
@@ -87,13 +135,16 @@ trainset, testset = buildsets()
for epoch in range(100):
print('Epoch: ' + str(epoch))
+ wandb.log({'epoch': epoch})
for X, label in tqdm(trainset):
net.zero_grad()
+ X.to(device)
output = net(X)
- loss = F.nll_loss(output.view(1, 10), label[0])
+ output.cpu()
+ loss = F.nll_loss(output.view(-1, 10), label)
loss.backward()
optimizer.step()
+ wandb.log({'loss': loss})
- print(loss)
- torch.save(net, './nets/net_' + str(epoch + 3) + '.pt')
+ torch.save(net, './nets/gpunets/net_' + str(epoch) + '.pt')
testnet(net, testset)
diff --git a/other_scripts/setcounter.py b/other_scripts/setcounter.py
index 9735f20..e9eb00c 100644
--- a/other_scripts/setcounter.py
+++ b/other_scripts/setcounter.py
@@ -7,9 +7,12 @@ data = datasets.MNIST('../datasets', train=True, download=True,
transforms.ToTensor()
]))
-loader = torch.utils.data.DataLoader(data, batch_size=1, shuffle=False)
+loader = torch.utils.data.DataLoader(data, batch_size=15, shuffle=False)
set = {'0': 0, '1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
+for data in loader:
+ print(data[1].shape)
+
for _, label in tqdm(loader):
set[str(label[0].item())] += 1

16
TicTacToe_AI/Net/wandb/run-20200128_102136-giubv24l/output.log Normal file
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109
TicTacToe_AI/Net/wandb/run-20200128_102136-giubv24l/requirements.txt Normal file
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@ -0,0 +1,109 @@
apturl==0.5.2
argh==0.26.2
asn1crypto==0.24.0
bcrypt==3.1.6
binwalk==2.1.2
blinker==1.4
brlapi==0.6.7
certifi==2018.8.24
chardet==3.0.4
click==7.0
command-not-found==0.3
configparser==4.0.2
cryptography==2.6.1
cupshelpers==1.0
cycler==0.10.0
dbus-python==1.2.12
decorator==4.3.0
defer==1.0.6
distro-info==0.21ubuntu4
distro==1.3.0
docker-pycreds==0.4.0
duplicity==0.8.4
entrypoints==0.3
fasteners==0.12.0
future==0.16.0
gitdb2==2.0.6
gitpython==3.0.5
gql==0.2.0
graphql-core==1.1
httplib2==0.11.3
idna==2.6
keyring==18.0.1
keyrings.alt==3.1.1
kiwisolver==1.0.1
language-selector==0.1
launchpadlib==1.10.7
lazr.restfulclient==0.14.2
lazr.uri==1.0.3
lockfile==0.12.2
louis==3.10.0
macaroonbakery==1.2.3
mako==1.0.7
markupsafe==1.1.0
matplotlib==3.0.2
monotonic==1.5
netifaces==0.10.4
numpy==1.16.2
nvidia-ml-py3==7.352.0
oauth==1.0.1
oauthlib==2.1.0
olefile==0.46
paramiko==2.6.0
pathtools==0.1.2
pexpect==4.6.0
pillow==6.1.0
pip==18.1
promise==2.3
protobuf==3.6.1
psutil==5.6.7
pycairo==1.16.2
pycrypto==2.6.1
pycups==1.9.73
pygments==2.3.1
pygobject==3.34.0
pyjwt==1.7.0
pymacaroons==0.13.0
pynacl==1.3.0
pyopengl==3.1.0
pyparsing==2.2.0
pyqt5==5.12.3
pyqtgraph==0.11.0.dev0
pyrfc3339==1.1
python-apt==1.9.0+ubuntu1.3
python-dateutil==2.7.3
python-debian==0.1.36
pytz==2019.2
pyxdg==0.25
pyyaml==5.1.2
reportlab==3.5.23
requests-unixsocket==0.1.5
requests==2.21.0
scipy==1.2.2
secretstorage==2.3.1
sentry-sdk==0.14.0
setuptools==41.1.0
shortuuid==0.5.0
simplejson==3.16.0
sip==4.19.18
six==1.12.0
smmap2==2.0.5
subprocess32==3.5.4
system-service==0.3
systemd-python==234
torch==1.3.1+cpu
torchvision==0.4.2+cpu
tqdm==4.41.0
ubuntu-advantage-tools==19.5
ubuntu-drivers-common==0.0.0
ufw==0.36
unattended-upgrades==0.1
urllib3==1.24.1
usb-creator==0.3.7
virtualenv==15.1.0
wadllib==1.3.3
wandb==0.8.22
watchdog==0.9.0
wheel==0.32.3
xkit==0.0.0
zope.interface==4.3.2

4
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@ -0,0 +1,4 @@
{"system.cpu": 73.8, "system.memory": 48.63, "system.disk": 8.1, "system.proc.memory.availableMB": 3957.04, "system.proc.memory.rssMB": 149.19, "system.proc.memory.percent": 1.94, "system.proc.cpu.threads": 4.0, "system.network.sent": 223467, "system.network.recv": 333380, "_wandb": true, "_timestamp": 1580206926, "_runtime": 29}
{"system.cpu": 46.9, "system.memory": 48.36, "system.disk": 8.1, "system.proc.memory.availableMB": 3978.55, "system.proc.memory.rssMB": 118.87, "system.proc.memory.percent": 1.54, "system.proc.cpu.threads": 6.0, "system.network.sent": 527285, "system.network.recv": 711501, "_wandb": true, "_timestamp": 1580206956, "_runtime": 59}
{"system.cpu": 32.65, "system.memory": 48.28, "system.disk": 8.1, "system.proc.memory.availableMB": 3984.82, "system.proc.memory.rssMB": 118.94, "system.proc.memory.percent": 1.54, "system.proc.cpu.threads": 6.0, "system.network.sent": 653819, "system.network.recv": 806592, "_wandb": true, "_timestamp": 1580206986, "_runtime": 89}
{"system.cpu": 36.03, "system.memory": 47.87, "system.disk": 8.1, "system.proc.memory.availableMB": 4014.99, "system.proc.memory.rssMB": 119.24, "system.proc.memory.percent": 1.55, "system.proc.cpu.threads": 5.93, "system.network.sent": 783435, "system.network.recv": 915032, "_wandb": true, "_timestamp": 1580207016, "_runtime": 119}

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{
"root": "/home/clemens/repositorys/pytorch-ai",
"program": "pytorch_ai.py",
"git": {
"remote": "git@github.com:Clemens-Dautermann/pytorch-ai.git",
"commit": "55cff9b18f8558ae7a9170e56a3d5c6f6665d9ab"
},
"email": "clemens.dautermann@gmail.com",
"startedAt": "2020-01-28T10:21:37.046817",
"host": "ubuntu-laptop",
"username": "clemens",
"executable": "/usr/bin/python3",
"os": "Linux-5.3.0-26-generic-x86_64-with-Ubuntu-19.10-eoan",
"python": "3.7.5",
"cpu_count": 2,
"args": [],
"state": "killed",
"jobType": null,
"mode": "run",
"project": "tictactoe",
"heartbeatAt": "2020-01-28T10:23:37.195037",
"exitcode": 255
}

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@ -0,0 +1,9 @@
wandb_version: 1
_wandb:
desc: null
value:
cli_version: 0.8.22
framework: torch
is_jupyter_run: false
python_version: 3.7.5

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diff --git a/TicTacToe_AI/Net/pytorch_ai.py b/TicTacToe_AI/Net/pytorch_ai.py
index efea5ae..ba862ae 100644
--- a/TicTacToe_AI/Net/pytorch_ai.py
+++ b/TicTacToe_AI/Net/pytorch_ai.py
@@ -4,6 +4,11 @@ import torch.optim as optim
from torch import nn
import torch.nn.functional as F
from tqdm import tqdm
+import wandb
+
+wandb.init(project="tictactoe")
+
+BATCH_SIZE = 3
def to_set(raw_list):
@@ -35,6 +40,40 @@ def to_set(raw_list):
return out_set
+def to_batched_set(raw_list):
+ counter = 0
+ out_set = []
+ boardtensor = torch.zeros((BATCH_SIZE, 1, 9))
+ labeltensor = torch.zeros(BATCH_SIZE, dtype=torch.long)
+ for line in tqdm(raw_list):
+ line = line.replace('\n', '')
+ raw_board, raw_label = line.split('|')[0], line.split('|')[1]
+
+ if not (int(raw_label) is -1):
+ labeltensor[counter] = int(raw_label)
+ else:
+ labeltensor[counter] = 9
+
+ raw_board = raw_board.split(',')
+ for n, block in enumerate(raw_board):
+ if int(block) is -1:
+ boardtensor[counter][0][n] = 0
+ elif int(block) is 0:
+ boardtensor[counter][0][n] = 0.5
+ elif int(block) is 1:
+ boardtensor[counter][0][n] = 1
+
+ if counter == (BATCH_SIZE - 1):
+ out_set.append([boardtensor, labeltensor])
+ boardtensor = torch.zeros((BATCH_SIZE, 1, 9))
+ labeltensor = torch.zeros(BATCH_SIZE, dtype=torch.long)
+ counter = 0
+ else:
+ counter += 1
+
+ return out_set
+
+
def buildsets():
with open('boards.bds', 'r') as infile:
print('Loading file...')
@@ -43,10 +82,10 @@ def buildsets():
random.shuffle(alllines)
print('Generating testset...')
- testset = to_set(alllines[0:10000])
+ testset = to_batched_set(alllines[0:10000])
print('Generating trainset...')
- trainset = to_set(alllines[10001:200000])
+ trainset = to_batched_set(alllines[10001:20000])
return trainset, testset
@@ -60,6 +99,7 @@ def testnet(net, testset):
if torch.argmax(output) == label[0]:
correct += 1
total += 1
+ wandb.log({'test_accuracy': correct / total})
print("Accuracy: ", round(correct / total, 3))
@@ -79,7 +119,15 @@ class Net(torch.nn.Module):
return F.log_softmax(x, dim=1)
-net = torch.load('./nets/net_3.pt')
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('running on %s' % device)
+
+# net = torch.load('./nets/net_3.pt')
+
+net = Net()
+wandb.watch(net)
+
+net.to(device)
optimizer = optim.Adam(net.parameters(), lr=0.001)
@@ -87,13 +135,18 @@ trainset, testset = buildsets()
for epoch in range(100):
print('Epoch: ' + str(epoch))
+ wandb.log({'epoch': epoch})
for X, label in tqdm(trainset):
net.zero_grad()
+ X.to(device)
output = net(X)
- loss = F.nll_loss(output.view(1, 10), label[0])
+ output.cpu()
+ print(output)
+ print(label)
+ loss = F.nll_loss(output.view(-1, 10), label)
loss.backward()
optimizer.step()
+ wandb.log({'loss': loss})
- print(loss)
- torch.save(net, './nets/net_' + str(epoch + 3) + '.pt')
+ torch.save(net, './nets/gpunets/net_' + str(epoch) + '.pt')
testnet(net, testset)
diff --git a/other_scripts/setcounter.py b/other_scripts/setcounter.py
index 9735f20..e9eb00c 100644
--- a/other_scripts/setcounter.py
+++ b/other_scripts/setcounter.py
@@ -7,9 +7,12 @@ data = datasets.MNIST('../datasets', train=True, download=True,
transforms.ToTensor()
]))
-loader = torch.utils.data.DataLoader(data, batch_size=1, shuffle=False)
+loader = torch.utils.data.DataLoader(data, batch_size=15, shuffle=False)
set = {'0': 0, '1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
+for data in loader:
+ print(data[1].shape)
+
for _, label in tqdm(loader):
set[str(label[0].item())] += 1

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@ -0,0 +1,126 @@
running on cpu
Loading file...
986410
Generating testset...
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Generating trainset...
0%| | 0/9999 [00:00<?, ?it/s] 4%|█▎ | 350/9999 [00:00<00:02, 3495.50it/s] 7%|██▋ | 715/9999 [00:00<00:02, 3538.49it/s] 11%|████ | 1081/9999 [00:00<00:02, 3573.92it/s] 14%|█████▎ | 1448/9999 [00:00<00:02, 3599.96it/s] 18%|██████▋ | 1819/9999 [00:00<00:02, 3629.98it/s] 22%|████████ | 2186/9999 [00:00<00:02, 3640.84it/s] 26%|█████████▍ | 2555/9999 [00:00<00:02, 3655.21it/s] 29%|██████████▊ | 2923/9999 [00:00<00:01, 3661.51it/s] 33%|████████████▏ | 3292/9999 [00:00<00:01, 3667.77it/s] 37%|█████████████▌ | 3661/9999 [00:01<00:01, 3672.84it/s] 40%|██████████████▊ | 4019/9999 [00:01<00:01, 3612.48it/s] 44%|████████████████▏ | 4384/9999 [00:01<00:01, 3622.65it/s] 48%|█████████████████▌ | 4752/9999 [00:01<00:01, 3638.28it/s] 51%|██████████████████▉ | 5114/9999 [00:01<00:01, 3631.61it/s] 55%|████████████████████▎ | 5475/9999 [00:01<00:01, 3592.49it/s] 58%|█████████████████████▌ | 5836/9999 [00:01<00:01, 3596.28it/s] 62%|██████████████████████▉ | 6195/9999 [00:01<00:01, 3556.03it/s] 66%|████████████████████████▎ | 6561/9999 [00:01<00:00, 3586.47it/s] 69%|█████████████████████████▋ | 6931/9999 [00:01<00:00, 3618.99it/s] 73%|██████████████████████████▉ | 7293/9999 [00:02<00:00, 3606.47it/s] 77%|████████████████████████████▎ | 7662/9999 [00:02<00:00, 3630.22it/s] 80%|█████████████████████████████▋ | 8029/9999 [00:02<00:00, 3640.63it/s] 84%|███████████████████████████████ | 8399/9999 [00:02<00:00, 3656.62it/s] 88%|████████████████████████████████▍ | 8766/9999 [00:02<00:00, 3659.70it/s] 91%|█████████████████████████████████▊ | 9133/9999 [00:02<00:00, 3662.23it/s] 95%|███████████████████████████████████▏ | 9500/9999 [00:02<00:00, 3636.64it/s] 99%|████████████████████████████████████▌| 9865/9999 [00:02<00:00, 3637.81it/s] 100%|█████████████████████████████████████| 9999/9999 [00:02<00:00, 3630.58it/s]
Epoch: 0
0%| | 0/3333 [00:00<?, ?it/s]tensor([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
tensor([2, 7, 8])
0%| | 1/3333 [00:00<14:08, 3.93it/s]tensor([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
tensor([9, 9, 3])
tensor([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
tensor([3, 9, 9])
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[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
tensor([9, 1, 4])
tensor([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
tensor([4, 2, 9])
0%| | 5/3333 [00:00<09:44, 5.69it/s]tensor([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
tensor([6, 4, 9])
0%| | 6/3333 [00:00<08:30, 6.52it/s]tensor([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
tensor([9, 9, 1])
0%| | 7/3333 [00:00<07:36, 7.28it/s]tensor([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
tensor([9, 7, 9])
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[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
tensor([1, 3, 0])
tensor([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
tensor([5, 2, 9])
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[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
tensor([9, 3, 9])
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[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
tensor([5, 4, 0])
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[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
tensor([7, 9, 3])
tensor([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
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[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
tensor([9, 9, 3])
tensor([[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],
[[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]]],
grad_fn=<LogSoftmaxBackward>)
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TicTacToe_AI/Net/wandb/run-20200128_102424-q6423sio/requirements.txt Normal file
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@ -0,0 +1,109 @@
apturl==0.5.2
argh==0.26.2
asn1crypto==0.24.0
bcrypt==3.1.6
binwalk==2.1.2
blinker==1.4
brlapi==0.6.7
certifi==2018.8.24
chardet==3.0.4
click==7.0
command-not-found==0.3
configparser==4.0.2
cryptography==2.6.1
cupshelpers==1.0
cycler==0.10.0
dbus-python==1.2.12
decorator==4.3.0
defer==1.0.6
distro-info==0.21ubuntu4
distro==1.3.0
docker-pycreds==0.4.0
duplicity==0.8.4
entrypoints==0.3
fasteners==0.12.0
future==0.16.0
gitdb2==2.0.6
gitpython==3.0.5
gql==0.2.0
graphql-core==1.1
httplib2==0.11.3
idna==2.6
keyring==18.0.1
keyrings.alt==3.1.1
kiwisolver==1.0.1
language-selector==0.1
launchpadlib==1.10.7
lazr.restfulclient==0.14.2
lazr.uri==1.0.3
lockfile==0.12.2
louis==3.10.0
macaroonbakery==1.2.3
mako==1.0.7
markupsafe==1.1.0
matplotlib==3.0.2
monotonic==1.5
netifaces==0.10.4
numpy==1.16.2
nvidia-ml-py3==7.352.0
oauth==1.0.1
oauthlib==2.1.0
olefile==0.46
paramiko==2.6.0
pathtools==0.1.2
pexpect==4.6.0
pillow==6.1.0
pip==18.1
promise==2.3
protobuf==3.6.1
psutil==5.6.7
pycairo==1.16.2
pycrypto==2.6.1
pycups==1.9.73
pygments==2.3.1
pygobject==3.34.0
pyjwt==1.7.0
pymacaroons==0.13.0
pynacl==1.3.0
pyopengl==3.1.0
pyparsing==2.2.0
pyqt5==5.12.3
pyqtgraph==0.11.0.dev0
pyrfc3339==1.1
python-apt==1.9.0+ubuntu1.3
python-dateutil==2.7.3
python-debian==0.1.36
pytz==2019.2
pyxdg==0.25
pyyaml==5.1.2
reportlab==3.5.23
requests-unixsocket==0.1.5
requests==2.21.0
scipy==1.2.2
secretstorage==2.3.1
sentry-sdk==0.14.0
setuptools==41.1.0
shortuuid==0.5.0
simplejson==3.16.0
sip==4.19.18
six==1.12.0
smmap2==2.0.5
subprocess32==3.5.4
system-service==0.3
systemd-python==234
torch==1.3.1+cpu
torchvision==0.4.2+cpu
tqdm==4.41.0
ubuntu-advantage-tools==19.5
ubuntu-drivers-common==0.0.0
ufw==0.36
unattended-upgrades==0.1
urllib3==1.24.1
usb-creator==0.3.7
virtualenv==15.1.0
wadllib==1.3.3
wandb==0.8.22
watchdog==0.9.0
wheel==0.32.3
xkit==0.0.0
zope.interface==4.3.2

1
TicTacToe_AI/Net/wandb/run-20200128_102424-q6423sio/wandb-events.jsonl Normal file
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@ -0,0 +1 @@
{"system.cpu": 49.09, "system.memory": 48.41, "system.disk": 8.1, "system.proc.memory.availableMB": 3974.46, "system.proc.memory.rssMB": 165.72, "system.proc.memory.percent": 2.15, "system.proc.cpu.threads": 2.71, "system.network.sent": 45246, "system.network.recv": 121493, "_wandb": true, "_timestamp": 1580207075, "_runtime": 11}

17
TicTacToe_AI/Net/wandb/run-20200128_102424-q6423sio/wandb-history.jsonl Normal file
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@ -0,0 +1,17 @@
{"epoch": 0, "_runtime": 10.482649564743042, "_timestamp": 1580207073.8952324, "_step": 0}
{"loss": 0.0, "_runtime": 10.755571603775024, "_timestamp": 1580207074.1681545, "_step": 1}
{"loss": 0.0, "_runtime": 10.839263677597046, "_timestamp": 1580207074.2518466, "_step": 2}
{"loss": 0.0, "_runtime": 10.93910551071167, "_timestamp": 1580207074.3516884, "_step": 3}
{"loss": 0.0, "_runtime": 11.038821935653687, "_timestamp": 1580207074.4514048, "_step": 4}
{"loss": 0.0, "_runtime": 11.138564825057983, "_timestamp": 1580207074.5511477, "_step": 5}
{"loss": 0.0, "_runtime": 11.237011909484863, "_timestamp": 1580207074.6495948, "_step": 6}
{"loss": 0.0, "_runtime": 11.339718103408813, "_timestamp": 1580207074.752301, "_step": 7}
{"loss": 0.0, "_runtime": 11.439802885055542, "_timestamp": 1580207074.8523858, "_step": 8}
{"loss": 0.0, "_runtime": 11.541845321655273, "_timestamp": 1580207074.9544282, "_step": 9}
{"loss": 0.0, "_runtime": 11.639827728271484, "_timestamp": 1580207075.0524106, "_step": 10}
{"loss": 0.0, "_runtime": 11.738152503967285, "_timestamp": 1580207075.1507354, "_step": 11}
{"loss": 0.0, "_runtime": 11.839798212051392, "_timestamp": 1580207075.252381, "_step": 12}
{"loss": 0.0, "_runtime": 11.939958333969116, "_timestamp": 1580207075.3525412, "_step": 13}
{"loss": 0.0, "_runtime": 12.040019989013672, "_timestamp": 1580207075.4526029, "_step": 14}
{"loss": 0.0, "_runtime": 12.139089345932007, "_timestamp": 1580207075.5516722, "_step": 15}
{"loss": 0.0, "_runtime": 12.240127325057983, "_timestamp": 1580207075.6527102, "_step": 16}

23
TicTacToe_AI/Net/wandb/run-20200128_102424-q6423sio/wandb-metadata.json Normal file
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{
"root": "/home/clemens/repositorys/pytorch-ai",
"program": "pytorch_ai.py",
"git": {
"remote": "git@github.com:Clemens-Dautermann/pytorch-ai.git",
"commit": "55cff9b18f8558ae7a9170e56a3d5c6f6665d9ab"
},
"email": "clemens.dautermann@gmail.com",
"startedAt": "2020-01-28T10:24:24.158270",
"host": "ubuntu-laptop",
"username": "clemens",
"executable": "/usr/bin/python3",
"os": "Linux-5.3.0-26-generic-x86_64-with-Ubuntu-19.10-eoan",
"python": "3.7.5",
"cpu_count": 2,
"args": [],
"state": "killed",
"jobType": null,
"mode": "run",
"project": "tictactoe",
"heartbeatAt": "2020-01-28T10:24:36.253604",
"exitcode": 255
}

1
TicTacToe_AI/Net/wandb/run-20200128_102424-q6423sio/wandb-summary.json Normal file
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{"epoch": 0, "_step": 16, "_runtime": 12.240127325057983, "_timestamp": 1580207075.6527102, "graph_0": {"_type": "graph", "format": "torch", "nodes": [{"name": "fc1", "id": 139735090147280, "class_name": "Linear(in_features=9, out_features=9, bias=True)", "parameters": [["weight", [9, 9]], ["bias", [9]]], "output_shape": [[3, 1, 9]], "num_parameters": [81, 9]}, {"name": "fc2", "id": 139735110388688, "class_name": "Linear(in_features=9, out_features=20, bias=True)", "parameters": [["weight", [20, 9]], ["bias", [20]]], "output_shape": [[3, 1, 20]], "num_parameters": [180, 20]}, {"name": "fc3", "id": 139735090146960, "class_name": "Linear(in_features=20, out_features=50, bias=True)", "parameters": [["weight", [50, 20]], ["bias", [50]]], "output_shape": [[3, 1, 50]], "num_parameters": [1000, 50]}, {"name": "fc4", "id": 139735090146768, "class_name": "Linear(in_features=50, out_features=10, bias=True)", "parameters": [["weight", [10, 50]], ["bias", [10]]], "output_shape": [[3, 1, 10]], "num_parameters": [500, 10]}], "edges": []}, "loss": 0.0}

9
TicTacToe_AI/Net/wandb/run-20200128_133032-5hk954l0/config.yaml Normal file
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@ -0,0 +1,9 @@
wandb_version: 1
_wandb:
desc: null
value:
cli_version: 0.8.22
framework: torch
is_jupyter_run: false
python_version: 3.7.5

136
TicTacToe_AI/Net/wandb/run-20200128_133032-5hk954l0/diff.patch Normal file
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diff --git a/TicTacToe_AI/Net/pytorch_ai.py b/TicTacToe_AI/Net/pytorch_ai.py
index efea5ae..f3bb291 100644
--- a/TicTacToe_AI/Net/pytorch_ai.py
+++ b/TicTacToe_AI/Net/pytorch_ai.py
@@ -4,6 +4,11 @@ import torch.optim as optim
from torch import nn
import torch.nn.functional as F
from tqdm import tqdm
+import wandb
+
+wandb.init(project="tictactoe")
+
+BATCH_SIZE = 3
def to_set(raw_list):
@@ -35,6 +40,40 @@ def to_set(raw_list):
return out_set
+def to_batched_set(raw_list):
+ counter = 0
+ out_set = []
+ boardtensor = torch.zeros((BATCH_SIZE, 1, 9))
+ labeltensor = torch.zeros(BATCH_SIZE, dtype=torch.long)
+ for line in tqdm(raw_list):
+ line = line.replace('\n', '')
+ raw_board, raw_label = line.split('|')[0], line.split('|')[1]
+
+ if not (int(raw_label) is -1):
+ labeltensor[counter] = int(raw_label)
+ else:
+ labeltensor[counter] = 9
+
+ raw_board = raw_board.split(',')
+ for n, block in enumerate(raw_board):
+ if int(block) is -1:
+ boardtensor[counter][0][n] = 0
+ elif int(block) is 0:
+ boardtensor[counter][0][n] = 0.5
+ elif int(block) is 1:
+ boardtensor[counter][0][n] = 1
+
+ if counter == (BATCH_SIZE - 1):
+ out_set.append([boardtensor, labeltensor])
+ boardtensor = torch.zeros((BATCH_SIZE, 1, 9))
+ labeltensor = torch.zeros(BATCH_SIZE, dtype=torch.long)
+ counter = 0
+ else:
+ counter += 1
+
+ return out_set
+
+
def buildsets():
with open('boards.bds', 'r') as infile:
print('Loading file...')
@@ -43,10 +82,10 @@ def buildsets():
random.shuffle(alllines)
print('Generating testset...')
- testset = to_set(alllines[0:10000])
+ testset = to_batched_set(alllines[0:10000])
print('Generating trainset...')
- trainset = to_set(alllines[10001:200000])
+ trainset = to_batched_set(alllines[10001:20000])
return trainset, testset
@@ -60,6 +99,7 @@ def testnet(net, testset):
if torch.argmax(output) == label[0]:
correct += 1
total += 1
+ wandb.log({'test_accuracy': correct / total})
print("Accuracy: ", round(correct / total, 3))
@@ -79,21 +119,35 @@ class Net(torch.nn.Module):
return F.log_softmax(x, dim=1)
-net = torch.load('./nets/net_3.pt')
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+print('running on %s' % device)
+
+# net = torch.load('./nets/net_3.pt')
+
+net = Net()
+wandb.watch(net)
+
+net.to(device)
optimizer = optim.Adam(net.parameters(), lr=0.001)
+loss_function = nn.CrossEntropyLoss()
trainset, testset = buildsets()
for epoch in range(100):
print('Epoch: ' + str(epoch))
+ wandb.log({'epoch': epoch})
for X, label in tqdm(trainset):
net.zero_grad()
+ X.to(device)
output = net(X)
- loss = F.nll_loss(output.view(1, 10), label[0])
+ output.cpu()
+ print(output)
+ print(label) # LABEL NEEDS TO BE ONE HOT VECTOR!!!!
+ loss = loss_function(output.view(-1, 10), label)
loss.backward()
optimizer.step()
+ wandb.log({'loss': loss})
- print(loss)
- torch.save(net, './nets/net_' + str(epoch + 3) + '.pt')
+ torch.save(net, './nets/gpunets/net_' + str(epoch) + '.pt')
testnet(net, testset)
diff --git a/other_scripts/setcounter.py b/other_scripts/setcounter.py
index 9735f20..e9eb00c 100644
--- a/other_scripts/setcounter.py
+++ b/other_scripts/setcounter.py
@@ -7,9 +7,12 @@ data = datasets.MNIST('../datasets', train=True, download=True,
transforms.ToTensor()
]))
-loader = torch.utils.data.DataLoader(data, batch_size=1, shuffle=False)
+loader = torch.utils.data.DataLoader(data, batch_size=15, shuffle=False)
set = {'0': 0, '1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
+for data in loader:
+ print(data[1].shape)
+
for _, label in tqdm(loader):
set[str(label[0].item())] += 1

1513
TicTacToe_AI/Net/wandb/run-20200128_133032-5hk954l0/output.log Normal file
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109
TicTacToe_AI/Net/wandb/run-20200128_133032-5hk954l0/requirements.txt Normal file
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@ -0,0 +1,109 @@
apturl==0.5.2
argh==0.26.2
asn1crypto==0.24.0
bcrypt==3.1.6
binwalk==2.1.2
blinker==1.4
brlapi==0.6.7
certifi==2018.8.24
chardet==3.0.4
click==7.0
command-not-found==0.3
configparser==4.0.2
cryptography==2.6.1
cupshelpers==1.0
cycler==0.10.0
dbus-python==1.2.12
decorator==4.3.0
defer==1.0.6
distro-info==0.21ubuntu4
distro==1.3.0
docker-pycreds==0.4.0
duplicity==0.8.4
entrypoints==0.3
fasteners==0.12.0
future==0.16.0
gitdb2==2.0.6
gitpython==3.0.5
gql==0.2.0
graphql-core==1.1
httplib2==0.11.3
idna==2.6
keyring==18.0.1
keyrings.alt==3.1.1
kiwisolver==1.0.1
language-selector==0.1
launchpadlib==1.10.7
lazr.restfulclient==0.14.2
lazr.uri==1.0.3
lockfile==0.12.2
louis==3.10.0
macaroonbakery==1.2.3
mako==1.0.7
markupsafe==1.1.0
matplotlib==3.0.2
monotonic==1.5
netifaces==0.10.4
numpy==1.16.2
nvidia-ml-py3==7.352.0
oauth==1.0.1
oauthlib==2.1.0
olefile==0.46
paramiko==2.6.0
pathtools==0.1.2
pexpect==4.6.0
pillow==6.1.0
pip==18.1
promise==2.3
protobuf==3.6.1
psutil==5.6.7
pycairo==1.16.2
pycrypto==2.6.1
pycups==1.9.73
pygments==2.3.1
pygobject==3.34.0
pyjwt==1.7.0
pymacaroons==0.13.0
pynacl==1.3.0
pyopengl==3.1.0
pyparsing==2.2.0
pyqt5==5.12.3
pyqtgraph==0.11.0.dev0
pyrfc3339==1.1
python-apt==1.9.0+ubuntu1.3
python-dateutil==2.7.3
python-debian==0.1.36
pytz==2019.2
pyxdg==0.25
pyyaml==5.1.2
reportlab==3.5.23
requests-unixsocket==0.1.5
requests==2.21.0
scipy==1.2.2
secretstorage==2.3.1
sentry-sdk==0.14.0
setuptools==41.1.0
shortuuid==0.5.0
simplejson==3.16.0
sip==4.19.18
six==1.12.0
smmap2==2.0.5
subprocess32==3.5.4
system-service==0.3
systemd-python==234
torch==1.3.1+cpu
torchvision==0.4.2+cpu
tqdm==4.41.0
ubuntu-advantage-tools==19.5
ubuntu-drivers-common==0.0.0
ufw==0.36
unattended-upgrades==0.1
urllib3==1.24.1
usb-creator==0.3.7
virtualenv==15.1.0
wadllib==1.3.3
wandb==0.8.22
watchdog==0.9.0
wheel==0.32.3
xkit==0.0.0
zope.interface==4.3.2

2
TicTacToe_AI/Net/wandb/run-20200128_133032-5hk954l0/wandb-events.jsonl Normal file
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@ -0,0 +1,2 @@
{"system.cpu": 37.01, "system.memory": 48.79, "system.disk": 8.1, "system.proc.memory.availableMB": 3945.33, "system.proc.memory.rssMB": 146.25, "system.proc.memory.percent": 1.9, "system.proc.cpu.threads": 4.53, "system.network.sent": 225678, "system.network.recv": 125572, "_wandb": true, "_timestamp": 1580218261, "_runtime": 29}
{"system.cpu": 29.1, "system.memory": 48.5, "system.disk": 8.1, "system.proc.memory.availableMB": 3968.06, "system.proc.memory.rssMB": 128.7, "system.proc.memory.percent": 1.67, "system.proc.cpu.threads": 5.5, "system.network.sent": 245582, "system.network.recv": 127391, "_wandb": true, "_timestamp": 1580218264, "_runtime": 31}

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TicTacToe_AI/Net/wandb/run-20200128_133032-5hk954l0/wandb-metadata.json Normal file
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@ -0,0 +1,23 @@
{
"root": "/home/clemens/repositorys/pytorch-ai",
"program": "pytorch_ai.py",
"git": {
"remote": "git@github.com:Clemens-Dautermann/pytorch-ai.git",
"commit": "55cff9b18f8558ae7a9170e56a3d5c6f6665d9ab"
},
"email": "clemens.dautermann@gmail.com",
"startedAt": "2020-01-28T13:30:32.604450",
"host": "ubuntu-laptop",
"username": "clemens",
"executable": "/usr/bin/python3",
"os": "Linux-5.3.0-26-generic-x86_64-with-Ubuntu-19.10-eoan",
"python": "3.7.5",
"cpu_count": 2,
"args": [],
"state": "killed",
"jobType": null,
"mode": "run",
"project": "tictactoe",
"heartbeatAt": "2020-01-28T13:31:04.636623",
"exitcode": 255
}

1
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5
TicTacToe_AI/Net/wandb/settings Normal file
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[default]
project = tictactoe
entity = cdautermann
disabled = true

5
other_scripts/setcounter.py
View file

@ -7,9 +7,12 @@ data = datasets.MNIST('../datasets', train=True, download=True,
transforms.ToTensor() transforms.ToTensor()
])) ]))
loader = torch.utils.data.DataLoader(data, batch_size=1, shuffle=False) loader = torch.utils.data.DataLoader(data, batch_size=15, shuffle=False)
set = {'0': 0, '1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0} set = {'0': 0, '1': 0, '2': 0, '3': 0, '4': 0, '5': 0, '6': 0, '7': 0, '8': 0, '9': 0}
for data in loader:
print(data[1].shape)
for _, label in tqdm(loader): for _, label in tqdm(loader):
set[str(label[0].item())] += 1 set[str(label[0].item())] += 1