From 1fe643d464bc16b62628d9cc0149a97a96b1f1a6 Mon Sep 17 00:00:00 2001 From: Clemens Dautermann Date: Sun, 26 Jan 2020 15:42:01 +0100 Subject: [PATCH] implemented wandb to classifier --- mnist_classifier.py | 18 +- wandb/debug.log | 173 +++++++++++++ .../run-20200126_144058-y7pjuj9j/config.yaml | 9 + wandb/run-20200126_144058-y7pjuj9j/diff.patch | 64 +++++ wandb/run-20200126_144058-y7pjuj9j/output.log | 1 + .../requirements.txt | 109 ++++++++ .../wandb-events.jsonl | 2 + .../wandb-history.jsonl | 235 ++++++++++++++++++ .../wandb-metadata.json | 23 ++ .../wandb-summary.json | 1 + wandb/settings | 2 + 11 files changed, 630 insertions(+), 7 deletions(-) create mode 100644 wandb/debug.log create mode 100644 wandb/run-20200126_144058-y7pjuj9j/config.yaml create mode 100644 wandb/run-20200126_144058-y7pjuj9j/diff.patch create mode 100644 wandb/run-20200126_144058-y7pjuj9j/output.log create mode 100644 wandb/run-20200126_144058-y7pjuj9j/requirements.txt create mode 100644 wandb/run-20200126_144058-y7pjuj9j/wandb-events.jsonl create mode 100644 wandb/run-20200126_144058-y7pjuj9j/wandb-history.jsonl create mode 100644 wandb/run-20200126_144058-y7pjuj9j/wandb-metadata.json create mode 100644 wandb/run-20200126_144058-y7pjuj9j/wandb-summary.json diff --git a/mnist_classifier.py b/mnist_classifier.py index 8eac991..4578ce3 100644 --- a/mnist_classifier.py +++ b/mnist_classifier.py @@ -3,7 +3,10 @@ import torch.nn as nn import torch.optim as optim import torch.nn.functional as F from torchvision import transforms, datasets +from tqdm import tqdm +import wandb +wandb.init(project='pytorch_ai') train = datasets.MNIST('./datasets', train=True, download=True, transform=transforms.Compose([ transforms.ToTensor() @@ -37,31 +40,32 @@ class Net(nn.Module): net = Net() +wandb.watch(net) loss_function = nn.CrossEntropyLoss() optimizer = optim.Adam(net.parameters(), lr=0.001) -for epoch in range(10): # 3 full passes over the data - for data in trainset: # `data` is a batch of data +for epoch in range(10): # 10 full passes over the data + for data in tqdm(trainset): # `data` is a batch of data X, y = data # X is the batch of features, y is the batch of targets. net.zero_grad() # sets gradients to 0 before loss calc. You will do this likely every step. output = net(X.view(-1, 784)) # pass in the reshaped batch (recall they are 28x28 atm) - loss = F.nll_loss(output, y) # calc and grab the loss value + loss = loss_function(output, y) # calc and grab the loss value loss.backward() # apply this loss backwards thru the network's parameters optimizer.step() # attempt to optimize weights to account for loss/gradients + wandb.log({'loss': loss}) - print(loss) # print loss. We hope loss (a measure of wrong-ness) declines! - torch.save(net, './nets/net_' + str(epoch) + ".pt") + # torch.save(net, './nets/net_' + str(epoch) + ".pt") correct = 0 total = 0 with torch.no_grad(): for data in testset: X, y = data output = net(X.view(-1, 784)) - # print(output) for idx, i in enumerate(output): - # print(torch.argmax(i), y[idx]) if torch.argmax(i) == y[idx]: correct += 1 total += 1 + wandb.log({'test_accuracy': correct / total}) print("Accuracy: ", round(correct / total, 3)) + wandb.log({'epoch': epoch}) diff --git a/wandb/debug.log b/wandb/debug.log new file mode 100644 index 0000000..0c37b2a --- /dev/null +++ b/wandb/debug.log @@ -0,0 +1,173 @@ +2020-01-26 15:40:59,370 DEBUG MainThread:14805 [wandb_config.py:_load_defaults():111] no defaults not found in config-defaults.yaml +2020-01-26 15:40:59,388 DEBUG MainThread:14805 [cmd.py:execute():728] Popen(['git', 'cat-file', '--batch-check'], cwd=/home/clemens/repositorys/pytorch-ai, universal_newlines=False, 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b/wandb/run-20200126_144058-y7pjuj9j/config.yaml new file mode 100644 index 0000000..1ead221 --- /dev/null +++ b/wandb/run-20200126_144058-y7pjuj9j/config.yaml @@ -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 diff --git a/wandb/run-20200126_144058-y7pjuj9j/diff.patch b/wandb/run-20200126_144058-y7pjuj9j/diff.patch new file mode 100644 index 0000000..522caea --- /dev/null +++ b/wandb/run-20200126_144058-y7pjuj9j/diff.patch @@ -0,0 +1,64 @@ +diff --git a/mnist_classifier.py b/mnist_classifier.py +index 8eac991..4578ce3 100644 +--- a/mnist_classifier.py ++++ b/mnist_classifier.py +@@ -3,7 +3,10 @@ import torch.nn as nn + import torch.optim as optim + import torch.nn.functional as F + from torchvision import transforms, datasets ++from tqdm import tqdm ++import wandb + ++wandb.init(project='pytorch_ai') + train = datasets.MNIST('./datasets', train=True, download=True, + transform=transforms.Compose([ + transforms.ToTensor() +@@ -37,31 +40,32 @@ class Net(nn.Module): + + + net = Net() ++wandb.watch(net) + + loss_function = nn.CrossEntropyLoss() + optimizer = optim.Adam(net.parameters(), lr=0.001) + +-for epoch in range(10): # 3 full passes over the data +- for data in trainset: # `data` is a batch of data ++for epoch in range(10): # 10 full passes over the data ++ for data in tqdm(trainset): # `data` is a batch of data + X, y = data # X is the batch of features, y is the batch of targets. + net.zero_grad() # sets gradients to 0 before loss calc. You will do this likely every step. + output = net(X.view(-1, 784)) # pass in the reshaped batch (recall they are 28x28 atm) +- loss = F.nll_loss(output, y) # calc and grab the loss value ++ loss = loss_function(output, y) # calc and grab the loss value + loss.backward() # apply this loss backwards thru the network's parameters + optimizer.step() # attempt to optimize weights to account for loss/gradients ++ wandb.log({'loss': loss}) + +- print(loss) # print loss. We hope loss (a measure of wrong-ness) declines! +- torch.save(net, './nets/net_' + str(epoch) + ".pt") ++ # torch.save(net, './nets/net_' + str(epoch) + ".pt") + correct = 0 + total = 0 + with torch.no_grad(): + for data in testset: + X, y = data + output = net(X.view(-1, 784)) +- # print(output) + for idx, i in enumerate(output): +- # print(torch.argmax(i), y[idx]) + if torch.argmax(i) == y[idx]: + correct += 1 + total += 1 ++ wandb.log({'test_accuracy': correct / total}) + print("Accuracy: ", round(correct / total, 3)) ++ wandb.log({'epoch': epoch}) +diff --git a/wandb/settings b/wandb/settings +index 8dae664..73c329e 100644 +--- a/wandb/settings ++++ b/wandb/settings +@@ -1,2 +1,4 @@ + [default] ++project = pytorch_ai ++entity = cdautermann + diff --git a/wandb/run-20200126_144058-y7pjuj9j/output.log b/wandb/run-20200126_144058-y7pjuj9j/output.log new file mode 100644 index 0000000..37b0c19 --- /dev/null +++ b/wandb/run-20200126_144058-y7pjuj9j/output.log @@ -0,0 +1 @@ + 0%| | 0/4000 [00:00