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adding tensorboard writer for training loss and cv loss

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Arec
2021-07-26 02:38:50 -07:00
parent 969812c5cc
commit 7b2ca6edc7
3 changed files with 17 additions and 6 deletions
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4
README.md
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@@ -33,6 +33,10 @@ You can then train a default behavior cloning policy with the following. Be sure
``` ```
python src/main.py --train python src/main.py --train
``` ```
You can run tensorboard by running the following and opening `localhost:6006` (or alternatively port-forwarding 6006 from the remote server)
```
tensorboard --logdir output/
```
You can then test the learned policy with the following, and see the animation file in `output/`: You can then test the learned policy with the following, and see the animation file in `output/`:
``` ```
python src/main.py --test python src/main.py --test

1
requirements.txt
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@@ -4,3 +4,4 @@ sklearn
pytest pytest
json5 json5
tqdm tqdm
tensorboard

16
src/bc/bc.py
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@@ -2,11 +2,11 @@ import torch
import torch.nn as nn import torch.nn as nn
from torch.utils.data import DataLoader from torch.utils.data import DataLoader
import pickle import pickle
#from torch.utils.tensorboard import SummaryWriter from torch.utils.tensorboard import SummaryWriter
from src.policies import DeepSetsPolicy from src.policies import DeepSetsPolicy
from src.util.transform import MinMaxScaler from src.util.transform import MinMaxScaler
import json5 from tqdm import tqdm
class BehaviorCloningPolicy(): class BehaviorCloningPolicy():
""" """
@@ -115,9 +115,9 @@ def generate_transforms(dataset):
def train(train_dataset, cv_dataset, policy, filestr, **kwargs): def train(train_dataset, cv_dataset, policy, filestr, **kwargs):
# hyperparams # hyperparams
train_epochs = 100 train_epochs = 1000
cv_every = 10 cv_every = 10
epoch_every = 1 epoch_every = 1000
train_batch_size = 64 train_batch_size = 64
cv_batch_size = 256 # doesn't matter cv_batch_size = 256 # doesn't matter
learning_rate = 1e-3 learning_rate = 1e-3
@@ -140,7 +140,10 @@ def train(train_dataset, cv_dataset, policy, filestr, **kwargs):
loss_fn = nn.HuberLoss(reduction='sum') loss_fn = nn.HuberLoss(reduction='sum')
optimizer = torch.optim.Adam(policy.parameters(), lr=learning_rate, weight_decay=weight_decay) optimizer = torch.optim.Adam(policy.parameters(), lr=learning_rate, weight_decay=weight_decay)
for i in range(train_epochs): # generate tensorboard writer
writer = SummaryWriter(filestr)
for i in tqdm(range(train_epochs)):
epoch_loss = 0 epoch_loss = 0
for (batch_idx, batch) in enumerate(training_loader): for (batch_idx, batch) in enumerate(training_loader):
@@ -160,6 +163,8 @@ def train(train_dataset, cv_dataset, policy, filestr, **kwargs):
epoch_loss += loss.item() / len(train_dataset) epoch_loss += loss.item() / len(train_dataset)
# Write epoch loss # Write epoch loss
writer.add_scalar('training loss',epoch_loss, i)
if i % epoch_every == 0: if i % epoch_every == 0:
print('Epoch: {}, Training Loss: {}'.format(i, epoch_loss)) print('Epoch: {}, Training Loss: {}'.format(i, epoch_loss))
@@ -171,6 +176,7 @@ def train(train_dataset, cv_dataset, policy, filestr, **kwargs):
pred_action = policy(batch) pred_action = policy(batch)
loss = loss_fn(pred_action, batch['action']) loss = loss_fn(pred_action, batch['action'])
cv_loss += loss.item() / len(cv_dataset) cv_loss += loss.item() / len(cv_dataset)
writer.add_scalar('cv loss', cv_loss, i)
print('Epoch: {}, CV Loss: {}'.format(i, cv_loss)) print('Epoch: {}, CV Loss: {}'.format(i, cv_loss))
policy.save_model(filestr) policy.save_model(filestr)