learning/annotated_deep_learning_paper_implementations
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cleanup some unused imports

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Varuna Jayasiri
2025-07-18 10:40:22 +05:30
parent 1b702523b9
commit f6d77c36b2
8 changed files with 16 additions and 37 deletions
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1
labml_nn/cfr/kuhn/__init__.py
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@ -41,7 +41,6 @@ import numpy as np
from labml import experiment from labml import experiment
from labml.configs import option from labml.configs import option
from labml_nn.cfr import History as _History, InfoSet as _InfoSet, Action, Player, CFRConfigs from labml_nn.cfr import History as _History, InfoSet as _InfoSet, Action, Player, CFRConfigs
from labml_nn.cfr.infoset_saver import InfoSetSaver
# Kuhn poker actions are pass (`p`) or bet (`b`) # Kuhn poker actions are pass (`p`) or bet (`b`)
ACTIONS = cast(List[Action], ['p', 'b']) ACTIONS = cast(List[Action], ['p', 'b'])

4
labml_nn/diffusion/ddpm/experiment.ipynb
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@ -71,11 +71,7 @@
} }
}, },
"source": [ "source": [
"import torch\n",
"import torch.nn as nn\n",
"\n",
"from labml import experiment\n", "from labml import experiment\n",
"from labml.configs import option\n",
"from labml_nn.diffusion.ddpm.experiment import Configs" "from labml_nn.diffusion.ddpm.experiment import Configs"
], ],
"outputs": [], "outputs": [],

4
labml_nn/normalization/deep_norm/experiment.ipynb
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@ -68,11 +68,7 @@
} }
}, },
"source": [ "source": [
"import torch\n",
"import torch.nn as nn\n",
"\n",
"from labml import experiment\n", "from labml import experiment\n",
"from labml.configs import option\n",
"from labml_nn.normalization.deep_norm.experiment import Configs" "from labml_nn.normalization.deep_norm.experiment import Configs"
], ],
"outputs": [], "outputs": [],

3
labml_nn/normalization/weight_standardization/experiment.ipynb
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@ -53,9 +53,6 @@
"id": "0hJXx_g0wS2C" "id": "0hJXx_g0wS2C"
}, },
"source": [ "source": [
"import torch\n",
"import torch.nn as nn\n",
"\n",
"from labml import experiment\n", "from labml import experiment\n",
"from labml_nn.normalization.weight_standardization.experiment import CIFAR10Configs as Configs" "from labml_nn.normalization.weight_standardization.experiment import CIFAR10Configs as Configs"
], ],

4
labml_nn/rl/ppo/__init__.py
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@ -26,8 +26,8 @@ The experiment uses [Generalized Advantage Estimation](gae.html).
""" """
import torch import torch
from labml_nn.rl.ppo.gae import GAE from labml_nn.rl.ppo.gae import GAE
from torch import nn
class ClippedPPOLoss(nn.Module): class ClippedPPOLoss(nn.Module):
@ -178,7 +178,7 @@ class ClippedPPOLoss(nn.Module):
return -policy_reward.mean() return -policy_reward.mean()
class ClippedValueFunctionLoss(Module): class ClippedValueFunctionLoss(nn.Module):
""" """
## Clipped Value Function Loss ## Clipped Value Function Loss

4
labml_nn/transformers/basic/autoregressive_experiment.ipynb
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@ -71,11 +71,7 @@
} }
}, },
"source": [ "source": [
"import torch\n",
"import torch.nn as nn\n",
"\n",
"from labml import experiment\n", "from labml import experiment\n",
"from labml.configs import option\n",
"from labml_nn.transformers.basic.autoregressive_experiment import Configs" "from labml_nn.transformers.basic.autoregressive_experiment import Configs"
], ],
"outputs": [], "outputs": [],

30
labml_nn/transformers/fast_weights/experiment.ipynb
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@ -67,11 +67,7 @@
"id": "0hJXx_g0wS2C" "id": "0hJXx_g0wS2C"
}, },
"source": [ "source": [
"import torch\n",
"import torch.nn as nn\n",
"\n",
"from labml import experiment\n", "from labml import experiment\n",
"from labml.configs import option\n",
"from labml_nn.transformers.fast_weights.experiment import Configs" "from labml_nn.transformers.fast_weights.experiment import Configs"
], ],
"outputs": [], "outputs": [],
@ -138,21 +134,21 @@
}, },
"source": [ "source": [
"experiment.configs(conf,\n", "experiment.configs(conf,\n",
" # A dictionary of configurations to override\n", " # A dictionary of configurations to override\n",
" {'tokenizer': 'character',\n", " {'tokenizer': 'character',\n",
" 'text': 'tiny_shakespeare',\n", " 'text': 'tiny_shakespeare',\n",
" 'optimizer.learning_rate': 1.0,\n", " 'optimizer.learning_rate': 1.0,\n",
" 'optimizer.optimizer': 'Noam',\n", " 'optimizer.optimizer': 'Noam',\n",
" 'prompt': 'It is',\n", " 'prompt': 'It is',\n",
" 'prompt_separator': '',\n", " 'prompt_separator': '',\n",
"\n", "\n",
" 'train_loader': 'shuffled_train_loader',\n", " 'train_loader': 'shuffled_train_loader',\n",
" 'valid_loader': 'shuffled_valid_loader',\n", " 'valid_loader': 'shuffled_valid_loader',\n",
"\n", "\n",
" 'seq_len': 128,\n", " 'seq_len': 128,\n",
" 'epochs': 128,\n", " 'epochs': 128,\n",
" 'batch_size': 16,\n", " 'batch_size': 16,\n",
" 'inner_iterations': 25})" " 'inner_iterations': 25})"
], ],
"outputs": [], "outputs": [],
"execution_count": null "execution_count": null

3
labml_nn/unet/carvana.py
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@ -13,13 +13,12 @@ You can find the download instructions
Save the training images inside `carvana/train` folder and the masks in `carvana/train_masks` folder. Save the training images inside `carvana/train` folder and the masks in `carvana/train_masks` folder.
""" """
from torch import nn
from pathlib import Path from pathlib import Path
import torch.utils.data
import torchvision.transforms.functional import torchvision.transforms.functional
from PIL import Image from PIL import Image
import torch.utils.data
from labml import lab from labml import lab