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Text classification in PyTorch to refactor with PyTorch lightning.ipynb
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text-classification-in-pytorch-to-refactor-with-pytorch-lightning.ipynb
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"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "SVhHUPSWPCHG"
},
"source": [
"The code below is a PyTorch text classifier obtained by getting code from https://pytorch.org/tutorials/beginner/text_sentiment_ngrams_tutorial.html, attempting (clumsily) a PyTorch lightning refactor.\r\n",
"How can I make this work with PyTorch lightning?"
]
},
{
"cell_type": "code",
"metadata": {
"id": "8-un-rXKQ2ec"
},
"source": [
"%matplotlib inline"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "RWuv_rZYRdXz"
},
"source": [
"pip install torchtext pytorch-lightning --upgrade --quiet"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "vOAdk0qdQ2ef",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "99cb63e9-b430-4096-a1a8-8126e76a2437"
},
"source": [
"import torch\n",
"import torchtext\n",
"from torchtext.datasets import text_classification\n",
"NGRAMS = 2\n",
"import os\n",
"if not os.path.isdir('./.data'):\n",
"\tos.mkdir('./.data')\n",
"train_dataset, test_dataset = text_classification.DATASETS['AG_NEWS'](\n",
" root='./.data', ngrams=NGRAMS, vocab=None)\n",
"BATCH_SIZE = 16"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"120000lines [00:06, 17747.80lines/s]\n",
"120000lines [00:15, 7709.20lines/s]\n",
"7600lines [00:00, 7603.03lines/s]\n"
],
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}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jEbcAxuuQ2ei"
},
"source": [
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"class TextSentiment(nn.Module):\n",
" def __init__(self, vocab_size, embed_dim, num_class):\n",
" super().__init__()\n",
" self.embedding = nn.EmbeddingBag(vocab_size, embed_dim, sparse=True)\n",
" self.fc = nn.Linear(embed_dim, num_class)\n",
" self.init_weights()\n",
"\n",
" def init_weights(self):\n",
" initrange = 0.5\n",
" self.embedding.weight.data.uniform_(-initrange, initrange)\n",
" self.fc.weight.data.uniform_(-initrange, initrange)\n",
" self.fc.bias.data.zero_()\n",
"\n",
" def forward(self, text, offsets):\n",
" embedded = self.embedding(text, offsets)\n",
" return self.fc(embedded)"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "SUOxiQOJQ2el"
},
"source": [
"VOCAB_SIZE = len(train_dataset.get_vocab())\n",
"EMBED_DIM = 32\n",
"NUM_CLASS = len(train_dataset.get_labels())"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "YJXial03Q2em"
},
"source": [
"Functions used to generate batch\n",
"--------------------------------\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "36rTk-S5Q2en"
},
"source": [
"def generate_batch(batch):\n",
" label = torch.tensor([entry[0] for entry in batch])\n",
" text = [entry[1] for entry in batch]\n",
" offsets = [0] + [len(entry) for entry in text]\n",
" # torch.Tensor.cumsum returns the cumulative sum\n",
" # of elements in the dimension dim.\n",
" # torch.Tensor([1.0, 2.0, 3.0]).cumsum(dim=0)\n",
"\n",
" offsets = torch.tensor(offsets[:-1]).cumsum(dim=0)\n",
" text = torch.cat(text)\n",
" return text, offsets, label"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "kiaOrOBhQ2ep"
},
"source": [
"Define functions to train the model and evaluate results.\n",
"---------------------------------------------------------\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "mMzZQ2e-Q2er"
},
"source": [
"import time\n",
"from torch.utils.data.dataset import random_split\n",
"\n",
"train_len = int(len(train_dataset) * 0.95)\n",
"sub_train_, sub_valid_ = \\\n",
" random_split(train_dataset, [train_len, len(train_dataset) - train_len])"
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "drSdUCLuSuhK"
},
"source": [
"from torch.utils.data import DataLoader\r\n",
"\r\n",
"train_dl = DataLoader(sub_train_, batch_size=BATCH_SIZE, shuffle=True,\r\n",
" collate_fn=generate_batch)\r\n",
"valid_dl = DataLoader(sub_valid_, batch_size=BATCH_SIZE, shuffle=True,\r\n",
" collate_fn=generate_batch)"
],
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "9-3usDU_TGY9"
},
"source": [
"import pytorch_lightning as pl\r\n",
"from torch import optim\r\n",
"from torch.nn import CrossEntropyLoss"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "qFUFjqu_S9Gp"
},
"source": [
"class LitTextClassifier(pl.LightningModule):\r\n",
" def __init__(self, num_class, criterion = CrossEntropyLoss):\r\n",
" super().__init__()\r\n",
" self.embedding = nn.EmbeddingBag(VOCAB_SIZE, EMBED_DIM, sparse=False)\r\n",
" self.fc = nn.Linear(EMBED_DIM, num_class)\r\n",
" self.init_weights()\r\n",
" self.criterion = criterion\r\n",
"\r\n",
" def init_weights(self):\r\n",
" initrange = 0.5\r\n",
" self.embedding.weight.data.uniform_(-initrange, initrange)\r\n",
" self.fc.weight.data.uniform_(-initrange, initrange)\r\n",
" self.fc.bias.data.zero_()\r\n",
"\r\n",
" def forward(self, text, offsets):\r\n",
" embedded = self.embedding(text, offsets)\r\n",
" return self.fc(embedded)\r\n",
"\r\n",
" def configure_optimizers(self):\r\n",
" optimizer = optim.SGD(self.parameters(), lr=4.0)\r\n",
" return optimizer\r\n",
"\r\n",
" def training_step(self, batch, batch_idx):\r\n",
" # I am messing up things here\r\n",
" text, offsets, cls = batch\r\n",
" output = self.forward(text, offsets)\r\n",
" loss = self.criterion(output, cls)\r\n",
"\r\n",
" return loss"
],
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "TgxhOV1YUCoD"
},
"source": [
"classifier = LitTextClassifier(NUM_CLASS)"
],
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
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"source": [
"trainer = pl.Trainer(gpus=1, max_epochs=1)\r\n",
"trainer.fit(classifier, train_dl)"
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"execution_count": 12,
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"GPU available: True, used: True\n",
"INFO:lightning:GPU available: True, used: True\n",
"TPU available: None, using: 0 TPU cores\n",
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"\n",
" | Name | Type | Params\n",
"-------------------------------------------\n",
"0 | embedding | EmbeddingBag | 41.9 M\n",
"1 | fc | Linear | 132 \n",
"-------------------------------------------\n",
"41.9 M Trainable params\n",
"0 Non-trainable params\n",
"41.9 M Total params\n",
"167.533 Total estimated model params size (MB)\n",
"INFO:lightning:\n",
" | Name | Type | Params\n",
"-------------------------------------------\n",
"0 | embedding | EmbeddingBag | 41.9 M\n",
"1 | fc | Linear | 132 \n",
"-------------------------------------------\n",
"41.9 M Trainable params\n",
"0 Non-trainable params\n",
"41.9 M Total params\n",
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-12-62647d0f905e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mtrainer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTrainer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgpus\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_dl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, model, train_dataloader, val_dataloaders, datamodule)\u001b[0m\n\u001b[1;32m 511\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 512\u001b[0m \u001b[0;31m# dispath `start_training` or `start_testing` or `start_predicting`\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 513\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 514\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 515\u001b[0m \u001b[0;31m# plugin will finalized fitting (e.g. ddp_spawn will load trained model)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mdispatch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 551\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 552\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 553\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_training\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 554\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 555\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtrain_or_test_or_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pytorch_lightning/accelerators/accelerator.py\u001b[0m in \u001b[0;36mstart_training\u001b[0;34m(self, trainer)\u001b[0m\n\u001b[1;32m 72\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mstart_training\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_type_plugin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart_training\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 75\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 76\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mstart_testing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pytorch_lightning/plugins/training_type/training_type_plugin.py\u001b[0m in \u001b[0;36mstart_training\u001b[0;34m(self, trainer)\u001b[0m\n\u001b[1;32m 109\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mstart_training\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Trainer'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[0;31m# double dispatch to initiate the training loop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_results\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 112\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 113\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mstart_testing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrainer\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'Trainer'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/trainer.py\u001b[0m in \u001b[0;36mrun_train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 642\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"run_training_epoch\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 643\u001b[0m \u001b[0;31m# run train epoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 644\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_loop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_training_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 645\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 646\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_steps\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_steps\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mglobal_step\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mrun_training_epoch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 490\u001b[0m \u001b[0;31m# ------------------------------------\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 491\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofiler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprofile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"run_training_batch\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 492\u001b[0;31m \u001b[0mbatch_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_training_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdataloader_idx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 494\u001b[0m \u001b[0;31m# when returning -1 from train_step, we end epoch early\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36mrun_training_batch\u001b[0;34m(self, batch, batch_idx, dataloader_idx)\u001b[0m\n\u001b[1;32m 648\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 649\u001b[0m \u001b[0;31m# optimizer step\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 650\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mopt_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_idx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_step_and_backward_closure\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 651\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 652\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pytorch_lightning/trainer/training_loop.py\u001b[0m in \u001b[0;36moptimizer_step\u001b[0;34m(self, optimizer, opt_idx, batch_idx, train_step_and_backward_closure)\u001b[0m\n\u001b[1;32m 432\u001b[0m \u001b[0mon_tpu\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_device_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mDeviceType\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTPU\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0m_TPU_AVAILABLE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 433\u001b[0m \u001b[0musing_native_amp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0musing_native_amp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 434\u001b[0;31m \u001b[0musing_lbfgs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mis_lbfgs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 435\u001b[0m )\n\u001b[1;32m 436\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, weight, size_average, ignore_index, reduce, reduction)\u001b[0m\n\u001b[1;32m 955\u001b[0m def __init__(self, weight: Optional[Tensor] = None, size_average=None, ignore_index: int = -100,\n\u001b[1;32m 956\u001b[0m reduce=None, reduction: str = 'mean') -> None:\n\u001b[0;32m--> 957\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCrossEntropyLoss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_average\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 958\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mignore_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 959\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, weight, size_average, reduce, reduction)\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0m_WeightedLoss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_Loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize_average\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'mean'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_WeightedLoss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize_average\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 26\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregister_buffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'weight'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mweight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, size_average, reduce, reduction)\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_Loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 17\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msize_average\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mreduce\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_Reduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlegacy_get_string\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msize_average\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 19\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduction\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/usr/local/lib/python3.6/dist-packages/torch/nn/_reduction.py\u001b[0m in \u001b[0;36mlegacy_get_string\u001b[0;34m(size_average, reduce, emit_warning)\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0mreduce\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 37\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0msize_average\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 38\u001b[0m \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'mean'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mreduce\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRuntimeError\u001b[0m: Boolean value of Tensor with more than one value is ambiguous"
]
}
]
}
]
}
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