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alchemistsrivastava/Version1OmAuthorNLP.ipynb Secret

Created November 26, 2017 23:24
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Version1OmAuthorNLP
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Version1OmAuthorNLP.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[93m Warning: no model found for 'en'\u001b[0m\n",
"\n",
" Only loading the 'en' tokenizer.\n",
"\n"
]
}
],
"source": [
"%reload_ext autoreload\n",
"%autoreload 2\n",
"%matplotlib inline\n",
"\n",
"from fastai.learner import *\n",
"\n",
"import torchtext\n",
"from torchtext import vocab, data\n",
"from torchtext.datasets import language_modeling\n",
"\n",
"from fastai.rnn_reg import *\n",
"from fastai.rnn_train import *\n",
"from fastai.nlp import *\n",
"from fastai.lm_rnn import *\n",
"\n",
"import dill as pickle\n",
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[0m\u001b[01;34mmodels\u001b[0m/ test.csv \u001b[01;34mtmp\u001b[0m/ \u001b[01;31mtrain.zip\u001b[0m\r\n",
"sample_submission.csv testData.txt train.csv\r\n",
"\u001b[01;31msample_submission.zip\u001b[0m \u001b[01;31mtest.zip\u001b[0m trainData.txt\r\n"
]
}
],
"source": [
"PATH='data/spooky-author-identification/'\n",
"\n",
"\n",
"%ls {PATH}"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def save_data(df, file_train):\n",
" trainData =\"\"\n",
" for idx, row in df.iterrows():\n",
" data = row['text']\n",
" if trainData == \"\":\n",
" trainData= data\n",
" else :\n",
" trainData=trainData + \" \" + data\n",
"\n",
" file_train.write(trainData)\n",
" file_train.close()\n",
" return trainData"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"file_train= open(f'{PATH}trainData.txt','w') "
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df_train = pd.read_csv(f'{PATH}train.csv')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"train_data= save_data(df_train,file_train)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"df_test = pd.read_csv(f'{PATH}test.csv')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"file_test= open(f'{PATH}testData.txt','w') "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"test_data= save_data(df_test,file_test)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"' '.join(spacy_tok(train_data))"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"TEXT = data.Field(lower=True, tokenize=spacy_tok)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"TRN_PATH = 'trainData.txt'\n",
"VAL_PATH = 'testData.txt'\n",
"TRN = f'{PATH}trainData.txt'\n",
"VAL = f'{PATH}testData.txt'"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'testData.txt'"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"VAL_PATH"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"bs=2; bptt=70"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"FILES = dict(train=TRN_PATH, validation=VAL_PATH, test=VAL_PATH)\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"md = LanguageModelData(PATH, TEXT, **FILES, bs=bs, bptt=bptt, min_freq=10)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"pickle.dump(TEXT, open(f'{PATH}models/TEXT.pkl','wb'))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(4250, 4930, 1, 595187)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(md.trn_dl), md.nt, len(md.trn_ds), len(md.trn_ds[0].text)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['<unk>', '<pad>', ',', 'the', 'of', '.', 'and', 'to', 'i', 'a', 'in', 'was']"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"TEXT.vocab.itos[:12]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"3"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 'stoi': 'string to int'\n",
"TEXT.vocab.stoi['the']"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['this',\n",
" 'process',\n",
" ',',\n",
" 'however',\n",
" ',',\n",
" 'afforded',\n",
" 'me',\n",
" 'no',\n",
" 'means',\n",
" 'of',\n",
" 'ascertaining',\n",
" 'the']"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"md.trn_ds[0].text[:12]"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Variable containing:\n",
" 31\n",
" 3075\n",
" 2\n",
" 153\n",
" 2\n",
" 1385\n",
" 27\n",
" 42\n",
" 304\n",
" 4\n",
" 0\n",
" 3\n",
"[torch.cuda.LongTensor of size 12x1 (GPU 0)]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"TEXT.numericalize([md.trn_ds[0].text[:12]])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"next(iter(md.trn_dl))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"em_sz = 200 # size of each embedding vector\n",
"nh = 50 # number of hidden activations per layer\n",
"nl = 3 # number of layers"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"opt_fn = partial(optim.Adam, betas=(0.7, 0.99))"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"learner = md.get_model(opt_fn, em_sz, nh, nl,\n",
" dropouti=0.05, dropout=0.05, wdrop=0.1, dropoute=0.02, dropouth=0.05)\n",
"learner.reg_fn = partial(seq2seq_reg, alpha=2, beta=1)\n",
"learner.clip=0.3"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learner.fit(3e-3, 4, wds=1e-6, cycle_len=1, cycle_mult=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learner.save_encoder('adam1_enc')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"learner.load_encoder('adam1_enc')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"TEXT = pickle.load(open(f'{PATH}models/TEXT.pkl','rb'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"IMDB_LABEL = data.Field(sequential=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"m=learner.model\n",
"ss=\"\"\". So, it wasn't quite was I was expecting, but I really liked it anyway! The best\"\"\"\n",
"s = [spacy_tok(ss)]\n",
"t=TEXT.numericalize(s)\n",
"' '.join(s[0])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Set batch size to 1\n",
"m[0].bs=1\n",
"# Turn off dropout\n",
"m.eval()\n",
"# Reset hidden state\n",
"m.reset()\n",
"# Get predictions from model\n",
"res,*_ = m(t)\n",
"# Put the batch size back to what it was\n",
"m[0].bs=bs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"nexts = torch.topk(res[-1], 10)[1]\n",
"[TEXT.vocab.itos[o] for o in to_np(nexts)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(ss,\"\\n\")\n",
"for i in range(50):\n",
" n=res[-1].topk(2)[1]\n",
" n = n[1] if n.data[0]==0 else n[0]\n",
" print(TEXT.vocab.itos[n.data[0]], end=' ')\n",
" res,*_ = m(n[0].unsqueeze(0))\n",
"print('...')"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"TEXT = pickle.load(open(f'{PATH}models/TEXT.pkl','rb'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"TEXT.vocab.itos"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\u001b[93m Warning: no model found for 'en'\u001b[0m\n",
"\n",
" Only loading the 'en' tokenizer.\n",
"\n"
]
}
],
"source": [
"import spacy\n",
"spacy_en = spacy.load('en')\n",
"\n",
"def tokenizer(text): # create a tokenizer function\n",
" return [tok.text for tok in spacy_en.tokenizer(text)]\n",
"\n",
"TEXT = data.Field(sequential=True, tokenize=tokenizer, lower=True, fix_length=150)\n",
"LABEL = data.Field(sequential=False, use_vocab=False)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"train, val, test = data.TabularDataset.splits(\n",
" PATH, train='train.csv',\n",
" validation='test.csv', test='test.csv', format='csv',\n",
" fields=[('Text', TEXT), ('Label', LABEL)])"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"splits=data.TabularDataset.splits(\n",
" PATH, train='train.csv',\n",
" validation='test.csv', test='test.csv', format='csv',\n",
" fields=[('Text', TEXT))"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"TEXT = pickle.load(open(f'{PATH}models/TEXT.pkl','rb'))"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"LABEL = data.Field(sequential=False, use_vocab=True)"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"splits = data.TabularDataset.splits(\n",
" PATH, train='train.csv',\n",
" validation='test.csv', test='test.csv', format='csv',\n",
" fields=[('text', TEXT), ('label', LABEL)])"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<bound method Field.build_vocab of <torchtext.data.field.Field object at 0x7f068117a438>>"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"TEXT.init_token"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "too many values to unpack (expected 2)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-24-9b7141513dd9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtrn_iter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mval_iter\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorchtext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mBucketIterator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msplits\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 2)"
]
}
],
"source": [
" trn_iter,val_iter = torchtext.data.BucketIterator.splits(splits, batch_size=bs)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"train_iter, val_iter, test_iter = torchtext.data.Iterator.splits(\n",
" (train, val, test), sort_key=lambda x: len(x.Text),\n",
" batch_sizes=(32, 256, 256))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"def from_splits(cls, path, splits, bs, text_name='text', label_name='label'):\n",
" text_fld = splits[0].fields[text_name]\n",
" print(text_fld )\n",
" label_fld = splits[0].fields[label_name]\n",
" print(label_fld)\n",
" label_fld.build_vocab(splits[0])\n",
" print(splits[0])\n",
" #trn_iter,val_iter = torchtext.data.BucketIterator.splits(splits, batch_size=bs)\n",
" trn_iter, val_iter, test_iter = torchtext.data.Iterator.splits(\n",
" (train, val, test), sort_key=lambda x: len(x.Text), batch_sizes=(32, 256, 256))\n",
" trn_dl = TextDataLoader(trn_iter, text_name, label_name)\n",
" val_dl = TextDataLoader(val_iter, text_name, label_name)\n",
" obj = TextData.from_dls(path, trn_dl, val_dl)\n",
" obj.bs = bs\n",
" obj.pad_idx = text_fld.vocab.stoi[text_fld.pad_token]\n",
" obj.nt = len(text_fld.vocab)\n",
" obj.c = len(label_fld.vocab)\n",
" return obj"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<torchtext.data.field.Field object at 0x7f06802f8b70>\n",
"<torchtext.data.field.Field object at 0x7f067f49c160>\n",
"<torchtext.data.dataset.TabularDataset object at 0x7f068392c748>\n"
]
}
],
"source": [
"md2=from_splits(TEXT, PATH, splits, bs, text_name='text', label_name='label')"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"19581"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"md2.c"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4930"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"md2.nt"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [],
"source": [
"??TextData.from_dls"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"??TextData.from_splits"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"md3 = md2.get_model(opt_fn, 1500, bptt, emb_sz=em_sz, n_hid=nh, n_layers=nl, \n",
" dropout=0.1, dropouti=0.4, wdrop=0.5, dropoute=0.05, dropouth=0.3)\n"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<bound method Learner.summary of SequentialRNN (\n",
" (0): MultiBatchRNN (\n",
" (encoder): Embedding(4930, 200, padding_idx=1)\n",
" (rnns): ModuleList (\n",
" (0): WeightDrop (\n",
" (module): LSTM(200, 50, dropout=0.3)\n",
" )\n",
" (1): WeightDrop (\n",
" (module): LSTM(50, 50, dropout=0.3)\n",
" )\n",
" (2): WeightDrop (\n",
" (module): LSTM(50, 200, dropout=0.3)\n",
" )\n",
" )\n",
" (dropouti): LockedDropout (\n",
" )\n",
" (dropouth): LockedDropout (\n",
" )\n",
" )\n",
" (1): PoolingLinearClassifier (\n",
" (decoder): Linear (600 -> 19581)\n",
" (dropout): LockedDropout (\n",
" )\n",
" )\n",
")>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"md3.summary"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "5648137448234ad1a35862cdda930ed9",
"version_major": 2,
"version_minor": 0
},
"text/html": [
"<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
"<p>\n",
" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
" that the widgets JavaScript is still loading. If this message persists, it\n",
" likely means that the widgets JavaScript library is either not installed or\n",
" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
" Widgets Documentation</a> for setup instructions.\n",
"</p>\n",
"<p>\n",
" If you're reading this message in another frontend (for example, a static\n",
" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
" it may mean that your frontend doesn't currently support widgets.\n",
"</p>\n"
],
"text/plain": [
"HBox(children=(IntProgress(value=0, description='Epoch', max=1), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 0%| | 0/611 [00:00<?, ?it/s]\n"
]
},
{
"ename": "AttributeError",
"evalue": "'Field' object has no attribute 'vocab'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;32m~/fastai/courses/dl1/fastai/learner.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, lrs, n_cycle, wds, **kwargs)\u001b[0m\n\u001b[1;32m 97\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msched\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0mlayer_opt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer_opt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 99\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_gen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlayer_opt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_cycle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 101\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mlr_find\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart_lr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1e-5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend_lr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwds\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~/fastai/courses/dl1/fastai/learner.py\u001b[0m in \u001b[0;36mfit_gen\u001b[0;34m(self, model, data, layer_opt, n_cycle, cycle_len, cycle_mult, cycle_save_name, metrics, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m 87\u001b[0m \u001b[0mn_epoch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum_geom\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcycle_len\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mcycle_len\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcycle_mult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_cycle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m fit(model, data, n_epoch, layer_opt.opt, self.crit,\n\u001b[0;32m---> 89\u001b[0;31m metrics=metrics, callbacks=callbacks, reg_fn=self.reg_fn, clip=self.clip, **kwargs)\n\u001b[0m\u001b[1;32m 90\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mget_layer_groups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_layer_groups\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/fastai/courses/dl1/fastai/model.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(model, data, epochs, opt, crit, metrics, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m 80\u001b[0m \u001b[0mstepper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreset\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 81\u001b[0m \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtqdm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrn_dl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mleave\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrn_dl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 83\u001b[0m \u001b[0mbatch_num\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 84\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstepper\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mV\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/src/anaconda3/envs/fastai/lib/python3.6/site-packages/tqdm/_tqdm.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 951\u001b[0m \"\"\", fp_write=getattr(self.fp, 'write', sys.stderr.write))\n\u001b[1;32m 952\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 953\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mobj\u001b[0m \u001b[0;32min\u001b[0m \u001b[0miterable\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 954\u001b[0m \u001b[0;32myield\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 955\u001b[0m \u001b[0;31m# Update and possibly print the progressbar.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/fastai/courses/dl1/fastai/dataset.py\u001b[0m in \u001b[0;36m__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 219\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m>=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 220\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 221\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 222\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 223\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m~/src/anaconda3/envs/fastai/lib/python3.6/site-packages/torchtext/data/iterator.py\u001b[0m in \u001b[0;36m__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 176\u001b[0m \u001b[0mminibatch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_key\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreverse\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 177\u001b[0m yield Batch(minibatch, self.dataset, self.device,\n\u001b[0;32m--> 178\u001b[0;31m self.train)\n\u001b[0m\u001b[1;32m 179\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 180\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mStopIteration\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/src/anaconda3/envs/fastai/lib/python3.6/site-packages/torchtext/data/batch.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, dataset, device, train)\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfield\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[0m\n\u001b[1;32m 21\u001b[0m \u001b[0mbatch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfield\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain\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 23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/src/anaconda3/envs/fastai/lib/python3.6/site-packages/torchtext/data/field.py\u001b[0m in \u001b[0;36mprocess\u001b[0;34m(self, batch, device, train)\u001b[0m\n\u001b[1;32m 182\u001b[0m \"\"\"\n\u001b[1;32m 183\u001b[0m \u001b[0mpadded\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 184\u001b[0;31m \u001b[0mtensor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumericalize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpadded\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 185\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mtensor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/src/anaconda3/envs/fastai/lib/python3.6/site-packages/torchtext/data/field.py\u001b[0m in \u001b[0;36mnumericalize\u001b[0;34m(self, arr, device, train)\u001b[0m\n\u001b[1;32m 274\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_vocab\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 275\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msequential\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 276\u001b[0;31m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstoi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mex\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 277\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstoi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/src/anaconda3/envs/fastai/lib/python3.6/site-packages/torchtext/data/field.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 274\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_vocab\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 275\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msequential\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 276\u001b[0;31m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstoi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mex\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 277\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstoi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/src/anaconda3/envs/fastai/lib/python3.6/site-packages/torchtext/data/field.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 274\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_vocab\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 275\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msequential\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 276\u001b[0;31m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstoi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mex\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 277\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstoi\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAttributeError\u001b[0m: 'Field' object has no attribute 'vocab'"
]
}
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Raw
Version1OmAuthorNLP.py
# coding: utf-8
# In[1]:
get_ipython().run_line_magic('reload_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().run_line_magic('matplotlib', 'inline')
from fastai.learner import *
import torchtext
from torchtext import vocab, data
from torchtext.datasets import language_modeling
from fastai.rnn_reg import *
from fastai.rnn_train import *
from fastai.nlp import *
from fastai.lm_rnn import *
import dill as pickle
import pandas as pd
import numpy as np
# In[2]:
PATH='data/spooky-author-identification/'
get_ipython().run_line_magic('ls', '{PATH}')
# In[3]:
def save_data(df, file_train):
trainData =""
for idx, row in df.iterrows():
data = row['text']
if trainData == "":
trainData= data
else :
trainData=trainData + " " + data
file_train.write(trainData)
file_train.close()
return trainData
# In[4]:
file_train= open(f'{PATH}trainData.txt','w')
# In[5]:
df_train = pd.read_csv(f'{PATH}train.csv')
# In[6]:
train_data= save_data(df_train,file_train)
# In[7]:
df_test = pd.read_csv(f'{PATH}test.csv')
# In[8]:
file_test= open(f'{PATH}testData.txt','w')
# In[9]:
test_data= save_data(df_test,file_test)
# In[ ]:
' '.join(spacy_tok(train_data))
# In[11]:
TEXT = data.Field(lower=True, tokenize=spacy_tok)
# In[12]:
TRN_PATH = 'trainData.txt'
VAL_PATH = 'testData.txt'
TRN = f'{PATH}trainData.txt'
VAL = f'{PATH}testData.txt'
# In[13]:
VAL_PATH
# In[14]:
bs=2; bptt=70
# In[15]:
FILES = dict(train=TRN_PATH, validation=VAL_PATH, test=VAL_PATH)
# In[16]:
md = LanguageModelData(PATH, TEXT, **FILES, bs=bs, bptt=bptt, min_freq=10)
# In[17]:
pickle.dump(TEXT, open(f'{PATH}models/TEXT.pkl','wb'))
# In[18]:
len(md.trn_dl), md.nt, len(md.trn_ds), len(md.trn_ds[0].text)
# In[19]:
TEXT.vocab.itos[:12]
# In[20]:
# 'stoi': 'string to int'
TEXT.vocab.stoi['the']
# In[21]:
md.trn_ds[0].text[:12]
# In[22]:
TEXT.numericalize([md.trn_ds[0].text[:12]])
# In[ ]:
next(iter(md.trn_dl))
# In[24]:
em_sz = 200 # size of each embedding vector
nh = 50 # number of hidden activations per layer
nl = 3 # number of layers
# In[25]:
opt_fn = partial(optim.Adam, betas=(0.7, 0.99))
# In[26]:
learner = md.get_model(opt_fn, em_sz, nh, nl,
dropouti=0.05, dropout=0.05, wdrop=0.1, dropoute=0.02, dropouth=0.05)
learner.reg_fn = partial(seq2seq_reg, alpha=2, beta=1)
learner.clip=0.3
# In[ ]:
learner.fit(3e-3, 4, wds=1e-6, cycle_len=1, cycle_mult=1)
# In[ ]:
learner.save_encoder('adam1_enc')
# In[ ]:
learner.load_encoder('adam1_enc')
# In[ ]:
TEXT = pickle.load(open(f'{PATH}models/TEXT.pkl','rb'))
# In[ ]:
IMDB_LABEL = data.Field(sequential=False)
# In[ ]:
m=learner.model
ss=""". So, it wasn't quite was I was expecting, but I really liked it anyway! The best"""
s = [spacy_tok(ss)]
t=TEXT.numericalize(s)
' '.join(s[0])
# In[ ]:
# Set batch size to 1
m[0].bs=1
# Turn off dropout
m.eval()
# Reset hidden state
m.reset()
# Get predictions from model
res,*_ = m(t)
# Put the batch size back to what it was
m[0].bs=bs
# In[ ]:
nexts = torch.topk(res[-1], 10)[1]
[TEXT.vocab.itos[o] for o in to_np(nexts)]
# In[ ]:
print(ss,"\n")
for i in range(50):
n=res[-1].topk(2)[1]
n = n[1] if n.data[0]==0 else n[0]
print(TEXT.vocab.itos[n.data[0]], end=' ')
res,*_ = m(n[0].unsqueeze(0))
print('...')
# In[22]:
TEXT = pickle.load(open(f'{PATH}models/TEXT.pkl','rb'))
# In[ ]:
TEXT.vocab.itos
# In[18]:
import spacy
spacy_en = spacy.load('en')
def tokenizer(text): # create a tokenizer function
return [tok.text for tok in spacy_en.tokenizer(text)]
TEXT = data.Field(sequential=True, tokenize=tokenizer, lower=True, fix_length=150)
LABEL = data.Field(sequential=False, use_vocab=False)
# In[19]:
train, val, test = data.TabularDataset.splits(
PATH, train='train.csv',
validation='test.csv', test='test.csv', format='csv',
fields=[('Text', TEXT), ('Label', LABEL)])
# In[27]:
splits=data.TabularDataset.splits(
PATH, train='train.csv',
validation='test.csv', test='test.csv', format='csv',
fields=[('Text', TEXT))
# In[28]:
TEXT = pickle.load(open(f'{PATH}models/TEXT.pkl','rb'))
# In[29]:
LABEL = data.Field(sequential=False, use_vocab=True)
# In[30]:
splits = data.TabularDataset.splits(
PATH, train='train.csv',
validation='test.csv', test='test.csv', format='csv',
fields=[('text', TEXT), ('label', LABEL)])
# In[23]:
TEXT.init_token
# In[24]:
trn_iter,val_iter = torchtext.data.BucketIterator.splits(splits, batch_size=bs)
# In[31]:
train_iter, val_iter, test_iter = torchtext.data.Iterator.splits(
(train, val, test), sort_key=lambda x: len(x.Text),
batch_sizes=(32, 256, 256))
# In[32]:
def from_splits(cls, path, splits, bs, text_name='text', label_name='label'):
text_fld = splits[0].fields[text_name]
print(text_fld )
label_fld = splits[0].fields[label_name]
print(label_fld)
label_fld.build_vocab(splits[0])
print(splits[0])
#trn_iter,val_iter = torchtext.data.BucketIterator.splits(splits, batch_size=bs)
trn_iter, val_iter, test_iter = torchtext.data.Iterator.splits(
(train, val, test), sort_key=lambda x: len(x.Text), batch_sizes=(32, 256, 256))
trn_dl = TextDataLoader(trn_iter, text_name, label_name)
val_dl = TextDataLoader(val_iter, text_name, label_name)
obj = TextData.from_dls(path, trn_dl, val_dl)
obj.bs = bs
obj.pad_idx = text_fld.vocab.stoi[text_fld.pad_token]
obj.nt = len(text_fld.vocab)
obj.c = len(label_fld.vocab)
return obj
# In[33]:
md2=from_splits(TEXT, PATH, splits, bs, text_name='text', label_name='label')
# In[34]:
md2.c
# In[35]:
md2.nt
# In[84]:
get_ipython().run_line_magic('pinfo2', 'TextData.from_dls')
# In[72]:
get_ipython().run_line_magic('pinfo2', 'TextData.from_splits')
# In[36]:
md3 = md2.get_model(opt_fn, 1500, bptt, emb_sz=em_sz, n_hid=nh, n_layers=nl,
dropout=0.1, dropouti=0.4, wdrop=0.5, dropoute=0.05, dropouth=0.3)
# In[37]:
md3.summary
# In[40]:
md3.fit(1e01, 1, metrics=[accuracy])
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