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ff-lm-bengio/model lm bengio - ODPOWIEDZI.ipynb
kubapok c5a7801af0 init
2021-05-24 14:11:26 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## importy"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/media/kuba/ssdsam/anaconda3/lib/python3.8/site-packages/gensim/similarities/__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.\n",
" warnings.warn(msg)\n"
]
}
],
"source": [
"from gensim.utils import tokenize\n",
"import numpy as np\n",
"import torch\n",
"from tqdm.notebook import tqdm"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"https://www.jmlr.org/papers/volume3/bengio03a/bengio03a.pdf"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"#device = 'cpu'"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using cuda device\n"
]
}
],
"source": [
"print('Using {} device'.format(device))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"device(type='cuda')"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"device"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## przygotowanie zbiorów"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"pan_tadeusz_path_train= '/home/kuba/Syncthing/przedmioty/2020-02/ISI/zajecia9_ngramowy_model_jDDezykowy/pan-tadeusz-train.txt'"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"corpora_train = open(pan_tadeusz_path_train).read()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"corpora_train_tokenized = list(tokenize(corpora_train,lowercase = True))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"vocab_itos = sorted(set(corpora_train_tokenized))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"16598"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(vocab_itos)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"vocab_itos = vocab_itos[:15005]\n",
"vocab_itos[15001] = \"<UNK>\"\n",
"vocab_itos[15002] = \"<BOS>\"\n",
"vocab_itos[15003] = \"<EOS>\"\n",
"vocab_itos[15004] = \"<PAD>\""
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"15005"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(vocab_itos)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"vocab_stoi = dict()\n",
"for i, token in enumerate(vocab_itos):\n",
" vocab_stoi[token] = i"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"NGRAMS = 4"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def get_token_id(dataset):\n",
" token_ids = [vocab_stoi['<PAD>']] * (NGRAMS-1) + [vocab_stoi['<BOS>']]\n",
" for token in dataset:\n",
" try:\n",
" token_ids.append(vocab_stoi[token])\n",
" except KeyError:\n",
" token_ids.append(vocab_stoi['<UNK>'])\n",
" token_ids.append(vocab_stoi['<EOS>'])\n",
" return token_ids"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"train_ids = get_token_id(corpora_train_tokenized)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"[15004,\n",
" 15004,\n",
" 15004,\n",
" 15002,\n",
" 7,\n",
" 5002,\n",
" 7247,\n",
" 11955,\n",
" 1432,\n",
" 7018,\n",
" 14739,\n",
" 5506,\n",
" 4696,\n",
" 4276,\n",
" 7505,\n",
" 2642,\n",
" 8477,\n",
" 7259,\n",
" 10870,\n",
" 10530,\n",
" 7506,\n",
" 12968,\n",
" 7997,\n",
" 1911,\n",
" 12479,\n",
" 11129,\n",
" 13069,\n",
" 11797,\n",
" 5819,\n",
" 6268]"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_ids[:30]"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def get_samples(dataset):\n",
" samples = []\n",
" for i in range(len(dataset)-NGRAMS):\n",
" samples.append(dataset[i:i+NGRAMS])\n",
" return samples"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"train_ids = get_samples(train_ids)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"train_ids = torch.tensor(train_ids, device = device)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[15004, 15004, 15004, 15002],\n",
" [15004, 15004, 15002, 7],\n",
" [15004, 15002, 7, 5002],\n",
" [15002, 7, 5002, 7247],\n",
" [ 7, 5002, 7247, 11955],\n",
" [ 5002, 7247, 11955, 1432],\n",
" [ 7247, 11955, 1432, 7018],\n",
" [11955, 1432, 7018, 14739],\n",
" [ 1432, 7018, 14739, 5506],\n",
" [ 7018, 14739, 5506, 4696],\n",
" [14739, 5506, 4696, 4276],\n",
" [ 5506, 4696, 4276, 7505],\n",
" [ 4696, 4276, 7505, 2642],\n",
" [ 4276, 7505, 2642, 8477],\n",
" [ 7505, 2642, 8477, 7259],\n",
" [ 2642, 8477, 7259, 10870],\n",
" [ 8477, 7259, 10870, 10530],\n",
" [ 7259, 10870, 10530, 7506],\n",
" [10870, 10530, 7506, 12968],\n",
" [10530, 7506, 12968, 7997],\n",
" [ 7506, 12968, 7997, 1911],\n",
" [12968, 7997, 1911, 12479],\n",
" [ 7997, 1911, 12479, 11129],\n",
" [ 1911, 12479, 11129, 13069],\n",
" [12479, 11129, 13069, 11797],\n",
" [11129, 13069, 11797, 5819],\n",
" [13069, 11797, 5819, 6268],\n",
" [11797, 5819, 6268, 2807],\n",
" [ 5819, 6268, 2807, 7831],\n",
" [ 6268, 2807, 7831, 12893]], device='cuda:0')"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_ids[:30]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## model"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"class NeuralLM(torch.nn.Module):\n",
"\n",
" def __init__(self):\n",
" super(NeuralLM, self).__init__()\n",
" self.emb = torch.nn.Embedding(len(vocab_itos),200)\n",
" self.fc1 = torch.nn.Linear( (NGRAMS - 1 ) * 200,len(vocab_itos)) \n",
"\n",
" def forward(self, x):\n",
" x = self.emb(x)\n",
" x = x.reshape(-1, (NGRAMS-1)*200)\n",
" x = self.fc1(x)\n",
" return x"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"lm = NeuralLM().to(device)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"criterion = torch.nn.CrossEntropyLoss()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"optimizer = torch.optim.Adam(lm.parameters())"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"BATCH_SIZE = 50\n",
"EPOCHS = 2"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
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"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=1140.0), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
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"updates: 1028/1140.44\tloss: 4.85994\t129.01682401556738\n",
"updates: 1030/1140.44\tloss: 4.85924\t128.92603097761628\n",
"updates: 1032/1140.44\tloss: 4.85834\t128.81061582186257\n",
"updates: 1034/1140.44\tloss: 4.85806\t128.7737779615554\n",
"updates: 1036/1140.44\tloss: 4.85808\t128.77709147187798\n",
"updates: 1038/1140.44\tloss: 4.85683\t128.6156449527718\n",
"updates: 1040/1140.44\tloss: 4.85576\t128.4776743645171\n",
"updates: 1042/1140.44\tloss: 4.85416\t128.2725933528742\n",
"updates: 1044/1140.44\tloss: 4.85432\t128.29383798212407\n",
"updates: 1046/1140.44\tloss: 4.85345\t128.18221392754577\n",
"updates: 1048/1140.44\tloss: 4.85213\t128.01267189980373\n",
"updates: 1050/1140.44\tloss: 4.85136\t127.9141155939983\n",
"updates: 1052/1140.44\tloss: 4.85021\t127.76665453554867\n",
"updates: 1054/1140.44\tloss: 4.8497\t127.70247001025903\n",
"updates: 1056/1140.44\tloss: 4.84919\t127.636758290674\n",
"updates: 1058/1140.44\tloss: 4.84834\t127.5281848635421\n",
"updates: 1060/1140.44\tloss: 4.84709\t127.36966885836036\n",
"updates: 1062/1140.44\tloss: 4.84665\t127.31306256224546\n",
"updates: 1064/1140.44\tloss: 4.84519\t127.12706815957381\n",
"updates: 1066/1140.44\tloss: 4.84352\t126.91553052440344\n",
"updates: 1068/1140.44\tloss: 4.84168\t126.68157966181045\n",
"updates: 1070/1140.44\tloss: 4.84101\t126.59656546362845\n",
"updates: 1072/1140.44\tloss: 4.84095\t126.59001104873029\n",
"updates: 1074/1140.44\tloss: 4.841\t126.59545824687845\n",
"updates: 1076/1140.44\tloss: 4.84058\t126.54293842006372\n",
"updates: 1078/1140.44\tloss: 4.83974\t126.43687704802781\n",
"updates: 1080/1140.44\tloss: 4.83928\t126.37820954505321\n",
"updates: 1082/1140.44\tloss: 4.83945\t126.39927992768688\n",
"updates: 1084/1140.44\tloss: 4.83913\t126.35940796794382\n",
"updates: 1086/1140.44\tloss: 4.8389\t126.32984520177155\n",
"updates: 1088/1140.44\tloss: 4.83849\t126.27815702758163\n",
"updates: 1090/1140.44\tloss: 4.8375\t126.15303370524808\n",
"updates: 1092/1140.44\tloss: 4.83684\t126.07006075398033\n",
"updates: 1094/1140.44\tloss: 4.83601\t125.96623438490424\n",
"updates: 1096/1140.44\tloss: 4.83539\t125.88809170272184\n",
"updates: 1098/1140.44\tloss: 4.83444\t125.76805500286181\n",
"updates: 1100/1140.44\tloss: 4.83399\t125.7121040294529\n",
"updates: 1102/1140.44\tloss: 4.83349\t125.6489488142964\n",
"updates: 1104/1140.44\tloss: 4.83283\t125.56569900080676\n",
"updates: 1106/1140.44\tloss: 4.83189\t125.44783103973653\n",
"updates: 1108/1140.44\tloss: 4.83169\t125.42259491603347\n",
"updates: 1110/1140.44\tloss: 4.83141\t125.388073018812\n",
"updates: 1112/1140.44\tloss: 4.83081\t125.31278363691462\n",
"updates: 1114/1140.44\tloss: 4.82998\t125.20810953985931\n",
"updates: 1116/1140.44\tloss: 4.82878\t125.05859369237062\n",
"updates: 1118/1140.44\tloss: 4.82766\t124.91879187540883\n",
"updates: 1120/1140.44\tloss: 4.82747\t124.89488799149356\n",
"updates: 1122/1140.44\tloss: 4.82709\t124.84750827625616\n",
"updates: 1124/1140.44\tloss: 4.82671\t124.79916803988598\n",
"updates: 1126/1140.44\tloss: 4.82583\t124.68999994273116\n",
"updates: 1128/1140.44\tloss: 4.82479\t124.5600589585652\n",
"updates: 1130/1140.44\tloss: 4.82376\t124.43179282419189\n",
"updates: 1132/1140.44\tloss: 4.82308\t124.34783719542669\n",
"updates: 1134/1140.44\tloss: 4.8215\t124.1509023840513\n",
"updates: 1136/1140.44\tloss: 4.82027\t123.99908809258902\n",
"updates: 1138/1140.44\tloss: 4.81874\t123.80952102600538\n",
"\n"
]
}
],
"source": [
"lm.train()\n",
"for epoch in range(EPOCHS):\n",
" batches = 0\n",
" loss_sum =0\n",
" acc_score = 0\n",
"\n",
" for i in tqdm(range(0, len(train_ids)-BATCH_SIZE+1, BATCH_SIZE)):\n",
" X = train_ids[i:i+BATCH_SIZE,:3]\n",
" Y = train_ids[i:i+BATCH_SIZE,3]\n",
" predictions = lm(X)\n",
" loss = criterion(predictions,Y)\n",
" \n",
" \n",
" \n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
" \n",
" loss_sum += loss.item()\n",
" batches += 1\n",
" \n",
" \n",
" \n",
" if i % 100 == 0:\n",
" loss_current = loss_sum / batches\n",
" print(f'updates: {i // BATCH_SIZE}/{len(train_ids) / BATCH_SIZE}', end ='\\t')\n",
" print('loss: ', round(loss_current,5) , end = '\\t')\n",
" print(np.exp(loss_current))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ewaluacja"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### zadanie:\n",
"\n",
"- wczytać zbiór testowy\n",
"- napisać funkcję liczącą perplexity dla dowolnego zbioru i policzyć perplexity dla zbioru testowego"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"pan_tadeusz_path_test= '/home/kuba/Syncthing/przedmioty/2020-02/ISI/zajecia9_ngramowy_model_jDDezykowy/pan-tadeusz-test.txt'"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"corpora_test = open(pan_tadeusz_path_test).read()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"corpora_test_tokenized = list(tokenize(corpora_test,lowercase = True))"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"test_ids = get_token_id(corpora_test_tokenized)"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"test_ids = torch.tensor(get_samples(test_ids), dtype = torch.long, device = device)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[15004, 15004, 15004, 15002],\n",
" [15004, 15004, 15002, 5873],\n",
" [15004, 15002, 5873, 15001],\n",
" ...,\n",
" [14552, 15001, 15001, 3116],\n",
" [15001, 15001, 3116, 15001],\n",
" [15001, 3116, 15001, 15001]], device='cuda:0')"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_ids"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"def get_ppl(dataset_ids):\n",
" lm.eval()\n",
"\n",
" batches = 0\n",
" loss_sum =0\n",
" acc_score = 0\n",
"\n",
" for i in tqdm(range(0, len(dataset_ids)-BATCH_SIZE+1, BATCH_SIZE)):\n",
" X = dataset_ids[i:i+BATCH_SIZE,:3]\n",
" Y = dataset_ids[i:i+BATCH_SIZE,3]\n",
" predictions = lm(X)\n",
" \n",
" # equally distributted\n",
" # predictions = torch.zeros_like(predictions)\n",
" \n",
" loss = criterion(predictions,Y)\n",
"\n",
" loss_sum += loss.item()\n",
" batches += 1\n",
"\n",
" if i % 1000 == 0:\n",
" loss_current = loss_sum / batches\n",
" print(f'updates: {i // BATCH_SIZE}/{len(train_ids) / BATCH_SIZE}', end ='\\t')\n",
" print('loss: ', round(loss_current,5) , end = '\\t')\n",
" print(np.exp(loss_current))\n",
" \n",
" #print(torch.argmax(torch.softmax(predictions,1),1))\n",
" return np.exp(loss_current)"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([[15004, 15004, 15004, 15002],\n",
" [15004, 15004, 15002, 5873],\n",
" [15004, 15002, 5873, 15001],\n",
" ...,\n",
" [14552, 15001, 15001, 3116],\n",
" [15001, 15001, 3116, 15001],\n",
" [15001, 3116, 15001, 15001]], device='cuda:0')"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_ids"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3c3e36b56499471fbb0084ae0819edc0",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=223.0), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"updates: 0/1140.44\tloss: 7.93691\t2798.69023798398\n",
"updates: 20/1140.44\tloss: 7.2917\t1468.0696196794395\n",
"updates: 40/1140.44\tloss: 7.02505\t1124.4520230095873\n",
"updates: 60/1140.44\tloss: 7.02153\t1120.498903823149\n",
"updates: 80/1140.44\tloss: 6.97483\t1069.377989926981\n",
"updates: 100/1140.44\tloss: 7.01626\t1114.614360205092\n",
"updates: 120/1140.44\tloss: 7.01344\t1111.4692099745646\n",
"updates: 140/1140.44\tloss: 7.02334\t1122.5331375146593\n",
"updates: 160/1140.44\tloss: 7.02724\t1126.9124589152598\n",
"updates: 180/1140.44\tloss: 7.047\t1149.409103844335\n",
"updates: 200/1140.44\tloss: 7.05975\t1164.1482767554144\n",
"updates: 220/1140.44\tloss: 7.06818\t1174.0069599198582\n",
"\n"
]
},
{
"data": {
"text/plain": [
"1174.0069599198582"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_ppl(test_ids)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## parametry modelu"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"[Parameter containing:\n",
" tensor([[ 1.2962, 2.2548, -0.5289, ..., 0.7910, 0.3451, 3.1669],\n",
" [ 0.6231, -1.0074, 0.4776, ..., 1.1894, 1.4892, 1.0638],\n",
" [ 0.6153, 0.2596, -1.9959, ..., -0.5816, 0.6102, 1.2497],\n",
" ...,\n",
" [ 3.6131, -1.3489, -0.8311, ..., 0.9109, 2.3637, -0.5194],\n",
" [ 0.1942, 0.9902, -0.4598, ..., 0.5558, 0.7963, -0.0882],\n",
" [ 0.1043, 1.7519, -0.5018, ..., -0.0734, 0.5121, -1.0132]],\n",
" device='cuda:0', requires_grad=True),\n",
" Parameter containing:\n",
" tensor([[ 0.0084, -0.0304, -0.0707, ..., 0.0578, -0.0801, -0.0080],\n",
" [-0.1369, -0.1782, 0.2099, ..., -0.0111, -0.0396, -0.1987],\n",
" [-0.0470, -0.0926, 0.0525, ..., -0.0092, 0.0290, -0.0435],\n",
" ...,\n",
" [-0.0967, 0.0055, 0.0719, ..., -0.0688, 0.0067, -0.0878],\n",
" [-0.1023, -0.1981, 0.1966, ..., -0.0676, -0.0961, -0.1946],\n",
" [-0.1015, -0.1989, 0.1953, ..., -0.0674, -0.0961, -0.1947]],\n",
" device='cuda:0', requires_grad=True),\n",
" Parameter containing:\n",
" tensor([ 0.7597, -0.4984, -0.0507, ..., -0.2868, -1.0517, -1.0112],\n",
" device='cuda:0', requires_grad=True)]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(lm.parameters())"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([15005, 200])"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(lm.parameters())[0].shape"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([15005, 600])"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(lm.parameters())[1].shape"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([15005])"
]
},
"execution_count": 39,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(lm.parameters())[2].shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inferencja"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Gości innych nie widział oprócz spółleśników'"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'Gości innych nie widział oprócz spółleśników'"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"tokenized = list(tokenize('Gości innych nie widział oprócz spółleśników',lowercase = True))"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [],
"source": [
"tokenized = tokenized[-NGRAMS :-1 ]"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['nie', 'widział', 'oprócz']"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tokenized"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [],
"source": [
"ids = []\n",
"for word in tokenized:\n",
" if word in vocab_stoi:\n",
" ids.append(vocab_stoi[word])\n",
" else:\n",
" ids.append(vocab_stoi['<UNK>'])"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[5873, 13240, 6938]"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ids"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"NeuralLM(\n",
" (emb): Embedding(15005, 200)\n",
" (fc1): Linear(in_features=600, out_features=15005, bias=True)\n",
")"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lm.eval()"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"ids = torch.tensor(ids, dtype = torch.long, device = device)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"tensor([ 5873, 13240, 6938], device='cuda:0')"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ids"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"<ipython-input-49-cbeeab1035ab>:1: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
" preds= lm(torch.tensor(ids, dtype = torch.long))\n"
]
}
],
"source": [
"preds= lm(torch.tensor(ids, dtype = torch.long))"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"10861"
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.argmax(torch.softmax(preds,1),1).item()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.return_types.max(\n",
"values=tensor([0.2408], device='cuda:0', grad_fn=<MaxBackward0>),\n",
"indices=tensor([10861], device='cuda:0'))"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"torch.max(torch.softmax(preds,1),1)"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'sposobu'"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"vocab_itos[torch.argmax(torch.softmax(preds,1),1).item()]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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