From 1dd6cd337d7bd9839908e559df09f2cea50b89b3 Mon Sep 17 00:00:00 2001 From: Leonard Starke Date: Sun, 30 Oct 2022 12:44:58 +0100 Subject: [PATCH] hack up a NN with nnTranformer model --- AutomaticSentenceCompletion.ipynb | 864 ++++++++++++++++++++++++++++-- 1 file changed, 821 insertions(+), 43 deletions(-) diff --git a/AutomaticSentenceCompletion.ipynb b/AutomaticSentenceCompletion.ipynb index 7334bc3..6724ae3 100644 --- a/AutomaticSentenceCompletion.ipynb +++ b/AutomaticSentenceCompletion.ipynb @@ -29,41 +29,10 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 29, "id": "e444b44c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Defaulting to user installation because normal site-packages is not writeable\n", - "Collecting Bio\n", - " Downloading bio-1.4.0-py3-none-any.whl (270 kB)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m270.9/270.9 kB\u001b[0m \u001b[31m2.6 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biopython>=1.79->Bio) (1.23.3)\n", - "Collecting biothings-client>=0.2.6\n", - " Downloading biothings_client-0.2.6-py2.py3-none-any.whl (37 kB)\n", - "Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3.10/site-packages (from requests->Bio) (3.4)\n", - "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3.10/site-packages (from requests->Bio) (1.26.12)\n", - "Installing collected packages: tqdm, biopython, biothings-client, mygene, Bio\n", - "\u001b[33m WARNING: The script tqdm is installed in '/home/hein/.local/bin' which is not on PATH.\n", - " Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n", - "\u001b[0m\u001b[33m WARNING: The scripts bio and fasta_filter.py are installed in '/home/hein/.local/bin' which is not on PATH.\n", - " Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n", - "\u001b[0mSuccessfully installed Bio-1.4.0 biopython-1.79 biothings-client-0.2.6 mygene-3.2.2 tqdm-4.64.1\n" - ] - } - ], + "outputs": [], "source": [ "try:\n", " from Bio import Entrez, Medline \n", @@ -82,12 +51,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 30, "id": "adfb256a", "metadata": {}, "outputs": [], "source": [ - "def getPapers(myQuery, maxPapers, myEmail =\"xxxxx@xxxxxxxx.xx\"):\n", + "def getPapers(myQuery, maxPapers, myEmail =\"leonard.starke@mailbox.tu-dresden.de\"):\n", " # Get articles from PubMed\n", " Entrez.email =myEmail\n", " record =Entrez.read(Entrez.esearch(db=\"pubmed\", term=myQuery, retmax=maxPapers))\n", @@ -107,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 31, "id": "00481ec9", "metadata": {}, "outputs": [ @@ -116,16 +85,37 @@ "output_type": "stream", "text": [ "\n", - "There are 1000 records for Cancer[tiab].\n" + "There are 1600 records for Cancer[tiab].\n" ] } ], "source": [ "myQuery =\"Cancer\"+\"[tiab]\" #query in title and abstract\n", - "maxPapers = 1000 # thinkabout outsourcing params to seperate section\n", + "maxPapers = 1600 # thinkabout outsourcing params to seperate section\n", "records = getPapers(myQuery, maxPapers)\n" ] }, + { + "cell_type": "code", + "execution_count": 32, + "id": "56cf72de", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1600" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(records)" + ] + }, { "cell_type": "markdown", "id": "b67747c6", @@ -136,14 +126,36 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 33, "id": "dcf5c217", "metadata": {}, "outputs": [], "source": [ "r_abstracts = []\n", "for r in records:\n", - " r_abstracts.append(r)" + " if not (r.get('AB') is None):\n", + " r_abstracts.append(r['AB'])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "eb1fb38b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1532" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(r_abstracts)" ] }, { @@ -156,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 35, "id": "c3199444", "metadata": {}, "outputs": [], @@ -164,9 +176,775 @@ "try:\n", " import torch\n", " from torch.utils.data import Dataset \n", + " from torchtext.data import get_tokenizer\n", "except:\n", - " !pip install pytorch\n", - " \n" + " !pip --default-timeout=1000 install torch\n", + " !pip --default-timeout=1000 install torchtext\n", + " import torch\n", + " from torch.utils.data import Dataset \n", + " from torchtext.data import get_tokenizer" + ] + }, + { + "cell_type": "markdown", + "id": "5b4007e8", + "metadata": {}, + "source": [ + "### Import numpy" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "daca9db6", + "metadata": {}, + "outputs": [], + "source": [ + "try:\n", + " import numpy as np\n", + "except:\n", + " !pip install numpy\n", + " import numpy as np\n" + ] + }, + { + "cell_type": "markdown", + "id": "4df1e449", + "metadata": {}, + "source": [ + "### define token iterators" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "8a128d3c", + "metadata": {}, + "outputs": [], + "source": [ + "def train_abstract_iter():\n", + " for abstract in r_abstracts[:1000]:\n", + " yield abstract" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "97e89986", + "metadata": {}, + "outputs": [], + "source": [ + "def val_abstract_iter():\n", + " for abstract in r_abstracts[1001:1300]:\n", + " yield abstract" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "0d6e89c4", + "metadata": {}, + "outputs": [], + "source": [ + "def test_abstract_iter():\n", + " for abstract in r_abstracts[1301:1542]:\n", + " yield abstract" + ] + }, + { + "cell_type": "markdown", + "id": "e5e9c5a2", + "metadata": {}, + "source": [ + "### define Tokenize function" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "0bdbc40a", + "metadata": {}, + "outputs": [], + "source": [ + "tokenizer = get_tokenizer(\"basic_english\")\n", + "def tokenize_abstract_iter():\n", + " for abstract in r_abstracts:\n", + " yield tokenizer(abstract)" + ] + }, + { + "cell_type": "markdown", + "id": "37da40bb", + "metadata": {}, + "source": [ + "### Map every world to a id to store inside torch tensor" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "a438ab1f", + "metadata": {}, + "outputs": [], + "source": [ + "from torchtext.vocab import build_vocab_from_iterator\n", + "token_generator = tokenize_abstract_iter()\n", + "vocab = build_vocab_from_iterator(token_generator, specials=[''])\n", + "vocab.set_default_index(vocab[''])\n" + ] + }, + { + "cell_type": "markdown", + "id": "221bdc48", + "metadata": {}, + "source": [ + "### now convert to tensor\n" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "0e5bc361", + "metadata": {}, + "outputs": [], + "source": [ + "def data_process(tokens_iter):\n", + " \"\"\"Converts raw text into a flat Tensor.\"\"\"\n", + " data = [torch.tensor(vocab(tokenizer(item)), dtype=torch.long) for item in tokens_iter]\n", + " return torch.cat(tuple(filter(lambda t: t.numel() > 0, data)))" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "dfd7400d", + "metadata": {}, + "outputs": [], + "source": [ + "train_generator = train_abstract_iter()\n", + "val_generator = val_abstract_iter()\n", + "test_generator = test_abstract_iter()\n", + "train_data = data_process(train_generator)\n", + "val_data = data_process(val_generator)\n", + "test_data = data_process(test_generator)" + ] + }, + { + "cell_type": "markdown", + "id": "c49a2734", + "metadata": {}, + "source": [ + "### check gpu" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "c155ee31", + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "79b2d248", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "device(type='cuda')" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "device" + ] + }, + { + "cell_type": "markdown", + "id": "2150ba71", + "metadata": {}, + "source": [ + "### define model" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "a33d722f", + "metadata": {}, + "outputs": [], + "source": [ + "import math\n", + "from typing import Tuple\n", + "\n", + "import torch\n", + "from torch import nn, Tensor\n", + "import torch.nn.functional as F\n", + "from torch.nn import TransformerEncoder, TransformerEncoderLayer\n", + "from torch.utils.data import dataset\n", + "\n", + "class TransformerModel(nn.Module):\n", + "\n", + " def __init__(self, ntoken: int, d_model: int, nhead: int, d_hid: int,\n", + " nlayers: int, dropout: float = 0.5):\n", + " super().__init__()\n", + " self.model_type = 'Transformer'\n", + " self.pos_encoder = PositionalEncoding(d_model, dropout)\n", + " encoder_layers = TransformerEncoderLayer(d_model, nhead, d_hid, dropout)\n", + " self.transformer_encoder = TransformerEncoder(encoder_layers, nlayers)\n", + " self.encoder = nn.Embedding(ntoken, d_model)\n", + " self.d_model = d_model\n", + " self.decoder = nn.Linear(d_model, ntoken)\n", + "\n", + " self.init_weights()\n", + "\n", + " def init_weights(self) -> None:\n", + " initrange = 0.1\n", + " self.encoder.weight.data.uniform_(-initrange, initrange)\n", + " self.decoder.bias.data.zero_()\n", + " self.decoder.weight.data.uniform_(-initrange, initrange)\n", + "\n", + " def forward(self, src: Tensor, src_mask: Tensor) -> Tensor:\n", + " \"\"\"\n", + " Args:\n", + " src: Tensor, shape [seq_len, batch_size]\n", + " src_mask: Tensor, shape [seq_len, seq_len]\n", + "\n", + " Returns:\n", + " output Tensor of shape [seq_len, batch_size, ntoken]\n", + " \"\"\"\n", + " src = self.encoder(src) * math.sqrt(self.d_model)\n", + " src = self.pos_encoder(src)\n", + " output = self.transformer_encoder(src, src_mask)\n", + " output = self.decoder(output)\n", + " return output\n", + "\n", + "\n", + "def generate_square_subsequent_mask(sz: int) -> Tensor:\n", + " \"\"\"Generates an upper-triangular matrix of -inf, with zeros on diag.\"\"\"\n", + " return torch.triu(torch.ones(sz, sz) * float('-inf'), diagonal=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da8fb12b", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "c2f6d33b", + "metadata": {}, + "outputs": [], + "source": [ + "class PositionalEncoding(nn.Module):\n", + "\n", + " def __init__(self, d_model: int, dropout: float = 0.1, max_len: int = 5000):\n", + " super().__init__()\n", + " self.dropout = nn.Dropout(p=dropout)\n", + "\n", + " position = torch.arange(max_len).unsqueeze(1)\n", + " div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))\n", + " pe = torch.zeros(max_len, 1, d_model)\n", + " pe[:, 0, 0::2] = torch.sin(position * div_term)\n", + " pe[:, 0, 1::2] = torch.cos(position * div_term)\n", + " self.register_buffer('pe', pe)\n", + "\n", + " def forward(self, x: Tensor) -> Tensor:\n", + " \"\"\"\n", + " Args:\n", + " x: Tensor, shape [seq_len, batch_size, embedding_dim]\n", + " \"\"\"\n", + " x = x + self.pe[:x.size(0)]\n", + " return self.dropout(x)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "9e184841", + "metadata": {}, + "outputs": [], + "source": [ + "def batchify(data: Tensor, bsz: int) -> Tensor:\n", + " \"\"\"Divides the data into bsz separate sequences, removing extra elements\n", + " that wouldn't cleanly fit.\n", + "\n", + " Args:\n", + " data: Tensor, shape [N]\n", + " bsz: int, batch size\n", + "\n", + " Returns:\n", + " Tensor of shape [N // bsz, bsz]\n", + " \"\"\"\n", + " seq_len = data.size(0) // bsz\n", + " data = data[:seq_len * bsz]\n", + " data = data.view(bsz, seq_len).t().contiguous()\n", + " return data.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "a4def1ac", + "metadata": {}, + "outputs": [], + "source": [ + "batch_size = 20\n", + "eval_batch_size = 10\n", + "train_data = batchify(train_data, batch_size) # shape [seq_len, batch_size]\n", + "val_data = batchify(val_data, eval_batch_size)\n", + "test_data = batchify(test_data, eval_batch_size)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "4ab5b8fd", + "metadata": {}, + "outputs": [], + "source": [ + "bptt = 35\n", + "def get_batch(source: Tensor, i: int) -> Tuple[Tensor, Tensor]:\n", + " \"\"\"\n", + " Args:\n", + " source: Tensor, shape [full_seq_len, batch_size]\n", + " i: int\n", + "\n", + " Returns:\n", + " tuple (data, target), where data has shape [seq_len, batch_size] and\n", + " target has shape [seq_len * batch_size]\n", + " \"\"\"\n", + " seq_len = min(bptt, len(source) - 1 - i)\n", + " data = source[i:i+seq_len]\n", + " target = source[i+1:i+1+seq_len].reshape(-1)\n", + " return data, target" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "c53764da", + "metadata": {}, + "outputs": [], + "source": [ + "ntokens = len(vocab) # size of vocabulary\n", + "emsize = 200 # embedding dimension\n", + "d_hid = 200 # dimension of the feedforward network model in nn.TransformerEncoder\n", + "nlayers = 2 # number of nn.TransformerEncoderLayer in nn.TransformerEncoder\n", + "nhead = 2 # number of heads in nn.MultiheadAttention\n", + "dropout = 0.2 # dropout probability\n", + "model = TransformerModel(ntokens, emsize, nhead, d_hid, nlayers, dropout).to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "50ab3fb6", + "metadata": {}, + "outputs": [], + "source": [ + "import copy\n", + "import time\n", + "\n", + "criterion = nn.CrossEntropyLoss()\n", + "lr = 5.0 # learning rate\n", + "optimizer = torch.optim.SGD(model.parameters(), lr=lr)\n", + "scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.95)\n", + "\n", + "def train(model: nn.Module) -> None:\n", + " model.train() # turn on train mode\n", + " total_loss = 0.\n", + " log_interval = 200\n", + " start_time = time.time()\n", + " src_mask = generate_square_subsequent_mask(bptt).to(device)\n", + "\n", + " num_batches = len(train_data) // bptt\n", + " for batch, i in enumerate(range(0, train_data.size(0) - 1, bptt)):\n", + " data, targets = get_batch(train_data, i)\n", + " seq_len = data.size(0)\n", + " if seq_len != bptt: # only on last batch\n", + " src_mask = src_mask[:seq_len, :seq_len]\n", + " output = model(data, src_mask)\n", + " loss = criterion(output.view(-1, ntokens), targets)\n", + "\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)\n", + " optimizer.step()\n", + "\n", + " total_loss += loss.item()\n", + " if batch % log_interval == 0 and batch > 0:\n", + " lr = scheduler.get_last_lr()[0]\n", + " ms_per_batch = (time.time() - start_time) * 1000 / log_interval\n", + " cur_loss = total_loss / log_interval\n", + " ppl = math.exp(cur_loss)\n", + " print(f'| epoch {epoch:3d} | {batch:5d}/{num_batches:5d} batches | '\n", + " f'lr {lr:02.2f} | ms/batch {ms_per_batch:5.2f} | '\n", + " f'loss {cur_loss:5.2f} | ppl {ppl:8.2f}')\n", + " total_loss = 0\n", + " start_time = time.time()\n", + "\n", + "def evaluate(model: nn.Module, eval_data: Tensor) -> float:\n", + " model.eval() # turn on evaluation mode\n", + " total_loss = 0.\n", + " src_mask = generate_square_subsequent_mask(bptt).to(device)\n", + " with torch.no_grad():\n", + " for i in range(0, eval_data.size(0) - 1, bptt):\n", + " data, targets = get_batch(eval_data, i)\n", + " seq_len = data.size(0)\n", + " if seq_len != bptt:\n", + " src_mask = src_mask[:seq_len, :seq_len]\n", + " output = model(data, src_mask)\n", + " output_flat = output.view(-1, ntokens)\n", + " total_loss += seq_len * criterion(output_flat, targets).item()\n", + " return total_loss / (len(eval_data) - 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "09c4d4ce", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| epoch 1 | 200/ 383 batches | lr 5.00 | ms/batch 63.06 | loss 8.09 | ppl 3258.10\n", + "-----------------------------------------------------------------------------------------\n", + "| end of epoch 1 | time: 17.05s | valid loss 6.34 | valid ppl 566.15\n", + "-----------------------------------------------------------------------------------------\n", + "| epoch 2 | 200/ 383 batches | lr 4.75 | ms/batch 19.83 | loss 6.14 | ppl 463.13\n", + "-----------------------------------------------------------------------------------------\n", + "| end of epoch 2 | time: 8.38s | valid loss 6.01 | valid ppl 406.56\n", + "-----------------------------------------------------------------------------------------\n", + "| epoch 3 | 200/ 383 batches | lr 4.51 | ms/batch 19.83 | loss 5.61 | ppl 273.67\n", + "-----------------------------------------------------------------------------------------\n", + "| end of epoch 3 | time: 8.38s | valid loss 5.95 | valid ppl 383.10\n", + "-----------------------------------------------------------------------------------------\n", + "| epoch 4 | 200/ 383 batches | lr 4.29 | ms/batch 19.89 | loss 5.25 | ppl 190.90\n", + "-----------------------------------------------------------------------------------------\n", + "| end of epoch 4 | time: 8.40s | valid loss 5.96 | valid ppl 386.38\n", + "-----------------------------------------------------------------------------------------\n", + "| epoch 5 | 200/ 383 batches | lr 4.07 | ms/batch 19.88 | loss 4.96 | ppl 142.55\n", + "-----------------------------------------------------------------------------------------\n", + "| end of epoch 5 | time: 8.40s | valid loss 5.99 | valid ppl 398.76\n", + "-----------------------------------------------------------------------------------------\n", + "| epoch 6 | 200/ 383 batches | lr 3.87 | ms/batch 19.89 | loss 4.71 | ppl 111.09\n", + "-----------------------------------------------------------------------------------------\n", + "| end of epoch 6 | time: 8.40s | valid loss 6.04 | valid ppl 421.64\n", + "-----------------------------------------------------------------------------------------\n", + "| epoch 7 | 200/ 383 batches | lr 3.68 | ms/batch 19.89 | loss 4.49 | ppl 89.44\n", + "-----------------------------------------------------------------------------------------\n", + "| end of epoch 7 | time: 8.40s | valid loss 6.11 | valid ppl 452.51\n", + "-----------------------------------------------------------------------------------------\n", + "| epoch 8 | 200/ 383 batches | lr 3.49 | ms/batch 19.92 | loss 4.30 | ppl 73.72\n", + "-----------------------------------------------------------------------------------------\n", + "| end of epoch 8 | time: 8.42s | valid loss 6.17 | valid ppl 479.04\n", + "-----------------------------------------------------------------------------------------\n", + "| epoch 9 | 200/ 383 batches | lr 3.32 | ms/batch 19.93 | loss 4.13 | ppl 62.43\n", + "-----------------------------------------------------------------------------------------\n", + "| end of epoch 9 | time: 8.42s | valid loss 6.26 | valid ppl 522.27\n", + "-----------------------------------------------------------------------------------------\n", + "| epoch 10 | 200/ 383 batches | lr 3.15 | ms/batch 19.95 | loss 3.99 | ppl 53.96\n", + "-----------------------------------------------------------------------------------------\n", + "| end of epoch 10 | time: 8.43s | valid loss 6.31 | valid ppl 548.35\n", + "-----------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "best_val_loss = float('inf')\n", + "epochs = 10\n", + "best_model = None\n", + "\n", + "for epoch in range(1, epochs + 1):\n", + " epoch_start_time = time.time()\n", + " train(model)\n", + " val_loss = evaluate(model, val_data)\n", + " val_ppl = math.exp(val_loss)\n", + " elapsed = time.time() - epoch_start_time\n", + " print('-' * 89)\n", + " print(f'| end of epoch {epoch:3d} | time: {elapsed:5.2f}s | '\n", + " f'valid loss {val_loss:5.2f} | valid ppl {val_ppl:8.2f}')\n", + " print('-' * 89)\n", + "\n", + " if val_loss < best_val_loss:\n", + " best_val_loss = val_loss\n", + " best_model = copy.deepcopy(model)\n", + "\n", + " scheduler.step()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "12fdd0aa", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "=========================================================================================\n", + "| End of training | test loss 5.80 | test ppl 329.59\n", + "=========================================================================================\n" + ] + } + ], + "source": [ + "test_loss = evaluate(best_model, test_data)\n", + "test_ppl = math.exp(test_loss)\n", + "print('=' * 89)\n", + "print(f'| End of training | test loss {test_loss:5.2f} | '\n", + " f'test ppl {test_ppl:8.2f}')\n", + "print('=' * 89)" + ] + }, + { + "cell_type": "markdown", + "id": "e685d3e1", + "metadata": {}, + "source": [ + "### define input batch " + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "id": "cfb30fe0", + "metadata": {}, + "outputs": [], + "source": [ + "input_batch = [\n", + " \"The brain is\",\n", + " \"The lung is\"\n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 275, + "id": "305853e8", + "metadata": {}, + "outputs": [], + "source": [ + "bptt = 3\n", + "src_mask = generate_square_subsequent_mask(bptt).to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 276, + "id": "afe585d6", + "metadata": {}, + "outputs": [], + "source": [ + "def predict_abstract_iter():\n", + " for batch in input_batch:\n", + " yield tokenizer(batch)\n", + "predict_generator = predict_abstract_iter()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f7ac6188", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 278, + "id": "0788b045", + "metadata": {}, + "outputs": [], + "source": [ + "data = [torch.tensor(vocab.lookup_indices(item)) for item in predict_generator]" + ] + }, + { + "cell_type": "code", + "execution_count": 279, + "id": "8bfaa8bd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[tensor([ 3, 555, 16]), tensor([ 3, 76, 16])]" + ] + }, + "execution_count": 279, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 280, + "id": "dd0e7310", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "device(type='cuda')" + ] + }, + "execution_count": 280, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "device" + ] + }, + { + "cell_type": "code", + "execution_count": 281, + "id": "1728f0fd", + "metadata": {}, + "outputs": [], + "source": [ + "for d in data:\n", + " d.to(device)" + ] + }, + { + "cell_type": "code", + "execution_count": 282, + "id": "49d27864", + "metadata": {}, + "outputs": [], + "source": [ + "best_model.eval()\n", + "for batch in data:\n", + " output = best_model(batch.to(device), src_mask)" + ] + }, + { + "cell_type": "code", + "execution_count": 283, + "id": "a3404169", + "metadata": {}, + "outputs": [], + "source": [ + "result_np = []\n", + "pred_np = output.cpu().detach().numpy()\n", + "for el in pred_np:\n", + " result_np.append(el)" + ] + }, + { + "cell_type": "code", + "execution_count": 284, + "id": "c7064c0c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([[-0.5258564 , 4.6108465 , 4.8358154 , ..., -0.46871045,\n", + " -0.04386039, -0.13068362],\n", + " [-0.35341978, 8.821181 , 9.57295 , ..., -1.2820313 ,\n", + " -0.989242 , -0.15542248],\n", + " [-0.39717233, 5.7111125 , 6.4295497 , ..., -0.27339834,\n", + " -1.5333815 , 0.16042188]], dtype=float32),\n", + " array([[-0.5291481 , 4.6452312 , 4.7958803 , ..., -0.4642661 ,\n", + " -0.04427804, -0.12225106],\n", + " [-0.35347146, 8.824585 , 9.5098095 , ..., -1.2693769 ,\n", + " -0.97772634, -0.13521233],\n", + " [-0.39733842, 5.693817 , 6.368334 , ..., -0.26423275,\n", + " -1.527182 , 0.16518843]], dtype=float32),\n", + " array([[-0.53349733, 4.6777644 , 4.7953978 , ..., -0.4346264 ,\n", + " -0.03433151, -0.11583059],\n", + " [-0.3695631 , 8.82613 , 9.477964 , ..., -1.2404828 ,\n", + " -0.9594678 , -0.11550146],\n", + " [-0.41203997, 5.699034 , 6.341655 , ..., -0.24370295,\n", + " -1.5179048 , 0.16230991]], dtype=float32)]" + ] + }, + "execution_count": 284, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result_np" + ] + }, + { + "cell_type": "code", + "execution_count": 285, + "id": "679e2316", + "metadata": {}, + "outputs": [], + "source": [ + "def predict(input_line, n_predictions=1):\n", + " print('\\n> %s' % input_line)\n", + " with torch.no_grad():\n", + " output = best_model(input_line.to(device), src_mask)\n", + "\n", + " # Get top N categories\n", + " topv, topi = output.topk(n_predictions, 1, True)\n", + " #x, y = output.topk(n_predictions, 1, True)\n", + " #print(x.shape)\n", + " #print(topv.shape)\n", + " # print(topi.shape)\n", + " predictions = []\n", + " for i in range(n_predictions):\n", + " value = topv[0][i]\n", + " v1, v2 = value.topk(1)\n", + " predict_token_index = v2.cpu().detach().numpy()\n", + " print(vocab.lookup_token(predict_token_index))\n", + " #print(category_index)\n", + " #print('(%.2f) %s' % (value, all_categories[category_index]))\n", + " #predictions.append([value, all_categories[category_index]])" + ] + }, + { + "cell_type": "code", + "execution_count": 286, + "id": "03389137", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "> tensor([ 3, 555, 16])\n", + "tumors\n", + "\n", + "> tensor([ 3, 76, 16])\n", + "cancer\n" + ] + } + ], + "source": [ + "for d in data:\n", + " predict(d)" ] } ],