leo
/
automatic-sentence-completion
2
1
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
Browse Source

hack up a NN with nnTranformer model

dev_neuralnet
Leonard Starke 2 years ago
parent
b910acbb7a
commit
1dd6cd337d
1 changed files with 821 additions and 43 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Unified View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 864
      AutomaticSentenceCompletion.ipynb

864
AutomaticSentenceCompletion.ipynb
View File

@ -29,41 +29,10 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2,
"execution_count": 29,
"id": "e444b44c", "id": "e444b44c",
"metadata": {}, "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 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
"\u001b[?25hCollecting mygene\n",
" Downloading mygene-3.2.2-py2.py3-none-any.whl (5.4 kB)\n",
"Collecting biopython>=1.79\n",
" Downloading biopython-1.79-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.7 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.7/2.7 MB\u001b[0m \u001b[31m12.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: requests in /usr/lib/python3.10/site-packages (from Bio) (2.28.1)\n",
"Collecting tqdm\n",
" Downloading tqdm-4.64.1-py2.py3-none-any.whl (78 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.5/78.5 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0mta \u001b[36m0:00:01\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: numpy in /usr/lib/python3.10/site-packages (from 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": [ "source": [
"try:\n", "try:\n",
" from Bio import Entrez, Medline \n", " from Bio import Entrez, Medline \n",
@ -82,12 +51,12 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7,
"execution_count": 30,
"id": "adfb256a", "id": "adfb256a",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "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", " # Get articles from PubMed\n",
" Entrez.email =myEmail\n", " Entrez.email =myEmail\n",
" record =Entrez.read(Entrez.esearch(db=\"pubmed\", term=myQuery, retmax=maxPapers))\n", " record =Entrez.read(Entrez.esearch(db=\"pubmed\", term=myQuery, retmax=maxPapers))\n",
@ -107,7 +76,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9,
"execution_count": 31,
"id": "00481ec9", "id": "00481ec9",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -116,16 +85,37 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"\n", "\n",
"There are 1000 records for Cancer[tiab].\n"
"There are 1600 records for Cancer[tiab].\n"
] ]
} }
], ],
"source": [ "source": [
"myQuery =\"Cancer\"+\"[tiab]\" #query in title and abstract\n", "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" "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", "cell_type": "markdown",
"id": "b67747c6", "id": "b67747c6",
@ -136,14 +126,36 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 30,
"execution_count": 33,
"id": "dcf5c217", "id": "dcf5c217",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"r_abstracts = []\n", "r_abstracts = []\n",
"for r in records:\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", "cell_type": "code",
"execution_count": 11,
"execution_count": 35,
"id": "c3199444", "id": "c3199444",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -164,9 +176,775 @@
"try:\n", "try:\n",
" import torch\n", " import torch\n",
" from torch.utils.data import Dataset \n", " from torch.utils.data import Dataset \n",
" from torchtext.data import get_tokenizer\n",
"except:\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", "except:\n",
" !pip install pytorch\n",
" \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=['<unk>'])\n",
"vocab.set_default_index(vocab['<unk>'])\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)"
] ]
} }
], ],

Write Preview
Loading…
Cancel
Save