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aitech-moj-2023/cw/14_Model_rekurencyjny_z_atencją.ipynb
Jakub Pokrywka 85d14a1c10 update
2022-07-05 11:24:56 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
"<div class=\"alert alert-block alert-info\">\n",
"<h1> Modelowanie Języka</h1>\n",
"<h2> 14. <i>Model rekurencyjny z atencją</i> [ćwiczenia]</h2> \n",
"<h3> Jakub Pokrywka (2022)</h3>\n",
"</div>\n",
"\n",
"![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"notebook na podstawie:\n",
"\n",
"# https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import unicode_literals, print_function, division\n",
"from io import open\n",
"import unicodedata\n",
"import string\n",
"import re\n",
"import random\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"from torch import optim\n",
"import torch.nn.functional as F\n",
"\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"SOS_token = 0\n",
"EOS_token = 1\n",
"\n",
"class Lang:\n",
" def __init__(self):\n",
" self.word2index = {}\n",
" self.word2count = {}\n",
" self.index2word = {0: \"SOS\", 1: \"EOS\"}\n",
" self.n_words = 2 # Count SOS and EOS\n",
"\n",
" def addSentence(self, sentence):\n",
" for word in sentence.split(' '):\n",
" self.addWord(word)\n",
"\n",
" def addWord(self, word):\n",
" if word not in self.word2index:\n",
" self.word2index[word] = self.n_words\n",
" self.word2count[word] = 1\n",
" self.index2word[self.n_words] = word\n",
" self.n_words += 1\n",
" else:\n",
" self.word2count[word] += 1"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"pairs = []\n",
"with open('data/eng-pol.txt') as f:\n",
" for line in f:\n",
" eng_line, pol_line = line.lower().rstrip().split('\\t')\n",
"\n",
" eng_line = re.sub(r\"([.!?])\", r\" \\1\", eng_line)\n",
" eng_line = re.sub(r\"[^a-zA-Z.!?]+\", r\" \", eng_line)\n",
"\n",
" pol_line = re.sub(r\"([.!?])\", r\" \\1\", pol_line)\n",
" pol_line = re.sub(r\"[^a-zA-Z.!?ąćęłńóśźżĄĆĘŁŃÓŚŹŻ]+\", r\" \", pol_line)\n",
"\n",
" pairs.append([eng_line, pol_line])\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['hi .', 'cześć .']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pairs[1]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"MAX_LENGTH = 10\n",
"eng_prefixes = (\n",
" \"i am \", \"i m \",\n",
" \"he is\", \"he s \",\n",
" \"she is\", \"she s \",\n",
" \"you are\", \"you re \",\n",
" \"we are\", \"we re \",\n",
" \"they are\", \"they re \"\n",
")\n",
"\n",
"pairs = [p for p in pairs if len(p[0].split(' ')) < MAX_LENGTH and len(p[1].split(' ')) < MAX_LENGTH]\n",
"pairs = [p for p in pairs if p[0].startswith(eng_prefixes)]\n",
"\n",
"eng_lang = Lang()\n",
"pol_lang = Lang()\n",
"\n",
"for pair in pairs:\n",
" eng_lang.addSentence(pair[0])\n",
" pol_lang.addSentence(pair[1])"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['i m ok .', 'ze mną wszystko w porządku .']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pairs[0]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['i m up .', 'wstałem .']"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pairs[1]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['i m tom .', 'jestem tom .']"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pairs[2]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1828"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eng_lang.n_words"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2883"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pol_lang.n_words"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"class EncoderRNN(nn.Module):\n",
" def __init__(self, input_size, embedding_size, hidden_size):\n",
" super(EncoderRNN, self).__init__()\n",
" self.embedding_size = 200\n",
" self.hidden_size = hidden_size\n",
"\n",
" self.embedding = nn.Embedding(input_size, self.embedding_size)\n",
" self.gru = nn.GRU(self.embedding_size, hidden_size)\n",
"\n",
" def forward(self, input, hidden):\n",
" embedded = self.embedding(input).view(1, 1, -1)\n",
" output = embedded\n",
" output, hidden = self.gru(output, hidden)\n",
" return output, hidden\n",
"\n",
" def initHidden(self):\n",
" return torch.zeros(1, 1, self.hidden_size, device=device)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"class DecoderRNN(nn.Module):\n",
" def __init__(self, embedding_size, hidden_size, output_size):\n",
" super(DecoderRNN, self).__init__()\n",
" self.embedding_size = embedding_size\n",
" self.hidden_size = hidden_size\n",
"\n",
" self.embedding = nn.Embedding(output_size, self.embedding_size)\n",
" self.gru = nn.GRU(self.embedding_size, hidden_size)\n",
" self.out = nn.Linear(hidden_size, output_size)\n",
" self.softmax = nn.LogSoftmax(dim=1)\n",
"\n",
" def forward(self, input, hidden):\n",
" output = self.embedding(input).view(1, 1, -1)\n",
" output = F.relu(output)\n",
" output, hidden = self.gru(output, hidden)\n",
" output = self.softmax(self.out(output[0]))\n",
" return output, hidden\n",
"\n",
" def initHidden(self):\n",
" return torch.zeros(1, 1, self.hidden_size, device=device)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"class AttnDecoderRNN(nn.Module):\n",
" def __init__(self, embedding_size, hidden_size, output_size, dropout_p=0.1, max_length=MAX_LENGTH):\n",
" super(AttnDecoderRNN, self).__init__()\n",
" self.embedding_size = embedding_size\n",
" self.hidden_size = hidden_size\n",
" self.output_size = output_size\n",
" self.dropout_p = dropout_p\n",
" self.max_length = max_length\n",
"\n",
" self.embedding = nn.Embedding(self.output_size, self.embedding_size)\n",
" self.attn = nn.Linear(self.hidden_size + self.embedding_size, self.max_length)\n",
" self.attn_combine = nn.Linear(self.hidden_size + self.embedding_size, self.embedding_size)\n",
" self.dropout = nn.Dropout(self.dropout_p)\n",
" self.gru = nn.GRU(self.embedding_size, self.hidden_size)\n",
" self.out = nn.Linear(self.hidden_size, self.output_size)\n",
"\n",
" def forward(self, input, hidden, encoder_outputs):\n",
" embedded = self.embedding(input).view(1, 1, -1)\n",
" embedded = self.dropout(embedded)\n",
"\n",
" attn_weights = F.softmax(\n",
" self.attn(torch.cat((embedded[0], hidden[0]), 1)), dim=1)\n",
" attn_applied = torch.bmm(attn_weights.unsqueeze(0),\n",
" encoder_outputs.unsqueeze(0))\n",
" import pdb; pdb.set_trace()\n",
"\n",
" output = torch.cat((embedded[0], attn_applied[0]), 1)\n",
" output = self.attn_combine(output).unsqueeze(0)\n",
"\n",
" output = F.relu(output)\n",
" output, hidden = self.gru(output, hidden)\n",
"\n",
" output = F.log_softmax(self.out(output[0]), dim=1)\n",
" return output, hidden, attn_weights\n",
"\n",
" def initHidden(self):\n",
" return torch.zeros(1, 1, self.hidden_size, device=device)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"def tensorFromSentence(sentence, lang):\n",
" indexes = [lang.word2index[word] for word in sentence.split(' ')]\n",
" indexes.append(EOS_token)\n",
" return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)\n"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"teacher_forcing_ratio = 0.5\n",
"\n",
"def train_one_batch(input_tensor, target_tensor, encoder, decoder, optimizer, criterion, max_length=MAX_LENGTH):\n",
" encoder_hidden = encoder.initHidden()\n",
"\n",
"\n",
" optimizer.zero_grad()\n",
"\n",
" input_length = input_tensor.size(0)\n",
" target_length = target_tensor.size(0)\n",
"\n",
" encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)\n",
"\n",
" loss = 0\n",
"\n",
" for ei in range(input_length):\n",
" encoder_output, encoder_hidden = encoder(input_tensor[ei], encoder_hidden)\n",
" encoder_outputs[ei] = encoder_output[0, 0]\n",
"\n",
" decoder_input = torch.tensor([[SOS_token]], device=device)\n",
"\n",
" decoder_hidden = encoder_hidden\n",
"\n",
" use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False\n",
"\n",
" if use_teacher_forcing:\n",
" for di in range(target_length):\n",
" decoder_output, decoder_hidden, decoder_attention = decoder(decoder_input, decoder_hidden, encoder_outputs)\n",
" loss += criterion(decoder_output, target_tensor[di])\n",
" decoder_input = target_tensor[di] # Teacher forcing\n",
"\n",
" else:\n",
" for di in range(target_length):\n",
" decoder_output, decoder_hidden, decoder_attention = decoder(decoder_input, decoder_hidden, encoder_outputs)\n",
" topv, topi = decoder_output.topk(1)\n",
" decoder_input = topi.squeeze().detach() # detach from history as input\n",
"\n",
" loss += criterion(decoder_output, target_tensor[di])\n",
" if decoder_input.item() == EOS_token:\n",
" break\n",
"\n",
" loss.backward()\n",
"\n",
" optimizer.step()\n",
"\n",
" return loss.item() / target_length"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"def trainIters(encoder, decoder, n_iters, print_every=1000, learning_rate=0.01):\n",
" print_loss_total = 0 # Reset every print_every\n",
" encoder.train()\n",
" decoder.train()\n",
"\n",
" optimizer = optim.SGD(list(encoder.parameters()) + list(decoder.parameters()), lr=learning_rate)\n",
" \n",
" training_pairs = [random.choice(pairs) for _ in range(n_iters)]\n",
" training_pairs = [(tensorFromSentence(p[0], eng_lang), tensorFromSentence(p[1], pol_lang)) for p in training_pairs]\n",
" \n",
" criterion = nn.NLLLoss()\n",
"\n",
" for i in range(1, n_iters + 1):\n",
" training_pair = training_pairs[i - 1]\n",
" input_tensor = training_pair[0]\n",
" target_tensor = training_pair[1]\n",
"\n",
" loss = train_one_batch(input_tensor,\n",
" target_tensor,\n",
" encoder,\n",
" decoder,\n",
" optimizer,\n",
"\n",
" criterion)\n",
" \n",
" print_loss_total += loss\n",
"\n",
" if i % print_every == 0:\n",
" print_loss_avg = print_loss_total / print_every\n",
" print_loss_total = 0\n",
" print(f'iter: {i}, loss: {print_loss_avg}')\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def evaluate(encoder, decoder, sentence, max_length=MAX_LENGTH):\n",
" encoder.eval()\n",
" decoder.eval()\n",
" with torch.no_grad():\n",
" input_tensor = tensorFromSentence(sentence, eng_lang)\n",
" input_length = input_tensor.size()[0]\n",
" encoder_hidden = encoder.initHidden()\n",
"\n",
" encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)\n",
"\n",
" for ei in range(input_length):\n",
" encoder_output, encoder_hidden = encoder(input_tensor[ei], encoder_hidden)\n",
" encoder_outputs[ei] += encoder_output[0, 0]\n",
"\n",
" decoder_input = torch.tensor([[SOS_token]], device=device)\n",
"\n",
" decoder_hidden = encoder_hidden\n",
"\n",
" decoded_words = []\n",
" decoder_attentions = torch.zeros(max_length, max_length)\n",
"\n",
" for di in range(max_length):\n",
" decoder_output, decoder_hidden, decoder_attention = decoder(\n",
" decoder_input, decoder_hidden, encoder_outputs)\n",
" decoder_attentions[di] = decoder_attention.data\n",
" topv, topi = decoder_output.data.topk(1)\n",
" if topi.item() == EOS_token:\n",
" decoded_words.append('<EOS>')\n",
" break\n",
" else:\n",
" decoded_words.append(pol_lang.index2word[topi.item()])\n",
"\n",
" decoder_input = topi.squeeze().detach()\n",
"\n",
" return decoded_words, decoder_attentions[:di + 1]"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def evaluateRandomly(encoder, decoder, n=10):\n",
" for i in range(n):\n",
" pair = random.choice(pairs)\n",
" print('>', pair[0])\n",
" print('=', pair[1])\n",
" output_words, attentions = evaluate(encoder, decoder, pair[0])\n",
" output_sentence = ' '.join(output_words)\n",
" print('<', output_sentence)\n",
" print('')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"embedding_size = 200\n",
"hidden_size = 256\n",
"encoder1 = EncoderRNN(eng_lang.n_words, embedding_size, hidden_size).to(device)\n",
"attn_decoder1 = AttnDecoderRNN(embedding_size, hidden_size, pol_lang.n_words, dropout_p=0.1).to(device)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"> \u001b[0;32m/tmp/ipykernel_41821/2519748186.py\u001b[0m(27)\u001b[0;36mforward\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m 25 \u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 26 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m---> 27 \u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membedded\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattn_applied\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 28 \u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattn_combine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 29 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n",
"ipdb> embedded\n",
"tensor([[[-0.7259, 0.0000, 2.2112, 1.1947, -0.1261, -1.0427, -1.4295,\n",
" 0.1567, -0.3949, -1.0815, 1.1206, 2.0630, 2.8148, -1.8538,\n",
" -1.5486, -0.4900, -0.0000, 0.0000, -1.5046, 2.0329, -0.5872,\n",
" 1.5764, -0.0000, 1.1447, -0.4200, -0.1560, 0.1723, 1.5950,\n",
" 1.2955, -0.5796, -0.0000, -0.8989, 0.4737, 1.7037, 0.8787,\n",
" -0.2064, 1.9589, 2.0400, -1.0883, 1.0515, 0.0540, 0.1436,\n",
" 1.2383, 0.4912, -1.7719, 1.6435, 1.5523, 2.3576, 0.0000,\n",
" 0.4063, -0.0821, -1.2872, 0.8372, -0.5638, 0.0706, 0.4151,\n",
" -0.0000, 1.1651, 1.7333, -0.1684, -0.0000, -0.8560, -0.0000,\n",
" 2.7717, -0.4485, -0.8488, 0.8165, 2.1787, -1.0720, -0.3146,\n",
" 1.5798, -0.6788, 0.0000, 0.5609, 0.7415, -0.5585, 2.0659,\n",
" 0.7054, 1.3791, -0.2697, -0.0458, 1.6028, -0.0304, -0.6326,\n",
" -1.3258, -0.8370, 0.6533, 2.2756, -0.5393, 0.4752, 0.4479,\n",
" -0.0186, -0.7785, -1.7858, 0.2345, 1.9794, -0.0314, -0.8594,\n",
" -0.0000, 0.0596, -2.6836, -1.9927, 0.2714, -1.4617, -0.8142,\n",
" -0.7790, 0.5029, -0.6001, -0.7932, 1.3418, 0.1305, -0.0000,\n",
" -1.2961, -2.7107, -2.3360, -0.7960, 0.5207, 1.6896, 0.9285,\n",
" 0.0000, 1.8187, -0.0000, 1.5908, 0.2745, -0.2589, 0.4066,\n",
" -0.0000, -1.3145, -0.5903, 0.3696, -1.9539, -1.9995, -0.8219,\n",
" 0.3937, -0.6068, 0.7947, 1.3940, 0.5513, 0.7498, 1.4578,\n",
" -0.0000, -0.5037, -0.6856, 0.7723, -0.6553, 1.0936, -0.2788,\n",
" -1.9658, 1.5950, 0.8480, 1.1166, 1.3168, -0.0000, 0.2597,\n",
" 1.0813, 0.1827, -1.6485, 0.5743, -0.4952, 0.7176, -0.4468,\n",
" -1.7915, -0.6303, 0.2046, 0.7791, 0.1586, 0.2322, -2.3935,\n",
" 1.3643, -1.2023, -1.6792, 0.5582, -2.0117, -0.6245, 2.4039,\n",
" 2.3736, 0.0559, 0.9173, 0.6446, -0.2068, -0.8805, -0.3070,\n",
" 0.7318, 1.9806, 1.9318, -1.1276, -0.1307, 0.0243, 0.8480,\n",
" 0.4865, -1.5352, 0.8082, 1.7595, -0.2168, 2.0735, -1.0444,\n",
" -0.0000, 1.0729, -0.2194, 0.5439]]], grad_fn=<MulBackward0>)\n",
"ipdb> embedded.shape\n",
"torch.Size([1, 1, 200])\n",
"ipdb> attn_weights\n",
"tensor([[0.0817, 0.1095, 0.1425, 0.1611, 0.0574, 0.0546, 0.0374, 0.0621, 0.0703,\n",
" 0.2234]], grad_fn=<SoftmaxBackward0>)\n",
"ipdb> attn_applied\n",
"tensor([[[ 0.0354, -0.0156, -0.0048, -0.0936, 0.0637, 0.1516, 0.1419,\n",
" 0.1106, 0.0511, 0.0235, -0.0622, 0.0725, 0.0709, -0.0624,\n",
" 0.1407, -0.0069, -0.1602, -0.1883, -0.1707, -0.1528, -0.0296,\n",
" -0.0500, 0.2115, 0.0705, -0.1385, -0.0487, -0.0165, -0.0128,\n",
" -0.0594, 0.0209, -0.1081, 0.0509, 0.0655, 0.1314, -0.0455,\n",
" -0.0049, -0.1527, -0.1900, -0.0019, 0.0295, -0.0308, 0.0886,\n",
" 0.1369, -0.1571, 0.0518, -0.0991, -0.0310, -0.1781, -0.0290,\n",
" 0.0558, 0.0585, -0.1045, -0.0027, -0.0476, -0.0377, -0.1026,\n",
" 0.0481, 0.0398, -0.0956, 0.0655, -0.1449, 0.0193, -0.0380,\n",
" 0.0401, 0.0491, -0.1925, 0.0669, 0.0774, 0.0604, 0.1187,\n",
" -0.0401, 0.1094, 0.0706, 0.0474, 0.0178, -0.0888, -0.0632,\n",
" 0.1180, -0.0257, -0.0180, -0.0807, 0.0867, -0.0428, -0.0982,\n",
" -0.0129, 0.1326, -0.0868, -0.0118, 0.0923, -0.0634, -0.1758,\n",
" -0.0835, -0.2328, 0.0578, 0.0184, 0.0602, -0.1132, -0.1089,\n",
" -0.1371, -0.0996, -0.0758, -0.1615, 0.0474, -0.0595, 0.1130,\n",
" -0.1329, 0.0068, -0.0485, -0.0376, 0.0170, 0.0743, 0.0284,\n",
" -0.1708, 0.0283, -0.0161, 0.1138, -0.0223, -0.0504, -0.0068,\n",
" 0.1297, 0.0962, 0.1806, -0.1773, -0.1658, 0.1612, 0.0569,\n",
" 0.0703, -0.0321, -0.1741, -0.0983, -0.0848, 0.0342, 0.1021,\n",
" -0.1319, 0.1122, -0.0467, 0.0927, -0.0528, -0.0696, 0.0227,\n",
" 0.0445, 0.0268, 0.1563, 0.0008, 0.0296, 0.0112, -0.0863,\n",
" -0.1705, -0.0137, -0.0336, -0.0533, 0.0015, -0.0134, -0.0530,\n",
" 0.0995, 0.0445, -0.1190, -0.1675, 0.1295, -0.1072, 0.0954,\n",
" 0.0559, 0.0572, 0.1595, 0.0054, -0.1020, 0.0309, -0.0821,\n",
" 0.0230, -0.1480, -0.0815, -0.0013, -0.0012, 0.1046, 0.0248,\n",
" 0.1121, 0.0055, 0.1006, -0.0891, -0.0237, -0.0231, -0.0891,\n",
" 0.0234, 0.0164, -0.0080, -0.0431, -0.0041, 0.2627, -0.2110,\n",
" 0.1026, -0.0049, 0.0077, -0.1126, 0.0161, 0.0039, 0.0700,\n",
" 0.0353, -0.0941, 0.0770, 0.1015, -0.1124, -0.1738, 0.0232,\n",
" 0.1839, -0.2329, 0.0488, 0.0791, 0.2002, 0.0389, -0.0985,\n",
" -0.0744, 0.1392, 0.0052, 0.1119, 0.0851, -0.1062, -0.0948,\n",
" 0.0718, 0.0308, 0.0136, 0.2036, -0.0510, 0.0615, 0.1164,\n",
" 0.0242, -0.0717, 0.0955, -0.0796, 0.0856, 0.0040, -0.1370,\n",
" -0.1614, 0.0605, -0.1396, -0.0286, 0.0295, 0.0515, -0.0880,\n",
" 0.0249, -0.2263, 0.0048, -0.0381, -0.0019, 0.0186, -0.0209,\n",
" -0.0929, -0.1371, 0.0052, -0.1237, -0.1090, -0.0606, 0.0524,\n",
" 0.0351, 0.0283, 0.0264, 0.0866]]], grad_fn=<BmmBackward0>)\n",
"ipdb> attn_applied.shape\n",
"torch.Size([1, 1, 256])\n",
"ipdb> attn_applied.shape\n",
"torch.Size([1, 1, 256])\n",
"ipdb> attn_weights.shape\n",
"torch.Size([1, 10])\n",
"ipdb> encoder_outputs.shape\n",
"torch.Size([10, 256])\n",
"ipdb> attn_applied.shape\n",
"torch.Size([1, 1, 256])\n",
"ipdb> attn_applied\n",
"tensor([[[ 0.0354, -0.0156, -0.0048, -0.0936, 0.0637, 0.1516, 0.1419,\n",
" 0.1106, 0.0511, 0.0235, -0.0622, 0.0725, 0.0709, -0.0624,\n",
" 0.1407, -0.0069, -0.1602, -0.1883, -0.1707, -0.1528, -0.0296,\n",
" -0.0500, 0.2115, 0.0705, -0.1385, -0.0487, -0.0165, -0.0128,\n",
" -0.0594, 0.0209, -0.1081, 0.0509, 0.0655, 0.1314, -0.0455,\n",
" -0.0049, -0.1527, -0.1900, -0.0019, 0.0295, -0.0308, 0.0886,\n",
" 0.1369, -0.1571, 0.0518, -0.0991, -0.0310, -0.1781, -0.0290,\n",
" 0.0558, 0.0585, -0.1045, -0.0027, -0.0476, -0.0377, -0.1026,\n",
" 0.0481, 0.0398, -0.0956, 0.0655, -0.1449, 0.0193, -0.0380,\n",
" 0.0401, 0.0491, -0.1925, 0.0669, 0.0774, 0.0604, 0.1187,\n",
" -0.0401, 0.1094, 0.0706, 0.0474, 0.0178, -0.0888, -0.0632,\n",
" 0.1180, -0.0257, -0.0180, -0.0807, 0.0867, -0.0428, -0.0982,\n",
" -0.0129, 0.1326, -0.0868, -0.0118, 0.0923, -0.0634, -0.1758,\n",
" -0.0835, -0.2328, 0.0578, 0.0184, 0.0602, -0.1132, -0.1089,\n",
" -0.1371, -0.0996, -0.0758, -0.1615, 0.0474, -0.0595, 0.1130,\n",
" -0.1329, 0.0068, -0.0485, -0.0376, 0.0170, 0.0743, 0.0284,\n",
" -0.1708, 0.0283, -0.0161, 0.1138, -0.0223, -0.0504, -0.0068,\n",
" 0.1297, 0.0962, 0.1806, -0.1773, -0.1658, 0.1612, 0.0569,\n",
" 0.0703, -0.0321, -0.1741, -0.0983, -0.0848, 0.0342, 0.1021,\n",
" -0.1319, 0.1122, -0.0467, 0.0927, -0.0528, -0.0696, 0.0227,\n",
" 0.0445, 0.0268, 0.1563, 0.0008, 0.0296, 0.0112, -0.0863,\n",
" -0.1705, -0.0137, -0.0336, -0.0533, 0.0015, -0.0134, -0.0530,\n",
" 0.0995, 0.0445, -0.1190, -0.1675, 0.1295, -0.1072, 0.0954,\n",
" 0.0559, 0.0572, 0.1595, 0.0054, -0.1020, 0.0309, -0.0821,\n",
" 0.0230, -0.1480, -0.0815, -0.0013, -0.0012, 0.1046, 0.0248,\n",
" 0.1121, 0.0055, 0.1006, -0.0891, -0.0237, -0.0231, -0.0891,\n",
" 0.0234, 0.0164, -0.0080, -0.0431, -0.0041, 0.2627, -0.2110,\n",
" 0.1026, -0.0049, 0.0077, -0.1126, 0.0161, 0.0039, 0.0700,\n",
" 0.0353, -0.0941, 0.0770, 0.1015, -0.1124, -0.1738, 0.0232,\n",
" 0.1839, -0.2329, 0.0488, 0.0791, 0.2002, 0.0389, -0.0985,\n",
" -0.0744, 0.1392, 0.0052, 0.1119, 0.0851, -0.1062, -0.0948,\n",
" 0.0718, 0.0308, 0.0136, 0.2036, -0.0510, 0.0615, 0.1164,\n",
" 0.0242, -0.0717, 0.0955, -0.0796, 0.0856, 0.0040, -0.1370,\n",
" -0.1614, 0.0605, -0.1396, -0.0286, 0.0295, 0.0515, -0.0880,\n",
" 0.0249, -0.2263, 0.0048, -0.0381, -0.0019, 0.0186, -0.0209,\n",
" -0.0929, -0.1371, 0.0052, -0.1237, -0.1090, -0.0606, 0.0524,\n",
" 0.0351, 0.0283, 0.0264, 0.0866]]], grad_fn=<BmmBackward0>)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ipdb> attn_weights.shape\n",
"torch.Size([1, 10])\n",
"ipdb> encoder_outputs.shape\n",
"torch.Size([10, 256])\n",
"ipdb> embedded.shape\n",
"torch.Size([1, 1, 200])\n",
"ipdb> attn_applied.shape\n",
"torch.Size([1, 1, 256])\n",
"ipdb> output = torch.cat((embedded[0], attn_applied[0]), 1)\n",
"ipdb> output.shape\n",
"torch.Size([1, 456])\n",
"ipdb> output = self.attn_combine(output).unsqueeze(0)\n",
"ipdb> output.shape\n",
"torch.Size([1, 1, 200])\n",
"ipdb> attn_weights\n",
"tensor([[0.0817, 0.1095, 0.1425, 0.1611, 0.0574, 0.0546, 0.0374, 0.0621, 0.0703,\n",
" 0.2234]], grad_fn=<SoftmaxBackward0>)\n",
"ipdb> attn_weights.shape\n",
"torch.Size([1, 10])\n",
"ipdb> attn_applied.shape\n",
"torch.Size([1, 1, 256])\n",
"ipdb> attn_applied.shape\n",
"torch.Size([1, 1, 256])\n",
"ipdb> attn_applied\n",
"tensor([[[ 0.0354, -0.0156, -0.0048, -0.0936, 0.0637, 0.1516, 0.1419,\n",
" 0.1106, 0.0511, 0.0235, -0.0622, 0.0725, 0.0709, -0.0624,\n",
" 0.1407, -0.0069, -0.1602, -0.1883, -0.1707, -0.1528, -0.0296,\n",
" -0.0500, 0.2115, 0.0705, -0.1385, -0.0487, -0.0165, -0.0128,\n",
" -0.0594, 0.0209, -0.1081, 0.0509, 0.0655, 0.1314, -0.0455,\n",
" -0.0049, -0.1527, -0.1900, -0.0019, 0.0295, -0.0308, 0.0886,\n",
" 0.1369, -0.1571, 0.0518, -0.0991, -0.0310, -0.1781, -0.0290,\n",
" 0.0558, 0.0585, -0.1045, -0.0027, -0.0476, -0.0377, -0.1026,\n",
" 0.0481, 0.0398, -0.0956, 0.0655, -0.1449, 0.0193, -0.0380,\n",
" 0.0401, 0.0491, -0.1925, 0.0669, 0.0774, 0.0604, 0.1187,\n",
" -0.0401, 0.1094, 0.0706, 0.0474, 0.0178, -0.0888, -0.0632,\n",
" 0.1180, -0.0257, -0.0180, -0.0807, 0.0867, -0.0428, -0.0982,\n",
" -0.0129, 0.1326, -0.0868, -0.0118, 0.0923, -0.0634, -0.1758,\n",
" -0.0835, -0.2328, 0.0578, 0.0184, 0.0602, -0.1132, -0.1089,\n",
" -0.1371, -0.0996, -0.0758, -0.1615, 0.0474, -0.0595, 0.1130,\n",
" -0.1329, 0.0068, -0.0485, -0.0376, 0.0170, 0.0743, 0.0284,\n",
" -0.1708, 0.0283, -0.0161, 0.1138, -0.0223, -0.0504, -0.0068,\n",
" 0.1297, 0.0962, 0.1806, -0.1773, -0.1658, 0.1612, 0.0569,\n",
" 0.0703, -0.0321, -0.1741, -0.0983, -0.0848, 0.0342, 0.1021,\n",
" -0.1319, 0.1122, -0.0467, 0.0927, -0.0528, -0.0696, 0.0227,\n",
" 0.0445, 0.0268, 0.1563, 0.0008, 0.0296, 0.0112, -0.0863,\n",
" -0.1705, -0.0137, -0.0336, -0.0533, 0.0015, -0.0134, -0.0530,\n",
" 0.0995, 0.0445, -0.1190, -0.1675, 0.1295, -0.1072, 0.0954,\n",
" 0.0559, 0.0572, 0.1595, 0.0054, -0.1020, 0.0309, -0.0821,\n",
" 0.0230, -0.1480, -0.0815, -0.0013, -0.0012, 0.1046, 0.0248,\n",
" 0.1121, 0.0055, 0.1006, -0.0891, -0.0237, -0.0231, -0.0891,\n",
" 0.0234, 0.0164, -0.0080, -0.0431, -0.0041, 0.2627, -0.2110,\n",
" 0.1026, -0.0049, 0.0077, -0.1126, 0.0161, 0.0039, 0.0700,\n",
" 0.0353, -0.0941, 0.0770, 0.1015, -0.1124, -0.1738, 0.0232,\n",
" 0.1839, -0.2329, 0.0488, 0.0791, 0.2002, 0.0389, -0.0985,\n",
" -0.0744, 0.1392, 0.0052, 0.1119, 0.0851, -0.1062, -0.0948,\n",
" 0.0718, 0.0308, 0.0136, 0.2036, -0.0510, 0.0615, 0.1164,\n",
" 0.0242, -0.0717, 0.0955, -0.0796, 0.0856, 0.0040, -0.1370,\n",
" -0.1614, 0.0605, -0.1396, -0.0286, 0.0295, 0.0515, -0.0880,\n",
" 0.0249, -0.2263, 0.0048, -0.0381, -0.0019, 0.0186, -0.0209,\n",
" -0.0929, -0.1371, 0.0052, -0.1237, -0.1090, -0.0606, 0.0524,\n",
" 0.0351, 0.0283, 0.0264, 0.0866]]], grad_fn=<BmmBackward0>)\n",
"ipdb> torch.cat((embedded[0], attn_applied[0]), 1)\n",
"tensor([[-7.2585e-01, 0.0000e+00, 2.2112e+00, 1.1947e+00, -1.2609e-01,\n",
" -1.0427e+00, -1.4295e+00, 1.5669e-01, -3.9488e-01, -1.0815e+00,\n",
" 1.1206e+00, 2.0630e+00, 2.8148e+00, -1.8538e+00, -1.5486e+00,\n",
" -4.8997e-01, -0.0000e+00, 0.0000e+00, -1.5046e+00, 2.0329e+00,\n",
" -5.8720e-01, 1.5764e+00, -0.0000e+00, 1.1447e+00, -4.2003e-01,\n",
" -1.5600e-01, 1.7233e-01, 1.5950e+00, 1.2955e+00, -5.7964e-01,\n",
" -0.0000e+00, -8.9891e-01, 4.7372e-01, 1.7037e+00, 8.7866e-01,\n",
" -2.0642e-01, 1.9589e+00, 2.0400e+00, -1.0883e+00, 1.0515e+00,\n",
" 5.3959e-02, 1.4358e-01, 1.2383e+00, 4.9123e-01, -1.7719e+00,\n",
" 1.6435e+00, 1.5523e+00, 2.3576e+00, 0.0000e+00, 4.0628e-01,\n",
" -8.2075e-02, -1.2872e+00, 8.3723e-01, -5.6378e-01, 7.0637e-02,\n",
" 4.1508e-01, -0.0000e+00, 1.1651e+00, 1.7333e+00, -1.6842e-01,\n",
" -0.0000e+00, -8.5601e-01, -0.0000e+00, 2.7717e+00, -4.4849e-01,\n",
" -8.4885e-01, 8.1650e-01, 2.1787e+00, -1.0720e+00, -3.1463e-01,\n",
" 1.5798e+00, -6.7880e-01, 0.0000e+00, 5.6090e-01, 7.4153e-01,\n",
" -5.5849e-01, 2.0659e+00, 7.0539e-01, 1.3791e+00, -2.6968e-01,\n",
" -4.5789e-02, 1.6028e+00, -3.0432e-02, -6.3259e-01, -1.3258e+00,\n",
" -8.3697e-01, 6.5333e-01, 2.2756e+00, -5.3934e-01, 4.7520e-01,\n",
" 4.4788e-01, -1.8612e-02, -7.7847e-01, -1.7858e+00, 2.3452e-01,\n",
" 1.9794e+00, -3.1421e-02, -8.5938e-01, -0.0000e+00, 5.9576e-02,\n",
" -2.6836e+00, -1.9927e+00, 2.7139e-01, -1.4617e+00, -8.1419e-01,\n",
" -7.7900e-01, 5.0293e-01, -6.0008e-01, -7.9323e-01, 1.3418e+00,\n",
" 1.3053e-01, -0.0000e+00, -1.2961e+00, -2.7107e+00, -2.3360e+00,\n",
" -7.9603e-01, 5.2071e-01, 1.6896e+00, 9.2845e-01, 0.0000e+00,\n",
" 1.8187e+00, -0.0000e+00, 1.5908e+00, 2.7451e-01, -2.5888e-01,\n",
" 4.0663e-01, -0.0000e+00, -1.3145e+00, -5.9031e-01, 3.6964e-01,\n",
" -1.9539e+00, -1.9995e+00, -8.2193e-01, 3.9374e-01, -6.0678e-01,\n",
" 7.9467e-01, 1.3940e+00, 5.5134e-01, 7.4983e-01, 1.4578e+00,\n",
" -0.0000e+00, -5.0368e-01, -6.8556e-01, 7.7229e-01, -6.5534e-01,\n",
" 1.0936e+00, -2.7885e-01, -1.9658e+00, 1.5950e+00, 8.4796e-01,\n",
" 1.1166e+00, 1.3168e+00, -0.0000e+00, 2.5968e-01, 1.0813e+00,\n",
" 1.8274e-01, -1.6485e+00, 5.7433e-01, -4.9516e-01, 7.1760e-01,\n",
" -4.4680e-01, -1.7915e+00, -6.3027e-01, 2.0462e-01, 7.7905e-01,\n",
" 1.5859e-01, 2.3222e-01, -2.3935e+00, 1.3643e+00, -1.2023e+00,\n",
" -1.6792e+00, 5.5823e-01, -2.0117e+00, -6.2452e-01, 2.4039e+00,\n",
" 2.3736e+00, 5.5896e-02, 9.1725e-01, 6.4464e-01, -2.0675e-01,\n",
" -8.8049e-01, -3.0703e-01, 7.3178e-01, 1.9806e+00, 1.9318e+00,\n",
" -1.1276e+00, -1.3072e-01, 2.4253e-02, 8.4797e-01, 4.8654e-01,\n",
" -1.5352e+00, 8.0822e-01, 1.7595e+00, -2.1682e-01, 2.0735e+00,\n",
" -1.0444e+00, -0.0000e+00, 1.0729e+00, -2.1940e-01, 5.4391e-01,\n",
" 3.5435e-02, -1.5585e-02, -4.8357e-03, -9.3600e-02, 6.3727e-02,\n",
" 1.5162e-01, 1.4191e-01, 1.1063e-01, 5.1059e-02, 2.3501e-02,\n",
" -6.2207e-02, 7.2538e-02, 7.0922e-02, -6.2352e-02, 1.4066e-01,\n",
" -6.8974e-03, -1.6019e-01, -1.8832e-01, -1.7067e-01, -1.5275e-01,\n",
" -2.9574e-02, -5.0036e-02, 2.1154e-01, 7.0534e-02, -1.3852e-01,\n",
" -4.8703e-02, -1.6496e-02, -1.2794e-02, -5.9357e-02, 2.0857e-02,\n",
" -1.0812e-01, 5.0935e-02, 6.5458e-02, 1.3136e-01, -4.5476e-02,\n",
" -4.8890e-03, -1.5270e-01, -1.9004e-01, -1.9268e-03, 2.9531e-02,\n",
" -3.0820e-02, 8.8608e-02, 1.3690e-01, -1.5715e-01, 5.1807e-02,\n",
" -9.9062e-02, -3.0984e-02, -1.7808e-01, -2.8995e-02, 5.5791e-02,\n",
" 5.8522e-02, -1.0453e-01, -2.7097e-03, -4.7650e-02, -3.7730e-02,\n",
" -1.0258e-01, 4.8142e-02, 3.9797e-02, -9.5571e-02, 6.5458e-02,\n",
" -1.4489e-01, 1.9339e-02, -3.8005e-02, 4.0136e-02, 4.9097e-02,\n",
" -1.9247e-01, 6.6852e-02, 7.7364e-02, 6.0379e-02, 1.1870e-01,\n",
" -4.0057e-02, 1.0945e-01, 7.0648e-02, 4.7377e-02, 1.7824e-02,\n",
" -8.8779e-02, -6.3218e-02, 1.1804e-01, -2.5733e-02, -1.7959e-02,\n",
" -8.0674e-02, 8.6741e-02, -4.2754e-02, -9.8244e-02, -1.2859e-02,\n",
" 1.3257e-01, -8.6784e-02, -1.1774e-02, 9.2331e-02, -6.3417e-02,\n",
" -1.7581e-01, -8.3526e-02, -2.3277e-01, 5.7765e-02, 1.8407e-02,\n",
" 6.0199e-02, -1.1321e-01, -1.0885e-01, -1.3705e-01, -9.9638e-02,\n",
" -7.5838e-02, -1.6146e-01, 4.7433e-02, -5.9514e-02, 1.1298e-01,\n",
" -1.3286e-01, 6.7797e-03, -4.8545e-02, -3.7572e-02, 1.7049e-02,\n",
" 7.4291e-02, 2.8442e-02, -1.7075e-01, 2.8328e-02, -1.6143e-02,\n",
" 1.1376e-01, -2.2335e-02, -5.0417e-02, -6.8320e-03, 1.2967e-01,\n",
" 9.6223e-02, 1.8056e-01, -1.7727e-01, -1.6582e-01, 1.6121e-01,\n",
" 5.6873e-02, 7.0338e-02, -3.2107e-02, -1.7414e-01, -9.8330e-02,\n",
" -8.4751e-02, 3.4170e-02, 1.0213e-01, -1.3191e-01, 1.1224e-01,\n",
" -4.6743e-02, 9.2736e-02, -5.2760e-02, -6.9552e-02, 2.2712e-02,\n",
" 4.4459e-02, 2.6758e-02, 1.5629e-01, 8.4847e-04, 2.9560e-02,\n",
" 1.1163e-02, -8.6294e-02, -1.7045e-01, -1.3690e-02, -3.3578e-02,\n",
" -5.3289e-02, 1.4815e-03, -1.3354e-02, -5.3049e-02, 9.9541e-02,\n",
" 4.4520e-02, -1.1904e-01, -1.6747e-01, 1.2955e-01, -1.0718e-01,\n",
" 9.5381e-02, 5.5950e-02, 5.7216e-02, 1.5949e-01, 5.4154e-03,\n",
" -1.0203e-01, 3.0928e-02, -8.2072e-02, 2.2982e-02, -1.4800e-01,\n",
" -8.1458e-02, -1.3399e-03, -1.2277e-03, 1.0457e-01, 2.4771e-02,\n",
" 1.1215e-01, 5.4644e-03, 1.0059e-01, -8.9117e-02, -2.3669e-02,\n",
" -2.3117e-02, -8.9104e-02, 2.3379e-02, 1.6435e-02, -8.0299e-03,\n",
" -4.3092e-02, -4.1300e-03, 2.6272e-01, -2.1100e-01, 1.0265e-01,\n",
" -4.9496e-03, 7.7325e-03, -1.1258e-01, 1.6118e-02, 3.8591e-03,\n",
" 6.9952e-02, 3.5275e-02, -9.4110e-02, 7.6992e-02, 1.0149e-01,\n",
" -1.1243e-01, -1.7381e-01, 2.3158e-02, 1.8389e-01, -2.3291e-01,\n",
" 4.8788e-02, 7.9070e-02, 2.0018e-01, 3.8932e-02, -9.8458e-02,\n",
" -7.4388e-02, 1.3917e-01, 5.1577e-03, 1.1188e-01, 8.5138e-02,\n",
" -1.0618e-01, -9.4835e-02, 7.1822e-02, 3.0813e-02, 1.3624e-02,\n",
" 2.0363e-01, -5.0962e-02, 6.1539e-02, 1.1643e-01, 2.4200e-02,\n",
" -7.1730e-02, 9.5475e-02, -7.9572e-02, 8.5584e-02, 3.9502e-03,\n",
" -1.3701e-01, -1.6142e-01, 6.0496e-02, -1.3962e-01, -2.8607e-02,\n",
" 2.9515e-02, 5.1506e-02, -8.7967e-02, 2.4942e-02, -2.2634e-01,\n",
" 4.7778e-03, -3.8064e-02, -1.9145e-03, 1.8559e-02, -2.0943e-02,\n",
" -9.2896e-02, -1.3714e-01, 5.1929e-03, -1.2374e-01, -1.0901e-01,\n",
" -6.0571e-02, 5.2448e-02, 3.5082e-02, 2.8269e-02, 2.6405e-02,\n",
" 8.6625e-02]], grad_fn=<CatBackward0>)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"ipdb> torch.cat((embedded[0], attn_applied[0]), 1).shape\n",
"torch.Size([1, 456])\n",
"ipdb> attnn_weights\n",
"*** NameError: name 'attnn_weights' is not defined\n",
"ipdb> attn_weights.shape\n",
"torch.Size([1, 10])\n",
"ipdb> attn_applied\n",
"tensor([[[ 0.0354, -0.0156, -0.0048, -0.0936, 0.0637, 0.1516, 0.1419,\n",
" 0.1106, 0.0511, 0.0235, -0.0622, 0.0725, 0.0709, -0.0624,\n",
" 0.1407, -0.0069, -0.1602, -0.1883, -0.1707, -0.1528, -0.0296,\n",
" -0.0500, 0.2115, 0.0705, -0.1385, -0.0487, -0.0165, -0.0128,\n",
" -0.0594, 0.0209, -0.1081, 0.0509, 0.0655, 0.1314, -0.0455,\n",
" -0.0049, -0.1527, -0.1900, -0.0019, 0.0295, -0.0308, 0.0886,\n",
" 0.1369, -0.1571, 0.0518, -0.0991, -0.0310, -0.1781, -0.0290,\n",
" 0.0558, 0.0585, -0.1045, -0.0027, -0.0476, -0.0377, -0.1026,\n",
" 0.0481, 0.0398, -0.0956, 0.0655, -0.1449, 0.0193, -0.0380,\n",
" 0.0401, 0.0491, -0.1925, 0.0669, 0.0774, 0.0604, 0.1187,\n",
" -0.0401, 0.1094, 0.0706, 0.0474, 0.0178, -0.0888, -0.0632,\n",
" 0.1180, -0.0257, -0.0180, -0.0807, 0.0867, -0.0428, -0.0982,\n",
" -0.0129, 0.1326, -0.0868, -0.0118, 0.0923, -0.0634, -0.1758,\n",
" -0.0835, -0.2328, 0.0578, 0.0184, 0.0602, -0.1132, -0.1089,\n",
" -0.1371, -0.0996, -0.0758, -0.1615, 0.0474, -0.0595, 0.1130,\n",
" -0.1329, 0.0068, -0.0485, -0.0376, 0.0170, 0.0743, 0.0284,\n",
" -0.1708, 0.0283, -0.0161, 0.1138, -0.0223, -0.0504, -0.0068,\n",
" 0.1297, 0.0962, 0.1806, -0.1773, -0.1658, 0.1612, 0.0569,\n",
" 0.0703, -0.0321, -0.1741, -0.0983, -0.0848, 0.0342, 0.1021,\n",
" -0.1319, 0.1122, -0.0467, 0.0927, -0.0528, -0.0696, 0.0227,\n",
" 0.0445, 0.0268, 0.1563, 0.0008, 0.0296, 0.0112, -0.0863,\n",
" -0.1705, -0.0137, -0.0336, -0.0533, 0.0015, -0.0134, -0.0530,\n",
" 0.0995, 0.0445, -0.1190, -0.1675, 0.1295, -0.1072, 0.0954,\n",
" 0.0559, 0.0572, 0.1595, 0.0054, -0.1020, 0.0309, -0.0821,\n",
" 0.0230, -0.1480, -0.0815, -0.0013, -0.0012, 0.1046, 0.0248,\n",
" 0.1121, 0.0055, 0.1006, -0.0891, -0.0237, -0.0231, -0.0891,\n",
" 0.0234, 0.0164, -0.0080, -0.0431, -0.0041, 0.2627, -0.2110,\n",
" 0.1026, -0.0049, 0.0077, -0.1126, 0.0161, 0.0039, 0.0700,\n",
" 0.0353, -0.0941, 0.0770, 0.1015, -0.1124, -0.1738, 0.0232,\n",
" 0.1839, -0.2329, 0.0488, 0.0791, 0.2002, 0.0389, -0.0985,\n",
" -0.0744, 0.1392, 0.0052, 0.1119, 0.0851, -0.1062, -0.0948,\n",
" 0.0718, 0.0308, 0.0136, 0.2036, -0.0510, 0.0615, 0.1164,\n",
" 0.0242, -0.0717, 0.0955, -0.0796, 0.0856, 0.0040, -0.1370,\n",
" -0.1614, 0.0605, -0.1396, -0.0286, 0.0295, 0.0515, -0.0880,\n",
" 0.0249, -0.2263, 0.0048, -0.0381, -0.0019, 0.0186, -0.0209,\n",
" -0.0929, -0.1371, 0.0052, -0.1237, -0.1090, -0.0606, 0.0524,\n",
" 0.0351, 0.0283, 0.0264, 0.0866]]], grad_fn=<BmmBackward0>)\n",
"ipdb> attn_applied.shape\n",
"torch.Size([1, 1, 256])\n",
"ipdb> torch.cat((embedded[0], attn_applied[0]), 1).shape\n",
"torch.Size([1, 456])\n",
"ipdb> self.attn_combine(output).unsqueeze(0).shape\n",
"*** RuntimeError: mat1 and mat2 shapes cannot be multiplied (1x200 and 456x200)\n",
"ipdb> output = self.attn_combine(output).unsqueeze(0)\n",
"*** RuntimeError: mat1 and mat2 shapes cannot be multiplied (1x200 and 456x200)\n",
"ipdb> output = torch.cat((embedded[0], attn_applied[0]), 1)\n",
"ipdb> output = torch.cat((embedded[0], attn_applied[0]), 1)\n",
"ipdb> c\n",
"> \u001b[0;32m/tmp/ipykernel_41821/2519748186.py\u001b[0m(27)\u001b[0;36mforward\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m 25 \u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m;\u001b[0m \u001b[0mpdb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 26 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m---> 27 \u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membedded\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattn_applied\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 28 \u001b[0;31m \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mattn_combine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munsqueeze\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\u001b[0;32m 29 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0m\n",
"ipdb> output = torch.cat((embedded[0], attn_applied[0]), 1)\n",
"ipdb> attn_weights.shape\n",
"torch.Size([1, 10])\n",
"ipdb> attn_applied.shape\n",
"torch.Size([1, 1, 256])\n",
"ipdb> output.shape\n",
"torch.Size([1, 456])\n",
"ipdb> self.attn_combine(output).unsqueeze(0).shape\n",
"torch.Size([1, 1, 200])\n"
]
}
],
"source": [
"trainIters(encoder1, attn_decoder1, 10_000, print_every=50)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"evaluateRandomly(encoder1, attn_decoder1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"## ZADANIE\n",
"\n",
"Gonito \"WMT2017 Czech-English machine translation challenge for news \"\n",
"\n",
"Proszę wytrenować najpierw model german -> english, a później dotrenować na czech-> english.\n",
"Można wziąć inicjalizować enkoder od nowa lub nie. Proszę w każdym razie użyć wytrenowanego dekodera."
]
}
],
"metadata": {
"author": "Jakub Pokrywka",
"email": "kubapok@wmi.amu.edu.pl",
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"lang": "pl",
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.3"
},
"subtitle": "0.Informacje na temat przedmiotu[ćwiczenia]",
"title": "Ekstrakcja informacji",
"year": "2021"
},
"nbformat": 4,
"nbformat_minor": 4
}
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