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Jakub Pokrywka 2022-05-29 19:05:03 +02:00
parent faa0ded62f
commit 195fc25de1
3 changed files with 46462 additions and 248 deletions
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498
cw/11_Model_rekurencyjny_z_atencją.ipynb
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@ -80,11 +80,11 @@
"metadata": {},
"outputs": [],
"source": [
"def unicodeToAscii(s):\n",
" return ''.join(\n",
" c for c in unicodedata.normalize('NFD', s)\n",
" if unicodedata.category(c) != 'Mn'\n",
" )"
"# def unicodeToAscii(s):\n",
"# return ''.join(\n",
"# c for c in unicodedata.normalize('NFD', s)\n",
"# if unicodedata.category(c) != 'Mn'\n",
"# )"
]
},
{
@ -94,20 +94,20 @@
"outputs": [],
"source": [
"pairs = []\n",
"with open('data/eng-fra.txt') as f:\n",
"with open('data/eng-pol.txt') as f:\n",
" for line in f:\n",
" eng_line, fra_line = line.lower().rstrip().split('\\t')\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",
" fra_line = re.sub(r\"([.!?])\", r\" \\1\", fra_line)\n",
" fra_line = re.sub(r\"[^a-zA-Z.!?]+\", r\" \", fra_line)\n",
" pol_line = re.sub(r\"([.!?])\", r\" \\1\", pol_line)\n",
" pol_line = re.sub(r\"[^a-zA-Z.!?]+\", r\" \", pol_line)\n",
" \n",
" eng_line = unicodeToAscii(eng_line)\n",
" fra_line = unicodeToAscii(fra_line)\n",
"# eng_line = unicodeToAscii(eng_line)\n",
"# pol_line = unicodeToAscii(pol_line)\n",
"\n",
" pairs.append([eng_line, fra_line])\n",
" pairs.append([eng_line, pol_line])\n",
"\n",
"\n"
]
@ -120,7 +120,7 @@
{
"data": {
"text/plain": [
"['run !', 'cours !']"
"['hi .', 'cze .']"
]
},
"execution_count": 6,
@ -152,11 +152,11 @@
"pairs = [p for p in pairs if p[0].startswith(eng_prefixes)]\n",
"\n",
"eng_lang = Lang()\n",
"fra_lang = Lang()\n",
"pol_lang = Lang()\n",
"\n",
"for pair in pairs:\n",
" eng_lang.addSentence(pair[0])\n",
" fra_lang.addSentence(pair[1])"
" pol_lang.addSentence(pair[1])"
]
},
{
@ -167,7 +167,7 @@
{
"data": {
"text/plain": [
"['i m .', 'j ai ans .']"
"['i m ok .', 'ze mn wszystko w porz dku .']"
]
},
"execution_count": 8,
@ -187,7 +187,7 @@
{
"data": {
"text/plain": [
"['i m ok .', 'je vais bien .']"
"['i m up .', 'wsta em .']"
]
},
"execution_count": 9,
@ -207,7 +207,7 @@
{
"data": {
"text/plain": [
"['i m ok .', ' a va .']"
"['i m tom .', 'jestem tom .']"
]
},
"execution_count": 10,
@ -347,8 +347,7 @@
" loss = 0\n",
"\n",
" for ei in range(input_length):\n",
" encoder_output, encoder_hidden = encoder(\n",
" input_tensor[ei], encoder_hidden)\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",
@ -358,18 +357,14 @@
" use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False\n",
"\n",
" if use_teacher_forcing:\n",
" # Teacher forcing: Feed the target as the next input\n",
" for di in range(target_length):\n",
" decoder_output, decoder_hidden, decoder_attention = decoder(\n",
" decoder_input, decoder_hidden, encoder_outputs)\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",
" # Without teacher forcing: use its own predictions as the next input\n",
" for di in range(target_length):\n",
" decoder_output, decoder_hidden, decoder_attention = decoder(\n",
" decoder_input, decoder_hidden, encoder_outputs)\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",
@ -398,7 +393,7 @@
" decoder_optimizer = optim.SGD(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], fra_lang)) for p in training_pairs]\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",
@ -410,7 +405,7 @@
" loss = train_one_batch(input_tensor,\n",
" target_tensor,\n",
" encoder,\n",
" encoder,\n",
" decoder,\n",
" encoder_optimizer,\n",
" decoder_optimizer,\n",
" criterion)\n",
@ -458,7 +453,7 @@
" decoded_words.append('<EOS>')\n",
" break\n",
" else:\n",
" decoded_words.append(fra_lang.index2word[topi.item()])\n",
" decoded_words.append(pol_lang.index2word[topi.item()])\n",
"\n",
" decoder_input = topi.squeeze().detach()\n",
"\n",
@ -484,244 +479,251 @@
},
{
"cell_type": "code",
"execution_count": 19,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Input \u001b[0;32mIn [19]\u001b[0m, in \u001b[0;36m<cell line: 5>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m encoder1 \u001b[38;5;241m=\u001b[39m EncoderRNN(eng_lang\u001b[38;5;241m.\u001b[39mn_words, hidden_size)\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[1;32m 3\u001b[0m attn_decoder1 \u001b[38;5;241m=\u001b[39m AttnDecoderRNN(hidden_size, fra_lang\u001b[38;5;241m.\u001b[39mn_words, dropout_p\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.1\u001b[39m)\u001b[38;5;241m.\u001b[39mto(device)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mtrainIters\u001b[49m\u001b[43m(\u001b[49m\u001b[43mencoder1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattn_decoder1\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m75000\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprint_every\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m)\u001b[49m\n",
"Input \u001b[0;32mIn [16]\u001b[0m, in \u001b[0;36mtrainIters\u001b[0;34m(encoder, decoder, n_iters, print_every, plot_every, learning_rate)\u001b[0m\n\u001b[1;32m 16\u001b[0m input_tensor \u001b[38;5;241m=\u001b[39m training_pair[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m 17\u001b[0m target_tensor \u001b[38;5;241m=\u001b[39m training_pair[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m---> 19\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_tensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget_tensor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 20\u001b[0m \u001b[43m \u001b[49m\u001b[43mdecoder\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoder_optimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdecoder_optimizer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 21\u001b[0m print_loss_total \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\n\u001b[1;32m 22\u001b[0m plot_loss_total \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\n",
"Input \u001b[0;32mIn [15]\u001b[0m, in \u001b[0;36mtrain\u001b[0;34m(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, max_length)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m decoder_input\u001b[38;5;241m.\u001b[39mitem() \u001b[38;5;241m==\u001b[39m EOS_token:\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m---> 48\u001b[0m \u001b[43mloss\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 50\u001b[0m encoder_optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m 51\u001b[0m decoder_optimizer\u001b[38;5;241m.\u001b[39mstep()\n",
"File \u001b[0;32m~/anaconda3/envs/zajeciaei/lib/python3.10/site-packages/torch/_tensor.py:363\u001b[0m, in \u001b[0;36mTensor.backward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m 354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m has_torch_function_unary(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m 355\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m handle_torch_function(\n\u001b[1;32m 356\u001b[0m Tensor\u001b[38;5;241m.\u001b[39mbackward,\n\u001b[1;32m 357\u001b[0m (\u001b[38;5;28mself\u001b[39m,),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 361\u001b[0m create_graph\u001b[38;5;241m=\u001b[39mcreate_graph,\n\u001b[1;32m 362\u001b[0m inputs\u001b[38;5;241m=\u001b[39minputs)\n\u001b[0;32m--> 363\u001b[0m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mautograd\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbackward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgradient\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/anaconda3/envs/zajeciaei/lib/python3.10/site-packages/torch/autograd/__init__.py:173\u001b[0m, in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m 168\u001b[0m retain_graph \u001b[38;5;241m=\u001b[39m create_graph\n\u001b[1;32m 170\u001b[0m \u001b[38;5;66;03m# The reason we repeat same the comment below is that\u001b[39;00m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;66;03m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[1;32m 172\u001b[0m \u001b[38;5;66;03m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[0;32m--> 173\u001b[0m \u001b[43mVariable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_execution_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_backward\u001b[49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;66;43;03m# Calls into the C++ engine to run the backward pass\u001b[39;49;00m\n\u001b[1;32m 174\u001b[0m \u001b[43m \u001b[49m\u001b[43mtensors\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgrad_tensors_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mretain_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcreate_graph\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 175\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_unreachable\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccumulate_grad\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
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]
}
],
"source": [
"hidden_size = 256\n",
"encoder1 = EncoderRNN(eng_lang.n_words, hidden_size).to(device)\n",
"attn_decoder1 = AttnDecoderRNN(hidden_size, fra_lang.n_words, dropout_p=0.1).to(device)\n",
"attn_decoder1 = AttnDecoderRNN(hidden_size, pol_lang.n_words, dropout_p=0.1).to(device)\n",
"\n",
"trainIters(encoder1, attn_decoder1, 75000, print_every=50)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"> you re sad .\n",
"= tu es triste .\n",
"< vous tes . . <EOS>\n",
"\n",
"> she is sewing a dress .\n",
"= elle coud une robe .\n",
"< elle est une une . . <EOS>\n",
"\n",
"> he is suffering from a headache .\n",
"= il souffre d un mal de t te .\n",
"< il est un un un un . <EOS>\n",
"\n",
"> i m glad to see you .\n",
"= je suis heureux de vous voir .\n",
"< je suis content de vous voir . <EOS>\n",
"\n",
"> you are only young once .\n",
"= on n est jeune qu une fois .\n",
"< vous tes trop plus une enfant . <EOS>\n",
"\n",
"> you re so sweet .\n",
"= vous tes si gentille !\n",
"< vous tes trop si . <EOS>\n",
"\n",
"> i m running out of closet space .\n",
"= je manque d espace dans mon placard .\n",
"< je suis un de de <EOS>\n",
"\n",
"> i m sort of an extrovert .\n",
"= je suis en quelque sorte extraverti .\n",
"< je suis un un . . <EOS>\n",
"\n",
"> i m out of practice .\n",
"= je manque de pratique .\n",
"< j ai ai pas de <EOS>\n",
"\n",
"> you re the last hope for humanity .\n",
"= tu es le dernier espoir de l humanit .\n",
"< vous tes le la la . . <EOS>\n",
"\n"
]
}
],
"outputs": [],
"source": [
"evaluateRandomly(encoder1, attn_decoder1)"
]

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Plik eng-pol.txt pochodzi z eng-pol.txt phttps://www.manythings.org/anki/o