From a40eac30260873ae52f45535d72c65d322901f10 Mon Sep 17 00:00:00 2001
From: Jakub Pokrywka <jakubpokrywka@gmail.com>
Date: Sun, 29 May 2022 20:00:36 +0200
Subject: [PATCH] 11 final

---
 cw/11_Model_rekurencyjny_z_atencją.ipynb | 941 ++++++++++++++++------
 1 file changed, 709 insertions(+), 232 deletions(-)

diff --git a/cw/11_Model_rekurencyjny_z_atencją.ipynb b/cw/11_Model_rekurencyjny_z_atencją.ipynb
index 117638e..4b1a7e8 100644
--- a/cw/11_Model_rekurencyjny_z_atencją.ipynb
+++ b/cw/11_Model_rekurencyjny_z_atencją.ipynb
@@ -15,11 +15,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 1,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
+    "notebook na podstawie:\n",
+    "\n",
     "# https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html"
    ]
   },
@@ -102,7 +102,7 @@
     "        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.!?]+\", r\" \", pol_line)\n",
+    "        pol_line = re.sub(r\"[^a-zA-Z.!?ąćęłńóśźżĄĆĘŁŃÓŚŹŻ]+\", r\" \", pol_line)\n",
     "        \n",
     "#         eng_line = unicodeToAscii(eng_line)\n",
     "#         pol_line = unicodeToAscii(pol_line)\n",
@@ -120,7 +120,7 @@
     {
      "data": {
       "text/plain": [
-       "['hi .', 'cze .']"
+       "['hi .', 'cześć .']"
       ]
      },
      "execution_count": 6,
@@ -167,7 +167,7 @@
     {
      "data": {
       "text/plain": [
-       "['i m ok .', 'ze mn wszystko w porz dku .']"
+       "['i m ok .', 'ze mną wszystko w porządku .']"
       ]
      },
      "execution_count": 8,
@@ -187,7 +187,7 @@
     {
      "data": {
       "text/plain": [
-       "['i m up .', 'wsta em .']"
+       "['i m up .', 'wstałem .']"
       ]
      },
      "execution_count": 9,
@@ -479,235 +479,645 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
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+     ]
+    },
+    {
+     "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, pol_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, learning_rate)\u001b[0m\n\u001b[1;32m     14\u001b[0m input_tensor \u001b[38;5;241m=\u001b[39m training_pair[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m     15\u001b[0m target_tensor \u001b[38;5;241m=\u001b[39m training_pair[\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m---> 17\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_one_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_tensor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     18\u001b[0m \u001b[43m                       \u001b[49m\u001b[43mtarget_tensor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     19\u001b[0m \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\n\u001b[1;32m     21\u001b[0m \u001b[43m                       \u001b[49m\u001b[43mencoder_optimizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     22\u001b[0m \u001b[43m                       \u001b[49m\u001b[43mdecoder_optimizer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     23\u001b[0m \u001b[43m                       \u001b[49m\u001b[43mcriterion\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     25\u001b[0m print_loss_total \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m loss\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m%\u001b[39m print_every \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
+      "Input \u001b[0;32mIn [15]\u001b[0m, in \u001b[0;36mtrain_one_batch\u001b[0;34m(input_tensor, target_tensor, encoder, decoder, encoder_optimizer, decoder_optimizer, criterion, max_length)\u001b[0m\n\u001b[1;32m     39\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     40\u001b[0m             \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m---> 42\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     44\u001b[0m encoder_optimizer\u001b[38;5;241m.\u001b[39mstep()\n\u001b[1;32m     45\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: "
      ]
     }
    ],
@@ -721,13 +1131,80 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "> he s a very important person .\n",
+      "= on jest bardzo ważnym człowiekiem .\n",
+      "< on jest bardzo ważnym człowiekiem . <EOS>\n",
+      "\n",
+      "> i m beautiful .\n",
+      "= jestem piękny .\n",
+      "< jestem piękna . <EOS>\n",
+      "\n",
+      "> we re quite certain of that .\n",
+      "= jesteśmy tego całkiem pewni .\n",
+      "< jesteśmy tego całkiem pewni . <EOS>\n",
+      "\n",
+      "> we are all looking forward to seeing you .\n",
+      "= miło nam będzie ponownie się z panem spotkać .\n",
+      "< miło nam nam ponownie się z tobą . . <EOS>\n",
+      "\n",
+      "> i m inside .\n",
+      "= jestem w środku .\n",
+      "< jestem w środku . <EOS>\n",
+      "\n",
+      "> i m giving up smoking .\n",
+      "= rzucam palenie .\n",
+      "< rzuciłem palenie . <EOS>\n",
+      "\n",
+      "> we re not arguing .\n",
+      "= nie kłócimy się .\n",
+      "< nie wychodzimy . <EOS>\n",
+      "\n",
+      "> i m not prepared to do that yet .\n",
+      "= nie jestem jeszcze przygotowany żeby to zrobić .\n",
+      "< nie jestem jeszcze przygotowany żeby to zrobić . <EOS>\n",
+      "\n",
+      "> i m a free man .\n",
+      "= jestem wolnym człowiekiem .\n",
+      "< jestem wolnym człowiekiem . <EOS>\n",
+      "\n",
+      "> i m still on the clock .\n",
+      "= jeszcze jestem w pracy .\n",
+      "< wciąż jestem w domu . <EOS>\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "evaluateRandomly(encoder1, attn_decoder1)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['i m ok .', 'ze mną wszystko w porządku .']"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pairs[0]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,