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luminousmen/Toxic.ipynb

Last active June 25, 2020 20:32
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Toxic.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"\n",
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from fastai.text import *\n",
"from fastai import *"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data_path = Path('data')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"toxic_comments.csv\")\n",
"df2 = pd.read_csv(\"toxic_comments2.csv\")\n",
"df = pd.concat([df, df2]) # <--- final data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"df['toxic'] = df['toxic'] + df['severe_toxic']\n",
"df = df.drop(columns=[\"id\", 'severe_toxic'])"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"df.loc[df['toxic'] == 2] = 1"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"df = df.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"mask = ((df['toxic'] == 0) & (df['obscene'] == 0) & (df['threat'] == 0) & (df['insult'] == 0) & (df['identity_hate'] == 0))\n",
"df_sample = pd.concat([df[mask].sample(20000), df[~mask].sample(22000)])"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"valid_mask = np.random.rand(len(df_sample)) < 0.8\n",
"train_df = df_sample[valid_mask]\n",
"valid_df = df_sample[~valid_mask]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"train_df.to_csv(\"train_df\", index=False)\n",
"valid_df.to_csv(\"valid_df\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"train_df = pd.read_csv(\"train_df\")\n",
"valid_df = pd.read_csv(\"valid_df\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Text classificator"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [],
"text/plain": [
"<IPython.core.display.HTML object>"
]
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"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data_bunch = TextDataBunch.from_df(\n",
" data_path, \n",
" train_df=train_df, \n",
" valid_df=valid_df,\n",
" text_cols=['comment_text'], \n",
" label_cols=['toxic', 'obscene', 'threat', 'insult', 'identity_hate'],\n",
" bs=32\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>text</th>\n",
" <th>target</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>xxbos i xxup am xxup an xxup loser xxrep 4 = i xxup am xxup an xxup loser = = = = i xxup am xxup an xxup loser xxrep 4 = i xxup am xxup an xxup loser = = = = i xxup am xxup an xxup loser xxrep 4 = i xxup am xxup an xxup loser = = = = i xxup am xxup an xxup</td>\n",
" <td>toxic</td>\n",
" </tr>\n",
" <tr>\n",
" <td>xxbos xxup it xxup is xxup users xxup like xxup you xxup that xxup make xxup wikipedia a xxup shithole . xxup you're a xxup fucker xxup and xxup get a xxup life . i xxup could xxup vandalize xxup all i xxup want xxup and i xxup dare xxup you xxup to xxup earse xxup it xxup agoian . xxup change xxup your xxup name xxup to xxup fuckbot</td>\n",
" <td>toxic;obscene;threat;insult</td>\n",
" </tr>\n",
" <tr>\n",
" <td>xxbos xxup tommy2010 xxup is a xxup nigger xxup tommy2010 xxup is a xxup nigger xxup tommy2010 xxup is a xxup nigger xxup tommy2010 xxup is a xxup nigger xxup tommy2010 xxup is a xxup nigger xxup tommy2010 xxup is a xxup nigger xxup tommy2010 xxup is a xxup nigger xxup tommy2010 xxup is a xxup nigger xxup tommy2010 xxup is a xxup nigger xxup tommy2010 xxup is a xxup</td>\n",
" <td>toxic;insult;identity_hate</td>\n",
" </tr>\n",
" <tr>\n",
" <td>xxbos xxup fuck xxup you xxup all ! xxup fuck xxup you xxup all ! xxup fuck xxup you xxup all ! xxup fuck xxup you xxup all ! xxup fuck xxup you xxup all ! xxup fuck xxup you xxup all ! xxup fuck xxup you xxup all ! xxup fuck xxup you xxup all ! xxup fuck xxup you xxup all ! xxup fuck xxup you xxup all</td>\n",
" <td>toxic;obscene;insult</td>\n",
" </tr>\n",
" <tr>\n",
" <td>xxbos \" \\n \\n xxmaj after reviewing the deletion , i think the error is technical . i am on a xxmaj hughes xxmaj sat network that uses transparent proxies . xxmaj this was causing me to log in and out from page to page . xxmaj the original article which was not the correct article went through and posted when i switched to xxup ssl wiki server .</td>\n",
" <td></td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data_bunch.show_batch()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"learn = text_classifier_learner(data_bunch, AWD_LSTM, drop_mult=0.5)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <div>\n",
" <style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
" </style>\n",
" <progress value='0' class='' max='1', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 0.00% [0/1 00:00<00:00]\n",
" </div>\n",
" \n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table><p>\n",
"\n",
" <div>\n",
" <style>\n",
" /* Turns off some styling */\n",
" progress {\n",
" /* gets rid of default border in Firefox and Opera. */\n",
" border: none;\n",
" /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
" background-size: auto;\n",
" }\n",
" .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
" background: #F44336;\n",
" }\n",
" </style>\n",
" <progress value='97' class='' max='1047', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" 9.26% [97/1047 00:18<03:00 2.3478]\n",
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" "
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},
"metadata": {},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n",
"Min numerical gradient: 6.92E-02\n",
"Min loss divided by 10: 4.37E-02\n"
]
},
{
"data": {
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NaeUwTRAA7v4Q8FCffbf12b4buLvPvnXAKWHGJiJSyBLJNGWx4dvEJCIiRyg+zIe5iojIERrundQiInKEhvtMahEROUKZmdTDd6KciIgcIXVSi4jIAdJpzzQxqZNaRESyJVLB7UbVByEiItl6E8SwnUk9nLV39fDKll2s2bqbtds6GD+qnFnjqzl+Yg1Tayvzeg9ZESl+iaQSRNFIp50Xmnbw6Kqt/Pblraxr2bcqSFVplM5Eau92ZWmUuceM5uQpozl56hjG15SxozNBW1eCnV09lMeijK8pY3xNGXU1ZVSVllARi1JRGiUWNYLlRfbqSiRZs2U3r27dzZb2OC0de2jdnSCeTDF38mjeeGwtpx47hpryGFt27aGprYvN7d2cNHkMx42vHrLPSEQGTzxZGE1MShB9xJMplqzfwbrWDl7f3sXr2ztZ3tROa0eckohxZsM4rj5tCidOGsWJE0cxYVQZnYkUr23NfIm/vHk3K5p3cs+zr5N4cv1hXTsWNUZXlDKmMsaYihjbOxNs2N6JZy2SXlsZo76mjIgZj7/WSiqdOVgSMZLp/VdTP6thHB84cxpvOXECJXnu7BKRgUsoQRQOd+eFpp38/PlmHlyxmfbuHgDKYxGmj6vizIZxvOXE8Zx//HhGV8QOOL+6rIRTj63l1GNr9+5LJNO8unU37d091FaWMq66lNEVMboTKVo64mzbFWd7Z5zOeIrunhTdiSS740l2dfewsyvzOH5CDVfOm8wJk2o4ceIoJo4u3+8XpjOeZEXzTl7YuJOOeJKptZVMHVtBfU0Zv395G/c9t5Eb7n2euupSpo+roq66jLqaUo4ZU8GcSaN4wzGjqa8pC/8DHmHSaWf3niRpd5zM71dPytnTk2JPMkW8J00sGqGyNEplWZSashgVpfkd7y6FZW+CyPM8iBGfIDrjSS6/5UnWtXRSVhLhkjdM5Ip5x3DS5MyXZ98mn4EqLYkwd/LoA/aXx6LUVpUye0LN0YZOVVkJZzXUcVZD3QHHTpg4ihvOa+APr2zj1y9uYuuuOGtbOnh2fZydXT17y00YVUZtZSnJtJNMpUm5U12WqcHUVsUYV1XGtHGVTBtXxYy6SjrjKZZsaGPphh280LSD0pIIk0ZVMGlMOceMqWDa2N6yVYyvKTugP6atM0Hjto6gZuREIxFKIkZFaZRp4yqZPq6K8tjR/adwd3Z29bAxaG6LRSNUlZVQVVpCVVmU6vISqssyTXtH+u/ba9PObp5eu52lG9rY2NZF845uNrd305Pq795YudVVlzE9+Jwn11YwrirzR8XYqlLqq8uoqy5jTGXsqOOV4qAaRIGoKithwax6bljQwKUnTaSm/MAaQrGKRoyL5kzgojkT9tvf3t3D6k27WLWpndWbdrE7niQWNUoiESIGHfEUO7sSvLq1g227Wtm1J3nAa08dW8EZM8cBsHnnHp7fuIOHXtq83xdjxDK1q+qyEqrKStjemaCtM3HIuI8ZXc7xE2uYP30sb5o+lpOnjKY0GqG9u4e2rsxrbO/I/GzrjLM92N7eGad1d4JNO7vZHT8w5r4iFnwx11Xt/XKOmNGdSNLdk6In5dTXlDFhVDkTR5VTEjWad3TT1NZFU1sXz2/cwYbtXQCMqYwxo66KU6aO4bKTJlFXXUo0YhhgZsSiEcpjEcpjUcpKIvSk0nQlUnQmUuzq7mHj9i42bO/kqcZWtu7es1+zYq9Y1BhbVUpJJIIZRMyIGEQiRsSMqBnV5SV7+7jqa8ooK4kSiRjRoFw67aScIDkb1WUl1JTHqCkv2VvTHVtVSkxNknmVSGX6NdVJXQC+8rY35DuEITW6IsaZDeM4s2HcgMrv7EqwvrWTDds7KY1GmT+9lgmjyg8ol0o7m3Z2s2F7Jxu2d7G1fQ8d8SQd8SSd8STzK2M01FfTML6amXVVxKIRUmmnJ5WmI55kw/YuNrR2sq6lg5WbdvHHNWuATP9K2p10P3+UV5eVUFddyrjqMo4dV8mZDeOYOraSY8dWMml0Oam005lI0hlP0RHvoSOeojOepGNPki279vD69k7+8EoLrR3Ne1+zIhYlGjE6ciQaM5hQU87cyaN4/5nTOathHMdPqBm00WvJVJqd3T20dSZo7YizvSNBy+44LR1xtnfESaUzX/C9n0k6eJ5KO7u6k7y6dTdPNrayO0diH6gxlTGmj6viuPHVNNRXM31cJWMqg/6xyhjja8qJarReaOI9qkFIkRhTWcqpx5bu18eSSzRiTB1bydSxlZw76/Cvc/KUMfttt3UmWPb6Dl7YuINoxKitzPx1W1tVurcJpray9KibpHp1xpOYQXnwVzdAdyLF1l172LJrD8mUM6U205wW5s3kS6KRTH9RddlRNUXu6UnRk0qTTu9LIplah2ER6Emm6Yyn2B3vYfeeJDu7ErQGNbOtu/awrqWTx19t4WfLmg947TGVMc6fXc+FJ05gwez6nH1zcuTiBTJRTglCCtbYqtKcTWRhqSo78L9DRWk00wRVVzUkMQym8lj0kMlz3ABGQrd399C8o4v2rp69NZvnN+7gsTUt/GL5JkoixmUnTeJj583kDccc2O8mh29fJ7UShIgUsNEVMUZX7P/Ff+0Z00ilneVNO/j1i1v4yZKNLF6xiXNn1fGxBQ2c1TBOE0aPgibKiUhRi0aM06aN5bRpY/n0W2Zx77Ov84OnNnDtfz3HlNoK3n7qZK564xRmFGHtK9/2JQgNcxWRIje6IsaNFxzH9efM4OGVW/jf55u55Y+NfPcPjZw5cxyfv2Q2p00bm+8wi4ZmUovIsFMei3LlqZO58tTJbGnfwwMv/IX/enI97/jPZ7jwhPF8/pLjOXHSqHyHWfASycww13wnCA12FpFQTBxdzsfPb+DxL57PFy45nj9vaOOy7z7BonuW8uy67XiuyR4CFM5y36pBiEioKktLuPGC47j2zdP4/hPruPe513l09VbmTBrFh8+ZwVWnTtacij4KpZNaNQgRGRKjK2N8/pLjeebmC/nnt59EMp3m8/+zgut/uIT2rOVfJJMgzDKTRPNJCUJEhlRFaZT3nH4sj3xmAV+7ci5PNbZy+S1P8vLmXfkOrWDEk5nbjeZ77S0lCBHJCzPj2jOmcf+iM4knU7z9e0+zeMWmfIdVEOLJdN77HyDkBGFmC81sjZk1mtnNOY5/wcyWB4+VZpYys7EDOVdEhofTptXy4CfPYe7kUXzqxy/wo2dfz3dIeZdIpfPe/wAhJggziwK3ApcCc4D3mNmc7DLu/g13n+fu84AvA39y97aBnCsiw8f4mnLu/cibecuJ4/n7X6zkrsO82dZwk0im8z5JDsKtQZwONLr7OndPAPcDVxyk/HuAHx/huSJS5MpKonzvfaex8A0T+adfreb2P63Nd0h5MxKamCYDTVnbzcG+A5hZJbAQ+N8jOHeRmS01s6UtLS1HHbSI5E9pSYT/eO+pXH7KMfzzb17h1j82wtq18IlPwKhREIlkfn7iE5n9w1Qimcr7Qn0QboLI1f3e38yYy4Gn3L3tcM919zvcfb67z6+vrz+CMEWkkMSiEb797nlcOe8Ylnzvv+mZexLceSfs3g3umZ933gknnwy/+U2+ww1FokBqEGFOlGsGpmZtTwH6G6JwDfualw73XBEZZqIR45tvrCb1wa8TS+w5sEBPT+Zx9dXw4ovQ0DD0QYaorTNBVdnw7oNYAswysxlmVkomCSzuW8jMRgPnAb883HNFZPgq+fa3KPXUwQv19MC3vjU0AQ2RXXt6eOkv7bxpev4XNwwtQbh7ErgJeAR4Gfipu68ysxvM7IasolcBj7p756HODStWESlA996L9RxihnVPD/zoR0MTzxB5du120g5nH1eX71DCXYvJ3R8CHuqz77Y+23cDdw/kXBEZQTo6BrdckXiqsZWKWJRTjx1z6MIhy38viIhILtUDuB/q4ZQrEk82tvLmmWOH/TwIEZEjd+21EIsdvEwsBu9//9DEMwQ2t3eztqWTcwqgeQmUIESkUH3ucwNLEJ/97NDEMwSefK0VKIz+B1CCEJFC1dAAP/sZVFYekCh6IlG6Y+W0/fC/h9UQ16caW6mrLuX4CTX5DgVQghCRQnbppZl5DosW7TeTuv391/H2j32PK9aNoqmtK99RDgp358nG7ZzVUEekQG6gpAQhIoWtoQFuuQXa2yGVgvZ26u7+Pl//4lXs6k7yrtufYV1L8Y9kenVrB60dcc6ZVRjNS6AEISJF6pSpY/jxR88gkUzzrtuf5c/r29jTc4iJdQXsycbC6n8A3ZNaRIrYnGNG8ZOPncF7v/8c77r9GQAmjS5nRl0Vn7t4NqdNy/9s5IF68rUWZtZVMXlMRb5D2Us1CBEpaseNr+GhT5/Ld66Zx2ffMpszZo6jcVsHn7zvBTriyXyHNyCJZJrn1rcVVO0BVIMQkWGgrrqMK+btuyPAstd3cPVtT/Nvj67hHy5/Qx4jG5gnXmuhK5FSghARCdtp02p5/xnTuPvpDVw5bzKnTM3/shV9teyO8+CKTfxi+V94sbmdMZUxzmwYl++w9qMmJhEZlr5wyfGMrynj5p+/RE8qne9w9rNqUzvn/usf+KdfrSaVdv72shN55DMLGF1xiImBQ0w1CBEZlmrKY/zj2+Zyw73LuOvJ9XzsvMKYUJdKO1/++UtUl8VYfNObmV0gk+JyUQ1CRIathXMncvGcCXzrd6/yy+V/wb2/m1oOnR89s4EXm9v5v5fPKejkAEoQIjLMffXKuTTUV/Pp+5dz5feeZumGtkOfFJLN7d1889FXWTC7nstPnpS3OAZKCUJEhrUJo8p58KZz+OY7T2FLezdX3/YMH793GetbOw8o25NKs3RDG50hDY/9yuJVJNNpvnbFXMwKYzmNg1EfhIgMe5GIcfVpU7jspIl8//H13P74Wn67eivXnjGNT/7VcUQjxn1/3sg9T7/Oll17GF9Txucuns3Vp00lOgjrIrk7D6/cwiOrtvKlhSdw7LjKQXhX4bNCaJMbLPPnz/elS5fmOwwRKXDbdu/h2797jZ8saaIiFiWZTrOnJ805x9Vx+SmTuH9JEy9s3MkJE2v48mUnsmBW3WH9xe/u/Hb1Vh5euYW1rZ2sa+lg954kx0+o4VefOodYtHAab8xsmbvPz3lMCUJERqrGbbu59Y9rKY9F+OBZ0zlh4igg8wX/65c28y8Pv0JTWzcnTKzhw2fP4G3zjqE8dvA7vb2wcQf/76GXWbJhB3XVZRw/sZqZddXMrK/i/5w0ifGjyofirQ2YEoSIyBGIJ1P84oW/8IOnNvDKlt2MrSrlk391HNedPeOAsjs6E/zdL1fy6xc3U1ddxmcvmsW750+lpIBqC7kcLEGoD0JEpB9lJVHe/aZjedf8qTyzbjv/+dha/vHB1XQlUtx4wXF7y7V1Jnjfnc+xtqWDT104i0ULZlJdVvxfr8X/DkREQmZmnNVQx5tnjONzP13ONx5ZQ1lJhI+cO5O2zgTv/f6zrGvt5M4PzGfB7Pp8hztolCBERAYoGjG++c5T6Ek5X/v1y8STaR5csYn1wzA5gBKEiMhhKYlG+PY180ik0ntrEnd+cD7nzhpeyQGUIEREDlssGuGW957Kd373Ggtm13PGzMJahXWwKEGIiByBspIoX1x4Qr7DCFWo46/MbKGZrTGzRjO7uZ8y55vZcjNbZWZ/ytq/wcxeCo5p7KqIyBALrQZhZlHgVuAioBlYYmaL3X11VpkxwPeAhe6+0czG93mZC9y9NawYRUSkf2HWIE4HGt19nbsngPuBK/qUeS/wc3ffCODu20KMR0REDkOYCWIy0JS13RzsyzYbqDWzx8xsmZl9IOuYA48G+xf1dxEzW2RmS81saUtLy6AFLyIy0oXZSZ1rZau+63qUAKcBFwIVwDNm9qy7vwqc7e6bgman35rZK+7++AEv6H4HcAdkltoY1HcgIjKChVmDaAamZm1PATblKPOwu3cGfQ2PA6cAuPum4Oc24AEyTVYiIjJEwkwQS4BZZjbDzEqBa4DFfcr8EjjXzErMrBJ4M/CymVWZWQ2AmVUBFwMrQ4xVRET6CK2Jyd2TZnYT8AgQBe5y91VmdkNw/DZ3f9nMHgZeBNLAne6+0sxmAg8E6+tly/sAAAgHSURBVK+XAPe5+8NhxSoiIgcaVst9m1k78FqOQ6OB9gFu53re+7MOOJJht32vN9DjufYfKtbs52HHPZAY+9t3sHiz9+X7Mx8pvyvZz/Mde7F+5oUWd39l+u6b5u651wlx92HzAO4YyP6Dbed6nvVz6WDGdbhxDyTWoYx7IDEezmfez768fuYj5XelkGIv1s+80OIeyO/FoR6FfSeLw/fgAPcfbDvX8/5ed6AOdf5A4+67L99x91dmIPsOFW+hfOYj5XdlINc+lJH+mRda3P2VGfB1h1UTU9jMbKn3c+elQlascUPxxl6scUPxxq64B99wq0GE7Y58B3CEijVuKN7YizVuKN7YFfcgUw1CRERyUg1CRERyUoIQEZGcRmyCMLO7zGybmR32DG0zOy24V0WjmX3Xghl9wbF3mdnq4P4W9w1u1OHEbWYfMrOW4N4by83sI4Mdd3CdUD7z4PjVZuZmNuidfSF95jdk3e/kSTObUyRx/3Xw+/2imf3ezKYNdtzBdcKIfYGZPW9mSTO7ulDi7ef1PmhmrwWPD2btn2FmzwX7fxKsUhGeIxl/OxwewALgjcDKIzj3z8CZZBYk/A1wabB/FvACUBtsjy+SuD8E3FKMn3lwrIbMOl7PAvOLIW5gVFaZt5FZk6wY4r4AqAyefxz4SbH8rgDTgZOBe4CrCyFe4DFgep99Y4F1wc/a4Hnvd8pPgWuC57cBHw/j8+99jNgahGdWhm3L3mdmDWb2cLDE+BNmdsD9BM1sEpn/3M945l/pHuDK4PBHgVvdfUdwjUG/v0VIcQ+JEGP/KvCvwJ5iidvdd2UVreLAlY4LNe4/untXUPRZMotwDrqQYt/g7r3L+hREvP24BPitu7cF3yW/BRYGNaG/An4WlPshIf8fHrEJoh93AJ9099OAz5O5211fk8msQtsr+z4Xs4HZZvaUmT1rZgtDjXafo40b4B1Bs8HPzGwqQ+eoYjezU4Gp7v6rsAPt46g/czO70czWkklunwox1myD8bvS63oyf6EPlcGMfSgMJN5c+ruXzjhgp7sn++wPTZj3gygqZlYNnAX8T1bzdlmuojn29f71V0Kmmel8Mn9ZPWFmc9195+BGmxXM4MT9IPBjd49bZjHFH5L5SyVURxu7mUWAb5FpIhsyg/SZ4+63Area2XuBvwM+mKP8oBmsuIPXuhaYD5w3mDH2ZzBjHwoHi9fMrgM+Hew7DnjIzBLAene/iv7fw5C/NyWIfSJksvO87J2Wubf2smBzMfCf7F+tzr7PRTPwrLv3AOvNbA2ZhLGkkON29+1Z+78P/Eto0e7vaGOvAeYCjwX/CScCi83sbe6+tIDj7uv+oGzYBiVuM3sL8LfAee4eDzXifQb7Mw9bzngB3P0HwA8AzOwx4EPuviGrSDOZPzJ7TSHTV9EKjDGzkqAWEf57C7ODo9AfZDqtVmZtPw28M3huwCn9nLcEOIN9nWCXBfsXAj8MnteRqSaOK4K4J2WVuYpMkiuKz7xPmccIoZM6pM98VlaZyznCBdvyEPepwNrs+IvtdwW4m0HupD7SeOm/k3o9mQ7q2uD52ODY/7B/J/UnQv03CPsfuVAfwI+BzUAPmYx9PTADeBhYAawG/m8/584ncwOjtcAt7JuRbsC/B+e+1PsPWQRx/zOwKjj/j8AJxfKZ9ynzGOGMYgrjM/9O8JkvDz7zNxRJ3L8DtgZxLwcWF8vvCvCm4LU6ge3AqnzHS44EEez/MNAYPK7L2j+TzCitRjLJoiyMz7/3oaU2REQkJ41iEhGRnJQgREQkJyUIERHJSQlCRERyUoIQEZGclCBkWDOzjiG+3tOD9Drnm1m7mb1gZq+Y2TcHcM6VFsKqsDJyKUGIHAYzO+jqA+5+1iBe7gl3P5XM5LS3mtnZhyh/JaAEIYNGS23IiGNmDcCtQD3QBXzU3V8xs8vJrIlUSmYi1fvcfauZfQU4hsxM2VYzexU4lsykpWOBb7v7d4PX7nD3ajM7H/gKmeUR5pJZDuJad3czu4zMhMpW4Hlgpru/tb943b3bzJazb4HCjwKLgjgbgfcD88gsG36emf0d8I7g9APe51F8dDLCqAYhI1F/q2w+CZwR/NV+P/DFrHNOA65w9/cG2yeQWZb5dOAfzCyW4zqnAp8h81f9TOBsMysHbidzj4JzyHx5H5SZ1ZJZ0+vxYNfP3f1N7n4K8DJwvbs/TWYtoi+4+zx3X3uQ9ykyIKpByIhyiFVBpwA/Ce4pUEpmDZxei929O2v7155ZqC5uZtuACey/zDTAn929ObjucjI1kA5gnbv3vvaPydQGcjnXzF4Ejge+7u5bgv1zzexrwBigGnjkMN+nyIAoQchI0+8qm8B/AP/u7ouzmoh6dfYpm72KaYrc/5dylcm1ZHN/nnD3t5rZbOBJM3vA3ZeTWWzuSndfYWYfYv+VP3sd7H2KDIiamGRE8cyd3Nab2TsBLOOU4PBo4C/B87DuzfAKMNPMpgfb7z7UCe7+KpkFFb8U7KoBNgfNWu/LKro7OHao9ykyIEoQMtxVmllz1uOvyXypXm9mK8isqHpFUPYrZJpkniDTgTzogmaqTwAPm9mTZFZGbR/AqbcBC8xsBvD3wHNkbkWZ3el8P/CFYGhsA/2/T5EB0WquIkPMzKrdvSO4x/CtwGvu/q18xyXSl2oQIkPvo0Gn9SoyzVq35zkekZxUgxARkZxUgxARkZyUIEREJCclCBERyUkJQkREclKCEBGRnP4/vmhSmHV9z7EAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"learn.lr_find()\n",
"learn.recorder.plot(suggestion=True)"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.483192</td>\n",
" <td>0.616437</td>\n",
" <td>02:36</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.490093</td>\n",
" <td>0.576607</td>\n",
" <td>02:40</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"learn.fit_one_cycle(2, slice(2.09E-01, 4.37E-02) , moms=(0.8,0.7))"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.397737</td>\n",
" <td>0.385522</td>\n",
" <td>02:48</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"learn.freeze_to(-2)\n",
"learn.fit_one_cycle(1, 3.63E-03, moms=(0.8,0.7))"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"learn.save('classificator')"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"RNNLearner(data=TextClasDataBunch;\n",
"\n",
"Train: LabelList (33516 items)\n",
"x: TextList\n",
"xxbos \" \n",
" \n",
" : : : i do not understand what you propose . \",xxbos xxup -i actually like the list . xxmaj there 's only 10 videos ... but i understand the need to simplify things . xxmaj maybe you should just list the current # 1 video , instead of the whole list ?,xxbos xxmaj what sort of idiot has never heard of the xxmaj xxunk ? xxmaj you xxmaj americans are not cultured at all !,xxbos \" : \" \" xxmaj on xxmaj june 30 , 2006 , an estimated xxunk % of black men were in prison or jail , compared to 1.9 % of xxmaj hispanic men and xxunk % of white men . \" \" \",xxbos \" \n",
" \n",
" xxmaj trolls and xxmaj xxunk \n",
" i have been xxunk from the discussion page so i can not really defend myself against the darkness ( the user darkness shine ) so let me have my say please the user has also attacked others calling them \" \" dimwits \" \" and reverted more 10 times today as have i so if justice is to be done we both should be blocked or warned or whatever the admins do to punish cheers again 109.150.60.235 \"\n",
"y: MultiCategoryList\n",
",,,,\n",
"Path: data;\n",
"\n",
"Valid: LabelList (8484 items)\n",
"x: TextList\n",
"xxbos xxmaj how is it xxrep 4 . \n",
" \n",
" xxmaj vandlism ? i created that picture , i could take it off if i wanted to .,xxbos xxmaj drafting xxmaj articles,xxbos \" \n",
" xxmaj sorry you feel that way , but aparently you do n't understand that you do n't xxup wp : xxup own xxup wp . xxunk \",xxbos \" \n",
" \n",
" xxmaj compromise ( i hope ) : xxmaj start a xxmaj minor xxmaj houses of a xxmaj song of xxmaj ice and xxmaj fire or xxunk . xxmaj move the info about their history there , as well as the key points there . xxmaj move complete xxmaj xxunk and xxmaj xxunk bios to minor characters . xxmaj however , i think this particular article should be kept around for 2 reasons : \n",
" 1 . xxmaj simply put , it 's a good idea to xxunk information . xxmaj somebody who 's reading minor houses and wants to read about xxmaj xxunk and xxmaj xxunk will find it easier and more intuitive to go to a xxmaj house xxmaj xxunk entry than a minor characters entry . \n",
" 2 . xxmaj from all indications , the xxmaj xxunk will be major players in xxup xxunk ( i think xxmaj martin saying that a xxmaj xxunk is coming back is better than mere \" \" speculation \" \" ) . xxmaj every major xxmaj xxunk institution that 's had a xxup pov centered on it ( major houses , xxmaj night 's xxmaj watch , xxmaj citadel ) has an article . xxmaj it seems pointless to get rid of this now and bring it back a year later . \n",
" i think that \" \" current \" \" is both too ambigious and potentially xxunk . xxmaj the former may be more verbose , but it 's also more exact and less troublesome . \",xxbos xxmaj xxunk conspiracy theory \n",
" \n",
" xxmaj thanks for your message on my talk page . i 've replied there but i 've noticed now that the xxmaj space xxmaj preservation xxmaj act situation is already properly explained in the article , at the end of the xxmaj xxunk vs xxunk section . xxmaj regards .\n",
"y: MultiCategoryList\n",
",,,,\n",
"Path: data;\n",
"\n",
"Test: None, model=SequentialRNN(\n",
" (0): MultiBatchEncoder(\n",
" (module): AWD_LSTM(\n",
" (encoder): Embedding(33832, 400, padding_idx=1)\n",
" (encoder_dp): EmbeddingDropout(\n",
" (emb): Embedding(33832, 400, padding_idx=1)\n",
" )\n",
" (rnns): ModuleList(\n",
" (0): WeightDropout(\n",
" (module): LSTM(400, 1152, batch_first=True)\n",
" )\n",
" (1): WeightDropout(\n",
" (module): LSTM(1152, 1152, batch_first=True)\n",
" )\n",
" (2): WeightDropout(\n",
" (module): LSTM(1152, 400, batch_first=True)\n",
" )\n",
" )\n",
" (input_dp): RNNDropout()\n",
" (hidden_dps): ModuleList(\n",
" (0): RNNDropout()\n",
" (1): RNNDropout()\n",
" (2): RNNDropout()\n",
" )\n",
" )\n",
" )\n",
" (1): PoolingLinearClassifier(\n",
" (layers): Sequential(\n",
" (0): BatchNorm1d(1200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (1): Dropout(p=0.2, inplace=False)\n",
" (2): Linear(in_features=1200, out_features=50, bias=True)\n",
" (3): ReLU(inplace=True)\n",
" (4): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (5): Dropout(p=0.1, inplace=False)\n",
" (6): Linear(in_features=50, out_features=5, bias=True)\n",
" )\n",
" )\n",
"), opt_func=functools.partial(<class 'torch.optim.adam.Adam'>, betas=(0.9, 0.99)), loss_func=FlattenedLoss of BCEWithLogitsLoss(), metrics=[], true_wd=True, bn_wd=True, wd=0.01, train_bn=True, path=PosixPath('data'), model_dir='models', callback_fns=[functools.partial(<class 'fastai.basic_train.Recorder'>, add_time=True, silent=False)], callbacks=[RNNTrainer\n",
"learn: ...\n",
"alpha: 2.0\n",
"beta: 1.0], layer_groups=[Sequential(\n",
" (0): Embedding(33832, 400, padding_idx=1)\n",
" (1): EmbeddingDropout(\n",
" (emb): Embedding(33832, 400, padding_idx=1)\n",
" )\n",
"), Sequential(\n",
" (0): WeightDropout(\n",
" (module): LSTM(400, 1152, batch_first=True)\n",
" )\n",
" (1): RNNDropout()\n",
"), Sequential(\n",
" (0): WeightDropout(\n",
" (module): LSTM(1152, 1152, batch_first=True)\n",
" )\n",
" (1): RNNDropout()\n",
"), Sequential(\n",
" (0): WeightDropout(\n",
" (module): LSTM(1152, 400, batch_first=True)\n",
" )\n",
" (1): RNNDropout()\n",
"), Sequential(\n",
" (0): PoolingLinearClassifier(\n",
" (layers): Sequential(\n",
" (0): BatchNorm1d(1200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (1): Dropout(p=0.2, inplace=False)\n",
" (2): Linear(in_features=1200, out_features=50, bias=True)\n",
" (3): ReLU(inplace=True)\n",
" (4): BatchNorm1d(50, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (5): Dropout(p=0.1, inplace=False)\n",
" (6): Linear(in_features=50, out_features=5, bias=True)\n",
" )\n",
" )\n",
")], add_time=True, silent=False)"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"learn.load('classificator')"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/envs/fastai/lib/python3.7/site-packages/ipykernel_launcher.py:2: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
" \n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:264: TracerWarning: Converting a tensor to a Python index might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" for i in range(0, sl, self.bptt):\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:265: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" r, o = self.module(input[:,i: min(i+self.bptt, sl)])\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/models/awd_lstm.py:106: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" if bs!=self.bs:\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/models/awd_lstm.py:123: TracerWarning: Converting a tensor to a Python index might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" return one_param(self).new(self.n_dir, self.bs, nh).zero_()\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:266: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" if i>(sl-self.max_len):\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:267: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" masks.append(input[:,i: min(i+self.bptt, sl)] == self.pad_idx)\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:265: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" r, o = self.module(input[:,i: min(i+self.bptt, sl)])\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/models/awd_lstm.py:106: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" if bs!=self.bs:\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:266: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" if i>(sl-self.max_len):\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:267: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" masks.append(input[:,i: min(i+self.bptt, sl)] == self.pad_idx)\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:265: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" r, o = self.module(input[:,i: min(i+self.bptt, sl)])\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/models/awd_lstm.py:106: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" if bs!=self.bs:\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:266: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" if i>(sl-self.max_len):\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:267: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" masks.append(input[:,i: min(i+self.bptt, sl)] == self.pad_idx)\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:265: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" r, o = self.module(input[:,i: min(i+self.bptt, sl)])\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/models/awd_lstm.py:106: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" if bs!=self.bs:\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:266: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" if i>(sl-self.max_len):\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:267: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" masks.append(input[:,i: min(i+self.bptt, sl)] == self.pad_idx)\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:265: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" r, o = self.module(input[:,i: min(i+self.bptt, sl)])\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/models/awd_lstm.py:106: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" if bs!=self.bs:\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:266: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" if i>(sl-self.max_len):\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:267: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" masks.append(input[:,i: min(i+self.bptt, sl)] == self.pad_idx)\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:265: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" r, o = self.module(input[:,i: min(i+self.bptt, sl)])\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:265: TracerWarning: Converting a tensor to a Python index might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" r, o = self.module(input[:,i: min(i+self.bptt, sl)])\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/models/awd_lstm.py:106: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" if bs!=self.bs:\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:266: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" if i>(sl-self.max_len):\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:267: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" masks.append(input[:,i: min(i+self.bptt, sl)] == self.pad_idx)\n",
"/opt/conda/envs/fastai/lib/python3.7/site-packages/fastai/text/learner.py:267: TracerWarning: Converting a tensor to a Python index might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
" masks.append(input[:,i: min(i+self.bptt, sl)] == self.pad_idx)\n"
]
},
{
"ename": "RuntimeError",
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+ 0xfb (0x556db64ac29b in /opt/conda/envs/fastai/bin/python)\nframe #32: _PyEval_EvalFrameDefault + 0x416 (0x556db6514806 in /opt/conda/envs/fastai/bin/python)\nframe #33: _PyFunction_FastCallKeywords + 0xfb (0x556db64ac29b in /opt/conda/envs/fastai/bin/python)\nframe #34: _PyEval_EvalFrameDefault + 0x6a0 (0x556db6514a90 in /opt/conda/envs/fastai/bin/python)\nframe #35: _PyEval_EvalCodeWithName + 0x2f9 (0x556db645c749 in /opt/conda/envs/fastai/bin/python)\nframe #36: _PyFunction_FastCallDict + 0x400 (0x556db645dab0 in /opt/conda/envs/fastai/bin/python)\nframe #37: _PyObject_Call_Prepend + 0x63 (0x556db647bb63 in /opt/conda/envs/fastai/bin/python)\nframe #38: PyObject_Call + 0x62 (0x556db646e522 in /opt/conda/envs/fastai/bin/python)\nframe #39: _PyEval_EvalFrameDefault + 0x1e53 (0x556db6516243 in /opt/conda/envs/fastai/bin/python)\nframe #40: _PyEval_EvalCodeWithName + 0x5da (0x556db645ca2a in /opt/conda/envs/fastai/bin/python)\nframe #41: _PyFunction_FastCallKeywords + 0x387 (0x556db64ac527 in /opt/conda/envs/fastai/bin/python)\nframe #42: _PyEval_EvalFrameDefault + 0x14e7 (0x556db65158d7 in /opt/conda/envs/fastai/bin/python)\nframe #43: <unknown function> + 0x16e5d9 (0x556db64b55d9 in /opt/conda/envs/fastai/bin/python)\nframe #44: _PyMethodDef_RawFastCallKeywords + 0xe9 (0x556db64acba9 in /opt/conda/envs/fastai/bin/python)\nframe #45: _PyCFunction_FastCallKeywords + 0x21 (0x556db64ace41 in /opt/conda/envs/fastai/bin/python)\nframe #46: _PyEval_EvalFrameDefault + 0x48d4 (0x556db6518cc4 in /opt/conda/envs/fastai/bin/python)\nframe #47: _PyEval_EvalCodeWithName + 0x5da (0x556db645ca2a in /opt/conda/envs/fastai/bin/python)\nframe #48: _PyFunction_FastCallKeywords + 0x387 (0x556db64ac527 in /opt/conda/envs/fastai/bin/python)\nframe #49: _PyEval_EvalFrameDefault + 0x6a0 (0x556db6514a90 in /opt/conda/envs/fastai/bin/python)\nframe #50: <unknown function> + 0x16e5d9 (0x556db64b55d9 in /opt/conda/envs/fastai/bin/python)\nframe #51: _PyMethodDef_RawFastCallKeywords + 0xe9 (0x556db64acba9 in /opt/conda/envs/fastai/bin/python)\nframe #52: _PyCFunction_FastCallKeywords + 0x21 (0x556db64ace41 in /opt/conda/envs/fastai/bin/python)\nframe #53: _PyEval_EvalFrameDefault + 0x48d4 (0x556db6518cc4 in /opt/conda/envs/fastai/bin/python)\nframe #54: _PyEval_EvalCodeWithName + 0x5da (0x556db645ca2a in /opt/conda/envs/fastai/bin/python)\nframe #55: _PyFunction_FastCallKeywords + 0x387 (0x556db64ac527 in /opt/conda/envs/fastai/bin/python)\nframe #56: _PyEval_EvalFrameDefault + 0x416 (0x556db6514806 in /opt/conda/envs/fastai/bin/python)\nframe #57: <unknown function> + 0x16e5d9 (0x556db64b55d9 in /opt/conda/envs/fastai/bin/python)\nframe #58: _PyMethodDef_RawFastCallKeywords + 0xe9 (0x556db64acba9 in /opt/conda/envs/fastai/bin/python)\nframe #59: _PyCFunction_FastCallKeywords + 0x21 (0x556db64ace41 in /opt/conda/envs/fastai/bin/python)\nframe #60: _PyEval_EvalFrameDefault + 0x48d4 (0x556db6518cc4 in /opt/conda/envs/fastai/bin/python)\nframe #61: _PyEval_EvalCodeWithName + 0x5da (0x556db645ca2a in /opt/conda/envs/fastai/bin/python)\nframe #62: _PyFunction_FastCallDict + 0x1d5 (0x556db645d885 in /opt/conda/envs/fastai/bin/python)\nframe #63: _PyObject_Call_Prepend + 0x63 (0x556db647bb63 in /opt/conda/envs/fastai/bin/python)\n",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-46-cf681b65882d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mtrace_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mones\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m400\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\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[1;32m 2\u001b[0m \u001b[0mtrace_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrace_input\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mint64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mjit_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrace_input\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mmodel_file\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'torch_model.pth'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msave\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjit_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_file\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/envs/fastai/lib/python3.7/site-packages/torch/jit/__init__.py\u001b[0m in \u001b[0;36mtrace\u001b[0;34m(func, example_inputs, optimize, check_trace, check_inputs, check_tolerance, _force_outplace, _module_class, _compilation_unit)\u001b[0m\n\u001b[1;32m 880\u001b[0m return trace_module(func, {'forward': example_inputs}, None,\n\u001b[1;32m 881\u001b[0m \u001b[0mcheck_trace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwrap_check_inputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcheck_inputs\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[0;32m--> 882\u001b[0;31m check_tolerance, _force_outplace, _module_class)\n\u001b[0m\u001b[1;32m 883\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 884\u001b[0m if (hasattr(func, '__self__') and isinstance(func.__self__, torch.nn.Module) and\n",
"\u001b[0;32m/opt/conda/envs/fastai/lib/python3.7/site-packages/torch/jit/__init__.py\u001b[0m in \u001b[0;36mtrace_module\u001b[0;34m(mod, inputs, optimize, check_trace, check_inputs, check_tolerance, _force_outplace, _module_class, _compilation_unit)\u001b[0m\n\u001b[1;32m 1032\u001b[0m \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmod\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mmethod_name\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"forward\"\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1033\u001b[0m \u001b[0mexample_inputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexample_inputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1034\u001b[0;31m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_c\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_method_from_trace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexample_inputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvar_lookup_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_force_outplace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1035\u001b[0m \u001b[0mcheck_trace_method\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_c\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1036\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mRuntimeError\u001b[0m: Only tensors or tuples of tensors can be output from traced functions (getOutput at /opt/conda/conda-bld/pytorch_1579022060824/work/torch/csrc/jit/tracer.cpp:212)\nframe #0: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x47 (0x7ff1452e8627 in /opt/conda/envs/fastai/lib/python3.7/site-packages/torch/lib/libc10.so)\nframe #1: torch::jit::tracer::TracingState::getOutput(c10::IValue const&, unsigned long) + 0x334 (0x7ff14fd6e024 in /opt/conda/envs/fastai/lib/python3.7/site-packages/torch/lib/libtorch.so)\nframe #2: torch::jit::tracer::TracingState::getOutput(c10::IValue const&, unsigned long) + 0xfe (0x7ff14fd6ddee in /opt/conda/envs/fastai/lib/python3.7/site-packages/torch/lib/libtorch.so)\nframe #3: torch::jit::tracer::trace(std::vector<c10::IValue, std::allocator<c10::IValue> >, std::function<std::vector<c10::IValue, std::allocator<c10::IValue> > (std::vector<c10::IValue, std::allocator<c10::IValue> >)> const&, std::function<std::string (at::Tensor const&)>, bool, torch::jit::script::Module*) + 0x539 (0x7ff14fd759f9 in /opt/conda/envs/fastai/lib/python3.7/site-packages/torch/lib/libtorch.so)\nframe #4: <unknown function> + 0x767641 (0x7ff17c274641 in /opt/conda/envs/fastai/lib/python3.7/site-packages/torch/lib/libtorch_python.so)\nframe #5: <unknown function> + 0x781581 (0x7ff17c28e581 in /opt/conda/envs/fastai/lib/python3.7/site-packages/torch/lib/libtorch_python.so)\nframe #6: <unknown function> + 0x28ba06 (0x7ff17bd98a06 in /opt/conda/envs/fastai/lib/python3.7/site-packages/torch/lib/libtorch_python.so)\nframe #7: _PyMethodDef_RawFastCallKeywords + 0x264 (0x556db64acd24 in /opt/conda/envs/fastai/bin/python)\nframe #8: _PyCFunction_FastCallKeywords + 0x21 (0x556db64ace41 in /opt/conda/envs/fastai/bin/python)\nframe #9: _PyEval_EvalFrameDefault + 0x533a (0x556db651972a in /opt/conda/envs/fastai/bin/python)\nframe #10: _PyEval_EvalCodeWithName + 0xab8 (0x556db645cf08 in /opt/conda/envs/fastai/bin/python)\nframe #11: _PyFunction_FastCallKeywords + 0x387 (0x556db64ac527 in /opt/conda/envs/fastai/bin/python)\nframe #12: _PyEval_EvalFrameDefault + 0x416 (0x556db6514806 in /opt/conda/envs/fastai/bin/python)\nframe #13: _PyEval_EvalCodeWithName + 0x2f9 (0x556db645c749 in /opt/conda/envs/fastai/bin/python)\nframe #14: _PyFunction_FastCallKeywords + 0x325 (0x556db64ac4c5 in /opt/conda/envs/fastai/bin/python)\nframe #15: _PyEval_EvalFrameDefault + 0x4c89 (0x556db6519079 in /opt/conda/envs/fastai/bin/python)\nframe #16: _PyEval_EvalCodeWithName + 0x2f9 (0x556db645c749 in /opt/conda/envs/fastai/bin/python)\nframe #17: PyEval_EvalCodeEx + 0x44 (0x556db645d674 in /opt/conda/envs/fastai/bin/python)\nframe #18: PyEval_EvalCode + 0x1c (0x556db645d69c in /opt/conda/envs/fastai/bin/python)\nframe #19: <unknown function> + 0x1dd06d (0x556db652406d in /opt/conda/envs/fastai/bin/python)\nframe #20: _PyMethodDef_RawFastCallKeywords + 0xe9 (0x556db64acba9 in /opt/conda/envs/fastai/bin/python)\nframe #21: _PyCFunction_FastCallKeywords + 0x21 (0x556db64ace41 in /opt/conda/envs/fastai/bin/python)\nframe #22: _PyEval_EvalFrameDefault + 0x48d4 (0x556db6518cc4 in /opt/conda/envs/fastai/bin/python)\nframe #23: _PyGen_Send + 0x2a2 (0x556db64b5b12 in /opt/conda/envs/fastai/bin/python)\nframe #24: _PyEval_EvalFrameDefault + 0x1adc (0x556db6515ecc in /opt/conda/envs/fastai/bin/python)\nframe #25: _PyGen_Send + 0x2a2 (0x556db64b5b12 in /opt/conda/envs/fastai/bin/python)\nframe #26: _PyEval_EvalFrameDefault + 0x1adc (0x556db6515ecc in /opt/conda/envs/fastai/bin/python)\nframe #27: _PyGen_Send + 0x2a2 (0x556db64b5b12 in /opt/conda/envs/fastai/bin/python)\nframe #28: _PyMethodDef_RawFastCallKeywords + 0x8d (0x556db64acb4d in /opt/conda/envs/fastai/bin/python)\nframe #29: _PyMethodDescr_FastCallKeywords + 0x4f (0x556db64b494f in /opt/conda/envs/fastai/bin/python)\nframe #30: _PyEval_EvalFrameDefault + 0x4ddd (0x556db65191cd in /opt/conda/envs/fastai/bin/python)\nframe #31: _PyFunction_FastCallKeywords + 0xfb (0x556db64ac29b in /opt/conda/envs/fastai/bin/python)\nframe #32: _PyEval_EvalFrameDefault + 0x416 (0x556db6514806 in /opt/conda/envs/fastai/bin/python)\nframe #33: _PyFunction_FastCallKeywords + 0xfb (0x556db64ac29b in /opt/conda/envs/fastai/bin/python)\nframe #34: _PyEval_EvalFrameDefault + 0x6a0 (0x556db6514a90 in /opt/conda/envs/fastai/bin/python)\nframe #35: _PyEval_EvalCodeWithName + 0x2f9 (0x556db645c749 in /opt/conda/envs/fastai/bin/python)\nframe #36: _PyFunction_FastCallDict + 0x400 (0x556db645dab0 in /opt/conda/envs/fastai/bin/python)\nframe #37: _PyObject_Call_Prepend + 0x63 (0x556db647bb63 in /opt/conda/envs/fastai/bin/python)\nframe #38: PyObject_Call + 0x62 (0x556db646e522 in /opt/conda/envs/fastai/bin/python)\nframe #39: _PyEval_EvalFrameDefault + 0x1e53 (0x556db6516243 in /opt/conda/envs/fastai/bin/python)\nframe #40: _PyEval_EvalCodeWithName + 0x5da (0x556db645ca2a in /opt/conda/envs/fastai/bin/python)\nframe #41: _PyFunction_FastCallKeywords + 0x387 (0x556db64ac527 in /opt/conda/envs/fastai/bin/python)\nframe #42: _PyEval_EvalFrameDefault + 0x14e7 (0x556db65158d7 in /opt/conda/envs/fastai/bin/python)\nframe #43: <unknown function> + 0x16e5d9 (0x556db64b55d9 in /opt/conda/envs/fastai/bin/python)\nframe #44: _PyMethodDef_RawFastCallKeywords + 0xe9 (0x556db64acba9 in /opt/conda/envs/fastai/bin/python)\nframe #45: _PyCFunction_FastCallKeywords + 0x21 (0x556db64ace41 in /opt/conda/envs/fastai/bin/python)\nframe #46: _PyEval_EvalFrameDefault + 0x48d4 (0x556db6518cc4 in /opt/conda/envs/fastai/bin/python)\nframe #47: _PyEval_EvalCodeWithName + 0x5da (0x556db645ca2a in /opt/conda/envs/fastai/bin/python)\nframe #48: _PyFunction_FastCallKeywords + 0x387 (0x556db64ac527 in /opt/conda/envs/fastai/bin/python)\nframe #49: _PyEval_EvalFrameDefault + 0x6a0 (0x556db6514a90 in /opt/conda/envs/fastai/bin/python)\nframe #50: <unknown function> + 0x16e5d9 (0x556db64b55d9 in /opt/conda/envs/fastai/bin/python)\nframe #51: _PyMethodDef_RawFastCallKeywords + 0xe9 (0x556db64acba9 in /opt/conda/envs/fastai/bin/python)\nframe #52: _PyCFunction_FastCallKeywords + 0x21 (0x556db64ace41 in /opt/conda/envs/fastai/bin/python)\nframe #53: _PyEval_EvalFrameDefault + 0x48d4 (0x556db6518cc4 in /opt/conda/envs/fastai/bin/python)\nframe #54: _PyEval_EvalCodeWithName + 0x5da (0x556db645ca2a in /opt/conda/envs/fastai/bin/python)\nframe #55: _PyFunction_FastCallKeywords + 0x387 (0x556db64ac527 in /opt/conda/envs/fastai/bin/python)\nframe #56: _PyEval_EvalFrameDefault + 0x416 (0x556db6514806 in /opt/conda/envs/fastai/bin/python)\nframe #57: <unknown function> + 0x16e5d9 (0x556db64b55d9 in /opt/conda/envs/fastai/bin/python)\nframe #58: _PyMethodDef_RawFastCallKeywords + 0xe9 (0x556db64acba9 in /opt/conda/envs/fastai/bin/python)\nframe #59: _PyCFunction_FastCallKeywords + 0x21 (0x556db64ace41 in /opt/conda/envs/fastai/bin/python)\nframe #60: _PyEval_EvalFrameDefault + 0x48d4 (0x556db6518cc4 in /opt/conda/envs/fastai/bin/python)\nframe #61: _PyEval_EvalCodeWithName + 0x5da (0x556db645ca2a in /opt/conda/envs/fastai/bin/python)\nframe #62: _PyFunction_FastCallDict + 0x1d5 (0x556db645d885 in /opt/conda/envs/fastai/bin/python)\nframe #63: _PyObject_Call_Prepend + 0x63 (0x556db647bb63 in /opt/conda/envs/fastai/bin/python)\n"
]
}
],
"source": [
"trace_input = torch.ones(1,400).cuda()\n",
"trace_input = torch.tensor(trace_input).to(torch.int64)\n",
"jit_model = torch.jit.trace(learn.model.float(), trace_input)\n",
"model_file='torch_model.pth'\n",
"torch.jit.save(jit_model, model_file)\n",
"# export classes text file\n",
"save_texts('classes.txt', data.classes)\n",
"tar_file='model.tar.gz'\n",
"classes_file='classes.txt'\n",
"# create a tarfile with the exported model and classes text file\n",
"with tarfile.open(tar_file, 'w:gz') as f:\n",
" f.add(model_file, arcname=model_file)\n",
" f.add(classes_file, arcname=classes_file)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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