12_3_char-seq-softmax-only.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cpprhtn/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/Users/cpprhtn/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/Users/cpprhtn/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/Users/cpprhtn/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/Users/cpprhtn/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/Users/cpprhtn/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /Users/cpprhtn/opt/anaconda3/lib/python3.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "\n",
      "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
      "For more information, please see:\n",
      "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
      "  * https://github.com/tensorflow/addons\n",
      "If you depend on functionality not listed there, please file an issue.\n",
      "\n",
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      "3 loss: 1.7802114 Prediction: yftyou yontyyou\n",
      "4 loss: 1.617033 Prediction: yf you yont you\n",
      "5 loss: 1.4682446 Prediction: yf you yont you\n",
      "6 loss: 1.3327205 Prediction: yf you yont you\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "573 loss: 0.27835837 Prediction: yf you yant you\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "909 loss: 0.277796 Prediction: yf you yant you\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1094 loss: 0.2776564 Prediction: yf you yant you\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1399 loss: 0.27752167 Prediction: yf you yant you\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1794 loss: 0.2774282 Prediction: yf you yant you\n",
      "1795 loss: 0.27742797 Prediction: yf you yant you\n",
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      "1970 loss: 0.27740133 Prediction: yf you yant you\n",
      "1971 loss: 0.27740118 Prediction: yf you yant you\n",
      "1972 loss: 0.2774011 Prediction: yf you yant you\n",
      "1973 loss: 0.27740094 Prediction: yf you yant you\n",
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      "1993 loss: 0.27739832 Prediction: yf you yant you\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1994 loss: 0.27739817 Prediction: yf you yant you\n",
      "1995 loss: 0.27739805 Prediction: yf you yant you\n",
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      "2010 loss: 0.27739608 Prediction: yf you yant you\n",
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      "2012 loss: 0.2773958 Prediction: yf you yant you\n",
      "2013 loss: 0.2773957 Prediction: yf you yant you\n",
      "2014 loss: 0.27739558 Prediction: yf you yant you\n",
      "2015 loss: 0.27739546 Prediction: yf you yant you\n",
      "2016 loss: 0.27739537 Prediction: yf you yant you\n",
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      "2018 loss: 0.27739507 Prediction: yf you yant you\n",
      "2019 loss: 0.27739495 Prediction: yf you yant you\n",
      "2020 loss: 0.27739486 Prediction: yf you yant you\n",
      "2021 loss: 0.2773947 Prediction: yf you yant you\n",
      "2022 loss: 0.27739456 Prediction: yf you yant you\n",
      "2023 loss: 0.2773944 Prediction: yf you yant you\n",
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      "2027 loss: 0.27739394 Prediction: yf you yant you\n",
      "2028 loss: 0.2773938 Prediction: yf you yant you\n",
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      "2031 loss: 0.27739343 Prediction: yf you yant you\n",
      "2032 loss: 0.27739328 Prediction: yf you yant you\n",
      "2033 loss: 0.27739316 Prediction: yf you yant you\n",
      "2034 loss: 0.27739304 Prediction: yf you yant you\n",
      "2035 loss: 0.27739292 Prediction: yf you yant you\n",
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      "2037 loss: 0.27739272 Prediction: yf you yant you\n",
      "2038 loss: 0.2773926 Prediction: yf you yant you\n",
      "2039 loss: 0.2773924 Prediction: yf you yant you\n",
      "2040 loss: 0.2773923 Prediction: yf you yant you\n",
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      "2042 loss: 0.2773921 Prediction: yf you yant you\n",
      "2043 loss: 0.2773919 Prediction: yf you yant you\n",
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      "2045 loss: 0.27739173 Prediction: yf you yant you\n",
      "2046 loss: 0.2773916 Prediction: yf you yant you\n",
      "2047 loss: 0.27739143 Prediction: yf you yant you\n",
      "2048 loss: 0.27739134 Prediction: yf you yant you\n",
      "2049 loss: 0.27739125 Prediction: yf you yant you\n",
      "2050 loss: 0.27739114 Prediction: yf you yant you\n",
      "2051 loss: 0.27739108 Prediction: yf you yant you\n",
      "2052 loss: 0.27739084 Prediction: yf you yant you\n",
      "2053 loss: 0.2773908 Prediction: yf you yant you\n",
      "2054 loss: 0.27739063 Prediction: yf you yant you\n",
      "2055 loss: 0.27739048 Prediction: yf you yant you\n",
      "2056 loss: 0.2773903 Prediction: yf you yant you\n",
      "2057 loss: 0.27739024 Prediction: yf you yant you\n",
      "2058 loss: 0.27739015 Prediction: yf you yant you\n",
      "2059 loss: 0.27739 Prediction: yf you yant you\n",
      "2060 loss: 0.27738988 Prediction: yf you yant you\n",
      "2061 loss: 0.27738976 Prediction: yf you yant you\n",
      "2062 loss: 0.27738968 Prediction: yf you yant you\n",
      "2063 loss: 0.2773896 Prediction: yf you yant you\n",
      "2064 loss: 0.27738944 Prediction: yf you yant you\n",
      "2065 loss: 0.2773893 Prediction: yf you yant you\n",
      "2066 loss: 0.27738917 Prediction: yf you yant you\n",
      "2067 loss: 0.27738902 Prediction: yf you yant you\n",
      "2068 loss: 0.27738896 Prediction: yf you yant you\n",
      "2069 loss: 0.27738878 Prediction: yf you yant you\n",
      "2070 loss: 0.2773887 Prediction: yf you yant you\n",
      "2071 loss: 0.27738857 Prediction: yf you yant you\n",
      "2072 loss: 0.27738845 Prediction: yf you yant you\n",
      "2073 loss: 0.27738836 Prediction: yf you yant you\n",
      "2074 loss: 0.27738822 Prediction: yf you yant you\n",
      "2075 loss: 0.2773881 Prediction: yf you yant you\n",
      "2076 loss: 0.277388 Prediction: yf you yant you\n",
      "2077 loss: 0.2773879 Prediction: yf you yant you\n",
      "2078 loss: 0.27738777 Prediction: yf you yant you\n",
      "2079 loss: 0.27738762 Prediction: yf you yant you\n",
      "2080 loss: 0.27738756 Prediction: yf you yant you\n",
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      "2082 loss: 0.27738726 Prediction: yf you yant you\n",
      "2083 loss: 0.2773871 Prediction: yf you yant you\n",
      "2084 loss: 0.27738705 Prediction: yf you yant you\n",
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      "2087 loss: 0.27738672 Prediction: yf you yant you\n",
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      "2089 loss: 0.27738646 Prediction: yf you yant you\n",
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      "2091 loss: 0.27738625 Prediction: yf you yant you\n",
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      "2094 loss: 0.27738592 Prediction: yf you yant you\n",
      "2095 loss: 0.27738577 Prediction: yf you yant you\n",
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      "2118 loss: 0.27738318 Prediction: yf you yant you\n",
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      "2120 loss: 0.27738294 Prediction: yf you yant you\n",
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      "2184 loss: 0.2773761 Prediction: yf you yant you\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2185 loss: 0.27737597 Prediction: yf you yant you\n",
      "2186 loss: 0.27737588 Prediction: yf you yant you\n",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "2356 loss: 0.27736005 Prediction: yf you yant you\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2532 loss: 0.2773465 Prediction: yf you yant you\n",
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      "2791 loss: 0.2773306 Prediction: yf you yant you\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2792 loss: 0.27733055 Prediction: yf you yant you\n",
      "2793 loss: 0.2773305 Prediction: yf you yant you\n",
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      "2879 loss: 0.27732617 Prediction: yf you yant you\n",
      "2880 loss: 0.27732608 Prediction: yf you yant you\n",
      "2881 loss: 0.277326 Prediction: yf you yant you\n",
      "2882 loss: 0.27732596 Prediction: yf you yant you\n",
      "2883 loss: 0.27732593 Prediction: yf you yant you\n",
      "2884 loss: 0.2773258 Prediction: yf you yant you\n",
      "2885 loss: 0.27732578 Prediction: yf you yant you\n",
      "2886 loss: 0.27732578 Prediction: yf you yant you\n",
      "2887 loss: 0.27732572 Prediction: yf you yant you\n",
      "2888 loss: 0.2773257 Prediction: yf you yant you\n",
      "2889 loss: 0.2773256 Prediction: yf you yant you\n",
      "2890 loss: 0.27732557 Prediction: yf you yant you\n",
      "2891 loss: 0.2773255 Prediction: yf you yant you\n",
      "2892 loss: 0.27732545 Prediction: yf you yant you\n",
      "2893 loss: 0.27732542 Prediction: yf you yant you\n",
      "2894 loss: 0.27732533 Prediction: yf you yant you\n",
      "2895 loss: 0.27732527 Prediction: yf you yant you\n",
      "2896 loss: 0.27732527 Prediction: yf you yant you\n",
      "2897 loss: 0.27732524 Prediction: yf you yant you\n",
      "2898 loss: 0.27732512 Prediction: yf you yant you\n",
      "2899 loss: 0.2773251 Prediction: yf you yant you\n",
      "2900 loss: 0.27732506 Prediction: yf you yant you\n",
      "2901 loss: 0.277325 Prediction: yf you yant you\n",
      "2902 loss: 0.27732497 Prediction: yf you yant you\n",
      "2903 loss: 0.2773249 Prediction: yf you yant you\n",
      "2904 loss: 0.27732486 Prediction: yf you yant you\n",
      "2905 loss: 0.2773248 Prediction: yf you yant you\n",
      "2906 loss: 0.2773248 Prediction: yf you yant you\n",
      "2907 loss: 0.27732468 Prediction: yf you yant you\n",
      "2908 loss: 0.27732468 Prediction: yf you yant you\n",
      "2909 loss: 0.27732462 Prediction: yf you yant you\n",
      "2910 loss: 0.27732462 Prediction: yf you yant you\n",
      "2911 loss: 0.27732456 Prediction: yf you yant you\n",
      "2912 loss: 0.2773245 Prediction: yf you yant you\n",
      "2913 loss: 0.27732444 Prediction: yf you yant you\n",
      "2914 loss: 0.27732438 Prediction: yf you yant you\n",
      "2915 loss: 0.27732435 Prediction: yf you yant you\n",
      "2916 loss: 0.27732432 Prediction: yf you yant you\n",
      "2917 loss: 0.2773242 Prediction: yf you yant you\n",
      "2918 loss: 0.2773242 Prediction: yf you yant you\n",
      "2919 loss: 0.27732414 Prediction: yf you yant you\n",
      "2920 loss: 0.2773241 Prediction: yf you yant you\n",
      "2921 loss: 0.2773241 Prediction: yf you yant you\n",
      "2922 loss: 0.27732408 Prediction: yf you yant you\n",
      "2923 loss: 0.277324 Prediction: yf you yant you\n",
      "2924 loss: 0.27732393 Prediction: yf you yant you\n",
      "2925 loss: 0.2773238 Prediction: yf you yant you\n",
      "2926 loss: 0.2773238 Prediction: yf you yant you\n",
      "2927 loss: 0.27732378 Prediction: yf you yant you\n",
      "2928 loss: 0.27732372 Prediction: yf you yant you\n",
      "2929 loss: 0.27732366 Prediction: yf you yant you\n",
      "2930 loss: 0.27732363 Prediction: yf you yant you\n",
      "2931 loss: 0.2773236 Prediction: yf you yant you\n",
      "2932 loss: 0.27732348 Prediction: yf you yant you\n",
      "2933 loss: 0.27732348 Prediction: yf you yant you\n",
      "2934 loss: 0.27732342 Prediction: yf you yant you\n",
      "2935 loss: 0.27732342 Prediction: yf you yant you\n",
      "2936 loss: 0.27732334 Prediction: yf you yant you\n",
      "2937 loss: 0.27732325 Prediction: yf you yant you\n",
      "2938 loss: 0.27732322 Prediction: yf you yant you\n",
      "2939 loss: 0.2773232 Prediction: yf you yant you\n",
      "2940 loss: 0.27732316 Prediction: yf you yant you\n",
      "2941 loss: 0.2773231 Prediction: yf you yant you\n",
      "2942 loss: 0.2773231 Prediction: yf you yant you\n",
      "2943 loss: 0.27732304 Prediction: yf you yant you\n",
      "2944 loss: 0.27732304 Prediction: yf you yant you\n",
      "2945 loss: 0.27732292 Prediction: yf you yant you\n",
      "2946 loss: 0.27732286 Prediction: yf you yant you\n",
      "2947 loss: 0.27732283 Prediction: yf you yant you\n",
      "2948 loss: 0.27732277 Prediction: yf you yant you\n",
      "2949 loss: 0.2773227 Prediction: yf you yant you\n",
      "2950 loss: 0.27732268 Prediction: yf you yant you\n",
      "2951 loss: 0.27732262 Prediction: yf you yant you\n",
      "2952 loss: 0.2773226 Prediction: yf you yant you\n",
      "2953 loss: 0.27732256 Prediction: yf you yant you\n",
      "2954 loss: 0.27732253 Prediction: yf you yant you\n",
      "2955 loss: 0.27732247 Prediction: yf you yant you\n",
      "2956 loss: 0.2773224 Prediction: yf you yant you\n",
      "2957 loss: 0.27732238 Prediction: yf you yant you\n",
      "2958 loss: 0.27732235 Prediction: yf you yant you\n",
      "2959 loss: 0.27732226 Prediction: yf you yant you\n",
      "2960 loss: 0.27732226 Prediction: yf you yant you\n",
      "2961 loss: 0.27732217 Prediction: yf you yant you\n",
      "2962 loss: 0.27732214 Prediction: yf you yant you\n",
      "2963 loss: 0.27732208 Prediction: yf you yant you\n",
      "2964 loss: 0.27732205 Prediction: yf you yant you\n",
      "2965 loss: 0.27732205 Prediction: yf you yant you\n",
      "2966 loss: 0.27732196 Prediction: yf you yant you\n",
      "2967 loss: 0.27732193 Prediction: yf you yant you\n",
      "2968 loss: 0.2773219 Prediction: yf you yant you\n",
      "2969 loss: 0.27732188 Prediction: yf you yant you\n",
      "2970 loss: 0.27732176 Prediction: yf you yant you\n",
      "2971 loss: 0.2773217 Prediction: yf you yant you\n",
      "2972 loss: 0.2773217 Prediction: yf you yant you\n",
      "2973 loss: 0.27732164 Prediction: yf you yant you\n",
      "2974 loss: 0.27732158 Prediction: yf you yant you\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2975 loss: 0.27732155 Prediction: yf you yant you\n",
      "2976 loss: 0.27732152 Prediction: yf you yant you\n",
      "2977 loss: 0.27732146 Prediction: yf you yant you\n",
      "2978 loss: 0.27732143 Prediction: yf you yant you\n",
      "2979 loss: 0.27732134 Prediction: yf you yant you\n",
      "2980 loss: 0.27732128 Prediction: yf you yant you\n",
      "2981 loss: 0.2773213 Prediction: yf you yant you\n",
      "2982 loss: 0.27732122 Prediction: yf you yant you\n",
      "2983 loss: 0.2773212 Prediction: yf you yant you\n",
      "2984 loss: 0.2773212 Prediction: yf you yant you\n",
      "2985 loss: 0.2773211 Prediction: yf you yant you\n",
      "2986 loss: 0.27732107 Prediction: yf you yant you\n",
      "2987 loss: 0.277321 Prediction: yf you yant you\n",
      "2988 loss: 0.27732095 Prediction: yf you yant you\n",
      "2989 loss: 0.27732095 Prediction: yf you yant you\n",
      "2990 loss: 0.27732086 Prediction: yf you yant you\n",
      "2991 loss: 0.2773208 Prediction: yf you yant you\n",
      "2992 loss: 0.27732077 Prediction: yf you yant you\n",
      "2993 loss: 0.27732074 Prediction: yf you yant you\n",
      "2994 loss: 0.27732074 Prediction: yf you yant you\n",
      "2995 loss: 0.27732068 Prediction: yf you yant you\n",
      "2996 loss: 0.27732062 Prediction: yf you yant you\n",
      "2997 loss: 0.27732056 Prediction: yf you yant you\n",
      "2998 loss: 0.27732053 Prediction: yf you yant you\n",
      "2999 loss: 0.2773205 Prediction: yf you yant you\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "\n",
    "sample = \" if you want you\"\n",
    "idx2char = list(set(sample))\n",
    "char2idx = {c: i for i, c in enumerate(idx2char)} \n",
    "\n",
    "# hyper parameters\n",
    "dic_size = len(char2idx)  \n",
    "rnn_hidden_size = len(char2idx) \n",
    "num_classes = len(char2idx)\n",
    "batch_size = 1 \n",
    "sequence_length = len(sample) - 1 \n",
    "learning_rate = 0.1\n",
    "\n",
    "sample_idx = [char2idx[c] for c in sample]  \n",
    "x_data = [sample_idx[:-1]]\n",
    "y_data = [sample_idx[1:]]   \n",
    "\n",
    "X = tf.placeholder(tf.int32, [None, sequence_length]) \n",
    "Y = tf.placeholder(tf.int32, [None, sequence_length]) \n",
    "\n",
    "X_one_hot = tf.one_hot(X, num_classes)\n",
    "X_for_softmax = tf.reshape(X_one_hot, [-1, rnn_hidden_size])\n",
    "\n",
    "# softmax layer (rnn_hidden_size -> num_classes)\n",
    "softmax_w = tf.get_variable(\"softmax_w\", [rnn_hidden_size, num_classes])\n",
    "softmax_b = tf.get_variable(\"softmax_b\", [num_classes])\n",
    "outputs = tf.matmul(X_for_softmax, softmax_w) + softmax_b\n",
    "\n",
    "# expend the data (revive the batches)\n",
    "outputs = tf.reshape(outputs, [batch_size, sequence_length, num_classes])\n",
    "weights = tf.ones([batch_size, sequence_length])\n",
    "\n",
    "# Compute sequence cost/loss\n",
    "sequence_loss = tf.contrib.seq2seq.sequence_loss(\n",
    "    logits=outputs, targets=Y, weights=weights)\n",
    "loss = tf.reduce_mean(sequence_loss) \n",
    "train = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)\n",
    "\n",
    "prediction = tf.argmax(outputs, axis=2)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    for i in range(3000):\n",
    "        l, _ = sess.run([loss, train], feed_dict={X: x_data, Y: y_data})\n",
    "        result = sess.run(prediction, feed_dict={X: x_data})\n",
    "\n",
    "        # print char using dic\n",
    "        result_str = [idx2char[c] for c in np.squeeze(result)]\n",
    "        print(i, \"loss:\", l, \"Prediction:\", ''.join(result_str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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