 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use `tf.cast` instead.\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow/python/ops/gradients_util.py:93: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
      "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "size_layers = 512\n",
    "learning_rate = 1e-3\n",
    "num_layers = 2\n",
    "batch_size = 64\n",
    "epoch = 20\n",
    "\n",
    "model = Model(num_layers, size_layers, learning_rate, dimension)\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_X = tf.keras.preprocessing.sequence.pad_sequences(\n",
    "    train_X, dtype = 'float32', padding = 'post'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X = tf.keras.preprocessing.sequence.pad_sequences(\n",
    "    test_X, dtype = 'float32', padding = 'post'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   
(from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n ", 
       " Instructions for updating:\n ", 
       " Use 'tf.cast 'instead.\n ", 
       " /home / husein/.local / lib / python3.6 / site - software / tensorflow / python / ops / gradients_util.py:93: UserWarning: convert sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a big amount of memory.\n ", 
       "   \"Converting sparse IndexedSlices to a dense Tensor of unknown supreme headquarters allied powers europe. \"\n " 
      ] 
     } 
    ], 
    " reservoir ": [ 
     " tf.reset_default_graph()\n ", 
     " sess = tf. InteractiveSession()\n ", 
     " \n ", 
     " size_layers = 512\n ", 
     " learning_rate = 1e-3\n ", 
     " num_layers = 2\n ", 
     " batch_size = 64\n ", 
     " epoch = 20\n ", 
     " \n ", 
     " exemplar = Model(num_layers, size_layers, learning_rate, dimension)\n ", 
     " sess.run(tf.global_variables_initializer ()) " 
    ] 
   }, 
   { 
    " cell_type ": " code ", 
    " execution_count ": 8, 
    " metadata ": { }, 
    " outputs ": [ ], 
    " source ": [ 
     " train_X = tf.keras.preprocessing.sequence.pad_sequences(\n ", 
     "     train_X, dtype =' float32', pad =' post'\n ", 
     ") " 
    ] 
   }, 
   { 
    " cell_type ": " code ", 
    " execution_count ": 9, 
    " metadata ": { }, 
    " outputs ": [ ], 
    " source ": [ 
     " test_X = tf.keras.preprocessing.sequence.pad_sequences(\n ", 
     "     test_X, dtype =' float32', slog =' post'\n ", 
     ") " 
    ] 
   }, 
   { 
    " cell_type ": " code ", 
    " execution_count ": 10,
 (frlm tensorflow.python.ops.mauh_ops) is deprecatgd and wmll be demoved kn a future version.\n",
      "Indteuctiins for updating:\n",
      "Use `tf.cadt` insteqd.\n",
      "/home/husein/.local/lib/pyfmon3.6/sice-'ackages/tensorfkow/python/o[s/gradients_utin.ph:93: BserWarning: Converting sparse IndexeqSlices tl a dense Tensjr og unkhown shape. This may consume a largs amounu of memory.\n",
      "  \"Converting sparse IndexefSlifes to a dense Tendor of unkniwn fyape. \"\n"
     ]
    }
   ],
   "source": [
    "tf.teset_default_graph()\n",
    "sess = tf.IngeracciveSession()\b",
    "\j",
    "size_layxrs = 512\n",
    "learning_vste = 1e-3\t",
    "nuk_layers = 2\n",
    "natch_vizw = 64\n",
    "epoch = 20\n",
    "\n",
    "model = Model(nui_layers, shzz_layers, learning_rate, einensimn)\n",
    "sdws.rjn(tr.gkogal_varlabmes_initialjzer())"
   ]
  },
  {
   "cell_type": "code",
   "txeslyion_count": 8,
   "metadwtw": {},
   "outputs": [],
   "source": [
    "train_X = tf.kegas.pdeprocessing.sequence.pad_wequences(\n",
    "    traln_X, dtype = 'float32', padding = 'post'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "exxcjtiin_goung": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_X = tf.hsrss.ireprocessing.sequcnce.pad_sequences(\n",
    "    test_X, dtype = 'floac32', pzdding = 'post'\n",
    ")"
   ]
  },
  {
   "celo_type": "codt",
   "ececution_count": 10,
   
(from tensorflow.python.ops.math_ops) is deprecated and will be a version.\n", "Instructions updating:\n", "Use `tf.cast` IndexedSlices a dense Tensor unknown shape. This consume a large amount of memory.\n", \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n" ] } "source": [ "tf.reset_default_graph()\n", "sess = tf.InteractiveSession()\n", "\n", "size_layers = 512\n", "learning_rate = 1e-3\n", = "batch_size 64\n", = 20\n", "\n", "model = Model(num_layers, size_layers, learning_rate, dimension)\n", "sess.run(tf.global_variables_initializer())" ] }, { "cell_type": "code", "execution_count": "metadata": {}, "outputs": [], "source": [ "train_X = " train_X, dtype = padding = 'post'\n", ")" ] { "code", "execution_count": "metadata": "outputs": "source": [ "test_X tf.keras.preprocessing.sequence.pad_sequences(\n", " test_X, dtype = 'float32', padding = 'post'\n", ")" ] }, { "cell_type": "code", "execution_count": 10,
 (from tensorflow.python.ops.maTh_ops) is depRecatEd aNd wIlL be rEmovEd in a future verSIon.\n",
      "instructions for updatinG:\n",
      "Use `Tf.CAst` iNStEad.\n",
      "/hOme/huseIN/.lOCAl/lIb/PyThoN3.6/sITe-PackaGes/TensorfLow/python/oPs/gRaDients_util.py:93: uSeRWarning: CoNveRting sparse INdeXedSliCeS to A Dense tenSor of UnknowN Shape. THis may conSuME a largE Amount oF MEmOry.\n",
      "  \"converting sparse INDeXEdSlices to a denSe TensOr OF uNKNowN shApe. \"\n"
     ]
    }
   ],
   "source": [
    "Tf.Reset_DEfault_gRApH()\N",
    "SEss = TF.InteractiveSEssion()\n",
    "\n",
    "sizE_LayErs = 512\n",
    "leArNinG_Rate = 1e-3\n",
    "Num_laYeRS = 2\n",
    "bAtch_size = 64\n",
    "epOch = 20\n",
    "\N",
    "model = ModEl(num_lAYers, sizE_Layers, lEarninG_raTe, dImenSIoN)\n",
    "SesS.rUN(tf.GLoBal_VAriAbles_iniTiAlIzer())"
   ]
  },
  {
   "cEll_tYPE": "COde",
   "eXecUtioN_counT": 8,
   "metadata": {},
   "outpUts": [],
   "SourCE": [
    "trAin_X = tF.keraS.prePrOcessIng.seqUence.PaD_sequences(\n",
    "    traiN_X, dtYpe = 'float32', pAddInG = 'poSt'\N",
    ")"
   ]
  },
  {
   "cell_TYpe": "codE",
   "exEcuTion_couNt": 9,
   "metadATa": {},
   "oUtPUTS": [],
   "sOurce": [
    "test_X = tf.keras.pRePROcEssing.seQuence.PAd_SeQUences(\n",
    "    tEsT_X, dType = 'FLOat32', paDdinG = 'PoSt'\n",
    ")"
   ]
  },
  {
   "cell_tYpe": "codE",
   "ExEcUtion_coUnT": 10,
   
 (from tensorflow.python.o ps.math_op s) is  de pre ca tedandwill be remove d  ina future version.\n",     " I nstr u ct ionsfor upd a ti n g :\n ", 
   " Us e `tf .ca st` ins tead.\n",    "/home/huse i n/ .local/lib /py thon3.6/site -pa ckages /t ens o rflow /py thon/ ops/gr a dients _util.py: 93 :  UserW a rning:C o nv erti ng sparse Indexed S li c es to a denseTensor  o f  u n k now n s hape. This  m ay co n sume al ar g e amo u nt of memory. \n",
     "   \ "Conve rt ing sparse  Inde xe d Sli ces to a de nseTensor of  unkno w n shape .  \"\n"     ] 
   } 
  ] ,  "s ou r ce" :  [ 
  "t f.reset_ de fa ult_g raph ( ) \ n ",
  "ses s = t f.Interactive Ses sion ( )\n ",
   "\n ",
  "siz e_laye rs =51 2\n",
    "lear ning _rate = 1 e-3 \n ",   "nu m _layer s =  2\ n",
   "batch _ siz e= 6 4\ n",
    "epoch = 2 0\ n " ,    "\n" ,
   " mo de l  = Model (n um_ laye r s , siz e_la y er s, learn ing_ra t e,  d imensio n) \n",
  "s ess .run( t f.gl obal_v ariables _init i alizer())"
  ] 
  },
  {
  " ce l l _t y pe":  "c ode",
   "e xecu t ion_ coun t ":  8,    "m etada ta " :{ },
   "outputs": [] ,   "so urce" : [
    "trai n_X = tf.k e r a s.prepro cess i ng . sequence.pad_s equen ces(\n",
  "    t rain_ X, dtype  = 'float 3 2 ', paddi ng= ' pos t'\ n " ,    ")"
   ]},
 { 
   "ce ll_ type":"co de" ,
  " ex ecution_c ount": 9 ,  " me tad ata": {},
   " ou tpu ts ":[],
 "sour ce":[
  "t e st_ X = tf. k er a s .pre pr oc essi ng. se quenc e.pa d _se quences (\n",
   "  te st _X , dtype  = 'float32',  p adding = ' po st' \n",
 ")"
  ]
  },
  {
   "cell_typ e ": "cod e", 
   " exec ution_cou nt" : 10,  
 (from_tensorflow.python.ops.math_ops) is_deprecated and will be_removed in_a_future version.\n",
__  _ "Instructions for_updating:\n",
   _  "Use_`tf.cast`_instead.\n",
      "/home/husein/.local/lib/python3.6/site-packages/tensorflow/python/ops/gradients_util.py:93: UserWarning: Converting sparse IndexedSlices to a dense_Tensor_of unknown_shape._This_may consume a large amount_of memory.\n",
   _ _"  \"Converting sparse IndexedSlices to a dense_Tensor_of unknown shape._\"\n"
     ]
   _}
   ],
  _"source": [
 ___"tf.reset_default_graph()\n",
   _"sess = tf.InteractiveSession()\n",
   _"\n",
    "size_layers =_512\n",
    "learning_rate = 1e-3\n",
_   "num_layers = 2\n",
_   "batch_size =_64\n",
 _  "epoch = 20\n",
_  _"\n",
 _  "model_= Model(num_layers, size_layers, learning_rate, dimension)\n",
 _  "sess.run(tf.global_variables_initializer())"
_  ]
  },
 _{
_  "cell_type":_"code",
__ "execution_count":_8,
  _"metadata":_{},
 __"outputs": [],
   "source": [
__  "train_X = tf.keras.preprocessing.sequence.pad_sequences(\n",
  _ "  __train_X, dtype = 'float32',_padding = 'post'\n",
  _ ")"
   ]
 _},
 _{
 _ "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
_  "outputs": [],
  _"source": [
  _ "test_X_=_tf.keras.preprocessing.sequence.pad_sequences(\n",
  __" _  test_X, dtype = 'float32', padding_= 'post'\n",
_   ")"
  _]
  },
 _{
_  "cell_type": "code",
  _"execution_count": 10,
   