LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1b58a27c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas_datareader as pdr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d9b341be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pdr.DataReader('AMZN','yahoo',start='2000-01-01',end='2022-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5b477b07",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1999-12-31</th>\n",
       "      <td>3.968750</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>3.965625</td>\n",
       "      <td>3.806250</td>\n",
       "      <td>145414000</td>\n",
       "      <td>3.806250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-03</th>\n",
       "      <td>4.478125</td>\n",
       "      <td>3.952344</td>\n",
       "      <td>4.075000</td>\n",
       "      <td>4.468750</td>\n",
       "      <td>322352000</td>\n",
       "      <td>4.468750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-04</th>\n",
       "      <td>4.575000</td>\n",
       "      <td>4.087500</td>\n",
       "      <td>4.268750</td>\n",
       "      <td>4.096875</td>\n",
       "      <td>349748000</td>\n",
       "      <td>4.096875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-05</th>\n",
       "      <td>3.756250</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>3.525000</td>\n",
       "      <td>3.487500</td>\n",
       "      <td>769148000</td>\n",
       "      <td>3.487500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000-01-06</th>\n",
       "      <td>3.634375</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>3.565625</td>\n",
       "      <td>3.278125</td>\n",
       "      <td>375040000</td>\n",
       "      <td>3.278125</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                High       Low      Open     Close     Volume  Adj Close\n",
       "Date                                                                    \n",
       "1999-12-31  3.968750  3.800000  3.965625  3.806250  145414000   3.806250\n",
       "2000-01-03  4.478125  3.952344  4.075000  4.468750  322352000   4.468750\n",
       "2000-01-04  4.575000  4.087500  4.268750  4.096875  349748000   4.096875\n",
       "2000-01-05  3.756250  3.400000  3.525000  3.487500  769148000   3.487500\n",
       "2000-01-06  3.634375  3.200000  3.565625  3.278125  375040000   3.278125"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "767bc2ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainrows=df.iloc[:4000]\n",
    "testrows=df.iloc[4000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c63ef3ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "window_size=50\n",
    "trainx=[ ]\n",
    "trainy=[ ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9ee4f1cd",
   "metadata": {},
   "outputs": [
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       " 2.740000009536743,\n",
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       " 2.5179998874664307,\n",
       " 2.424999952316284,\n",
       " 2.4765000343322754,\n",
       " 2.442500114440918,\n",
       " 2.428999900817871,\n",
       " 2.566499948501587,\n",
       " 2.623500108718872,\n",
       " 2.6480000019073486,\n",
       " 2.698499917984009,\n",
       " 2.7325000762939453,\n",
       " 2.696500062942505,\n",
       " 2.575500011444092,\n",
       " 2.5899999141693115,\n",
       " 2.578000068664551,\n",
       " 2.553999900817871,\n",
       " 2.4670000076293945,\n",
       " 2.4600000381469727,\n",
       " 2.5399999618530273,\n",
       " 2.549499988555908,\n",
       " 2.5204999446868896,\n",
       " 2.4749999046325684,\n",
       " 2.378999948501587,\n",
       " 2.4375,\n",
       " 2.4619998931884766,\n",
       " 2.494499921798706,\n",
       " 2.624000072479248,\n",
       " ...]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainrows=trainrows['Close'].tolist()\n",
    "trainrows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a9cf816c",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(trainrows)-window_size):\n",
    "    trainx.append(trainrows[i:i+window_size])\n",
    "    trainy.append(trainrows[i+window_size])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cfa610a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "trainx=np.array(trainx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "47889819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.8062501 ,  4.46875   ,  4.09687519, ...,  3.44062495,\n",
       "         3.34375   ,  3.265625  ],\n",
       "       [ 4.46875   ,  4.09687519,  3.48749995, ...,  3.34375   ,\n",
       "         3.265625  ,  3.28125   ],\n",
       "       [ 4.09687519,  3.48749995,  3.27812505, ...,  3.265625  ,\n",
       "         3.28125   ,  3.1875    ],\n",
       "       ...,\n",
       "       [26.11199951, 26.47200012, 26.06900024, ..., 32.39049911,\n",
       "        32.16500092, 33.17699814],\n",
       "       [26.47200012, 26.06900024, 26.11849976, ..., 32.16500092,\n",
       "        33.17699814, 33.06349945],\n",
       "       [26.06900024, 26.11849976, 26.36949921, ..., 33.17699814,\n",
       "        33.06349945, 33.42250061]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "73da5603",
   "metadata": {},
   "outputs": [
    {
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       " 2.174999952316284,\n",
       " ...]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainy"
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  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ecda29c7",
   "metadata": {},
   "outputs": [
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       " 90.23300170898438,\n",
       " 90.08550262451172,\n",
       " 89.78849792480469,\n",
       " 89.41000366210938,\n",
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       " 88.5824966430664,\n",
       " 88.9000015258789,\n",
       " 87.65550231933594,\n",
       " ...]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testrows=testrows['Close'].tolist()\n",
    "testrows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "61b7147c",
   "metadata": {},
   "outputs": [],
   "source": [
    "testx=[]\n",
    "testy=[]\n",
    "for i in range(len(testrows)-window_size):\n",
    "    testx.append(testrows[i:i+window_size])\n",
    "    testy.append(testrows[i+window_size])\n",
    "testx=np.array(testx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d2c26703",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Input,Dense,SimpleRNN,LSTM,GRU\n",
    "from tensorflow.keras import Sequential\n",
    "from tensorflow.keras.losses import MeanAbsoluteError\n",
    "from tensorflow.keras.optimizers import SGD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3b048639",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential([\n",
    "    LSTM(10,input_shape=(window_size,1),return_sequences=True),\n",
    "    LSTM(10,),\n",
    "    Dense(100,activation='relu'),\n",
    "    Dense(1, activation='relu')\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b5f34ebb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " lstm (LSTM)                 (None, 50, 10)            480       \n",
      "                                                                 \n",
      " lstm_1 (LSTM)               (None, 10)                840       \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 100)               1100      \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 1)                 101       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 2,521\n",
      "Trainable params: 2,521\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "55a38d3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "train=model.compile(loss=MeanAbsoluteError(),optimizer=SGD(),metrics=['mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0b36f0ff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "62/62 [==============================] - 7s 38ms/step - loss: 5.6302 - mae: 5.6302\n",
      "Epoch 2/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 4.1571 - mae: 4.1571\n",
      "Epoch 3/100\n",
      "62/62 [==============================] - 2s 40ms/step - loss: 2.7154 - mae: 2.7154\n",
      "Epoch 4/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 1.5360 - mae: 1.5360\n",
      "Epoch 5/100\n",
      "62/62 [==============================] - 2s 39ms/step - loss: 1.0603 - mae: 1.0603\n",
      "Epoch 6/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.9353 - mae: 0.9353\n",
      "Epoch 7/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.8318 - mae: 0.8318\n",
      "Epoch 8/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.7785 - mae: 0.7785\n",
      "Epoch 9/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.7544 - mae: 0.7544\n",
      "Epoch 10/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.7299 - mae: 0.7299\n",
      "Epoch 11/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.6954 - mae: 0.6954\n",
      "Epoch 12/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.7350 - mae: 0.7350\n",
      "Epoch 13/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.6750 - mae: 0.6750\n",
      "Epoch 14/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.6726 - mae: 0.6726\n",
      "Epoch 15/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.6331 - mae: 0.6331\n",
      "Epoch 16/100\n",
      "62/62 [==============================] - 2s 39ms/step - loss: 0.6346 - mae: 0.6346\n",
      "Epoch 17/100\n",
      "62/62 [==============================] - 2s 39ms/step - loss: 0.5930 - mae: 0.5930\n",
      "Epoch 18/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.6117 - mae: 0.6117\n",
      "Epoch 19/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.5918 - mae: 0.5918\n",
      "Epoch 20/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.5693 - mae: 0.5693\n",
      "Epoch 21/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.5662 - mae: 0.5662\n",
      "Epoch 22/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.5236 - mae: 0.5236\n",
      "Epoch 23/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.5602 - mae: 0.5602\n",
      "Epoch 24/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.4591 - mae: 0.4591\n",
      "Epoch 25/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.5732 - mae: 0.5732\n",
      "Epoch 26/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.5189 - mae: 0.5189\n",
      "Epoch 27/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.5088 - mae: 0.5088\n",
      "Epoch 28/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.4975 - mae: 0.4975\n",
      "Epoch 29/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.4629 - mae: 0.4629\n",
      "Epoch 30/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.4794 - mae: 0.4794\n",
      "Epoch 31/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.4630 - mae: 0.4630\n",
      "Epoch 32/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.4733 - mae: 0.4733\n",
      "Epoch 33/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.4641 - mae: 0.4641\n",
      "Epoch 34/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.4736 - mae: 0.4736\n",
      "Epoch 35/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.4396 - mae: 0.4396\n",
      "Epoch 36/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.4273 - mae: 0.4273\n",
      "Epoch 37/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.4425 - mae: 0.4425\n",
      "Epoch 38/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.4360 - mae: 0.4360\n",
      "Epoch 39/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.4327 - mae: 0.4327\n",
      "Epoch 40/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.4420 - mae: 0.4420\n",
      "Epoch 41/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.4275 - mae: 0.4275\n",
      "Epoch 42/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.4138 - mae: 0.4138\n",
      "Epoch 43/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.4187 - mae: 0.4187\n",
      "Epoch 44/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.4241 - mae: 0.4241\n",
      "Epoch 45/100\n",
      "62/62 [==============================] - 2s 39ms/step - loss: 0.4265 - mae: 0.4265\n",
      "Epoch 46/100\n",
      "62/62 [==============================] - 2s 40ms/step - loss: 0.3868 - mae: 0.3868\n",
      "Epoch 47/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.4231 - mae: 0.4231\n",
      "Epoch 48/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.4004 - mae: 0.4004\n",
      "Epoch 49/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.4035 - mae: 0.4035\n",
      "Epoch 50/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.3515 - mae: 0.3515\n",
      "Epoch 51/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.4016 - mae: 0.4016\n",
      "Epoch 52/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3789 - mae: 0.3789\n",
      "Epoch 53/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3854 - mae: 0.3854\n",
      "Epoch 54/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3754 - mae: 0.3754\n",
      "Epoch 55/100\n",
      "62/62 [==============================] - 2s 39ms/step - loss: 0.3803 - mae: 0.3803\n",
      "Epoch 56/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3669 - mae: 0.3669\n",
      "Epoch 57/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.3756 - mae: 0.3756\n",
      "Epoch 58/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3890 - mae: 0.3890\n",
      "Epoch 59/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3700 - mae: 0.3700\n",
      "Epoch 60/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3413 - mae: 0.3413\n",
      "Epoch 61/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3560 - mae: 0.3560\n",
      "Epoch 62/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.3775 - mae: 0.3775\n",
      "Epoch 63/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3624 - mae: 0.3624\n",
      "Epoch 64/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3725 - mae: 0.3725\n",
      "Epoch 65/100\n",
      "62/62 [==============================] - 2s 40ms/step - loss: 0.3477 - mae: 0.3477\n",
      "Epoch 66/100\n",
      "62/62 [==============================] - 2s 39ms/step - loss: 0.3578 - mae: 0.3578\n",
      "Epoch 67/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.3618 - mae: 0.3618\n",
      "Epoch 68/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.3524 - mae: 0.3524\n",
      "Epoch 69/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3319 - mae: 0.3319\n",
      "Epoch 70/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3422 - mae: 0.3422\n",
      "Epoch 71/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3326 - mae: 0.3326\n",
      "Epoch 72/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3493 - mae: 0.3493\n",
      "Epoch 73/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3523 - mae: 0.3523\n",
      "Epoch 74/100\n",
      "62/62 [==============================] - 2s 39ms/step - loss: 0.3506 - mae: 0.3506\n",
      "Epoch 75/100\n",
      "62/62 [==============================] - 3s 41ms/step - loss: 0.3385 - mae: 0.3385\n",
      "Epoch 76/100\n",
      "62/62 [==============================] - 3s 40ms/step - loss: 0.3419 - mae: 0.3419\n",
      "Epoch 77/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3321 - mae: 0.3321\n",
      "Epoch 78/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3581 - mae: 0.3581\n",
      "Epoch 79/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3336 - mae: 0.3336\n",
      "Epoch 80/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3219 - mae: 0.3219\n",
      "Epoch 81/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3437 - mae: 0.3437\n",
      "Epoch 82/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3339 - mae: 0.3339\n",
      "Epoch 83/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3169 - mae: 0.3169\n",
      "Epoch 84/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3337 - mae: 0.3337\n",
      "Epoch 85/100\n",
      "62/62 [==============================] - 2s 38ms/step - loss: 0.3162 - mae: 0.3162\n",
      "Epoch 86/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "62/62 [==============================] - 2s 37ms/step - loss: 0.3274 - mae: 0.3274\n",
      "Epoch 87/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.3273 - mae: 0.3273\n",
      "Epoch 88/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.3373 - mae: 0.3373\n",
      "Epoch 89/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.3437 - mae: 0.3437\n",
      "Epoch 90/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.3211 - mae: 0.3211\n",
      "Epoch 91/100\n",
      "62/62 [==============================] - 2s 36ms/step - loss: 0.3264 - mae: 0.3264\n",
      "Epoch 92/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.3228 - mae: 0.3228\n",
      "Epoch 93/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.3305 - mae: 0.3305\n",
      "Epoch 94/100\n",
      "62/62 [==============================] - 2s 36ms/step - loss: 0.3090 - mae: 0.3090\n",
      "Epoch 95/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.3067 - mae: 0.3067\n",
      "Epoch 96/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.3142 - mae: 0.3142\n",
      "Epoch 97/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.3011 - mae: 0.3011\n",
      "Epoch 98/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.2983 - mae: 0.2983\n",
      "Epoch 99/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.3221 - mae: 0.3221\n",
      "Epoch 100/100\n",
      "62/62 [==============================] - 2s 37ms/step - loss: 0.2980 - mae: 0.2980\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x23994e627f0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(trainx.tolist(),trainy,epochs=100,batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28f0d14f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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