Codes/LSTM Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1149/1149 - 3s - loss: 0.0029 - 3s/epoch - 2ms/step\n",
      "Epoch 2/100\n",
      "1149/1149 - 1s - loss: 8.6871e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 3/100\n",
      "1149/1149 - 1s - loss: 7.5598e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 4/100\n",
      "1149/1149 - 1s - loss: 7.5390e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 5/100\n",
      "1149/1149 - 2s - loss: 6.8331e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 6/100\n",
      "1149/1149 - 2s - loss: 6.9378e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 7/100\n",
      "1149/1149 - 1s - loss: 5.6127e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 8/100\n",
      "1149/1149 - 1s - loss: 5.7069e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 9/100\n",
      "1149/1149 - 1s - loss: 5.3311e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 10/100\n",
      "1149/1149 - 2s - loss: 5.1760e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 11/100\n",
      "1149/1149 - 1s - loss: 4.8103e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 12/100\n",
      "1149/1149 - 2s - loss: 4.5163e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 13/100\n",
      "1149/1149 - 2s - loss: 4.6110e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 14/100\n",
      "1149/1149 - 1s - loss: 4.6722e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 15/100\n",
      "1149/1149 - 1s - loss: 4.4103e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 16/100\n",
      "1149/1149 - 1s - loss: 4.3738e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 17/100\n",
      "1149/1149 - 2s - loss: 4.3715e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 18/100\n",
      "1149/1149 - 2s - loss: 4.1187e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 19/100\n",
      "1149/1149 - 2s - loss: 4.5410e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 20/100\n",
      "1149/1149 - 2s - loss: 4.1467e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 21/100\n",
      "1149/1149 - 2s - loss: 4.1411e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 22/100\n",
      "1149/1149 - 1s - loss: 4.2400e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 23/100\n",
      "1149/1149 - 2s - loss: 4.2518e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 24/100\n",
      "1149/1149 - 1s - loss: 4.2891e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 25/100\n",
      "1149/1149 - 1s - loss: 4.3251e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 26/100\n",
      "1149/1149 - 2s - loss: 4.2570e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 27/100\n",
      "1149/1149 - 2s - loss: 4.2067e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 28/100\n",
      "1149/1149 - 1s - loss: 4.3212e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 29/100\n",
      "1149/1149 - 2s - loss: 4.2605e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 30/100\n",
      "1149/1149 - 1s - loss: 4.0103e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 31/100\n",
      "1149/1149 - 2s - loss: 4.3370e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 32/100\n",
      "1149/1149 - 1s - loss: 4.1258e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 33/100\n",
      "1149/1149 - 1s - loss: 4.1829e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 34/100\n",
      "1149/1149 - 1s - loss: 3.9316e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 35/100\n",
      "1149/1149 - 2s - loss: 4.0413e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 36/100\n",
      "1149/1149 - 2s - loss: 3.9223e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 37/100\n",
      "1149/1149 - 1s - loss: 4.0426e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 38/100\n",
      "1149/1149 - 2s - loss: 3.9492e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 39/100\n",
      "1149/1149 - 2s - loss: 4.1107e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 40/100\n",
      "1149/1149 - 2s - loss: 4.0664e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 41/100\n",
      "1149/1149 - 2s - loss: 4.1523e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 42/100\n",
      "1149/1149 - 2s - loss: 4.2126e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 43/100\n",
      "1149/1149 - 1s - loss: 4.0723e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 44/100\n",
      "1149/1149 - 2s - loss: 4.0909e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 45/100\n",
      "1149/1149 - 1s - loss: 3.9383e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 46/100\n",
      "1149/1149 - 2s - loss: 3.9190e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 47/100\n",
      "1149/1149 - 2s - loss: 3.9551e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 48/100\n",
      "1149/1149 - 2s - loss: 4.0269e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 49/100\n",
      "1149/1149 - 2s - loss: 3.9560e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 50/100\n",
      "1149/1149 - 2s - loss: 3.8568e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 51/100\n",
      "1149/1149 - 2s - loss: 4.0589e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 52/100\n",
      "1149/1149 - 2s - loss: 3.8607e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 53/100\n",
      "1149/1149 - 2s - loss: 3.8997e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 54/100\n",
      "1149/1149 - 2s - loss: 3.9775e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 55/100\n",
      "1149/1149 - 2s - loss: 3.9812e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 56/100\n",
      "1149/1149 - 1s - loss: 3.7762e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 57/100\n",
      "1149/1149 - 1s - loss: 4.0063e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 58/100\n",
      "1149/1149 - 2s - loss: 4.0259e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 59/100\n",
      "1149/1149 - 1s - loss: 4.0483e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 60/100\n",
      "1149/1149 - 1s - loss: 3.8798e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 61/100\n",
      "1149/1149 - 1s - loss: 3.9800e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 62/100\n",
      "1149/1149 - 1s - loss: 4.0074e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 63/100\n",
      "1149/1149 - 1s - loss: 3.7303e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 64/100\n",
      "1149/1149 - 1s - loss: 3.8095e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 65/100\n",
      "1149/1149 - 1s - loss: 3.6779e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 66/100\n",
      "1149/1149 - 1s - loss: 4.0476e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 67/100\n",
      "1149/1149 - 1s - loss: 4.0114e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 68/100\n",
      "1149/1149 - 1s - loss: 3.9168e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 69/100\n",
      "1149/1149 - 1s - loss: 3.9130e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 70/100\n",
      "1149/1149 - 1s - loss: 3.6833e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 71/100\n",
      "1149/1149 - 1s - loss: 4.0782e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 72/100\n",
      "1149/1149 - 1s - loss: 3.9823e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 73/100\n",
      "1149/1149 - 1s - loss: 3.7493e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 74/100\n",
      "1149/1149 - 1s - loss: 3.9728e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 75/100\n",
      "1149/1149 - 1s - loss: 3.8058e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 76/100\n",
      "1149/1149 - 1s - loss: 3.8342e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 77/100\n",
      "1149/1149 - 1s - loss: 3.7591e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 78/100\n",
      "1149/1149 - 1s - loss: 4.0194e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 79/100\n",
      "1149/1149 - 1s - loss: 3.8847e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 80/100\n",
      "1149/1149 - 2s - loss: 3.8282e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 81/100\n",
      "1149/1149 - 1s - loss: 3.8311e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 82/100\n",
      "1149/1149 - 1s - loss: 3.9507e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 83/100\n",
      "1149/1149 - 2s - loss: 3.7428e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 84/100\n",
      "1149/1149 - 1s - loss: 3.8213e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 85/100\n",
      "1149/1149 - 1s - loss: 3.7480e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 86/100\n",
      "1149/1149 - 1s - loss: 3.8918e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 87/100\n",
      "1149/1149 - 1s - loss: 3.7682e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 88/100\n",
      "1149/1149 - 2s - loss: 3.8424e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 89/100\n",
      "1149/1149 - 2s - loss: 3.8731e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 90/100\n",
      "1149/1149 - 2s - loss: 3.7724e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 91/100\n",
      "1149/1149 - 2s - loss: 3.8307e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 92/100\n",
      "1149/1149 - 2s - loss: 3.7978e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 93/100\n",
      "1149/1149 - 2s - loss: 3.8426e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 94/100\n",
      "1149/1149 - 2s - loss: 4.0042e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 95/100\n",
      "1149/1149 - 2s - loss: 3.7915e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 96/100\n",
      "1149/1149 - 2s - loss: 3.8507e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 97/100\n",
      "1149/1149 - 2s - loss: 3.7485e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 98/100\n",
      "1149/1149 - 2s - loss: 3.6677e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 99/100\n",
      "1149/1149 - 1s - loss: 3.6318e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 100/100\n",
      "1149/1149 - 2s - loss: 3.8393e-04 - 2s/epoch - 1ms/step\n",
      "16/16 [==============================] - 0s 339us/step\n",
      "\n",
      "\n",
      "Evaluation Metrics for All Tokens:\n",
      "  Symbol         MSE       RMSE       MAE      MAPE      Corr       R2\n",
      "0    LTC  108.692053  10.425548  5.671813  4.789613  0.991807  0.98261\n",
      "Epoch 1/100\n",
      "1149/1149 - 3s - loss: 4.1777e-04 - 3s/epoch - 2ms/step\n",
      "Epoch 2/100\n",
      "1149/1149 - 1s - loss: 1.0214e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 3/100\n",
      "1149/1149 - 1s - loss: 9.6756e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 4/100\n",
      "1149/1149 - 2s - loss: 9.1265e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 5/100\n",
      "1149/1149 - 1s - loss: 8.5730e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 6/100\n",
      "1149/1149 - 1s - loss: 8.5784e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 7/100\n",
      "1149/1149 - 2s - loss: 7.0263e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 8/100\n",
      "1149/1149 - 1s - loss: 6.2127e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 9/100\n",
      "1149/1149 - 2s - loss: 6.2125e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 10/100\n",
      "1149/1149 - 2s - loss: 6.5621e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 11/100\n",
      "1149/1149 - 2s - loss: 5.3808e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 12/100\n",
      "1149/1149 - 2s - loss: 5.4010e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 13/100\n",
      "1149/1149 - 1s - loss: 4.7933e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 14/100\n",
      "1149/1149 - 2s - loss: 5.0177e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 15/100\n",
      "1149/1149 - 2s - loss: 5.1065e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 16/100\n",
      "1149/1149 - 1s - loss: 4.8151e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 17/100\n",
      "1149/1149 - 2s - loss: 4.8765e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 18/100\n",
      "1149/1149 - 2s - loss: 4.9796e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 19/100\n",
      "1149/1149 - 2s - loss: 4.3405e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 20/100\n",
      "1149/1149 - 2s - loss: 4.8837e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 21/100\n",
      "1149/1149 - 2s - loss: 4.8350e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 22/100\n",
      "1149/1149 - 2s - loss: 4.9492e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 23/100\n",
      "1149/1149 - 1s - loss: 5.0249e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 24/100\n",
      "1149/1149 - 2s - loss: 4.3410e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 25/100\n",
      "1149/1149 - 1s - loss: 4.9542e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 26/100\n",
      "1149/1149 - 2s - loss: 4.7736e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 27/100\n",
      "1149/1149 - 2s - loss: 4.6251e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 28/100\n",
      "1149/1149 - 2s - loss: 4.5042e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 29/100\n",
      "1149/1149 - 2s - loss: 4.6279e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 30/100\n",
      "1149/1149 - 2s - loss: 4.6890e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 31/100\n",
      "1149/1149 - 2s - loss: 4.3663e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 32/100\n",
      "1149/1149 - 2s - loss: 4.4126e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 33/100\n",
      "1149/1149 - 2s - loss: 4.5914e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 34/100\n",
      "1149/1149 - 2s - loss: 4.5417e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 35/100\n",
      "1149/1149 - 2s - loss: 4.5708e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 36/100\n",
      "1149/1149 - 1s - loss: 4.1770e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 37/100\n",
      "1149/1149 - 1s - loss: 4.4996e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 38/100\n",
      "1149/1149 - 2s - loss: 4.5691e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 39/100\n",
      "1149/1149 - 2s - loss: 4.5727e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 40/100\n",
      "1149/1149 - 1s - loss: 4.4093e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 41/100\n",
      "1149/1149 - 2s - loss: 4.6186e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 42/100\n",
      "1149/1149 - 2s - loss: 4.6253e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 43/100\n",
      "1149/1149 - 2s - loss: 4.4724e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 44/100\n",
      "1149/1149 - 2s - loss: 4.2821e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 45/100\n",
      "1149/1149 - 2s - loss: 4.2685e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 46/100\n",
      "1149/1149 - 2s - loss: 4.3178e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 47/100\n",
      "1149/1149 - 1s - loss: 4.5960e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 48/100\n",
      "1149/1149 - 2s - loss: 4.1821e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 49/100\n",
      "1149/1149 - 2s - loss: 4.3742e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 50/100\n",
      "1149/1149 - 2s - loss: 4.3330e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 51/100\n",
      "1149/1149 - 2s - loss: 4.3688e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 52/100\n",
      "1149/1149 - 1s - loss: 4.2084e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 53/100\n",
      "1149/1149 - 2s - loss: 4.4197e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 54/100\n",
      "1149/1149 - 2s - loss: 4.4574e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 55/100\n",
      "1149/1149 - 2s - loss: 4.2548e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 56/100\n",
      "1149/1149 - 1s - loss: 4.4106e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 57/100\n",
      "1149/1149 - 1s - loss: 4.1891e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 58/100\n",
      "1149/1149 - 2s - loss: 4.5355e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 59/100\n",
      "1149/1149 - 1s - loss: 4.2791e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 60/100\n",
      "1149/1149 - 1s - loss: 4.2323e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 61/100\n",
      "1149/1149 - 2s - loss: 4.2423e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 62/100\n",
      "1149/1149 - 2s - loss: 4.0908e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 63/100\n",
      "1149/1149 - 2s - loss: 4.0796e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 64/100\n",
      "1149/1149 - 1s - loss: 4.3932e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 65/100\n",
      "1149/1149 - 2s - loss: 4.2350e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 66/100\n",
      "1149/1149 - 2s - loss: 4.1448e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 67/100\n",
      "1149/1149 - 1s - loss: 4.2251e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 68/100\n",
      "1149/1149 - 1s - loss: 4.2912e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 69/100\n",
      "1149/1149 - 2s - loss: 4.0578e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 70/100\n",
      "1149/1149 - 2s - loss: 4.1459e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 71/100\n",
      "1149/1149 - 2s - loss: 4.1285e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 72/100\n",
      "1149/1149 - 2s - loss: 4.0543e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 73/100\n",
      "1149/1149 - 2s - loss: 3.9221e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 74/100\n",
      "1149/1149 - 2s - loss: 4.0299e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 75/100\n",
      "1149/1149 - 2s - loss: 4.0822e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 76/100\n",
      "1149/1149 - 2s - loss: 4.3731e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 77/100\n",
      "1149/1149 - 1s - loss: 3.9703e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 78/100\n",
      "1149/1149 - 2s - loss: 3.9493e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 79/100\n",
      "1149/1149 - 2s - loss: 4.0797e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 80/100\n",
      "1149/1149 - 1s - loss: 3.8082e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 81/100\n",
      "1149/1149 - 1s - loss: 4.0187e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 82/100\n",
      "1149/1149 - 1s - loss: 4.2967e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 83/100\n",
      "1149/1149 - 1s - loss: 4.2114e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 84/100\n",
      "1149/1149 - 1s - loss: 4.0949e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 85/100\n",
      "1149/1149 - 2s - loss: 4.0543e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 86/100\n",
      "1149/1149 - 2s - loss: 4.0386e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 87/100\n",
      "1149/1149 - 2s - loss: 4.0018e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 88/100\n",
      "1149/1149 - 1s - loss: 4.2128e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 89/100\n",
      "1149/1149 - 2s - loss: 4.0640e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 90/100\n",
      "1149/1149 - 1s - loss: 4.0923e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 91/100\n",
      "1149/1149 - 2s - loss: 4.0080e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 92/100\n",
      "1149/1149 - 2s - loss: 4.1362e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 93/100\n",
      "1149/1149 - 2s - loss: 3.9161e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 94/100\n",
      "1149/1149 - 1s - loss: 4.0486e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 95/100\n",
      "1149/1149 - 2s - loss: 3.8739e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 96/100\n",
      "1149/1149 - 2s - loss: 4.0160e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 97/100\n",
      "1149/1149 - 2s - loss: 3.7427e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 98/100\n",
      "1149/1149 - 2s - loss: 4.0602e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 99/100\n",
      "1149/1149 - 2s - loss: 4.1826e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 100/100\n",
      "1149/1149 - 2s - loss: 4.0090e-05 - 2s/epoch - 1ms/step\n",
      "16/16 [==============================] - 0s 775us/step\n",
      "\n",
      "\n",
      "Evaluation Metrics for All Tokens:\n",
      "  Symbol           MSE        RMSE        MAE      MAPE      Corr        R2\n",
      "0    LTC    108.692053   10.425548   5.671813  4.789613  0.991807  0.982610\n",
      "1    ETH  10431.272901  102.133603  49.534535  4.337902  0.994356  0.988519\n",
      "Epoch 1/100\n",
      "1149/1149 - 3s - loss: 2.9935e-06 - 3s/epoch - 2ms/step\n",
      "Epoch 2/100\n",
      "1149/1149 - 2s - loss: 1.1483e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 3/100\n",
      "1149/1149 - 2s - loss: 8.7579e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 4/100\n",
      "1149/1149 - 2s - loss: 8.1746e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 5/100\n",
      "1149/1149 - 2s - loss: 7.9278e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 6/100\n",
      "1149/1149 - 2s - loss: 8.5359e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 7/100\n",
      "1149/1149 - 2s - loss: 6.9296e-07 - 2s/epoch - 1ms/step\n",
      "Epoch 8/100\n",
      "1149/1149 - 2s - loss: 7.3184e-07 - 2s/epoch - 1ms/step\n",
      "Epoch 9/100\n",
      "1149/1149 - 2s - loss: 6.8106e-07 - 2s/epoch - 1ms/step\n",
      "Epoch 10/100\n",
      "1149/1149 - 2s - loss: 5.4965e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 11/100\n",
      "1149/1149 - 2s - loss: 6.8147e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 12/100\n",
      "1149/1149 - 2s - loss: 6.1270e-07 - 2s/epoch - 1ms/step\n",
      "Epoch 13/100\n",
      "1149/1149 - 1s - loss: 5.7286e-07 - 1s/epoch - 1ms/step\n",
      "Epoch 14/100\n",
      "1149/1149 - 2s - loss: 5.9385e-07 - 2s/epoch - 1ms/step\n",
      "Epoch 15/100\n",
      "1149/1149 - 2s - loss: 5.6311e-07 - 2s/epoch - 1ms/step\n",
      "Epoch 16/100\n",
      "1149/1149 - 2s - loss: 5.6714e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 17/100\n",
      "1149/1149 - 2s - loss: 4.9827e-07 - 2s/epoch - 1ms/step\n",
      "Epoch 18/100\n",
      "1149/1149 - 2s - loss: 5.9766e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 19/100\n",
      "1149/1149 - 2s - loss: 5.2876e-07 - 2s/epoch - 1ms/step\n",
      "Epoch 20/100\n",
      "1149/1149 - 2s - loss: 5.7201e-07 - 2s/epoch - 1ms/step\n",
      "Epoch 21/100\n",
      "1149/1149 - 2s - loss: 5.1653e-07 - 2s/epoch - 1ms/step\n",
      "Epoch 22/100\n",
      "1149/1149 - 2s - loss: 5.1498e-07 - 2s/epoch - 1ms/step\n",
      "Epoch 23/100\n",
      "1149/1149 - 3s - loss: 4.3902e-07 - 3s/epoch - 3ms/step\n",
      "Epoch 24/100\n",
      "1149/1149 - 3s - loss: 4.1231e-07 - 3s/epoch - 3ms/step\n",
      "Epoch 25/100\n",
      "1149/1149 - 3s - loss: 5.4618e-07 - 3s/epoch - 3ms/step\n",
      "Epoch 26/100\n",
      "1149/1149 - 3s - loss: 5.5693e-07 - 3s/epoch - 3ms/step\n",
      "Epoch 27/100\n",
      "1149/1149 - 2s - loss: 5.3396e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 28/100\n",
      "1149/1149 - 2s - loss: 4.8562e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 29/100\n",
      "1149/1149 - 2s - loss: 4.5471e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 30/100\n",
      "1149/1149 - 2s - loss: 4.5173e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 31/100\n",
      "1149/1149 - 2s - loss: 5.3545e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 32/100\n",
      "1149/1149 - 2s - loss: 5.1195e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 33/100\n",
      "1149/1149 - 2s - loss: 5.1788e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 34/100\n",
      "1149/1149 - 2s - loss: 4.3407e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 35/100\n",
      "1149/1149 - 2s - loss: 4.3635e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 36/100\n",
      "1149/1149 - 2s - loss: 5.2327e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 37/100\n",
      "1149/1149 - 2s - loss: 4.9375e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 38/100\n",
      "1149/1149 - 2s - loss: 4.8097e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 39/100\n",
      "1149/1149 - 2s - loss: 4.4685e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 40/100\n",
      "1149/1149 - 2s - loss: 4.6958e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 41/100\n",
      "1149/1149 - 2s - loss: 4.4215e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 42/100\n",
      "1149/1149 - 2s - loss: 4.7126e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 43/100\n",
      "1149/1149 - 2s - loss: 4.7840e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 44/100\n",
      "1149/1149 - 2s - loss: 4.8957e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 45/100\n",
      "1149/1149 - 2s - loss: 4.8382e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 46/100\n",
      "1149/1149 - 2s - loss: 4.1281e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 47/100\n",
      "1149/1149 - 2s - loss: 5.4427e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 48/100\n",
      "1149/1149 - 2s - loss: 4.7749e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 49/100\n",
      "1149/1149 - 2s - loss: 4.3270e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 50/100\n",
      "1149/1149 - 2s - loss: 4.3388e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 51/100\n",
      "1149/1149 - 2s - loss: 4.3770e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 52/100\n",
      "1149/1149 - 2s - loss: 5.1795e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 53/100\n",
      "1149/1149 - 4s - loss: 4.6098e-07 - 4s/epoch - 3ms/step\n",
      "Epoch 54/100\n",
      "1149/1149 - 2s - loss: 4.3994e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 55/100\n",
      "1149/1149 - 3s - loss: 4.7276e-07 - 3s/epoch - 2ms/step\n",
      "Epoch 56/100\n",
      "1149/1149 - 2s - loss: 4.5236e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 57/100\n",
      "1149/1149 - 2s - loss: 4.2428e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 58/100\n",
      "1149/1149 - 2s - loss: 5.0111e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 59/100\n",
      "1149/1149 - 2s - loss: 5.1284e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 60/100\n",
      "1149/1149 - 2s - loss: 4.6153e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 61/100\n",
      "1149/1149 - 2s - loss: 4.2726e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 62/100\n",
      "1149/1149 - 2s - loss: 4.4393e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 63/100\n",
      "1149/1149 - 2s - loss: 4.9128e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 64/100\n",
      "1149/1149 - 2s - loss: 4.7023e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 65/100\n",
      "1149/1149 - 2s - loss: 4.4576e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 66/100\n",
      "1149/1149 - 2s - loss: 4.0789e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 67/100\n",
      "1149/1149 - 2s - loss: 4.4822e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 68/100\n",
      "1149/1149 - 2s - loss: 4.5640e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 69/100\n",
      "1149/1149 - 2s - loss: 4.9707e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 70/100\n",
      "1149/1149 - 2s - loss: 4.0150e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 71/100\n",
      "1149/1149 - 2s - loss: 4.7230e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 72/100\n",
      "1149/1149 - 2s - loss: 4.3273e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 73/100\n",
      "1149/1149 - 2s - loss: 4.6808e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 74/100\n",
      "1149/1149 - 2s - loss: 4.3759e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 75/100\n",
      "1149/1149 - 2s - loss: 4.8231e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 76/100\n",
      "1149/1149 - 2s - loss: 4.2697e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 77/100\n",
      "1149/1149 - 2s - loss: 4.3160e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 78/100\n",
      "1149/1149 - 2s - loss: 4.6925e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 79/100\n",
      "1149/1149 - 2s - loss: 4.9401e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 80/100\n",
      "1149/1149 - 2s - loss: 4.7313e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 81/100\n",
      "1149/1149 - 2s - loss: 4.7529e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 82/100\n",
      "1149/1149 - 2s - loss: 4.5021e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 83/100\n",
      "1149/1149 - 2s - loss: 4.0765e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 84/100\n",
      "1149/1149 - 2s - loss: 4.7684e-07 - 2s/epoch - 1ms/step\n",
      "Epoch 85/100\n",
      "1149/1149 - 2s - loss: 4.3823e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 86/100\n",
      "1149/1149 - 2s - loss: 4.7411e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 87/100\n",
      "1149/1149 - 2s - loss: 4.2976e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 88/100\n",
      "1149/1149 - 2s - loss: 4.6028e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 89/100\n",
      "1149/1149 - 2s - loss: 4.2204e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 90/100\n",
      "1149/1149 - 2s - loss: 4.4972e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 91/100\n",
      "1149/1149 - 2s - loss: 4.3787e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 92/100\n",
      "1149/1149 - 2s - loss: 4.4561e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 93/100\n",
      "1149/1149 - 2s - loss: 4.8901e-07 - 2s/epoch - 1ms/step\n",
      "Epoch 94/100\n",
      "1149/1149 - 2s - loss: 4.5809e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 95/100\n",
      "1149/1149 - 2s - loss: 4.8210e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 96/100\n",
      "1149/1149 - 2s - loss: 4.4202e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 97/100\n",
      "1149/1149 - 2s - loss: 3.8923e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 98/100\n",
      "1149/1149 - 2s - loss: 4.1846e-07 - 2s/epoch - 2ms/step\n",
      "Epoch 99/100\n",
      "1149/1149 - 2s - loss: 3.9821e-07 - 2s/epoch - 1ms/step\n",
      "Epoch 100/100\n",
      "1149/1149 - 2s - loss: 4.7685e-07 - 2s/epoch - 2ms/step\n",
      "16/16 [==============================] - 1s 631us/step\n",
      "\n",
      "\n",
      "Evaluation Metrics for All Tokens:\n",
      "  Symbol           MSE        RMSE        MAE      MAPE      Corr        R2\n",
      "0    LTC    108.692053   10.425548   5.671813  4.789613  0.991807  0.982610\n",
      "1    ETH  10431.272901  102.133603  49.534535  4.337902  0.994356  0.988519\n",
      "2   DOGE      0.000801    0.028308   0.009414  8.082299  0.986679  0.955656\n",
      "Epoch 1/100\n",
      "1149/1149 - 3s - loss: 3.7006e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 2/100\n",
      "1149/1149 - 2s - loss: 1.0926e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 3/100\n",
      "1149/1149 - 2s - loss: 8.5323e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 4/100\n",
      "1149/1149 - 2s - loss: 8.7088e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 5/100\n",
      "1149/1149 - 2s - loss: 7.7649e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 6/100\n",
      "1149/1149 - 2s - loss: 7.9790e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 7/100\n",
      "1149/1149 - 2s - loss: 6.5866e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 8/100\n",
      "1149/1149 - 2s - loss: 6.3855e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 9/100\n",
      "1149/1149 - 2s - loss: 6.0874e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 10/100\n",
      "1149/1149 - 2s - loss: 5.1618e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 11/100\n",
      "1149/1149 - 2s - loss: 5.1640e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 12/100\n",
      "1149/1149 - 2s - loss: 5.1161e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 13/100\n",
      "1149/1149 - 2s - loss: 4.9066e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 14/100\n",
      "1149/1149 - 2s - loss: 4.7779e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 15/100\n",
      "1149/1149 - 2s - loss: 5.1571e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 16/100\n",
      "1149/1149 - 2s - loss: 5.2271e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 17/100\n",
      "1149/1149 - 2s - loss: 4.7146e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 18/100\n",
      "1149/1149 - 2s - loss: 4.6325e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 19/100\n",
      "1149/1149 - 2s - loss: 4.8353e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 20/100\n",
      "1149/1149 - 2s - loss: 5.0718e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 21/100\n",
      "1149/1149 - 2s - loss: 4.6255e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 22/100\n",
      "1149/1149 - 2s - loss: 4.8454e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 23/100\n",
      "1149/1149 - 2s - loss: 4.6095e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 24/100\n",
      "1149/1149 - 2s - loss: 4.7267e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 25/100\n",
      "1149/1149 - 2s - loss: 4.6597e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 26/100\n",
      "1149/1149 - 2s - loss: 4.8439e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 27/100\n",
      "1149/1149 - 2s - loss: 4.5550e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 28/100\n",
      "1149/1149 - 2s - loss: 4.7311e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 29/100\n",
      "1149/1149 - 2s - loss: 4.7089e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 30/100\n",
      "1149/1149 - 2s - loss: 4.5981e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 31/100\n",
      "1149/1149 - 2s - loss: 4.6113e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 32/100\n",
      "1149/1149 - 2s - loss: 4.7814e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 33/100\n",
      "1149/1149 - 2s - loss: 4.5847e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 34/100\n",
      "1149/1149 - 2s - loss: 4.3430e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 35/100\n",
      "1149/1149 - 2s - loss: 4.7153e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 36/100\n",
      "1149/1149 - 2s - loss: 4.5631e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 37/100\n",
      "1149/1149 - 2s - loss: 4.3820e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 38/100\n",
      "1149/1149 - 2s - loss: 4.4565e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 39/100\n",
      "1149/1149 - 2s - loss: 4.2222e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 40/100\n",
      "1149/1149 - 2s - loss: 4.4699e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 41/100\n",
      "1149/1149 - 2s - loss: 4.4768e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 42/100\n",
      "1149/1149 - 2s - loss: 4.3472e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 43/100\n",
      "1149/1149 - 2s - loss: 4.6302e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 44/100\n",
      "1149/1149 - 2s - loss: 4.6077e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 45/100\n",
      "1149/1149 - 2s - loss: 4.2638e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 46/100\n",
      "1149/1149 - 2s - loss: 4.4641e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 47/100\n",
      "1149/1149 - 1s - loss: 4.3753e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 48/100\n",
      "1149/1149 - 2s - loss: 4.5229e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 49/100\n",
      "1149/1149 - 1s - loss: 4.2849e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 50/100\n",
      "1149/1149 - 2s - loss: 4.4136e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 51/100\n",
      "1149/1149 - 1s - loss: 4.2636e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 52/100\n",
      "1149/1149 - 2s - loss: 4.3709e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 53/100\n",
      "1149/1149 - 2s - loss: 4.1924e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 54/100\n",
      "1149/1149 - 2s - loss: 4.3891e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 55/100\n",
      "1149/1149 - 2s - loss: 4.4693e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 56/100\n",
      "1149/1149 - 1s - loss: 4.3483e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 57/100\n",
      "1149/1149 - 2s - loss: 4.2138e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 58/100\n",
      "1149/1149 - 2s - loss: 4.3820e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 59/100\n",
      "1149/1149 - 2s - loss: 4.5219e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 60/100\n",
      "1149/1149 - 2s - loss: 4.4229e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 61/100\n",
      "1149/1149 - 2s - loss: 4.3718e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 62/100\n",
      "1149/1149 - 2s - loss: 4.1091e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 63/100\n",
      "1149/1149 - 2s - loss: 4.2439e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 64/100\n",
      "1149/1149 - 2s - loss: 4.3694e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 65/100\n",
      "1149/1149 - 2s - loss: 4.2017e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 66/100\n",
      "1149/1149 - 1s - loss: 4.2701e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 67/100\n",
      "1149/1149 - 2s - loss: 4.1742e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 68/100\n",
      "1149/1149 - 1s - loss: 4.2678e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 69/100\n",
      "1149/1149 - 2s - loss: 4.4044e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 70/100\n",
      "1149/1149 - 1s - loss: 4.3337e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 71/100\n",
      "1149/1149 - 2s - loss: 4.3114e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 72/100\n",
      "1149/1149 - 1s - loss: 4.1636e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 73/100\n",
      "1149/1149 - 2s - loss: 4.2031e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 74/100\n",
      "1149/1149 - 2s - loss: 4.3233e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 75/100\n",
      "1149/1149 - 2s - loss: 4.1137e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 76/100\n",
      "1149/1149 - 2s - loss: 4.3315e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 77/100\n",
      "1149/1149 - 2s - loss: 4.2344e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 78/100\n",
      "1149/1149 - 1s - loss: 4.1958e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 79/100\n",
      "1149/1149 - 2s - loss: 4.3047e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 80/100\n",
      "1149/1149 - 1s - loss: 4.2812e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 81/100\n",
      "1149/1149 - 2s - loss: 4.0866e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 82/100\n",
      "1149/1149 - 2s - loss: 4.2577e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 83/100\n",
      "1149/1149 - 2s - loss: 4.3068e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 84/100\n",
      "1149/1149 - 2s - loss: 4.2032e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 85/100\n",
      "1149/1149 - 2s - loss: 4.1205e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 86/100\n",
      "1149/1149 - 2s - loss: 4.2869e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 87/100\n",
      "1149/1149 - 2s - loss: 4.1914e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 88/100\n",
      "1149/1149 - 2s - loss: 4.1603e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 89/100\n",
      "1149/1149 - 2s - loss: 4.2825e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 90/100\n",
      "1149/1149 - 2s - loss: 4.2248e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 91/100\n",
      "1149/1149 - 2s - loss: 4.2767e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 92/100\n",
      "1149/1149 - 2s - loss: 4.1486e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 93/100\n",
      "1149/1149 - 2s - loss: 4.2068e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 94/100\n",
      "1149/1149 - 2s - loss: 4.1879e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 95/100\n",
      "1149/1149 - 2s - loss: 4.2598e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 96/100\n",
      "1149/1149 - 2s - loss: 4.3265e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 97/100\n",
      "1149/1149 - 2s - loss: 4.0571e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 98/100\n",
      "1149/1149 - 2s - loss: 4.2437e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 99/100\n",
      "1149/1149 - 2s - loss: 3.9915e-05 - 2s/epoch - 1ms/step\n",
      "Epoch 100/100\n",
      "1149/1149 - 2s - loss: 4.2093e-05 - 2s/epoch - 1ms/step\n",
      "16/16 [==============================] - 0s 807us/step\n",
      "\n",
      "\n",
      "Evaluation Metrics for All Tokens:\n",
      "  Symbol           MSE         RMSE          MAE      MAPE      Corr        R2\n",
      "0    LTC  1.086921e+02    10.425548     5.671813  4.789613  0.991807  0.982610\n",
      "1    ETH  1.043127e+04   102.133603    49.534535  4.337902  0.994356  0.988519\n",
      "2   DOGE  8.013556e-04     0.028308     0.009414  8.082299  0.986679  0.955656\n",
      "3    BTC  7.371176e+06  2714.991025  1564.615526  4.473806  0.996693  0.977252\n",
      "Epoch 1/100\n",
      "1005/1005 - 3s - loss: 2.9783e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 2/100\n",
      "1005/1005 - 1s - loss: 6.4654e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 3/100\n",
      "1005/1005 - 2s - loss: 5.5103e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 4/100\n",
      "1005/1005 - 2s - loss: 5.8638e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 5/100\n",
      "1005/1005 - 1s - loss: 5.4979e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 6/100\n",
      "1005/1005 - 1s - loss: 4.5712e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 7/100\n",
      "1005/1005 - 1s - loss: 4.0198e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 8/100\n",
      "1005/1005 - 1s - loss: 4.0462e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 9/100\n",
      "1005/1005 - 1s - loss: 3.5521e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 10/100\n",
      "1005/1005 - 1s - loss: 3.8051e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 11/100\n",
      "1005/1005 - 2s - loss: 3.2952e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 12/100\n",
      "1005/1005 - 1s - loss: 3.0567e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 13/100\n",
      "1005/1005 - 1s - loss: 3.1628e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 14/100\n",
      "1005/1005 - 1s - loss: 3.3705e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 15/100\n",
      "1005/1005 - 1s - loss: 3.0173e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 16/100\n",
      "1005/1005 - 2s - loss: 3.0263e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 17/100\n",
      "1005/1005 - 1s - loss: 2.9826e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 18/100\n",
      "1005/1005 - 1s - loss: 3.2397e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 19/100\n",
      "1005/1005 - 2s - loss: 3.2326e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 20/100\n",
      "1005/1005 - 2s - loss: 2.9668e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 21/100\n",
      "1005/1005 - 1s - loss: 2.9644e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 22/100\n",
      "1005/1005 - 1s - loss: 3.2068e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 23/100\n",
      "1005/1005 - 2s - loss: 3.0528e-06 - 2s/epoch - 1ms/step\n",
      "Epoch 24/100\n",
      "1005/1005 - 2s - loss: 3.0824e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 25/100\n",
      "1005/1005 - 1s - loss: 2.9181e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 26/100\n",
      "1005/1005 - 1s - loss: 2.9568e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 27/100\n",
      "1005/1005 - 2s - loss: 2.9031e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 28/100\n",
      "1005/1005 - 1s - loss: 2.8141e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 29/100\n",
      "1005/1005 - 1s - loss: 2.4602e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 30/100\n",
      "1005/1005 - 2s - loss: 2.8416e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 31/100\n",
      "1005/1005 - 1s - loss: 2.9560e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 32/100\n",
      "1005/1005 - 1s - loss: 2.6884e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 33/100\n",
      "1005/1005 - 2s - loss: 3.1398e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 34/100\n",
      "1005/1005 - 1s - loss: 3.0933e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 35/100\n",
      "1005/1005 - 1s - loss: 2.7443e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 36/100\n",
      "1005/1005 - 1s - loss: 2.6956e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 37/100\n",
      "1005/1005 - 1s - loss: 2.9381e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 38/100\n",
      "1005/1005 - 2s - loss: 2.7743e-06 - 2s/epoch - 1ms/step\n",
      "Epoch 39/100\n",
      "1005/1005 - 1s - loss: 2.8004e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 40/100\n",
      "1005/1005 - 1s - loss: 2.8857e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 41/100\n",
      "1005/1005 - 2s - loss: 2.5018e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 42/100\n",
      "1005/1005 - 2s - loss: 3.0510e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 43/100\n",
      "1005/1005 - 1s - loss: 2.4635e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 44/100\n",
      "1005/1005 - 1s - loss: 3.1318e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 45/100\n",
      "1005/1005 - 2s - loss: 2.6678e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 46/100\n",
      "1005/1005 - 2s - loss: 2.9606e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 47/100\n",
      "1005/1005 - 2s - loss: 2.6324e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 48/100\n",
      "1005/1005 - 1s - loss: 2.5912e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 49/100\n",
      "1005/1005 - 2s - loss: 2.7363e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 50/100\n",
      "1005/1005 - 1s - loss: 2.7868e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 51/100\n",
      "1005/1005 - 1s - loss: 2.7453e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 52/100\n",
      "1005/1005 - 2s - loss: 2.5753e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 53/100\n",
      "1005/1005 - 2s - loss: 2.5028e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 54/100\n",
      "1005/1005 - 1s - loss: 2.8061e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 55/100\n",
      "1005/1005 - 2s - loss: 2.5201e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 56/100\n",
      "1005/1005 - 1s - loss: 2.5795e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 57/100\n",
      "1005/1005 - 1s - loss: 2.7771e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 58/100\n",
      "1005/1005 - 1s - loss: 2.5152e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 59/100\n",
      "1005/1005 - 2s - loss: 2.6990e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 60/100\n",
      "1005/1005 - 2s - loss: 2.7391e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 61/100\n",
      "1005/1005 - 2s - loss: 3.1098e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 62/100\n",
      "1005/1005 - 1s - loss: 2.5681e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 63/100\n",
      "1005/1005 - 2s - loss: 2.7279e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 64/100\n",
      "1005/1005 - 2s - loss: 2.5786e-06 - 2s/epoch - 1ms/step\n",
      "Epoch 65/100\n",
      "1005/1005 - 2s - loss: 2.6184e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 66/100\n",
      "1005/1005 - 1s - loss: 2.5734e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 67/100\n",
      "1005/1005 - 2s - loss: 2.5545e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 68/100\n",
      "1005/1005 - 2s - loss: 2.8304e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 69/100\n",
      "1005/1005 - 2s - loss: 2.6560e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 70/100\n",
      "1005/1005 - 1s - loss: 2.5918e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 71/100\n",
      "1005/1005 - 1s - loss: 2.7015e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 72/100\n",
      "1005/1005 - 2s - loss: 2.4544e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 73/100\n",
      "1005/1005 - 2s - loss: 2.4386e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 74/100\n",
      "1005/1005 - 2s - loss: 2.7589e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 75/100\n",
      "1005/1005 - 1s - loss: 2.7737e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 76/100\n",
      "1005/1005 - 2s - loss: 2.6559e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 77/100\n",
      "1005/1005 - 2s - loss: 2.5874e-06 - 2s/epoch - 1ms/step\n",
      "Epoch 78/100\n",
      "1005/1005 - 2s - loss: 2.6221e-06 - 2s/epoch - 1ms/step\n",
      "Epoch 79/100\n",
      "1005/1005 - 1s - loss: 2.6166e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 80/100\n",
      "1005/1005 - 2s - loss: 2.6146e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 81/100\n",
      "1005/1005 - 1s - loss: 2.7955e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 82/100\n",
      "1005/1005 - 2s - loss: 2.6378e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 83/100\n",
      "1005/1005 - 2s - loss: 2.5510e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 84/100\n",
      "1005/1005 - 2s - loss: 2.6729e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 85/100\n",
      "1005/1005 - 1s - loss: 2.3649e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 86/100\n",
      "1005/1005 - 1s - loss: 2.5945e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 87/100\n",
      "1005/1005 - 2s - loss: 2.4436e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 88/100\n",
      "1005/1005 - 2s - loss: 2.6733e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 89/100\n",
      "1005/1005 - 2s - loss: 2.5080e-06 - 2s/epoch - 1ms/step\n",
      "Epoch 90/100\n",
      "1005/1005 - 2s - loss: 2.3646e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 91/100\n",
      "1005/1005 - 2s - loss: 2.4583e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 92/100\n",
      "1005/1005 - 1s - loss: 2.5445e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 93/100\n",
      "1005/1005 - 1s - loss: 2.6126e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 94/100\n",
      "1005/1005 - 1s - loss: 2.5508e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 95/100\n",
      "1005/1005 - 2s - loss: 2.5801e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 96/100\n",
      "1005/1005 - 2s - loss: 2.6240e-06 - 2s/epoch - 1ms/step\n",
      "Epoch 97/100\n",
      "1005/1005 - 2s - loss: 2.4211e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 98/100\n",
      "1005/1005 - 1s - loss: 2.5892e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 99/100\n",
      "1005/1005 - 1s - loss: 2.4485e-06 - 1s/epoch - 1ms/step\n",
      "Epoch 100/100\n",
      "1005/1005 - 1s - loss: 2.7525e-06 - 1s/epoch - 1ms/step\n",
      "14/14 [==============================] - 0s 1ms/step\n",
      "\n",
      "\n",
      "Evaluation Metrics for All Tokens:\n",
      "  Symbol           MSE         RMSE          MAE      MAPE      Corr        R2\n",
      "0    LTC  1.086921e+02    10.425548     5.671813  4.789613  0.991807  0.982610\n",
      "1    ETH  1.043127e+04   102.133603    49.534535  4.337902  0.994356  0.988519\n",
      "2   DOGE  8.013556e-04     0.028308     0.009414  8.082299  0.986679  0.955656\n",
      "3    BTC  7.371176e+06  2714.991025  1564.615526  4.473806  0.996693  0.977252\n",
      "4    BNB  1.122289e+03    33.500578    15.217210  5.603422  0.993032  0.965664\n",
      "Epoch 1/100\n",
      "957/957 - 3s - loss: 7.3763e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 2/100\n",
      "957/957 - 1s - loss: 2.6516e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 3/100\n",
      "957/957 - 1s - loss: 2.6817e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 4/100\n",
      "957/957 - 1s - loss: 2.1547e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 5/100\n",
      "957/957 - 1s - loss: 2.1196e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 6/100\n",
      "957/957 - 1s - loss: 2.1514e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 7/100\n",
      "957/957 - 1s - loss: 1.8098e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 8/100\n",
      "957/957 - 1s - loss: 1.6735e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 9/100\n",
      "957/957 - 1s - loss: 1.6751e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 10/100\n",
      "957/957 - 1s - loss: 1.5663e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 11/100\n",
      "957/957 - 2s - loss: 1.4343e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 12/100\n",
      "957/957 - 1s - loss: 1.2833e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 13/100\n",
      "957/957 - 1s - loss: 1.3497e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 14/100\n",
      "957/957 - 1s - loss: 1.2678e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 15/100\n",
      "957/957 - 1s - loss: 1.1685e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 16/100\n",
      "957/957 - 1s - loss: 1.2955e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 17/100\n",
      "957/957 - 1s - loss: 1.1704e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 18/100\n",
      "957/957 - 2s - loss: 1.1380e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 19/100\n",
      "957/957 - 2s - loss: 1.2700e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 20/100\n",
      "957/957 - 2s - loss: 1.0946e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 21/100\n",
      "957/957 - 2s - loss: 1.2142e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 22/100\n",
      "957/957 - 1s - loss: 1.1344e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 23/100\n",
      "957/957 - 1s - loss: 1.1850e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 24/100\n",
      "957/957 - 1s - loss: 1.2548e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 25/100\n",
      "957/957 - 1s - loss: 1.1580e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 26/100\n",
      "957/957 - 1s - loss: 1.1324e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 27/100\n",
      "957/957 - 1s - loss: 1.1221e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 28/100\n",
      "957/957 - 1s - loss: 1.0359e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 29/100\n",
      "957/957 - 1s - loss: 1.1003e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 30/100\n",
      "957/957 - 1s - loss: 1.0609e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 31/100\n",
      "957/957 - 1s - loss: 1.0961e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 32/100\n",
      "957/957 - 1s - loss: 1.0474e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 33/100\n",
      "957/957 - 1s - loss: 1.0208e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 34/100\n",
      "957/957 - 1s - loss: 1.1004e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 35/100\n",
      "957/957 - 2s - loss: 1.0094e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 36/100\n",
      "957/957 - 2s - loss: 1.0312e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 37/100\n",
      "957/957 - 2s - loss: 1.1330e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 38/100\n",
      "957/957 - 2s - loss: 1.1379e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 39/100\n",
      "957/957 - 1s - loss: 9.5473e-05 - 1s/epoch - 2ms/step\n",
      "Epoch 40/100\n",
      "957/957 - 1s - loss: 1.0338e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 41/100\n",
      "957/957 - 1s - loss: 1.1109e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 42/100\n",
      "957/957 - 1s - loss: 1.0034e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 43/100\n",
      "957/957 - 1s - loss: 1.0728e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 44/100\n",
      "957/957 - 1s - loss: 1.0916e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 45/100\n",
      "957/957 - 2s - loss: 1.0654e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 46/100\n",
      "957/957 - 2s - loss: 1.0881e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 47/100\n",
      "957/957 - 2s - loss: 1.0503e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 48/100\n",
      "957/957 - 2s - loss: 1.0716e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 49/100\n",
      "957/957 - 2s - loss: 1.1187e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 50/100\n",
      "957/957 - 2s - loss: 1.0077e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 51/100\n",
      "957/957 - 2s - loss: 1.0601e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 52/100\n",
      "957/957 - 1s - loss: 1.0262e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 53/100\n",
      "957/957 - 1s - loss: 1.0750e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 54/100\n",
      "957/957 - 1s - loss: 1.1030e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 55/100\n",
      "957/957 - 1s - loss: 1.0502e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 56/100\n",
      "957/957 - 1s - loss: 9.3715e-05 - 1s/epoch - 2ms/step\n",
      "Epoch 57/100\n",
      "957/957 - 1s - loss: 1.0588e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 58/100\n",
      "957/957 - 2s - loss: 9.2616e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 59/100\n",
      "957/957 - 2s - loss: 1.0771e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 60/100\n",
      "957/957 - 2s - loss: 9.8225e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 61/100\n",
      "957/957 - 1s - loss: 9.7990e-05 - 1s/epoch - 2ms/step\n",
      "Epoch 62/100\n",
      "957/957 - 1s - loss: 8.8862e-05 - 1s/epoch - 2ms/step\n",
      "Epoch 63/100\n",
      "957/957 - 1s - loss: 1.0960e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 64/100\n",
      "957/957 - 1s - loss: 1.0189e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 65/100\n",
      "957/957 - 1s - loss: 1.0305e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 66/100\n",
      "957/957 - 1s - loss: 1.0271e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 67/100\n",
      "957/957 - 1s - loss: 1.0099e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 68/100\n",
      "957/957 - 2s - loss: 9.2835e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 69/100\n",
      "957/957 - 1s - loss: 1.0570e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 70/100\n",
      "957/957 - 1s - loss: 1.0668e-04 - 1s/epoch - 2ms/step\n",
      "Epoch 71/100\n",
      "957/957 - 2s - loss: 1.0263e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 72/100\n",
      "957/957 - 2s - loss: 9.9204e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 73/100\n",
      "957/957 - 2s - loss: 1.0036e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 74/100\n",
      "957/957 - 2s - loss: 9.8304e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 75/100\n",
      "957/957 - 2s - loss: 9.3669e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 76/100\n",
      "957/957 - 2s - loss: 1.0569e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 77/100\n",
      "957/957 - 2s - loss: 1.0425e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 78/100\n",
      "957/957 - 1s - loss: 1.0574e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 79/100\n",
      "957/957 - 2s - loss: 9.8777e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 80/100\n",
      "957/957 - 2s - loss: 1.0574e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 81/100\n",
      "957/957 - 1s - loss: 1.0328e-04 - 1s/epoch - 1ms/step\n",
      "Epoch 82/100\n",
      "957/957 - 2s - loss: 1.0361e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 83/100\n",
      "957/957 - 1s - loss: 9.9813e-05 - 1s/epoch - 2ms/step\n",
      "Epoch 84/100\n",
      "957/957 - 1s - loss: 9.7060e-05 - 1s/epoch - 2ms/step\n",
      "Epoch 85/100\n",
      "957/957 - 2s - loss: 9.7996e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 86/100\n",
      "957/957 - 2s - loss: 9.9564e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 87/100\n",
      "957/957 - 2s - loss: 9.8145e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 88/100\n",
      "957/957 - 2s - loss: 1.0015e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 89/100\n",
      "957/957 - 3s - loss: 9.6668e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 90/100\n",
      "957/957 - 1s - loss: 9.8063e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 91/100\n",
      "957/957 - 2s - loss: 1.0726e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 92/100\n",
      "957/957 - 2s - loss: 1.0193e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 93/100\n",
      "957/957 - 1s - loss: 9.6589e-05 - 1s/epoch - 1ms/step\n",
      "Epoch 94/100\n",
      "957/957 - 2s - loss: 1.0140e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 95/100\n",
      "957/957 - 2s - loss: 9.3200e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 96/100\n",
      "957/957 - 1s - loss: 9.9976e-05 - 1s/epoch - 2ms/step\n",
      "Epoch 97/100\n",
      "957/957 - 2s - loss: 1.0167e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 98/100\n",
      "957/957 - 2s - loss: 9.9362e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 99/100\n",
      "957/957 - 2s - loss: 9.8816e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 100/100\n",
      "957/957 - 1s - loss: 9.4964e-05 - 1s/epoch - 2ms/step\n",
      "13/13 [==============================] - 0s 2ms/step\n",
      "\n",
      "\n",
      "Evaluation Metrics for All Tokens:\n",
      "  Symbol           MSE         RMSE          MAE      MAPE      Corr        R2\n",
      "0    LTC  1.086921e+02    10.425548     5.671813  4.789613  0.991807  0.982610\n",
      "1    ETH  1.043127e+04   102.133603    49.534535  4.337902  0.994356  0.988519\n",
      "2   DOGE  8.013556e-04     0.028308     0.009414  8.082299  0.986679  0.955656\n",
      "3    BTC  7.371176e+06  2714.991025  1564.615526  4.473806  0.996693  0.977252\n",
      "4    BNB  1.122289e+03    33.500578    15.217210  5.603422  0.993032  0.965664\n",
      "5    ADA  1.054776e-02     0.102702     0.055291  7.480709  0.993433  0.970547\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from math import sqrt\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# load data\n",
    "df = pd.read_csv('sixcrypto.csv')\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df.set_index('Date', inplace=True)\n",
    "df.sort_index(inplace=True)\n",
    "\n",
    "# create a list of unique tokens offered on the exchange\n",
    "tokens = df['Symbol'].unique()\n",
    "\n",
    "# create a dictionary to store the models and their performance metrics\n",
    "models = {}\n",
    "\n",
    "# create a 2x3 grid of subplots\n",
    "fig, axs = plt.subplots(2, 3, figsize=(15, 10))\n",
    "\n",
    "# loop through each token and plot its actual vs predicted values on a subplot\n",
    "for i, token in enumerate(tokens):\n",
    "    # select the data for the current token\n",
    "    data = df[df['Symbol'] == token]['Close'].values.reshape(-1, 1)\n",
    "\n",
    "    # preprocess data\n",
    "    scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "    scaled_data = scaler.fit_transform(data)\n",
    "\n",
    "    # split data into training and testing sets\n",
    "    train_size = int(len(scaled_data) * 0.7)\n",
    "    train_data = scaled_data[:train_size, :]\n",
    "    test_data = scaled_data[train_size:, :]\n",
    "\n",
    "    # function to create LSTM dataset\n",
    "    def create_dataset(dataset, look_back=1):\n",
    "        X, y = [], []\n",
    "        for i in range(len(dataset)-look_back-1):\n",
    "            a = dataset[i:(i+look_back), 0]\n",
    "            X.append(a)\n",
    "            y.append(dataset[i + look_back, 0])\n",
    "        return np.array(X), np.array(y)\n",
    "\n",
    "    # create training and testing data for LSTM model\n",
    "    look_back = 3\n",
    "    X_train, y_train = create_dataset(train_data, look_back)\n",
    "    X_test, y_test = create_dataset(test_data, look_back)\n",
    "\n",
    "    # reshape input to be [samples, time steps, features]\n",
    "    X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
    "    X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
    "\n",
    "    # create and train LSTM model\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(50, input_shape=(look_back, 1)))\n",
    "    model.add(Dense(1))\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "    model.fit(X_train, y_train, epochs=100, batch_size=1, verbose=2)\n",
    "\n",
    "    # make predictions on test data\n",
    "    y_pred = model.predict(X_test)\n",
    "\n",
    "    # inverse transform the scaled data\n",
    "    y_test = scaler.inverse_transform(y_test.reshape(-1, 1))\n",
    "    y_pred = scaler.inverse_transform(y_pred)\n",
    "\n",
    "    # evaluate model using various performance metrics\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    rmse = sqrt(mse)\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    mape = np.mean(np.abs((y_test - y_pred) / y_test)) * 100\n",
    "    corr = np.corrcoef(y_pred.T, y_test.T)[0, 1]\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "    # add the model and its performance metrics to\n",
    "    models[token] = {'model': model, 'mse': mse, 'rmse': rmse, 'mae': mae, 'mape': mape, 'corr': corr, 'r2': r2}\n",
    "\n",
    "    # plot actual vs predicted values on a subplot\n",
    "    row = i // 3\n",
    "    col = i % 3\n",
    "    axs[row, col].plot(y_test, label='Actual')\n",
    "    axs[row, col].plot(y_pred, label='Predicted')\n",
    "    axs[row, col].set_title('{} Actual vs Predicted Values'.format(token))\n",
    "    axs[row, col].set_xlabel('Time')\n",
    "    axs[row, col].set_ylabel('Price')\n",
    "    axs[row, col].legend()\n",
    "\n",
    "        # create a table to display evaluation metrics for all models\n",
    "    results = pd.DataFrame(columns=['Symbol', 'MSE', 'RMSE', 'MAE', 'MAPE', 'Corr', 'R2'])\n",
    "    for token, metrics in models.items():\n",
    "        results = results.append({'Symbol': token,\n",
    "                              'MSE': metrics['mse'],\n",
    "                              'RMSE': metrics['rmse'],\n",
    "                              'MAE': metrics['mae'],\n",
    "                              'MAPE': metrics['mape'],\n",
    "                              'Corr': metrics['corr'],\n",
    "                              'R2': metrics['r2']}, ignore_index=True)\n",
    "\n",
    "    # display results table\n",
    "    print('\\n\\nEvaluation Metrics for All Tokens:')\n",
    "    print(results)\n",
    "\n",
    "# adjust subplot layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "1149/1149 - 3s - loss: 0.0062 - 3s/epoch - 3ms/step\n",
      "Epoch 2/100\n",
      "1149/1149 - 2s - loss: 0.0045 - 2s/epoch - 1ms/step\n",
      "Epoch 3/100\n",
      "1149/1149 - 2s - loss: 0.0038 - 2s/epoch - 2ms/step\n",
      "Epoch 4/100\n",
      "1149/1149 - 2s - loss: 0.0043 - 2s/epoch - 2ms/step\n",
      "Epoch 5/100\n",
      "1149/1149 - 2s - loss: 0.0039 - 2s/epoch - 2ms/step\n",
      "Epoch 6/100\n",
      "1149/1149 - 2s - loss: 0.0036 - 2s/epoch - 2ms/step\n",
      "Epoch 7/100\n",
      "1149/1149 - 2s - loss: 0.0031 - 2s/epoch - 2ms/step\n",
      "Epoch 8/100\n",
      "1149/1149 - 2s - loss: 0.0034 - 2s/epoch - 2ms/step\n",
      "Epoch 9/100\n",
      "1149/1149 - 2s - loss: 0.0027 - 2s/epoch - 2ms/step\n",
      "Epoch 10/100\n",
      "1149/1149 - 2s - loss: 0.0028 - 2s/epoch - 2ms/step\n",
      "Epoch 11/100\n",
      "1149/1149 - 2s - loss: 0.0025 - 2s/epoch - 2ms/step\n",
      "Epoch 12/100\n",
      "1149/1149 - 2s - loss: 0.0023 - 2s/epoch - 2ms/step\n",
      "Epoch 13/100\n",
      "1149/1149 - 2s - loss: 0.0028 - 2s/epoch - 2ms/step\n",
      "Epoch 14/100\n",
      "1149/1149 - 2s - loss: 0.0021 - 2s/epoch - 2ms/step\n",
      "Epoch 15/100\n",
      "1149/1149 - 2s - loss: 0.0021 - 2s/epoch - 2ms/step\n",
      "Epoch 16/100\n",
      "1149/1149 - 2s - loss: 0.0018 - 2s/epoch - 2ms/step\n",
      "Epoch 17/100\n",
      "1149/1149 - 2s - loss: 0.0016 - 2s/epoch - 2ms/step\n",
      "Epoch 18/100\n",
      "1149/1149 - 2s - loss: 0.0017 - 2s/epoch - 2ms/step\n",
      "Epoch 19/100\n",
      "1149/1149 - 2s - loss: 0.0017 - 2s/epoch - 2ms/step\n",
      "Epoch 20/100\n",
      "1149/1149 - 2s - loss: 0.0014 - 2s/epoch - 2ms/step\n",
      "Epoch 21/100\n",
      "1149/1149 - 2s - loss: 0.0017 - 2s/epoch - 2ms/step\n",
      "Epoch 22/100\n",
      "1149/1149 - 2s - loss: 0.0014 - 2s/epoch - 2ms/step\n",
      "Epoch 23/100\n",
      "1149/1149 - 2s - loss: 0.0015 - 2s/epoch - 2ms/step\n",
      "Epoch 24/100\n",
      "1149/1149 - 3s - loss: 0.0015 - 3s/epoch - 2ms/step\n",
      "Epoch 25/100\n",
      "1149/1149 - 2s - loss: 0.0017 - 2s/epoch - 2ms/step\n",
      "Epoch 26/100\n",
      "1149/1149 - 2s - loss: 0.0013 - 2s/epoch - 2ms/step\n",
      "Epoch 27/100\n",
      "1149/1149 - 2s - loss: 0.0014 - 2s/epoch - 2ms/step\n",
      "Epoch 28/100\n",
      "1149/1149 - 2s - loss: 0.0017 - 2s/epoch - 2ms/step\n",
      "Epoch 29/100\n",
      "1149/1149 - 2s - loss: 0.0014 - 2s/epoch - 2ms/step\n",
      "Epoch 30/100\n",
      "1149/1149 - 2s - loss: 0.0016 - 2s/epoch - 2ms/step\n",
      "Epoch 31/100\n",
      "1149/1149 - 2s - loss: 0.0014 - 2s/epoch - 2ms/step\n",
      "Epoch 32/100\n",
      "1149/1149 - 2s - loss: 0.0014 - 2s/epoch - 2ms/step\n",
      "Epoch 33/100\n",
      "1149/1149 - 2s - loss: 0.0015 - 2s/epoch - 2ms/step\n",
      "Epoch 34/100\n",
      "1149/1149 - 2s - loss: 0.0015 - 2s/epoch - 2ms/step\n",
      "Epoch 35/100\n",
      "1149/1149 - 2s - loss: 0.0013 - 2s/epoch - 2ms/step\n",
      "Epoch 36/100\n",
      "1149/1149 - 2s - loss: 0.0012 - 2s/epoch - 2ms/step\n",
      "Epoch 37/100\n",
      "1149/1149 - 2s - loss: 0.0012 - 2s/epoch - 2ms/step\n",
      "Epoch 38/100\n",
      "1149/1149 - 2s - loss: 0.0013 - 2s/epoch - 2ms/step\n",
      "Epoch 39/100\n",
      "1149/1149 - 2s - loss: 0.0013 - 2s/epoch - 2ms/step\n",
      "Epoch 40/100\n",
      "1149/1149 - 2s - loss: 0.0012 - 2s/epoch - 2ms/step\n",
      "Epoch 41/100\n",
      "1149/1149 - 2s - loss: 0.0014 - 2s/epoch - 2ms/step\n",
      "Epoch 42/100\n",
      "1149/1149 - 2s - loss: 0.0011 - 2s/epoch - 2ms/step\n",
      "Epoch 43/100\n",
      "1149/1149 - 2s - loss: 0.0012 - 2s/epoch - 2ms/step\n",
      "Epoch 44/100\n",
      "1149/1149 - 2s - loss: 0.0011 - 2s/epoch - 2ms/step\n",
      "Epoch 45/100\n",
      "1149/1149 - 2s - loss: 0.0011 - 2s/epoch - 2ms/step\n",
      "Epoch 46/100\n",
      "1149/1149 - 2s - loss: 0.0010 - 2s/epoch - 2ms/step\n",
      "Epoch 47/100\n",
      "1149/1149 - 2s - loss: 0.0011 - 2s/epoch - 2ms/step\n",
      "Epoch 48/100\n",
      "1149/1149 - 2s - loss: 0.0012 - 2s/epoch - 2ms/step\n",
      "Epoch 49/100\n",
      "1149/1149 - 2s - loss: 0.0012 - 2s/epoch - 2ms/step\n",
      "Epoch 50/100\n",
      "1149/1149 - 2s - loss: 0.0011 - 2s/epoch - 2ms/step\n",
      "Epoch 51/100\n",
      "1149/1149 - 2s - loss: 0.0011 - 2s/epoch - 2ms/step\n",
      "Epoch 52/100\n",
      "1149/1149 - 2s - loss: 0.0010 - 2s/epoch - 2ms/step\n",
      "Epoch 53/100\n",
      "1149/1149 - 2s - loss: 0.0011 - 2s/epoch - 2ms/step\n",
      "Epoch 54/100\n",
      "1149/1149 - 2s - loss: 0.0011 - 2s/epoch - 2ms/step\n",
      "Epoch 55/100\n",
      "1149/1149 - 2s - loss: 9.4691e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 56/100\n",
      "1149/1149 - 2s - loss: 8.6947e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 57/100\n",
      "1149/1149 - 2s - loss: 9.4919e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 58/100\n",
      "1149/1149 - 2s - loss: 9.6647e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 59/100\n",
      "1149/1149 - 2s - loss: 0.0010 - 2s/epoch - 2ms/step\n",
      "Epoch 60/100\n",
      "1149/1149 - 2s - loss: 9.9527e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 61/100\n",
      "1149/1149 - 2s - loss: 0.0010 - 2s/epoch - 2ms/step\n",
      "Epoch 62/100\n",
      "1149/1149 - 2s - loss: 0.0011 - 2s/epoch - 2ms/step\n",
      "Epoch 63/100\n",
      "1149/1149 - 2s - loss: 0.0011 - 2s/epoch - 2ms/step\n",
      "Epoch 64/100\n",
      "1149/1149 - 2s - loss: 9.1370e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 65/100\n",
      "1149/1149 - 2s - loss: 8.6119e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 66/100\n",
      "1149/1149 - 2s - loss: 0.0010 - 2s/epoch - 2ms/step\n",
      "Epoch 67/100\n",
      "1149/1149 - 2s - loss: 0.0011 - 2s/epoch - 2ms/step\n",
      "Epoch 68/100\n",
      "1149/1149 - 2s - loss: 9.2309e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 69/100\n",
      "1149/1149 - 2s - loss: 8.5412e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 70/100\n",
      "1149/1149 - 2s - loss: 8.9600e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 71/100\n",
      "1149/1149 - 2s - loss: 9.2462e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 72/100\n",
      "1149/1149 - 2s - loss: 8.7964e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 73/100\n",
      "1149/1149 - 2s - loss: 9.1716e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 74/100\n",
      "1149/1149 - 2s - loss: 9.7216e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 75/100\n",
      "1149/1149 - 2s - loss: 7.7704e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 76/100\n",
      "1149/1149 - 2s - loss: 9.5323e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 77/100\n",
      "1149/1149 - 2s - loss: 8.0510e-04 - 2s/epoch - 1ms/step\n",
      "Epoch 78/100\n",
      "1149/1149 - 2s - loss: 9.9980e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 79/100\n",
      "1149/1149 - 2s - loss: 9.0600e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 80/100\n",
      "1149/1149 - 2s - loss: 8.3522e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 81/100\n",
      "1149/1149 - 2s - loss: 7.4112e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 82/100\n",
      "1149/1149 - 2s - loss: 9.3305e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 83/100\n",
      "1149/1149 - 2s - loss: 7.1396e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 84/100\n",
      "1149/1149 - 2s - loss: 7.5844e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 85/100\n",
      "1149/1149 - 2s - loss: 9.8073e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 86/100\n",
      "1149/1149 - 2s - loss: 7.5732e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 87/100\n",
      "1149/1149 - 2s - loss: 7.6084e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 88/100\n",
      "1149/1149 - 2s - loss: 7.7225e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 89/100\n",
      "1149/1149 - 2s - loss: 7.7635e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 90/100\n",
      "1149/1149 - 2s - loss: 6.8154e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 91/100\n",
      "1149/1149 - 2s - loss: 9.2148e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 92/100\n",
      "1149/1149 - 2s - loss: 6.4424e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 93/100\n",
      "1149/1149 - 2s - loss: 8.0937e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 94/100\n",
      "1149/1149 - 2s - loss: 7.2521e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 95/100\n",
      "1149/1149 - 2s - loss: 7.5871e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 96/100\n",
      "1149/1149 - 2s - loss: 6.6219e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 97/100\n",
      "1149/1149 - 2s - loss: 8.0312e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 98/100\n",
      "1149/1149 - 2s - loss: 7.1014e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 99/100\n",
      "1149/1149 - 2s - loss: 7.7988e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 100/100\n",
      "1149/1149 - 2s - loss: 8.6858e-04 - 2s/epoch - 2ms/step\n",
      "16/16 [==============================] - 0s 1ms/step\n",
      "\n",
      "\n",
      "Evaluation Metrics for All Tokens: Dropout Model\n",
      "  Symbol         MSE       RMSE        MAE       MAPE      Corr        R2\n",
      "0    LTC  644.997651  25.396804  16.600663  11.201755  0.990316  0.896802\n",
      "Epoch 1/100\n",
      "1149/1149 - 3s - loss: 0.0011 - 3s/epoch - 3ms/step\n",
      "Epoch 2/100\n",
      "1149/1149 - 2s - loss: 7.5143e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 3/100\n",
      "1149/1149 - 2s - loss: 7.7676e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 4/100\n",
      "1149/1149 - 2s - loss: 6.0614e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 5/100\n",
      "1149/1149 - 2s - loss: 5.8689e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 6/100\n",
      "1149/1149 - 2s - loss: 4.9222e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 7/100\n",
      "1149/1149 - 2s - loss: 5.4491e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 8/100\n",
      "1149/1149 - 2s - loss: 5.2008e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 9/100\n",
      "1149/1149 - 2s - loss: 6.2991e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 10/100\n",
      "1149/1149 - 2s - loss: 5.3901e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 11/100\n",
      "1149/1149 - 2s - loss: 5.9301e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 12/100\n",
      "1149/1149 - 2s - loss: 5.1158e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 13/100\n",
      "1149/1149 - 2s - loss: 5.0741e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 14/100\n",
      "1149/1149 - 2s - loss: 5.0090e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 15/100\n",
      "1149/1149 - 2s - loss: 4.3781e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 16/100\n",
      "1149/1149 - 2s - loss: 3.8096e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 17/100\n",
      "1149/1149 - 2s - loss: 4.3431e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 18/100\n",
      "1149/1149 - 2s - loss: 3.5343e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 19/100\n",
      "1149/1149 - 2s - loss: 3.1058e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 20/100\n",
      "1149/1149 - 2s - loss: 3.1872e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 21/100\n",
      "1149/1149 - 2s - loss: 3.6646e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 22/100\n",
      "1149/1149 - 2s - loss: 3.1371e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 23/100\n",
      "1149/1149 - 2s - loss: 2.8243e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 24/100\n",
      "1149/1149 - 2s - loss: 2.6568e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 25/100\n",
      "1149/1149 - 2s - loss: 2.9359e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 26/100\n",
      "1149/1149 - 2s - loss: 2.4720e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 27/100\n",
      "1149/1149 - 2s - loss: 2.7740e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 28/100\n",
      "1149/1149 - 2s - loss: 2.1801e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 29/100\n",
      "1149/1149 - 2s - loss: 1.9257e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 30/100\n",
      "1149/1149 - 2s - loss: 2.2728e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 31/100\n",
      "1149/1149 - 2s - loss: 2.1296e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 32/100\n",
      "1149/1149 - 2s - loss: 2.2567e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 33/100\n",
      "1149/1149 - 2s - loss: 1.8481e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 34/100\n",
      "1149/1149 - 2s - loss: 1.7713e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 35/100\n",
      "1149/1149 - 2s - loss: 2.0819e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 36/100\n",
      "1149/1149 - 2s - loss: 1.8166e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 37/100\n",
      "1149/1149 - 2s - loss: 1.9667e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 38/100\n",
      "1149/1149 - 2s - loss: 1.8994e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 39/100\n",
      "1149/1149 - 2s - loss: 1.9832e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 40/100\n",
      "1149/1149 - 2s - loss: 1.8953e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 41/100\n",
      "1149/1149 - 2s - loss: 1.9324e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 42/100\n",
      "1149/1149 - 2s - loss: 1.7772e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 43/100\n",
      "1149/1149 - 2s - loss: 1.8787e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 44/100\n",
      "1149/1149 - 2s - loss: 1.6608e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 45/100\n",
      "1149/1149 - 2s - loss: 1.9392e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 46/100\n",
      "1149/1149 - 2s - loss: 1.6181e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 47/100\n",
      "1149/1149 - 2s - loss: 1.8171e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 48/100\n",
      "1149/1149 - 2s - loss: 1.6850e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 49/100\n",
      "1149/1149 - 2s - loss: 1.7234e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 50/100\n",
      "1149/1149 - 2s - loss: 1.8366e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 51/100\n",
      "1149/1149 - 2s - loss: 1.9517e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 52/100\n",
      "1149/1149 - 2s - loss: 1.6057e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 53/100\n",
      "1149/1149 - 2s - loss: 1.6127e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 54/100\n",
      "1149/1149 - 2s - loss: 1.6586e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 55/100\n",
      "1149/1149 - 2s - loss: 1.7008e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 56/100\n",
      "1149/1149 - 2s - loss: 1.6513e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 57/100\n",
      "1149/1149 - 2s - loss: 1.7145e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 58/100\n",
      "1149/1149 - 2s - loss: 1.7728e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 59/100\n",
      "1149/1149 - 2s - loss: 1.5717e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 60/100\n",
      "1149/1149 - 2s - loss: 1.8182e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 61/100\n",
      "1149/1149 - 2s - loss: 1.7866e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 62/100\n",
      "1149/1149 - 2s - loss: 1.7362e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 63/100\n",
      "1149/1149 - 2s - loss: 1.3318e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 64/100\n",
      "1149/1149 - 2s - loss: 1.5801e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 65/100\n",
      "1149/1149 - 2s - loss: 1.7528e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 66/100\n",
      "1149/1149 - 2s - loss: 1.4626e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 67/100\n",
      "1149/1149 - 2s - loss: 1.5274e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 68/100\n",
      "1149/1149 - 2s - loss: 1.6238e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 69/100\n",
      "1149/1149 - 2s - loss: 1.5229e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 70/100\n",
      "1149/1149 - 2s - loss: 1.4182e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 71/100\n",
      "1149/1149 - 2s - loss: 1.6794e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 72/100\n",
      "1149/1149 - 2s - loss: 1.2689e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 73/100\n",
      "1149/1149 - 2s - loss: 1.4608e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 74/100\n",
      "1149/1149 - 2s - loss: 1.3347e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 75/100\n",
      "1149/1149 - 2s - loss: 1.5956e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 76/100\n",
      "1149/1149 - 2s - loss: 1.4121e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 77/100\n",
      "1149/1149 - 2s - loss: 1.6168e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 78/100\n",
      "1149/1149 - 2s - loss: 1.2878e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 79/100\n",
      "1149/1149 - 2s - loss: 1.3405e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 80/100\n",
      "1149/1149 - 2s - loss: 1.4949e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 81/100\n",
      "1149/1149 - 2s - loss: 1.3464e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 82/100\n",
      "1149/1149 - 2s - loss: 1.3470e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 83/100\n",
      "1149/1149 - 2s - loss: 1.3097e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 84/100\n",
      "1149/1149 - 2s - loss: 1.3924e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 85/100\n",
      "1149/1149 - 2s - loss: 1.4229e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 86/100\n",
      "1149/1149 - 2s - loss: 1.3180e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 87/100\n",
      "1149/1149 - 2s - loss: 1.5686e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 88/100\n",
      "1149/1149 - 2s - loss: 1.2779e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 89/100\n",
      "1149/1149 - 2s - loss: 1.2226e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 90/100\n",
      "1149/1149 - 2s - loss: 1.3714e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 91/100\n",
      "1149/1149 - 2s - loss: 1.4491e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 92/100\n",
      "1149/1149 - 2s - loss: 1.5476e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 93/100\n",
      "1149/1149 - 2s - loss: 1.1764e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 94/100\n",
      "1149/1149 - 2s - loss: 1.3784e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 95/100\n",
      "1149/1149 - 2s - loss: 1.3725e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 96/100\n",
      "1149/1149 - 2s - loss: 1.3376e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 97/100\n",
      "1149/1149 - 2s - loss: 1.5563e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 98/100\n",
      "1149/1149 - 2s - loss: 1.6204e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 99/100\n",
      "1149/1149 - 2s - loss: 1.6883e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 100/100\n",
      "1149/1149 - 2s - loss: 1.2908e-04 - 2s/epoch - 2ms/step\n",
      "16/16 [==============================] - 0s 2ms/step\n",
      "\n",
      "\n",
      "Evaluation Metrics for All Tokens: Dropout Model\n",
      "  Symbol            MSE        RMSE         MAE       MAPE      Corr        R2\n",
      "0    LTC     644.997651   25.396804   16.600663  11.201755  0.990316  0.896802\n",
      "1    ETH  185669.338720  430.893651  270.635298  22.260517  0.990041  0.795638\n",
      "Epoch 1/100\n",
      "1149/1149 - 3s - loss: 6.9731e-06 - 3s/epoch - 3ms/step\n",
      "Epoch 2/100\n",
      "1149/1149 - 2s - loss: 4.1673e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 3/100\n",
      "1149/1149 - 2s - loss: 3.4058e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 4/100\n",
      "1149/1149 - 2s - loss: 2.9103e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 5/100\n",
      "1149/1149 - 2s - loss: 2.7693e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 6/100\n",
      "1149/1149 - 2s - loss: 3.3179e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 7/100\n",
      "1149/1149 - 2s - loss: 3.6618e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 8/100\n",
      "1149/1149 - 2s - loss: 2.9930e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 9/100\n",
      "1149/1149 - 2s - loss: 2.8721e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 10/100\n",
      "1149/1149 - 2s - loss: 2.6352e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 11/100\n",
      "1149/1149 - 2s - loss: 3.1352e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 12/100\n",
      "1149/1149 - 2s - loss: 2.7923e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 13/100\n",
      "1149/1149 - 2s - loss: 2.7086e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 14/100\n",
      "1149/1149 - 2s - loss: 3.4442e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 15/100\n",
      "1149/1149 - 2s - loss: 2.9877e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 16/100\n",
      "1149/1149 - 2s - loss: 3.5792e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 17/100\n",
      "1149/1149 - 2s - loss: 2.6312e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 18/100\n",
      "1149/1149 - 2s - loss: 2.7868e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 19/100\n",
      "1149/1149 - 2s - loss: 3.4390e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 20/100\n",
      "1149/1149 - 2s - loss: 2.6052e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 21/100\n",
      "1149/1149 - 2s - loss: 2.5794e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 22/100\n",
      "1149/1149 - 2s - loss: 2.9291e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 23/100\n",
      "1149/1149 - 2s - loss: 3.1488e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 24/100\n",
      "1149/1149 - 2s - loss: 2.7169e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 25/100\n",
      "1149/1149 - 2s - loss: 3.1348e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 26/100\n",
      "1149/1149 - 2s - loss: 3.1502e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 27/100\n",
      "1149/1149 - 2s - loss: 2.4450e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 28/100\n",
      "1149/1149 - 2s - loss: 3.2409e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 29/100\n",
      "1149/1149 - 2s - loss: 3.3606e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 30/100\n",
      "1149/1149 - 2s - loss: 2.7521e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 31/100\n",
      "1149/1149 - 2s - loss: 2.1315e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 32/100\n",
      "1149/1149 - 2s - loss: 2.6551e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 33/100\n",
      "1149/1149 - 2s - loss: 2.7446e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 34/100\n",
      "1149/1149 - 2s - loss: 2.7925e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 35/100\n",
      "1149/1149 - 2s - loss: 2.4080e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 36/100\n",
      "1149/1149 - 2s - loss: 2.7048e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 37/100\n",
      "1149/1149 - 2s - loss: 2.7541e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 38/100\n",
      "1149/1149 - 2s - loss: 2.5819e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 39/100\n",
      "1149/1149 - 2s - loss: 2.6953e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 40/100\n",
      "1149/1149 - 2s - loss: 3.0034e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 41/100\n",
      "1149/1149 - 2s - loss: 2.8546e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 42/100\n",
      "1149/1149 - 2s - loss: 2.6395e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 43/100\n",
      "1149/1149 - 2s - loss: 2.8482e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 44/100\n",
      "1149/1149 - 3s - loss: 2.5246e-06 - 3s/epoch - 2ms/step\n",
      "Epoch 45/100\n",
      "1149/1149 - 3s - loss: 2.2273e-06 - 3s/epoch - 3ms/step\n",
      "Epoch 46/100\n",
      "1149/1149 - 3s - loss: 2.5416e-06 - 3s/epoch - 2ms/step\n",
      "Epoch 47/100\n",
      "1149/1149 - 2s - loss: 2.7748e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 48/100\n",
      "1149/1149 - 2s - loss: 2.9784e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 49/100\n",
      "1149/1149 - 2s - loss: 2.6466e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 50/100\n",
      "1149/1149 - 2s - loss: 3.6454e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 51/100\n",
      "1149/1149 - 2s - loss: 2.2637e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 52/100\n",
      "1149/1149 - 2s - loss: 2.9721e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 53/100\n",
      "1149/1149 - 2s - loss: 2.1788e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 54/100\n",
      "1149/1149 - 2s - loss: 2.9871e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 55/100\n",
      "1149/1149 - 2s - loss: 2.5303e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 56/100\n",
      "1149/1149 - 2s - loss: 2.6930e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 57/100\n",
      "1149/1149 - 2s - loss: 2.8029e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 58/100\n",
      "1149/1149 - 2s - loss: 2.5206e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 59/100\n",
      "1149/1149 - 2s - loss: 2.3801e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 60/100\n",
      "1149/1149 - 2s - loss: 2.4465e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 61/100\n",
      "1149/1149 - 2s - loss: 2.4809e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 62/100\n",
      "1149/1149 - 2s - loss: 2.9277e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 63/100\n",
      "1149/1149 - 2s - loss: 3.0891e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 64/100\n",
      "1149/1149 - 2s - loss: 2.5422e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 65/100\n",
      "1149/1149 - 2s - loss: 2.7378e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 66/100\n",
      "1149/1149 - 2s - loss: 2.4911e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 67/100\n",
      "1149/1149 - 2s - loss: 2.5380e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 68/100\n",
      "1149/1149 - 2s - loss: 2.5013e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 69/100\n",
      "1149/1149 - 2s - loss: 2.5870e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 70/100\n",
      "1149/1149 - 2s - loss: 2.7777e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 71/100\n",
      "1149/1149 - 2s - loss: 2.4339e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 72/100\n",
      "1149/1149 - 2s - loss: 2.2764e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 73/100\n",
      "1149/1149 - 2s - loss: 2.5639e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 74/100\n",
      "1149/1149 - 2s - loss: 2.3105e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 75/100\n",
      "1149/1149 - 2s - loss: 2.9072e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 76/100\n",
      "1149/1149 - 2s - loss: 2.7280e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 77/100\n",
      "1149/1149 - 2s - loss: 2.4535e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 78/100\n",
      "1149/1149 - 2s - loss: 2.1088e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 79/100\n",
      "1149/1149 - 2s - loss: 2.7964e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 80/100\n",
      "1149/1149 - 2s - loss: 2.6558e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 81/100\n",
      "1149/1149 - 2s - loss: 2.3236e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 82/100\n",
      "1149/1149 - 2s - loss: 3.0360e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 83/100\n",
      "1149/1149 - 2s - loss: 2.3259e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 84/100\n",
      "1149/1149 - 2s - loss: 2.4457e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 85/100\n",
      "1149/1149 - 2s - loss: 2.4853e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 86/100\n",
      "1149/1149 - 2s - loss: 2.3939e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 87/100\n",
      "1149/1149 - 2s - loss: 2.8522e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 88/100\n",
      "1149/1149 - 2s - loss: 2.4511e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 89/100\n",
      "1149/1149 - 2s - loss: 2.5975e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 90/100\n",
      "1149/1149 - 2s - loss: 2.1263e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 91/100\n",
      "1149/1149 - 2s - loss: 2.9141e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 92/100\n",
      "1149/1149 - 3s - loss: 3.1264e-06 - 3s/epoch - 2ms/step\n",
      "Epoch 93/100\n",
      "1149/1149 - 2s - loss: 2.4620e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 94/100\n",
      "1149/1149 - 2s - loss: 2.8615e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 95/100\n",
      "1149/1149 - 2s - loss: 2.3014e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 96/100\n",
      "1149/1149 - 2s - loss: 2.7195e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 97/100\n",
      "1149/1149 - 2s - loss: 2.3756e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 98/100\n",
      "1149/1149 - 2s - loss: 2.8051e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 99/100\n",
      "1149/1149 - 2s - loss: 2.5374e-06 - 2s/epoch - 2ms/step\n",
      "Epoch 100/100\n",
      "1149/1149 - 2s - loss: 2.3264e-06 - 2s/epoch - 2ms/step\n",
      "16/16 [==============================] - 0s 1ms/step\n",
      "\n",
      "\n",
      "Evaluation Metrics for All Tokens: Dropout Model\n",
      "  Symbol            MSE        RMSE         MAE       MAPE      Corr        R2\n",
      "0    LTC     644.997651   25.396804   16.600663  11.201755  0.990316  0.896802\n",
      "1    ETH  185669.338720  430.893651  270.635298  22.260517  0.990041  0.795638\n",
      "2   DOGE       0.008233    0.090735    0.036417  19.170134  0.969997  0.544419\n",
      "Epoch 1/100\n",
      "1149/1149 - 3s - loss: 0.0012 - 3s/epoch - 3ms/step\n",
      "Epoch 2/100\n",
      "1149/1149 - 2s - loss: 8.5297e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 3/100\n",
      "1149/1149 - 2s - loss: 9.0876e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 4/100\n",
      "1149/1149 - 2s - loss: 7.8160e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 5/100\n",
      "1149/1149 - 2s - loss: 6.7479e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 6/100\n",
      "1149/1149 - 2s - loss: 6.7031e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 7/100\n",
      "1149/1149 - 2s - loss: 5.9545e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 8/100\n",
      "1149/1149 - 2s - loss: 5.9879e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 9/100\n",
      "1149/1149 - 2s - loss: 5.1328e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 10/100\n",
      "1149/1149 - 2s - loss: 4.3343e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 11/100\n",
      "1149/1149 - 2s - loss: 4.9449e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 12/100\n",
      "1149/1149 - 2s - loss: 3.7996e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 13/100\n",
      "1149/1149 - 2s - loss: 3.9231e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 14/100\n",
      "1149/1149 - 3s - loss: 3.6066e-04 - 3s/epoch - 2ms/step\n",
      "Epoch 15/100\n",
      "1149/1149 - 2s - loss: 3.5580e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 16/100\n",
      "1149/1149 - 2s - loss: 4.2875e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 17/100\n",
      "1149/1149 - 2s - loss: 3.6348e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 18/100\n",
      "1149/1149 - 3s - loss: 3.9100e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 19/100\n",
      "1149/1149 - 3s - loss: 3.7409e-04 - 3s/epoch - 2ms/step\n",
      "Epoch 20/100\n",
      "1149/1149 - 2s - loss: 3.8538e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 21/100\n",
      "1149/1149 - 2s - loss: 3.9692e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 22/100\n",
      "1149/1149 - 2s - loss: 3.2455e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 23/100\n",
      "1149/1149 - 2s - loss: 3.3581e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 24/100\n",
      "1149/1149 - 3s - loss: 3.6987e-04 - 3s/epoch - 2ms/step\n",
      "Epoch 25/100\n",
      "1149/1149 - 2s - loss: 3.7745e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 26/100\n",
      "1149/1149 - 2s - loss: 3.7770e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 27/100\n",
      "1149/1149 - 2s - loss: 3.3972e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 28/100\n",
      "1149/1149 - 2s - loss: 3.5333e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 29/100\n",
      "1149/1149 - 2s - loss: 3.2972e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 30/100\n",
      "1149/1149 - 2s - loss: 2.9619e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 31/100\n",
      "1149/1149 - 2s - loss: 2.6650e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 32/100\n",
      "1149/1149 - 2s - loss: 2.3275e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 33/100\n",
      "1149/1149 - 2s - loss: 2.5577e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 34/100\n",
      "1149/1149 - 2s - loss: 2.8951e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 35/100\n",
      "1149/1149 - 2s - loss: 2.0229e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 36/100\n",
      "1149/1149 - 2s - loss: 2.1421e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 37/100\n",
      "1149/1149 - 2s - loss: 1.8534e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 38/100\n",
      "1149/1149 - 2s - loss: 1.7841e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 39/100\n",
      "1149/1149 - 2s - loss: 2.0384e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 40/100\n",
      "1149/1149 - 2s - loss: 1.8734e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 41/100\n",
      "1149/1149 - 2s - loss: 1.7127e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 42/100\n",
      "1149/1149 - 2s - loss: 1.8848e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 43/100\n",
      "1149/1149 - 2s - loss: 1.6888e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 44/100\n",
      "1149/1149 - 2s - loss: 1.7790e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 45/100\n",
      "1149/1149 - 2s - loss: 1.8515e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 46/100\n",
      "1149/1149 - 2s - loss: 1.7319e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 47/100\n",
      "1149/1149 - 2s - loss: 1.6162e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 48/100\n",
      "1149/1149 - 2s - loss: 1.7355e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 49/100\n",
      "1149/1149 - 2s - loss: 1.6203e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 50/100\n",
      "1149/1149 - 2s - loss: 1.5992e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 51/100\n",
      "1149/1149 - 2s - loss: 1.7290e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 52/100\n",
      "1149/1149 - 2s - loss: 1.6124e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 53/100\n",
      "1149/1149 - 2s - loss: 1.8347e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 54/100\n",
      "1149/1149 - 2s - loss: 1.5802e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 55/100\n",
      "1149/1149 - 2s - loss: 1.4038e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 56/100\n",
      "1149/1149 - 2s - loss: 1.6039e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 57/100\n",
      "1149/1149 - 2s - loss: 1.8363e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 58/100\n",
      "1149/1149 - 2s - loss: 1.5499e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 59/100\n",
      "1149/1149 - 2s - loss: 1.5840e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 60/100\n",
      "1149/1149 - 2s - loss: 1.4578e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 61/100\n",
      "1149/1149 - 2s - loss: 1.4433e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 62/100\n",
      "1149/1149 - 2s - loss: 1.4686e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 63/100\n",
      "1149/1149 - 2s - loss: 1.6004e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 64/100\n",
      "1149/1149 - 2s - loss: 1.7514e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 65/100\n",
      "1149/1149 - 2s - loss: 1.4415e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 66/100\n",
      "1149/1149 - 2s - loss: 1.5882e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 67/100\n",
      "1149/1149 - 2s - loss: 1.4680e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 68/100\n",
      "1149/1149 - 2s - loss: 1.2519e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 69/100\n",
      "1149/1149 - 3s - loss: 1.3644e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 70/100\n",
      "1149/1149 - 2s - loss: 1.5418e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 71/100\n",
      "1149/1149 - 3s - loss: 1.3978e-04 - 3s/epoch - 2ms/step\n",
      "Epoch 72/100\n",
      "1149/1149 - 3s - loss: 1.3188e-04 - 3s/epoch - 2ms/step\n",
      "Epoch 73/100\n",
      "1149/1149 - 4s - loss: 1.3915e-04 - 4s/epoch - 3ms/step\n",
      "Epoch 74/100\n",
      "1149/1149 - 2s - loss: 1.3507e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 75/100\n",
      "1149/1149 - 2s - loss: 1.4912e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 76/100\n",
      "1149/1149 - 3s - loss: 1.4493e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 77/100\n",
      "1149/1149 - 2s - loss: 1.5236e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 78/100\n",
      "1149/1149 - 2s - loss: 1.2796e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 79/100\n",
      "1149/1149 - 2s - loss: 1.5732e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 80/100\n",
      "1149/1149 - 3s - loss: 1.3871e-04 - 3s/epoch - 2ms/step\n",
      "Epoch 81/100\n",
      "1149/1149 - 3s - loss: 1.4293e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 82/100\n",
      "1149/1149 - 4s - loss: 1.3277e-04 - 4s/epoch - 4ms/step\n",
      "Epoch 83/100\n",
      "1149/1149 - 2s - loss: 1.4744e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 84/100\n",
      "1149/1149 - 2s - loss: 1.2247e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 85/100\n",
      "1149/1149 - 2s - loss: 1.5076e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 86/100\n",
      "1149/1149 - 2s - loss: 1.3903e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 87/100\n",
      "1149/1149 - 2s - loss: 1.3116e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 88/100\n",
      "1149/1149 - 3s - loss: 1.2339e-04 - 3s/epoch - 2ms/step\n",
      "Epoch 89/100\n",
      "1149/1149 - 2s - loss: 1.3969e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 90/100\n",
      "1149/1149 - 3s - loss: 1.2969e-04 - 3s/epoch - 2ms/step\n",
      "Epoch 91/100\n",
      "1149/1149 - 3s - loss: 1.3787e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 92/100\n",
      "1149/1149 - 3s - loss: 1.4169e-04 - 3s/epoch - 2ms/step\n",
      "Epoch 93/100\n",
      "1149/1149 - 3s - loss: 1.3391e-04 - 3s/epoch - 2ms/step\n",
      "Epoch 94/100\n",
      "1149/1149 - 3s - loss: 1.2811e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 95/100\n",
      "1149/1149 - 2s - loss: 1.3631e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 96/100\n",
      "1149/1149 - 3s - loss: 1.5977e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 97/100\n",
      "1149/1149 - 3s - loss: 1.3496e-04 - 3s/epoch - 2ms/step\n",
      "Epoch 98/100\n",
      "1149/1149 - 2s - loss: 1.4001e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 99/100\n",
      "1149/1149 - 3s - loss: 1.3278e-04 - 3s/epoch - 2ms/step\n",
      "Epoch 100/100\n",
      "1149/1149 - 2s - loss: 1.2924e-04 - 2s/epoch - 2ms/step\n",
      "16/16 [==============================] - 0s 1ms/step\n",
      "\n",
      "\n",
      "Evaluation Metrics for All Tokens: Dropout Model\n",
      "  Symbol           MSE          RMSE          MAE       MAPE      Corr  \\\n",
      "0    LTC  6.449977e+02     25.396804    16.600663  11.201755  0.990316   \n",
      "1    ETH  1.856693e+05    430.893651   270.635298  22.260517  0.990041   \n",
      "2   DOGE  8.232917e-03      0.090735     0.036417  19.170134  0.969997   \n",
      "3    BTC  1.476139e+08  12149.645190  8342.910071  25.921750  0.990583   \n",
      "\n",
      "         R2  \n",
      "0  0.896802  \n",
      "1  0.795638  \n",
      "2  0.544419  \n",
      "3  0.544458  \n",
      "Epoch 1/100\n",
      "1005/1005 - 4s - loss: 7.0791e-05 - 4s/epoch - 4ms/step\n",
      "Epoch 2/100\n",
      "1005/1005 - 2s - loss: 5.4783e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 3/100\n",
      "1005/1005 - 2s - loss: 5.0858e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 4/100\n",
      "1005/1005 - 2s - loss: 4.6901e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 5/100\n",
      "1005/1005 - 2s - loss: 4.9536e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 6/100\n",
      "1005/1005 - 2s - loss: 5.1593e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 7/100\n",
      "1005/1005 - 2s - loss: 4.7664e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 8/100\n",
      "1005/1005 - 2s - loss: 5.4630e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 9/100\n",
      "1005/1005 - 2s - loss: 4.8904e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 10/100\n",
      "1005/1005 - 2s - loss: 4.2320e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 11/100\n",
      "1005/1005 - 2s - loss: 4.2142e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 12/100\n",
      "1005/1005 - 3s - loss: 4.3118e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 13/100\n",
      "1005/1005 - 2s - loss: 3.6594e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 14/100\n",
      "1005/1005 - 2s - loss: 4.5046e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 15/100\n",
      "1005/1005 - 3s - loss: 4.3394e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 16/100\n",
      "1005/1005 - 2s - loss: 3.3760e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 17/100\n",
      "1005/1005 - 2s - loss: 3.6233e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 18/100\n",
      "1005/1005 - 3s - loss: 3.8115e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 19/100\n",
      "1005/1005 - 2s - loss: 3.9783e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 20/100\n",
      "1005/1005 - 3s - loss: 3.8729e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 21/100\n",
      "1005/1005 - 2s - loss: 3.7022e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 22/100\n",
      "1005/1005 - 2s - loss: 3.5086e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 23/100\n",
      "1005/1005 - 2s - loss: 3.1867e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 24/100\n",
      "1005/1005 - 2s - loss: 3.2729e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 25/100\n",
      "1005/1005 - 3s - loss: 3.4016e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 26/100\n",
      "1005/1005 - 3s - loss: 3.5247e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 27/100\n",
      "1005/1005 - 2s - loss: 3.2872e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 28/100\n",
      "1005/1005 - 2s - loss: 3.1639e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 29/100\n",
      "1005/1005 - 2s - loss: 3.1596e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 30/100\n",
      "1005/1005 - 3s - loss: 3.0928e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 31/100\n",
      "1005/1005 - 2s - loss: 2.7657e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 32/100\n",
      "1005/1005 - 3s - loss: 2.9723e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 33/100\n",
      "1005/1005 - 2s - loss: 2.6817e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 34/100\n",
      "1005/1005 - 2s - loss: 2.9387e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 35/100\n",
      "1005/1005 - 2s - loss: 2.7286e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 36/100\n",
      "1005/1005 - 3s - loss: 2.7028e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 37/100\n",
      "1005/1005 - 2s - loss: 2.7705e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 38/100\n",
      "1005/1005 - 3s - loss: 2.5996e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 39/100\n",
      "1005/1005 - 2s - loss: 2.6154e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 40/100\n",
      "1005/1005 - 3s - loss: 2.7793e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 41/100\n",
      "1005/1005 - 2s - loss: 2.6933e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 42/100\n",
      "1005/1005 - 2s - loss: 2.9849e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 43/100\n",
      "1005/1005 - 3s - loss: 2.3843e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 44/100\n",
      "1005/1005 - 2s - loss: 2.1630e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 45/100\n",
      "1005/1005 - 2s - loss: 2.2903e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 46/100\n",
      "1005/1005 - 2s - loss: 2.3151e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 47/100\n",
      "1005/1005 - 2s - loss: 2.4106e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 48/100\n",
      "1005/1005 - 2s - loss: 2.3356e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 49/100\n",
      "1005/1005 - 2s - loss: 2.3200e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 50/100\n",
      "1005/1005 - 2s - loss: 2.2918e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 51/100\n",
      "1005/1005 - 3s - loss: 2.3681e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 52/100\n",
      "1005/1005 - 2s - loss: 2.4622e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 53/100\n",
      "1005/1005 - 3s - loss: 2.3686e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 54/100\n",
      "1005/1005 - 3s - loss: 2.7905e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 55/100\n",
      "1005/1005 - 3s - loss: 2.4228e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 56/100\n",
      "1005/1005 - 3s - loss: 2.0410e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 57/100\n",
      "1005/1005 - 2s - loss: 2.2303e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 58/100\n",
      "1005/1005 - 2s - loss: 2.3990e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 59/100\n",
      "1005/1005 - 3s - loss: 2.5060e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 60/100\n",
      "1005/1005 - 2s - loss: 2.5589e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 61/100\n",
      "1005/1005 - 3s - loss: 2.0886e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 62/100\n",
      "1005/1005 - 3s - loss: 2.1336e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 63/100\n",
      "1005/1005 - 2s - loss: 1.8825e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 64/100\n",
      "1005/1005 - 2s - loss: 2.2621e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 65/100\n",
      "1005/1005 - 2s - loss: 2.2702e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 66/100\n",
      "1005/1005 - 3s - loss: 2.1084e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 67/100\n",
      "1005/1005 - 2s - loss: 2.2974e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 68/100\n",
      "1005/1005 - 3s - loss: 2.1110e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 69/100\n",
      "1005/1005 - 3s - loss: 2.2174e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 70/100\n",
      "1005/1005 - 2s - loss: 2.0137e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 71/100\n",
      "1005/1005 - 2s - loss: 2.0231e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 72/100\n",
      "1005/1005 - 2s - loss: 2.0443e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 73/100\n",
      "1005/1005 - 2s - loss: 2.2516e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 74/100\n",
      "1005/1005 - 3s - loss: 2.1713e-05 - 3s/epoch - 2ms/step\n",
      "Epoch 75/100\n",
      "1005/1005 - 3s - loss: 2.1409e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 76/100\n",
      "1005/1005 - 2s - loss: 2.1402e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 77/100\n",
      "1005/1005 - 3s - loss: 2.0454e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 78/100\n",
      "1005/1005 - 3s - loss: 2.1856e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 79/100\n",
      "1005/1005 - 3s - loss: 2.2595e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 80/100\n",
      "1005/1005 - 3s - loss: 2.1576e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 81/100\n",
      "1005/1005 - 3s - loss: 2.1024e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 82/100\n",
      "1005/1005 - 3s - loss: 2.2037e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 83/100\n",
      "1005/1005 - 2s - loss: 2.0439e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 84/100\n",
      "1005/1005 - 3s - loss: 2.0303e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 85/100\n",
      "1005/1005 - 3s - loss: 2.4311e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 86/100\n",
      "1005/1005 - 2s - loss: 1.9661e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 87/100\n",
      "1005/1005 - 2s - loss: 1.8813e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 88/100\n",
      "1005/1005 - 2s - loss: 1.9759e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 89/100\n",
      "1005/1005 - 2s - loss: 2.1179e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 90/100\n",
      "1005/1005 - 2s - loss: 2.0930e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 91/100\n",
      "1005/1005 - 3s - loss: 2.0506e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 92/100\n",
      "1005/1005 - 2s - loss: 1.9936e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 93/100\n",
      "1005/1005 - 2s - loss: 2.2824e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 94/100\n",
      "1005/1005 - 3s - loss: 1.8112e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 95/100\n",
      "1005/1005 - 2s - loss: 1.8416e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 96/100\n",
      "1005/1005 - 3s - loss: 2.0611e-05 - 3s/epoch - 3ms/step\n",
      "Epoch 97/100\n",
      "1005/1005 - 2s - loss: 1.9310e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 98/100\n",
      "1005/1005 - 2s - loss: 1.6634e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 99/100\n",
      "1005/1005 - 2s - loss: 2.1162e-05 - 2s/epoch - 2ms/step\n",
      "Epoch 100/100\n",
      "1005/1005 - 3s - loss: 1.9305e-05 - 3s/epoch - 3ms/step\n",
      "14/14 [==============================] - 0s 1ms/step\n",
      "\n",
      "\n",
      "Evaluation Metrics for All Tokens: Dropout Model\n",
      "  Symbol           MSE          RMSE          MAE       MAPE      Corr  \\\n",
      "0    LTC  6.449977e+02     25.396804    16.600663  11.201755  0.990316   \n",
      "1    ETH  1.856693e+05    430.893651   270.635298  22.260517  0.990041   \n",
      "2   DOGE  8.232917e-03      0.090735     0.036417  19.170134  0.969997   \n",
      "3    BTC  1.476139e+08  12149.645190  8342.910071  25.921750  0.990583   \n",
      "4    BNB  1.348784e+04    116.137140    60.826012  20.827440  0.980301   \n",
      "\n",
      "         R2  \n",
      "0  0.896802  \n",
      "1  0.795638  \n",
      "2  0.544419  \n",
      "3  0.544458  \n",
      "4  0.587346  \n",
      "Epoch 1/100\n",
      "957/957 - 4s - loss: 9.0313e-04 - 4s/epoch - 4ms/step\n",
      "Epoch 2/100\n",
      "957/957 - 2s - loss: 6.9812e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 3/100\n",
      "957/957 - 2s - loss: 8.5349e-04 - 2s/epoch - 3ms/step\n",
      "Epoch 4/100\n",
      "957/957 - 3s - loss: 8.0986e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 5/100\n",
      "957/957 - 3s - loss: 5.5763e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 6/100\n",
      "957/957 - 2s - loss: 7.3818e-04 - 2s/epoch - 3ms/step\n",
      "Epoch 7/100\n",
      "957/957 - 2s - loss: 9.3050e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 8/100\n",
      "957/957 - 2s - loss: 5.6129e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 9/100\n",
      "957/957 - 2s - loss: 6.0479e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 10/100\n",
      "957/957 - 2s - loss: 9.5673e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 11/100\n",
      "957/957 - 2s - loss: 5.0292e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 12/100\n",
      "957/957 - 2s - loss: 6.5274e-04 - 2s/epoch - 3ms/step\n",
      "Epoch 13/100\n",
      "957/957 - 2s - loss: 5.2128e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 14/100\n",
      "957/957 - 2s - loss: 6.1657e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 15/100\n",
      "957/957 - 2s - loss: 4.8525e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 16/100\n",
      "957/957 - 2s - loss: 7.4682e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 17/100\n",
      "957/957 - 2s - loss: 3.4036e-04 - 2s/epoch - 3ms/step\n",
      "Epoch 18/100\n",
      "957/957 - 2s - loss: 5.0551e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 19/100\n",
      "957/957 - 2s - loss: 8.1402e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 20/100\n",
      "957/957 - 2s - loss: 6.2023e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 21/100\n",
      "957/957 - 2s - loss: 5.1336e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 22/100\n",
      "957/957 - 2s - loss: 5.3920e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 23/100\n",
      "957/957 - 2s - loss: 5.1512e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 24/100\n",
      "957/957 - 2s - loss: 5.4992e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 25/100\n",
      "957/957 - 2s - loss: 3.7528e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 26/100\n",
      "957/957 - 2s - loss: 4.4026e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 27/100\n",
      "957/957 - 2s - loss: 4.1209e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 28/100\n",
      "957/957 - 2s - loss: 5.6673e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 29/100\n",
      "957/957 - 2s - loss: 5.1260e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 30/100\n",
      "957/957 - 2s - loss: 5.9605e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 31/100\n",
      "957/957 - 2s - loss: 3.2393e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 32/100\n",
      "957/957 - 2s - loss: 3.6133e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 33/100\n",
      "957/957 - 2s - loss: 3.4881e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 34/100\n",
      "957/957 - 2s - loss: 3.2646e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 35/100\n",
      "957/957 - 2s - loss: 4.3335e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 36/100\n",
      "957/957 - 2s - loss: 4.0215e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 37/100\n",
      "957/957 - 2s - loss: 4.4218e-04 - 2s/epoch - 3ms/step\n",
      "Epoch 38/100\n",
      "957/957 - 2s - loss: 3.2423e-04 - 2s/epoch - 3ms/step\n",
      "Epoch 39/100\n",
      "957/957 - 2s - loss: 3.1592e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 40/100\n",
      "957/957 - 2s - loss: 3.7566e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 41/100\n",
      "957/957 - 2s - loss: 3.7335e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 42/100\n",
      "957/957 - 2s - loss: 3.5686e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 43/100\n",
      "957/957 - 2s - loss: 3.3423e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 44/100\n",
      "957/957 - 3s - loss: 3.1406e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 45/100\n",
      "957/957 - 3s - loss: 3.7367e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 46/100\n",
      "957/957 - 2s - loss: 3.8588e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 47/100\n",
      "957/957 - 2s - loss: 3.5047e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 48/100\n",
      "957/957 - 2s - loss: 3.6937e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 49/100\n",
      "957/957 - 2s - loss: 3.1925e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 50/100\n",
      "957/957 - 2s - loss: 2.4724e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 51/100\n",
      "957/957 - 2s - loss: 3.5743e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 52/100\n",
      "957/957 - 2s - loss: 2.1976e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 53/100\n",
      "957/957 - 2s - loss: 2.9545e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 54/100\n",
      "957/957 - 2s - loss: 2.8223e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 55/100\n",
      "957/957 - 2s - loss: 3.1844e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 56/100\n",
      "957/957 - 2s - loss: 4.4310e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 57/100\n",
      "957/957 - 3s - loss: 3.1659e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 58/100\n",
      "957/957 - 2s - loss: 3.4555e-04 - 2s/epoch - 3ms/step\n",
      "Epoch 59/100\n",
      "957/957 - 2s - loss: 2.9596e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 60/100\n",
      "957/957 - 2s - loss: 3.0355e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 61/100\n",
      "957/957 - 2s - loss: 2.9753e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 62/100\n",
      "957/957 - 2s - loss: 4.0319e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 63/100\n",
      "957/957 - 2s - loss: 2.5421e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 64/100\n",
      "957/957 - 2s - loss: 2.7151e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 65/100\n",
      "957/957 - 2s - loss: 3.0051e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 66/100\n",
      "957/957 - 3s - loss: 3.0358e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 67/100\n",
      "957/957 - 2s - loss: 2.5358e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 68/100\n",
      "957/957 - 2s - loss: 2.4897e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 69/100\n",
      "957/957 - 2s - loss: 3.2457e-04 - 2s/epoch - 3ms/step\n",
      "Epoch 70/100\n",
      "957/957 - 2s - loss: 3.4525e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 71/100\n",
      "957/957 - 3s - loss: 2.7278e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 72/100\n",
      "957/957 - 2s - loss: 2.6325e-04 - 2s/epoch - 3ms/step\n",
      "Epoch 73/100\n",
      "957/957 - 2s - loss: 3.3520e-04 - 2s/epoch - 3ms/step\n",
      "Epoch 74/100\n",
      "957/957 - 3s - loss: 3.9899e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 75/100\n",
      "957/957 - 2s - loss: 2.8368e-04 - 2s/epoch - 3ms/step\n",
      "Epoch 76/100\n",
      "957/957 - 3s - loss: 3.3132e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 77/100\n",
      "957/957 - 3s - loss: 2.5526e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 78/100\n",
      "957/957 - 3s - loss: 2.8412e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 79/100\n",
      "957/957 - 3s - loss: 2.3062e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 80/100\n",
      "957/957 - 3s - loss: 2.5353e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 81/100\n",
      "957/957 - 2s - loss: 2.5131e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 82/100\n",
      "957/957 - 2s - loss: 2.4158e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 83/100\n",
      "957/957 - 2s - loss: 2.9375e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 84/100\n",
      "957/957 - 2s - loss: 2.9135e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 85/100\n",
      "957/957 - 2s - loss: 2.7792e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 86/100\n",
      "957/957 - 2s - loss: 2.5780e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 87/100\n",
      "957/957 - 2s - loss: 2.6245e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 88/100\n",
      "957/957 - 2s - loss: 2.9833e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 89/100\n",
      "957/957 - 2s - loss: 3.3835e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 90/100\n",
      "957/957 - 2s - loss: 2.6511e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 91/100\n",
      "957/957 - 2s - loss: 3.2268e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 92/100\n",
      "957/957 - 2s - loss: 2.6987e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 93/100\n",
      "957/957 - 2s - loss: 2.7572e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 94/100\n",
      "957/957 - 2s - loss: 2.4611e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 95/100\n",
      "957/957 - 3s - loss: 2.5695e-04 - 3s/epoch - 3ms/step\n",
      "Epoch 96/100\n",
      "957/957 - 2s - loss: 2.5628e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 97/100\n",
      "957/957 - 2s - loss: 2.6870e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 98/100\n",
      "957/957 - 2s - loss: 3.0642e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 99/100\n",
      "957/957 - 2s - loss: 2.8870e-04 - 2s/epoch - 2ms/step\n",
      "Epoch 100/100\n",
      "957/957 - 2s - loss: 2.7369e-04 - 2s/epoch - 2ms/step\n",
      "13/13 [==============================] - 0s 1ms/step\n",
      "\n",
      "\n",
      "Evaluation Metrics for All Tokens: Dropout Model\n",
      "  Symbol           MSE          RMSE          MAE       MAPE      Corr  \\\n",
      "0    LTC  6.449977e+02     25.396804    16.600663  11.201755  0.990316   \n",
      "1    ETH  1.856693e+05    430.893651   270.635298  22.260517  0.990041   \n",
      "2   DOGE  8.232917e-03      0.090735     0.036417  19.170134  0.969997   \n",
      "3    BTC  1.476139e+08  12149.645190  8342.910071  25.921750  0.990583   \n",
      "4    BNB  1.348784e+04    116.137140    60.826012  20.827440  0.980301   \n",
      "5    ADA  3.842086e-02      0.196012     0.112370  12.956438  0.990834   \n",
      "\n",
      "         R2  \n",
      "0  0.896802  \n",
      "1  0.795638  \n",
      "2  0.544419  \n",
      "3  0.544458  \n",
      "4  0.587346  \n",
      "5  0.892716  \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM, Dropout\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from math import sqrt\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# load data\n",
    "df = pd.read_csv('sixcrypto.csv')\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df.set_index('Date', inplace=True)\n",
    "df.sort_index(inplace=True)\n",
    "\n",
    "# create a list of unique tokens offered on the exchange\n",
    "tokens = df['Symbol'].unique()\n",
    "\n",
    "# create a dictionary to store the models and their performance metrics\n",
    "models = {}\n",
    "\n",
    "# create a 2x3 grid of subplots\n",
    "fig, axs = plt.subplots(2, 3, figsize=(15, 10))\n",
    "\n",
    "# loop through each token and plot its actual vs predicted values on a subplot\n",
    "for i, token in enumerate(tokens):\n",
    "    # select the data for the current token\n",
    "    data = df[df['Symbol'] == token]['Close'].values.reshape(-1, 1)\n",
    "\n",
    "    # preprocess data\n",
    "    scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "    scaled_data = scaler.fit_transform(data)\n",
    "\n",
    "    # split data into training and testing sets\n",
    "    train_size = int(len(scaled_data) * 0.7)\n",
    "    train_data = scaled_data[:train_size, :]\n",
    "    test_data = scaled_data[train_size:, :]\n",
    "\n",
    "    # function to create LSTM dataset\n",
    "    def create_dataset(dataset, look_back=1):\n",
    "        X, y = [], []\n",
    "        for i in range(len(dataset)-look_back-1):\n",
    "            a = dataset[i:(i+look_back), 0]\n",
    "            X.append(a)\n",
    "            y.append(dataset[i + look_back, 0])\n",
    "        return np.array(X), np.array(y)\n",
    "\n",
    "    # create training and testing data for LSTM model\n",
    "    look_back = 3\n",
    "    X_train, y_train = create_dataset(train_data, look_back)\n",
    "    X_test, y_test = create_dataset(test_data, look_back)\n",
    "\n",
    "    # reshape input to be [samples, time steps, features]\n",
    "    X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))\n",
    "    X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n",
    "\n",
    "    # create and train LSTM model with dropout layer\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(50, input_shape=(look_back, 1), dropout=0.2, recurrent_dropout=0.2))\n",
    "    model.add(Dense(1))\n",
    "    model.compile(loss='mean_squared_error', optimizer='adam')\n",
    "    model.fit(X_train, y_train, epochs=100, batch_size=1, verbose=2)\n",
    "\n",
    "    # make predictions on test data\n",
    "    y_pred = model.predict(X_test)\n",
    "\n",
    "    # inverse transform the scaled data\n",
    "    y_test = scaler.inverse_transform(y_test.reshape(-1, 1))\n",
    "    y_pred = scaler.inverse_transform(y_pred)\n",
    "\n",
    "    # evaluate model using various performance metrics\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    rmse = sqrt(mse)\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    mape = np.mean(np.abs((y_test - y_pred) / y_test)) * 100\n",
    "    corr = np.corrcoef(y_pred.T, y_test.T)[0, 1]\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "    # add the model and its performance metrics to\n",
    "    models[token] = {'model': model, 'mse': mse, 'rmse': rmse, 'mae': mae, 'mape': mape, 'corr': corr, 'r2': r2}\n",
    "\n",
    "        # plot actual vs predicted values on a subplot\n",
    "    row = i // 3\n",
    "    col = i % 3\n",
    "    axs[row, col].plot(y_test, label='Actual')\n",
    "    axs[row, col].plot(y_pred, label='Predicted')\n",
    "    axs[row, col].set_title('{} Actual vs Predicted Values - Dropout Model'.format(token))\n",
    "    axs[row, col].set_xlabel('Time')\n",
    "    axs[row, col].set_ylabel('Price')\n",
    "    axs[row, col].legend()\n",
    "\n",
    "    # evaluate model using various performance metrics\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    rmse = sqrt(mse)\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    mape = np.mean(np.abs((y_test - y_pred) / y_test)) * 100\n",
    "    corr = np.corrcoef(y_pred.T, y_test.T)[0, 1]\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "    # add the model and its performance metrics to\n",
    "    models[token] = {'model': model, 'mse': mse, 'rmse': rmse, 'mae': mae, 'mape': mape, 'corr': corr, 'r2': r2}\n",
    "    \n",
    "    # create a table to display evaluation metrics for all models\n",
    "    results = pd.DataFrame(columns=['Symbol', 'MSE', 'RMSE', 'MAE', 'MAPE', 'Corr', 'R2'])\n",
    "    for token, metrics in models.items():\n",
    "        results = results.append({'Symbol': token,\n",
    "                              'MSE': metrics['mse'],\n",
    "                              'RMSE': metrics['rmse'],\n",
    "                              'MAE': metrics['mae'],\n",
    "                              'MAPE': metrics['mape'],\n",
    "                              'Corr': metrics['corr'],\n",
    "                              'R2': metrics['r2']}, ignore_index=True)\n",
    "\n",
    "    # display results table\n",
    "    print('\\n\\nEvaluation Metrics for All Tokens: Dropout Model')\n",
    "    print(results)\n",
    "\n",
    "# adjust subplot layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# show plot\n",
    "plt.show()"
   ]
  }
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