diff --git a/doc/HandWrittenNotes/2023/NotesNov9.pdf b/doc/HandWrittenNotes/2023/NotesNov9.pdf
new file mode 100644
index 000000000..67cfee93c
Binary files /dev/null and b/doc/HandWrittenNotes/2023/NotesNov9.pdf differ
diff --git a/doc/pub/week45/ipynb/week45.ipynb b/doc/pub/week45/ipynb/week45.ipynb
index a85902747..3c7f78419 100644
--- a/doc/pub/week45/ipynb/week45.ipynb
+++ b/doc/pub/week45/ipynb/week45.ipynb
@@ -815,7 +815,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 8,
    "id": "7252c0b8",
    "metadata": {},
    "outputs": [
@@ -823,15 +823,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Model: \"sequential_1\"\n",
+      "Model: \"sequential_2\"\n",
       "_________________________________________________________________\n",
       " Layer (type)                Output Shape              Param #   \n",
       "=================================================================\n",
-      " simple_rnn_1 (SimpleRNN)    (None, 32)                1184      \n",
+      " simple_rnn_2 (SimpleRNN)    (None, 32)                1184      \n",
       "                                                                 \n",
-      " dense_2 (Dense)             (None, 8)                 264       \n",
+      " dense_4 (Dense)             (None, 8)                 264       \n",
       "                                                                 \n",
-      " dense_3 (Dense)             (None, 1)                 9         \n",
+      " dense_5 (Dense)             (None, 1)                 9         \n",
       "                                                                 \n",
       "=================================================================\n",
       "Total params: 1,457\n",
@@ -839,211 +839,211 @@
       "Non-trainable params: 0\n",
       "_________________________________________________________________\n",
       "Epoch 1/100\n",
-      "50/50 - 3s - loss: 0.1823 - 3s/epoch - 53ms/step\n",
+      "50/50 - 3s - loss: 0.4503 - 3s/epoch - 69ms/step\n",
       "Epoch 2/100\n",
-      "50/50 - 0s - loss: 0.0071 - 485ms/epoch - 10ms/step\n",
+      "50/50 - 1s - loss: 0.4265 - 770ms/epoch - 15ms/step\n",
       "Epoch 3/100\n",
-      "50/50 - 1s - loss: 0.0017 - 583ms/epoch - 12ms/step\n",
+      "50/50 - 1s - loss: 0.4127 - 571ms/epoch - 11ms/step\n",
       "Epoch 4/100\n",
-      "50/50 - 1s - loss: 0.0015 - 518ms/epoch - 10ms/step\n",
+      "50/50 - 1s - loss: 0.4051 - 604ms/epoch - 12ms/step\n",
       "Epoch 5/100\n",
-      "50/50 - 0s - loss: 0.0013 - 455ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.4030 - 522ms/epoch - 10ms/step\n",
       "Epoch 6/100\n",
-      "50/50 - 0s - loss: 0.0012 - 459ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.4041 - 531ms/epoch - 11ms/step\n",
       "Epoch 7/100\n",
-      "50/50 - 0s - loss: 0.0010 - 451ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.4015 - 507ms/epoch - 10ms/step\n",
       "Epoch 8/100\n",
-      "50/50 - 0s - loss: 7.6110e-04 - 451ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3995 - 510ms/epoch - 10ms/step\n",
       "Epoch 9/100\n",
-      "50/50 - 0s - loss: 6.2400e-04 - 453ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.4005 - 514ms/epoch - 10ms/step\n",
       "Epoch 10/100\n",
-      "50/50 - 0s - loss: 5.0527e-04 - 454ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3977 - 504ms/epoch - 10ms/step\n",
       "Epoch 11/100\n",
-      "50/50 - 0s - loss: 4.7144e-04 - 452ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.4007 - 501ms/epoch - 10ms/step\n",
       "Epoch 12/100\n",
-      "50/50 - 0s - loss: 3.8245e-04 - 455ms/epoch - 9ms/step\n",
+      "50/50 - 0s - loss: 0.3978 - 493ms/epoch - 10ms/step\n",
       "Epoch 13/100\n",
-      "50/50 - 0s - loss: 3.7650e-04 - 453ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.4000 - 502ms/epoch - 10ms/step\n",
       "Epoch 14/100\n",
-      "50/50 - 0s - loss: 3.7521e-04 - 455ms/epoch - 9ms/step\n",
+      "50/50 - 0s - loss: 0.3963 - 495ms/epoch - 10ms/step\n",
       "Epoch 15/100\n",
-      "50/50 - 0s - loss: 2.7278e-04 - 453ms/epoch - 9ms/step\n",
+      "50/50 - 0s - loss: 0.3960 - 493ms/epoch - 10ms/step\n",
       "Epoch 16/100\n",
-      "50/50 - 0s - loss: 2.5569e-04 - 452ms/epoch - 9ms/step\n",
+      "50/50 - 0s - loss: 0.3951 - 483ms/epoch - 10ms/step\n",
       "Epoch 17/100\n",
-      "50/50 - 0s - loss: 2.3474e-04 - 452ms/epoch - 9ms/step\n",
+      "50/50 - 0s - loss: 0.3947 - 490ms/epoch - 10ms/step\n",
       "Epoch 18/100\n",
-      "50/50 - 1s - loss: 2.3310e-04 - 544ms/epoch - 11ms/step\n",
+      "50/50 - 0s - loss: 0.3939 - 483ms/epoch - 10ms/step\n",
       "Epoch 19/100\n",
-      "50/50 - 1s - loss: 1.8887e-04 - 580ms/epoch - 12ms/step\n",
+      "50/50 - 0s - loss: 0.3925 - 481ms/epoch - 10ms/step\n",
       "Epoch 20/100\n",
-      "50/50 - 1s - loss: 2.0727e-04 - 579ms/epoch - 12ms/step\n",
+      "50/50 - 0s - loss: 0.3926 - 487ms/epoch - 10ms/step\n",
       "Epoch 21/100\n",
-      "50/50 - 1s - loss: 1.6970e-04 - 531ms/epoch - 11ms/step\n",
+      "50/50 - 1s - loss: 0.3935 - 502ms/epoch - 10ms/step\n",
       "Epoch 22/100\n",
-      "50/50 - 0s - loss: 2.1364e-04 - 459ms/epoch - 9ms/step\n",
+      "50/50 - 0s - loss: 0.3892 - 493ms/epoch - 10ms/step\n",
       "Epoch 23/100\n",
-      "50/50 - 0s - loss: 1.6468e-04 - 466ms/epoch - 9ms/step\n",
+      "50/50 - 0s - loss: 0.3913 - 483ms/epoch - 10ms/step\n",
       "Epoch 24/100\n",
-      "50/50 - 0s - loss: 1.8675e-04 - 454ms/epoch - 9ms/step\n",
+      "50/50 - 0s - loss: 0.3897 - 483ms/epoch - 10ms/step\n",
       "Epoch 25/100\n",
-      "50/50 - 0s - loss: 1.7464e-04 - 455ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3894 - 502ms/epoch - 10ms/step\n",
       "Epoch 26/100\n",
-      "50/50 - 0s - loss: 1.7150e-04 - 454ms/epoch - 9ms/step\n",
+      "50/50 - 0s - loss: 0.3886 - 488ms/epoch - 10ms/step\n",
       "Epoch 27/100\n",
-      "50/50 - 0s - loss: 1.7167e-04 - 453ms/epoch - 9ms/step\n",
+      "50/50 - 0s - loss: 0.3909 - 480ms/epoch - 10ms/step\n",
       "Epoch 28/100\n",
-      "50/50 - 0s - loss: 1.5447e-04 - 456ms/epoch - 9ms/step\n",
+      "50/50 - 0s - loss: 0.3862 - 491ms/epoch - 10ms/step\n",
       "Epoch 29/100\n",
-      "50/50 - 1s - loss: 1.6784e-04 - 516ms/epoch - 10ms/step\n",
+      "50/50 - 0s - loss: 0.3883 - 494ms/epoch - 10ms/step\n",
       "Epoch 30/100\n",
-      "50/50 - 1s - loss: 1.5286e-04 - 573ms/epoch - 11ms/step\n",
+      "50/50 - 0s - loss: 0.3890 - 488ms/epoch - 10ms/step\n",
       "Epoch 31/100\n",
-      "50/50 - 1s - loss: 1.5793e-04 - 565ms/epoch - 11ms/step\n",
+      "50/50 - 0s - loss: 0.3883 - 486ms/epoch - 10ms/step\n",
       "Epoch 32/100\n",
-      "50/50 - 1s - loss: 1.6390e-04 - 533ms/epoch - 11ms/step\n",
+      "50/50 - 0s - loss: 0.3868 - 486ms/epoch - 10ms/step\n",
       "Epoch 33/100\n",
-      "50/50 - 0s - loss: 1.6676e-04 - 461ms/epoch - 9ms/step\n",
+      "50/50 - 0s - loss: 0.3862 - 495ms/epoch - 10ms/step\n",
       "Epoch 34/100\n",
-      "50/50 - 0s - loss: 1.8529e-04 - 464ms/epoch - 9ms/step\n",
+      "50/50 - 0s - loss: 0.3873 - 492ms/epoch - 10ms/step\n",
       "Epoch 35/100\n",
-      "50/50 - 0s - loss: 1.3479e-04 - 455ms/epoch - 9ms/step\n",
+      "50/50 - 0s - loss: 0.3869 - 487ms/epoch - 10ms/step\n",
       "Epoch 36/100\n",
-      "50/50 - 0s - loss: 1.6066e-04 - 451ms/epoch - 9ms/step\n",
+      "50/50 - 0s - loss: 0.3873 - 487ms/epoch - 10ms/step\n",
       "Epoch 37/100\n",
-      "50/50 - 0s - loss: 1.4952e-04 - 454ms/epoch - 9ms/step\n",
+      "50/50 - 0s - loss: 0.3865 - 479ms/epoch - 10ms/step\n",
       "Epoch 38/100\n",
-      "50/50 - 1s - loss: 1.6409e-04 - 542ms/epoch - 11ms/step\n",
+      "50/50 - 0s - loss: 0.3849 - 499ms/epoch - 10ms/step\n",
       "Epoch 39/100\n",
-      "50/50 - 1s - loss: 1.5115e-04 - 565ms/epoch - 11ms/step\n",
+      "50/50 - 0s - loss: 0.3871 - 491ms/epoch - 10ms/step\n",
       "Epoch 40/100\n",
-      "50/50 - 1s - loss: 1.3440e-04 - 529ms/epoch - 11ms/step\n",
+      "50/50 - 0s - loss: 0.3845 - 490ms/epoch - 10ms/step\n",
       "Epoch 41/100\n",
-      "50/50 - 1s - loss: 1.5595e-04 - 505ms/epoch - 10ms/step\n",
+      "50/50 - 0s - loss: 0.3840 - 498ms/epoch - 10ms/step\n",
       "Epoch 42/100\n",
-      "50/50 - 1s - loss: 1.4974e-04 - 551ms/epoch - 11ms/step\n",
+      "50/50 - 0s - loss: 0.3817 - 490ms/epoch - 10ms/step\n",
       "Epoch 43/100\n",
-      "50/50 - 0s - loss: 1.4582e-04 - 457ms/epoch - 9ms/step\n",
+      "50/50 - 0s - loss: 0.3838 - 500ms/epoch - 10ms/step\n",
       "Epoch 44/100\n",
-      "50/50 - 1s - loss: 1.3210e-04 - 531ms/epoch - 11ms/step\n",
+      "50/50 - 0s - loss: 0.3824 - 485ms/epoch - 10ms/step\n",
       "Epoch 45/100\n",
-      "50/50 - 1s - loss: 1.5804e-04 - 559ms/epoch - 11ms/step\n",
+      "50/50 - 0s - loss: 0.3840 - 497ms/epoch - 10ms/step\n",
       "Epoch 46/100\n",
-      "50/50 - 0s - loss: 1.4973e-04 - 459ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3826 - 515ms/epoch - 10ms/step\n",
       "Epoch 47/100\n",
-      "50/50 - 0s - loss: 1.9301e-04 - 469ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3791 - 533ms/epoch - 11ms/step\n",
       "Epoch 48/100\n",
-      "50/50 - 0s - loss: 1.1564e-04 - 452ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3823 - 540ms/epoch - 11ms/step\n",
       "Epoch 49/100\n",
-      "50/50 - 0s - loss: 1.4595e-04 - 489ms/epoch - 10ms/step\n",
+      "50/50 - 1s - loss: 0.3829 - 540ms/epoch - 11ms/step\n",
       "Epoch 50/100\n",
-      "50/50 - 1s - loss: 1.4399e-04 - 568ms/epoch - 11ms/step\n",
+      "50/50 - 1s - loss: 0.3813 - 618ms/epoch - 12ms/step\n",
       "Epoch 51/100\n",
-      "50/50 - 1s - loss: 1.6464e-04 - 540ms/epoch - 11ms/step\n",
+      "50/50 - 1s - loss: 0.3821 - 563ms/epoch - 11ms/step\n",
       "Epoch 52/100\n",
-      "50/50 - 1s - loss: 1.1713e-04 - 586ms/epoch - 12ms/step\n",
+      "50/50 - 1s - loss: 0.3821 - 581ms/epoch - 12ms/step\n",
       "Epoch 53/100\n",
-      "50/50 - 1s - loss: 1.5229e-04 - 555ms/epoch - 11ms/step\n",
+      "50/50 - 1s - loss: 0.3806 - 620ms/epoch - 12ms/step\n",
       "Epoch 54/100\n",
-      "50/50 - 1s - loss: 1.2272e-04 - 558ms/epoch - 11ms/step\n",
+      "50/50 - 1s - loss: 0.3782 - 601ms/epoch - 12ms/step\n",
       "Epoch 55/100\n",
-      "50/50 - 0s - loss: 1.4453e-04 - 496ms/epoch - 10ms/step\n",
+      "50/50 - 1s - loss: 0.3793 - 584ms/epoch - 12ms/step\n",
       "Epoch 56/100\n",
-      "50/50 - 1s - loss: 1.0907e-04 - 593ms/epoch - 12ms/step\n",
+      "50/50 - 1s - loss: 0.3778 - 578ms/epoch - 12ms/step\n",
       "Epoch 57/100\n",
-      "50/50 - 1s - loss: 1.5891e-04 - 524ms/epoch - 10ms/step\n",
+      "50/50 - 1s - loss: 0.3793 - 567ms/epoch - 11ms/step\n",
       "Epoch 58/100\n",
-      "50/50 - 0s - loss: 1.4634e-04 - 458ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3805 - 595ms/epoch - 12ms/step\n",
       "Epoch 59/100\n",
-      "50/50 - 0s - loss: 1.4984e-04 - 456ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3798 - 571ms/epoch - 11ms/step\n",
       "Epoch 60/100\n",
-      "50/50 - 0s - loss: 1.3223e-04 - 495ms/epoch - 10ms/step\n",
+      "50/50 - 1s - loss: 0.3779 - 534ms/epoch - 11ms/step\n",
       "Epoch 61/100\n",
-      "50/50 - 1s - loss: 1.2282e-04 - 552ms/epoch - 11ms/step\n",
+      "50/50 - 1s - loss: 0.3768 - 521ms/epoch - 10ms/step\n",
       "Epoch 62/100\n",
-      "50/50 - 0s - loss: 1.2260e-04 - 457ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3778 - 525ms/epoch - 11ms/step\n",
       "Epoch 63/100\n",
-      "50/50 - 0s - loss: 1.4930e-04 - 494ms/epoch - 10ms/step\n",
+      "50/50 - 1s - loss: 0.3786 - 530ms/epoch - 11ms/step\n",
       "Epoch 64/100\n",
-      "50/50 - 0s - loss: 1.2384e-04 - 452ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3766 - 533ms/epoch - 11ms/step\n",
       "Epoch 65/100\n",
-      "50/50 - 0s - loss: 1.4297e-04 - 458ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3784 - 523ms/epoch - 10ms/step\n",
       "Epoch 66/100\n",
-      "50/50 - 1s - loss: 1.1002e-04 - 517ms/epoch - 10ms/step\n",
+      "50/50 - 1s - loss: 0.3771 - 520ms/epoch - 10ms/step\n",
       "Epoch 67/100\n",
-      "50/50 - 1s - loss: 1.3300e-04 - 505ms/epoch - 10ms/step\n",
+      "50/50 - 1s - loss: 0.3740 - 515ms/epoch - 10ms/step\n",
       "Epoch 68/100\n",
-      "50/50 - 1s - loss: 1.2980e-04 - 517ms/epoch - 10ms/step\n",
+      "50/50 - 1s - loss: 0.3755 - 520ms/epoch - 10ms/step\n",
       "Epoch 69/100\n",
-      "50/50 - 1s - loss: 1.3353e-04 - 515ms/epoch - 10ms/step\n",
+      "50/50 - 1s - loss: 0.3758 - 507ms/epoch - 10ms/step\n",
       "Epoch 70/100\n",
-      "50/50 - 1s - loss: 1.4278e-04 - 511ms/epoch - 10ms/step\n",
+      "50/50 - 1s - loss: 0.3746 - 503ms/epoch - 10ms/step\n",
       "Epoch 71/100\n",
-      "50/50 - 1s - loss: 1.3547e-04 - 515ms/epoch - 10ms/step\n",
+      "50/50 - 1s - loss: 0.3755 - 512ms/epoch - 10ms/step\n",
       "Epoch 72/100\n",
-      "50/50 - 1s - loss: 1.3195e-04 - 519ms/epoch - 10ms/step\n",
+      "50/50 - 1s - loss: 0.3734 - 501ms/epoch - 10ms/step\n",
       "Epoch 73/100\n",
-      "50/50 - 0s - loss: 1.3806e-04 - 482ms/epoch - 10ms/step\n",
+      "50/50 - 1s - loss: 0.3752 - 520ms/epoch - 10ms/step\n",
       "Epoch 74/100\n",
-      "50/50 - 0s - loss: 1.2993e-04 - 453ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3746 - 515ms/epoch - 10ms/step\n",
       "Epoch 75/100\n",
-      "50/50 - 0s - loss: 1.1680e-04 - 454ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3759 - 506ms/epoch - 10ms/step\n",
       "Epoch 76/100\n",
-      "50/50 - 0s - loss: 1.4084e-04 - 453ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3741 - 525ms/epoch - 10ms/step\n",
       "Epoch 77/100\n",
-      "50/50 - 0s - loss: 1.2300e-04 - 452ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3723 - 513ms/epoch - 10ms/step\n",
       "Epoch 78/100\n",
-      "50/50 - 0s - loss: 1.3992e-04 - 452ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3701 - 526ms/epoch - 11ms/step\n",
       "Epoch 79/100\n",
-      "50/50 - 0s - loss: 1.1784e-04 - 450ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3703 - 521ms/epoch - 10ms/step\n",
       "Epoch 80/100\n",
-      "50/50 - 0s - loss: 1.1756e-04 - 451ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3730 - 616ms/epoch - 12ms/step\n",
       "Epoch 81/100\n",
-      "50/50 - 0s - loss: 1.1931e-04 - 453ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3734 - 630ms/epoch - 13ms/step\n",
       "Epoch 82/100\n",
-      "50/50 - 0s - loss: 1.0862e-04 - 452ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3735 - 585ms/epoch - 12ms/step\n",
       "Epoch 83/100\n",
-      "50/50 - 0s - loss: 1.2957e-04 - 453ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3721 - 520ms/epoch - 10ms/step\n",
       "Epoch 84/100\n",
-      "50/50 - 0s - loss: 1.1890e-04 - 451ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3715 - 545ms/epoch - 11ms/step\n",
       "Epoch 85/100\n",
-      "50/50 - 0s - loss: 1.2060e-04 - 452ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3705 - 620ms/epoch - 12ms/step\n",
       "Epoch 86/100\n",
-      "50/50 - 0s - loss: 9.6054e-05 - 451ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3719 - 800ms/epoch - 16ms/step\n",
       "Epoch 87/100\n",
-      "50/50 - 0s - loss: 1.2433e-04 - 451ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3705 - 817ms/epoch - 16ms/step\n",
       "Epoch 88/100\n",
-      "50/50 - 0s - loss: 1.3120e-04 - 451ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3715 - 585ms/epoch - 12ms/step\n",
       "Epoch 89/100\n",
-      "50/50 - 0s - loss: 9.3067e-05 - 453ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3705 - 647ms/epoch - 13ms/step\n",
       "Epoch 90/100\n",
-      "50/50 - 0s - loss: 1.0844e-04 - 451ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3712 - 600ms/epoch - 12ms/step\n",
       "Epoch 91/100\n",
-      "50/50 - 0s - loss: 1.2138e-04 - 453ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3656 - 523ms/epoch - 10ms/step\n",
       "Epoch 92/100\n",
-      "50/50 - 0s - loss: 9.8372e-05 - 451ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3687 - 515ms/epoch - 10ms/step\n",
       "Epoch 93/100\n",
-      "50/50 - 0s - loss: 1.1158e-04 - 452ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3692 - 530ms/epoch - 11ms/step\n",
       "Epoch 94/100\n",
-      "50/50 - 0s - loss: 1.1435e-04 - 452ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3700 - 507ms/epoch - 10ms/step\n",
       "Epoch 95/100\n",
-      "50/50 - 0s - loss: 1.1319e-04 - 453ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3683 - 598ms/epoch - 12ms/step\n",
       "Epoch 96/100\n",
-      "50/50 - 0s - loss: 9.1814e-05 - 452ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3683 - 625ms/epoch - 12ms/step\n",
       "Epoch 97/100\n",
-      "50/50 - 0s - loss: 1.0696e-04 - 450ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3683 - 598ms/epoch - 12ms/step\n",
       "Epoch 98/100\n",
-      "50/50 - 0s - loss: 1.0741e-04 - 451ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3676 - 571ms/epoch - 11ms/step\n",
       "Epoch 99/100\n",
-      "50/50 - 0s - loss: 1.0522e-04 - 449ms/epoch - 9ms/step\n",
+      "50/50 - 1s - loss: 0.3707 - 577ms/epoch - 12ms/step\n",
       "Epoch 100/100\n",
-      "50/50 - 0s - loss: 1.0165e-04 - 450ms/epoch - 9ms/step\n",
-      "3.525180363794789e-05\n"
+      "50/50 - 1s - loss: 0.3663 - 599ms/epoch - 12ms/step\n",
+      "0.37175318598747253\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1082,7 +1082,7 @@
     "Tp = 800    \n",
     "\n",
     "t=np.arange(0,N)\n",
-    "x=np.sin(0.02*t)#+2*np.random.rand(N)\n",
+    "x=np.sin(0.02*t)+2*np.random.rand(N)\n",
     "df = pd.DataFrame(x)\n",
     "df.head()\n",
     "\n",
@@ -2616,10 +2616,740 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "id": "5c3d5a53",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"model_2\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " input_3 (InputLayer)        [(None, 2, 1)]            0         \n",
+      "                                                                 \n",
+      " dnn (Dense)                 (None, 2, 125)            250       \n",
+      "                                                                 \n",
+      " dnn1 (Dense)                (None, 2, 125)            15750     \n",
+      "                                                                 \n",
+      " RNN1 (GRU)                  (None, 2, 250)            282750    \n",
+      "                                                                 \n",
+      " RNN (GRU)                   (None, 250)               376500    \n",
+      "                                                                 \n",
+      " dense (Dense)               (None, 1)                 251       \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 675,501\n",
+      "Trainable params: 675,501\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "Epoch 1/150\n",
+      "1/1 [==============================] - 10s 10s/step - loss: 0.2483 - val_loss: 0.6297\n",
+      "Epoch 2/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 0.1954 - val_loss: 0.4846\n",
+      "Epoch 3/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 0.1452 - val_loss: 0.3423\n",
+      "Epoch 4/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 0.0972 - val_loss: 0.2069\n",
+      "Epoch 5/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.0536 - val_loss: 0.0905\n",
+      "Epoch 6/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 0.0197 - val_loss: 0.0152\n",
+      "Epoch 7/150\n",
+      "1/1 [==============================] - 0s 43ms/step - loss: 0.0045 - val_loss: 0.0037\n",
+      "Epoch 8/150\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 0.0152 - val_loss: 0.0381\n",
+      "Epoch 9/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0395 - val_loss: 0.0573\n",
+      "Epoch 10/150\n",
+      "1/1 [==============================] - 0s 44ms/step - loss: 0.0501 - val_loss: 0.0436\n",
+      "Epoch 11/150\n",
+      "1/1 [==============================] - 0s 41ms/step - loss: 0.0416 - val_loss: 0.0177\n",
+      "Epoch 12/150\n",
+      "1/1 [==============================] - 0s 44ms/step - loss: 0.0248 - val_loss: 0.0014\n",
+      "Epoch 13/150\n",
+      "1/1 [==============================] - 0s 44ms/step - loss: 0.0105 - val_loss: 0.0037\n",
+      "Epoch 14/150\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 0.0039 - val_loss: 0.0215\n",
+      "Epoch 15/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0045 - val_loss: 0.0456\n",
+      "Epoch 16/150\n",
+      "1/1 [==============================] - 0s 41ms/step - loss: 0.0094 - val_loss: 0.0669\n",
+      "Epoch 17/150\n",
+      "1/1 [==============================] - 0s 41ms/step - loss: 0.0148 - val_loss: 0.0796\n",
+      "Epoch 18/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0184 - val_loss: 0.0813\n",
+      "Epoch 19/150\n",
+      "1/1 [==============================] - 0s 44ms/step - loss: 0.0190 - val_loss: 0.0728\n",
+      "Epoch 20/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 0.0168 - val_loss: 0.0569\n",
+      "Epoch 21/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 0.0126 - val_loss: 0.0377\n",
+      "Epoch 22/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 0.0078 - val_loss: 0.0195\n",
+      "Epoch 23/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 0.0040 - val_loss: 0.0064\n",
+      "Epoch 24/150\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 0.0023 - val_loss: 5.0631e-04\n",
+      "Epoch 25/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0031 - val_loss: 6.7572e-04\n",
+      "Epoch 26/150\n",
+      "1/1 [==============================] - 0s 51ms/step - loss: 0.0055 - val_loss: 0.0034\n",
+      "Epoch 27/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 0.0079 - val_loss: 0.0048\n",
+      "Epoch 28/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 0.0087 - val_loss: 0.0035\n",
+      "Epoch 29/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 0.0074 - val_loss: 0.0011\n",
+      "Epoch 30/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0049 - val_loss: 7.6232e-06\n",
+      "Epoch 31/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 0.0026 - val_loss: 0.0018\n",
+      "Epoch 32/150\n",
+      "1/1 [==============================] - 0s 41ms/step - loss: 0.0014 - val_loss: 0.0061\n",
+      "Epoch 33/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 0.0015 - val_loss: 0.0114\n",
+      "Epoch 34/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 0.0024 - val_loss: 0.0157\n",
+      "Epoch 35/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 0.0034 - val_loss: 0.0174\n",
+      "Epoch 36/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 0.0038 - val_loss: 0.0161\n",
+      "Epoch 37/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 0.0036 - val_loss: 0.0124\n",
+      "Epoch 38/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 0.0027 - val_loss: 0.0077\n",
+      "Epoch 39/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 0.0017 - val_loss: 0.0034\n",
+      "Epoch 40/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 8.7355e-04 - val_loss: 7.7774e-04\n",
+      "Epoch 41/150\n",
+      "1/1 [==============================] - 0s 35ms/step - loss: 6.1643e-04 - val_loss: 6.3507e-09\n",
+      "Epoch 42/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 8.8923e-04 - val_loss: 4.5979e-04\n",
+      "Epoch 43/150\n",
+      "1/1 [==============================] - 0s 35ms/step - loss: 0.0014 - val_loss: 0.0011\n",
+      "Epoch 44/150\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 0.0017 - val_loss: 0.0012\n",
+      "Epoch 45/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 0.0016 - val_loss: 7.0269e-04\n",
+      "Epoch 46/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 0.0012 - val_loss: 1.3053e-04\n",
+      "Epoch 47/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 6.2995e-04 - val_loss: 4.8583e-05\n",
+      "Epoch 48/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 2.8498e-04 - val_loss: 6.2473e-04\n",
+      "Epoch 49/150\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 2.4506e-04 - val_loss: 0.0016\n",
+      "Epoch 50/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 4.2695e-04 - val_loss: 0.0023\n",
+      "Epoch 51/150\n",
+      "1/1 [==============================] - 0s 41ms/step - loss: 6.4272e-04 - val_loss: 0.0025\n",
+      "Epoch 52/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 7.2663e-04 - val_loss: 0.0021\n",
+      "Epoch 53/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 6.2148e-04 - val_loss: 0.0012\n",
+      "Epoch 54/150\n",
+      "1/1 [==============================] - 0s 35ms/step - loss: 3.9107e-04 - val_loss: 4.2612e-04\n",
+      "Epoch 55/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.6624e-04 - val_loss: 1.9689e-05\n",
+      "Epoch 56/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 6.0193e-05 - val_loss: 1.1690e-04\n",
+      "Epoch 57/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.0090e-04 - val_loss: 5.1530e-04\n",
+      "Epoch 58/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 2.2103e-04 - val_loss: 8.7068e-04\n",
+      "Epoch 59/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 3.1189e-04 - val_loss: 9.3426e-04\n",
+      "Epoch 60/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 3.0217e-04 - val_loss: 6.9561e-04\n",
+      "Epoch 61/150\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 2.0220e-04 - val_loss: 3.3902e-04\n",
+      "Epoch 62/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 8.3827e-05 - val_loss: 7.6302e-05\n",
+      "Epoch 63/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 1.9915e-05 - val_loss: 2.5929e-07\n",
+      "Epoch 64/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 3.4305e-05 - val_loss: 5.6229e-05\n",
+      "Epoch 65/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 9.5200e-05 - val_loss: 1.2152e-04\n",
+      "Epoch 66/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.4696e-04 - val_loss: 1.1280e-04\n",
+      "Epoch 67/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 1.5109e-04 - val_loss: 4.3254e-05\n",
+      "Epoch 68/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 1.0838e-04 - val_loss: 3.1677e-08\n",
+      "Epoch 69/150\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 38ms/step - loss: 5.1779e-05 - val_loss: 6.8193e-05\n",
+      "Epoch 70/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 1.9148e-05 - val_loss: 2.5886e-04\n",
+      "Epoch 71/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 2.6256e-05 - val_loss: 4.9361e-04\n",
+      "Epoch 72/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 5.8516e-05 - val_loss: 6.5561e-04\n",
+      "Epoch 73/150\n",
+      "1/1 [==============================] - 0s 43ms/step - loss: 8.5646e-05 - val_loss: 6.6774e-04\n",
+      "Epoch 74/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 8.6241e-05 - val_loss: 5.3811e-04\n",
+      "Epoch 75/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 6.2354e-05 - val_loss: 3.4303e-04\n",
+      "Epoch 76/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 3.4073e-05 - val_loss: 1.6893e-04\n",
+      "Epoch 77/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 2.1296e-05 - val_loss: 6.1795e-05\n",
+      "Epoch 78/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 2.8907e-05 - val_loss: 1.7120e-05\n",
+      "Epoch 79/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 4.5900e-05 - val_loss: 6.0411e-06\n",
+      "Epoch 80/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 5.6421e-05 - val_loss: 8.8481e-06\n",
+      "Epoch 81/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 5.2302e-05 - val_loss: 2.8892e-05\n",
+      "Epoch 82/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 3.7757e-05 - val_loss: 8.0624e-05\n",
+      "Epoch 83/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 2.4167e-05 - val_loss: 1.6640e-04\n",
+      "Epoch 84/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 2.0343e-05 - val_loss: 2.6374e-04\n",
+      "Epoch 85/150\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 2.6195e-05 - val_loss: 3.3455e-04\n",
+      "Epoch 86/150\n",
+      "1/1 [==============================] - 0s 41ms/step - loss: 3.4193e-05 - val_loss: 3.4873e-04\n",
+      "Epoch 87/150\n",
+      "1/1 [==============================] - 0s 45ms/step - loss: 3.6467e-05 - val_loss: 3.0325e-04\n",
+      "Epoch 88/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 3.1109e-05 - val_loss: 2.2189e-04\n",
+      "Epoch 89/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 2.2697e-05 - val_loss: 1.3812e-04\n",
+      "Epoch 90/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 1.7504e-05 - val_loss: 7.5586e-05\n",
+      "Epoch 91/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 1.8179e-05 - val_loss: 4.0254e-05\n",
+      "Epoch 92/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 2.2281e-05 - val_loss: 2.6136e-05\n",
+      "Epoch 93/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 2.5138e-05 - val_loss: 2.6217e-05\n",
+      "Epoch 94/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 2.3974e-05 - val_loss: 3.8516e-05\n",
+      "Epoch 95/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 1.9796e-05 - val_loss: 6.3681e-05\n",
+      "Epoch 96/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.5934e-05 - val_loss: 9.8592e-05\n",
+      "Epoch 97/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.4945e-05 - val_loss: 1.3324e-04\n",
+      "Epoch 98/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 1.6646e-05 - val_loss: 1.5450e-04\n",
+      "Epoch 99/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 1.8715e-05 - val_loss: 1.5383e-04\n",
+      "Epoch 100/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 1.8982e-05 - val_loss: 1.3246e-04\n",
+      "Epoch 101/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 1.7220e-05 - val_loss: 9.9974e-05\n",
+      "Epoch 102/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.4998e-05 - val_loss: 6.8027e-05\n",
+      "Epoch 103/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.4034e-05 - val_loss: 4.4328e-05\n",
+      "Epoch 104/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 1.4703e-05 - val_loss: 3.0887e-05\n",
+      "Epoch 105/150\n",
+      "1/1 [==============================] - 0s 35ms/step - loss: 1.5958e-05 - val_loss: 2.6339e-05\n",
+      "Epoch 106/150\n",
+      "1/1 [==============================] - 0s 35ms/step - loss: 1.6465e-05 - val_loss: 2.9071e-05\n",
+      "Epoch 107/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 1.5786e-05 - val_loss: 3.8250e-05\n",
+      "Epoch 108/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 1.4597e-05 - val_loss: 5.2506e-05\n",
+      "Epoch 109/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 1.3908e-05 - val_loss: 6.8403e-05\n",
+      "Epoch 110/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 1.4132e-05 - val_loss: 8.0828e-05\n",
+      "Epoch 111/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 1.4824e-05 - val_loss: 8.5325e-05\n",
+      "Epoch 112/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.5215e-05 - val_loss: 8.0591e-05\n",
+      "Epoch 113/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.4945e-05 - val_loss: 6.9029e-05\n",
+      "Epoch 114/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.4309e-05 - val_loss: 5.5127e-05\n",
+      "Epoch 115/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 1.3880e-05 - val_loss: 4.3015e-05\n",
+      "Epoch 116/150\n",
+      "1/1 [==============================] - 0s 54ms/step - loss: 1.3946e-05 - val_loss: 3.5029e-05\n",
+      "Epoch 117/150\n",
+      "1/1 [==============================] - 0s 44ms/step - loss: 1.4303e-05 - val_loss: 3.1805e-05\n",
+      "Epoch 118/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 1.4523e-05 - val_loss: 3.3115e-05\n",
+      "Epoch 119/150\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 1.4381e-05 - val_loss: 3.8382e-05\n",
+      "Epoch 120/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.4019e-05 - val_loss: 4.6483e-05\n",
+      "Epoch 121/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.3755e-05 - val_loss: 5.5428e-05\n",
+      "Epoch 122/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.3761e-05 - val_loss: 6.2647e-05\n",
+      "Epoch 123/150\n",
+      "1/1 [==============================] - 0s 35ms/step - loss: 1.3933e-05 - val_loss: 6.5962e-05\n",
+      "Epoch 124/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.4033e-05 - val_loss: 6.4612e-05\n",
+      "Epoch 125/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.3933e-05 - val_loss: 5.9548e-05\n",
+      "Epoch 126/150\n",
+      "1/1 [==============================] - 0s 35ms/step - loss: 1.3717e-05 - val_loss: 5.2822e-05\n",
+      "Epoch 127/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 1.3561e-05 - val_loss: 4.6609e-05\n",
+      "Epoch 128/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.3558e-05 - val_loss: 4.2449e-05\n",
+      "Epoch 129/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.3639e-05 - val_loss: 4.1070e-05\n",
+      "Epoch 130/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.3673e-05 - val_loss: 4.2536e-05\n",
+      "Epoch 131/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 1.3596e-05 - val_loss: 4.6376e-05\n",
+      "Epoch 132/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 1.3462e-05 - val_loss: 5.1620e-05\n",
+      "Epoch 133/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.3373e-05 - val_loss: 5.6878e-05\n",
+      "Epoch 134/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 1.3369e-05 - val_loss: 6.0703e-05\n",
+      "Epoch 135/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.3404e-05 - val_loss: 6.2090e-05\n",
+      "Epoch 136/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 1.3404e-05 - val_loss: 6.0884e-05\n",
+      "Epoch 137/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 1.3344e-05 - val_loss: 5.7783e-05\n",
+      "Epoch 138/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 1.3263e-05 - val_loss: 5.3980e-05\n",
+      "Epoch 139/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 1.3215e-05 - val_loss: 5.0676e-05\n",
+      "Epoch 140/150\n",
+      "1/1 [==============================] - 0s 35ms/step - loss: 1.3214e-05 - val_loss: 4.8729e-05\n",
+      "Epoch 141/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 1.3225e-05 - val_loss: 4.8520e-05\n",
+      "Epoch 142/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 1.3209e-05 - val_loss: 4.9967e-05\n",
+      "Epoch 143/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.3160e-05 - val_loss: 5.2587e-05\n",
+      "Epoch 144/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 1.3108e-05 - val_loss: 5.5601e-05\n",
+      "Epoch 145/150\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 37ms/step - loss: 1.3078e-05 - val_loss: 5.8127e-05\n",
+      "Epoch 146/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 1.3072e-05 - val_loss: 5.9447e-05\n",
+      "Epoch 147/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 1.3065e-05 - val_loss: 5.9255e-05\n",
+      "Epoch 148/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 1.3041e-05 - val_loss: 5.7739e-05\n",
+      "Epoch 149/150\n",
+      "1/1 [==============================] - 0s 35ms/step - loss: 1.3002e-05 - val_loss: 5.5476e-05\n",
+      "Epoch 150/150\n",
+      "1/1 [==============================] - 0s 35ms/step - loss: 1.2967e-05 - val_loss: 5.3191e-05\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/td/3yk470mj5p931p9dtkk0y6jw0000gn/T/ipykernel_11679/3370771014.py:33: DeprecationWarning: setting an array element with a sequence. This was supported in some cases where the elements are arrays with a single element. For example `np.array([1, np.array([2])], dtype=int)`. In the future this will raise the same ValueError as `np.array([1, [2]], dtype=int)`.\n",
+      "  next_input = np.array([[last, next[0]]], dtype=np.float64)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MSE:  0.0025297125391877074\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:  18.10655679099989\n",
+      "Model: \"model_3\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " input_4 (InputLayer)        [(None, 1, 1)]            0         \n",
+      "                                                                 \n",
+      " RNN (SimpleRNN)             (None, 200)               40400     \n",
+      "                                                                 \n",
+      " dense (Dense)               (None, 1)                 201       \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 40,601\n",
+      "Trainable params: 40,601\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n",
+      "Epoch 1/150\n",
+      "1/1 [==============================] - 3s 3s/step - loss: 2.4544 - val_loss: 4.6542\n",
+      "Epoch 2/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 1.8163 - val_loss: 3.5777\n",
+      "Epoch 3/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 1.2813 - val_loss: 2.6541\n",
+      "Epoch 4/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 0.8500 - val_loss: 1.8841\n",
+      "Epoch 5/150\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 0.5206 - val_loss: 1.2650\n",
+      "Epoch 6/150\n",
+      "1/1 [==============================] - 0s 33ms/step - loss: 0.2887 - val_loss: 0.7903\n",
+      "Epoch 7/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 0.1465 - val_loss: 0.4481\n",
+      "Epoch 8/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 0.0819 - val_loss: 0.2213\n",
+      "Epoch 9/150\n",
+      "1/1 [==============================] - 0s 32ms/step - loss: 0.0787 - val_loss: 0.0878\n",
+      "Epoch 10/150\n",
+      "1/1 [==============================] - 0s 30ms/step - loss: 0.1177 - val_loss: 0.0228\n",
+      "Epoch 11/150\n",
+      "1/1 [==============================] - 0s 31ms/step - loss: 0.1785 - val_loss: 0.0014\n",
+      "Epoch 12/150\n",
+      "1/1 [==============================] - 0s 29ms/step - loss: 0.2423 - val_loss: 0.0021\n",
+      "Epoch 13/150\n",
+      "1/1 [==============================] - 0s 29ms/step - loss: 0.2946 - val_loss: 0.0093\n",
+      "Epoch 14/150\n",
+      "1/1 [==============================] - 0s 28ms/step - loss: 0.3267 - val_loss: 0.0141\n",
+      "Epoch 15/150\n",
+      "1/1 [==============================] - 0s 31ms/step - loss: 0.3350 - val_loss: 0.0133\n",
+      "Epoch 16/150\n",
+      "1/1 [==============================] - 0s 30ms/step - loss: 0.3209 - val_loss: 0.0080\n",
+      "Epoch 17/150\n",
+      "1/1 [==============================] - 0s 30ms/step - loss: 0.2887 - val_loss: 0.0020\n",
+      "Epoch 18/150\n",
+      "1/1 [==============================] - 0s 29ms/step - loss: 0.2446 - val_loss: 1.8482e-04\n",
+      "Epoch 19/150\n",
+      "1/1 [==============================] - 0s 29ms/step - loss: 0.1950 - val_loss: 0.0069\n",
+      "Epoch 20/150\n",
+      "1/1 [==============================] - 0s 28ms/step - loss: 0.1461 - val_loss: 0.0254\n",
+      "Epoch 21/150\n",
+      "1/1 [==============================] - 0s 28ms/step - loss: 0.1028 - val_loss: 0.0573\n",
+      "Epoch 22/150\n",
+      "1/1 [==============================] - 0s 30ms/step - loss: 0.0687 - val_loss: 0.1018\n",
+      "Epoch 23/150\n",
+      "1/1 [==============================] - 0s 29ms/step - loss: 0.0456 - val_loss: 0.1565\n",
+      "Epoch 24/150\n",
+      "1/1 [==============================] - 0s 27ms/step - loss: 0.0335 - val_loss: 0.2172\n",
+      "Epoch 25/150\n",
+      "1/1 [==============================] - 0s 29ms/step - loss: 0.0313 - val_loss: 0.2787\n",
+      "Epoch 26/150\n",
+      "1/1 [==============================] - 0s 30ms/step - loss: 0.0365 - val_loss: 0.3354\n",
+      "Epoch 27/150\n",
+      "1/1 [==============================] - 0s 30ms/step - loss: 0.0461 - val_loss: 0.3823\n",
+      "Epoch 28/150\n",
+      "1/1 [==============================] - 0s 30ms/step - loss: 0.0570 - val_loss: 0.4154\n",
+      "Epoch 29/150\n",
+      "1/1 [==============================] - 0s 29ms/step - loss: 0.0665 - val_loss: 0.4323\n",
+      "Epoch 30/150\n",
+      "1/1 [==============================] - 0s 30ms/step - loss: 0.0727 - val_loss: 0.4324\n",
+      "Epoch 31/150\n",
+      "1/1 [==============================] - 0s 35ms/step - loss: 0.0743 - val_loss: 0.4166\n",
+      "Epoch 32/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 0.0713 - val_loss: 0.3873\n",
+      "Epoch 33/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0643 - val_loss: 0.3480\n",
+      "Epoch 34/150\n",
+      "1/1 [==============================] - 0s 45ms/step - loss: 0.0545 - val_loss: 0.3023\n",
+      "Epoch 35/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0434 - val_loss: 0.2542\n",
+      "Epoch 36/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 0.0326 - val_loss: 0.2071\n",
+      "Epoch 37/150\n",
+      "1/1 [==============================] - 0s 52ms/step - loss: 0.0233 - val_loss: 0.1636\n",
+      "Epoch 38/150\n",
+      "1/1 [==============================] - 0s 43ms/step - loss: 0.0163 - val_loss: 0.1257\n",
+      "Epoch 39/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.0121 - val_loss: 0.0942\n",
+      "Epoch 40/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 0.0105 - val_loss: 0.0695\n",
+      "Epoch 41/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0111 - val_loss: 0.0509\n",
+      "Epoch 42/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.0131 - val_loss: 0.0377\n",
+      "Epoch 43/150\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 0.0156 - val_loss: 0.0289\n",
+      "Epoch 44/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 0.0179 - val_loss: 0.0235\n",
+      "Epoch 45/150\n",
+      "1/1 [==============================] - 0s 31ms/step - loss: 0.0193 - val_loss: 0.0208\n",
+      "Epoch 46/150\n",
+      "1/1 [==============================] - 0s 30ms/step - loss: 0.0196 - val_loss: 0.0201\n",
+      "Epoch 47/150\n",
+      "1/1 [==============================] - 0s 30ms/step - loss: 0.0186 - val_loss: 0.0213\n",
+      "Epoch 48/150\n",
+      "1/1 [==============================] - 0s 28ms/step - loss: 0.0167 - val_loss: 0.0241\n",
+      "Epoch 49/150\n",
+      "1/1 [==============================] - 0s 29ms/step - loss: 0.0142 - val_loss: 0.0285\n",
+      "Epoch 50/150\n",
+      "1/1 [==============================] - 0s 30ms/step - loss: 0.0114 - val_loss: 0.0344\n",
+      "Epoch 51/150\n",
+      "1/1 [==============================] - 0s 32ms/step - loss: 0.0089 - val_loss: 0.0415\n",
+      "Epoch 52/150\n",
+      "1/1 [==============================] - 0s 28ms/step - loss: 0.0070 - val_loss: 0.0494\n",
+      "Epoch 53/150\n",
+      "1/1 [==============================] - 0s 28ms/step - loss: 0.0058 - val_loss: 0.0577\n",
+      "Epoch 54/150\n",
+      "1/1 [==============================] - 0s 27ms/step - loss: 0.0053 - val_loss: 0.0656\n",
+      "Epoch 55/150\n",
+      "1/1 [==============================] - 0s 27ms/step - loss: 0.0055 - val_loss: 0.0726\n",
+      "Epoch 56/150\n",
+      "1/1 [==============================] - 0s 27ms/step - loss: 0.0060 - val_loss: 0.0780\n",
+      "Epoch 57/150\n",
+      "1/1 [==============================] - 0s 26ms/step - loss: 0.0068 - val_loss: 0.0815\n",
+      "Epoch 58/150\n",
+      "1/1 [==============================] - 0s 28ms/step - loss: 0.0074 - val_loss: 0.0827\n",
+      "Epoch 59/150\n",
+      "1/1 [==============================] - 0s 24ms/step - loss: 0.0078 - val_loss: 0.0818\n",
+      "Epoch 60/150\n",
+      "1/1 [==============================] - 0s 27ms/step - loss: 0.0078 - val_loss: 0.0787\n",
+      "Epoch 61/150\n",
+      "1/1 [==============================] - 0s 44ms/step - loss: 0.0075 - val_loss: 0.0740\n",
+      "Epoch 62/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.0070 - val_loss: 0.0681\n",
+      "Epoch 63/150\n",
+      "1/1 [==============================] - 0s 44ms/step - loss: 0.0063 - val_loss: 0.0616\n",
+      "Epoch 64/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.0056 - val_loss: 0.0548\n",
+      "Epoch 65/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 0.0050 - val_loss: 0.0484\n",
+      "Epoch 66/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 0.0046 - val_loss: 0.0425\n",
+      "Epoch 67/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 0.0044 - val_loss: 0.0374\n",
+      "Epoch 68/150\n",
+      "1/1 [==============================] - 0s 43ms/step - loss: 0.0044 - val_loss: 0.0333\n",
+      "Epoch 69/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 0.0046 - val_loss: 0.0301\n",
+      "Epoch 70/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.0048 - val_loss: 0.0278\n",
+      "Epoch 71/150\n",
+      "1/1 [==============================] - 0s 30ms/step - loss: 0.0049 - val_loss: 0.0264\n",
+      "Epoch 72/150\n",
+      "1/1 [==============================] - 0s 24ms/step - loss: 0.0050 - val_loss: 0.0259\n",
+      "Epoch 73/150\n",
+      "1/1 [==============================] - 0s 26ms/step - loss: 0.0050 - val_loss: 0.0261\n",
+      "Epoch 74/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 0.0049 - val_loss: 0.0269\n",
+      "Epoch 75/150\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1/1 [==============================] - 0s 41ms/step - loss: 0.0047 - val_loss: 0.0282\n",
+      "Epoch 76/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 0.0045 - val_loss: 0.0300\n",
+      "Epoch 77/150\n",
+      "1/1 [==============================] - 0s 43ms/step - loss: 0.0043 - val_loss: 0.0321\n",
+      "Epoch 78/150\n",
+      "1/1 [==============================] - 0s 50ms/step - loss: 0.0041 - val_loss: 0.0343\n",
+      "Epoch 79/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 0.0040 - val_loss: 0.0365\n",
+      "Epoch 80/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 0.0040 - val_loss: 0.0385\n",
+      "Epoch 81/150\n",
+      "1/1 [==============================] - 0s 43ms/step - loss: 0.0040 - val_loss: 0.0402\n",
+      "Epoch 82/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0040 - val_loss: 0.0414\n",
+      "Epoch 83/150\n",
+      "1/1 [==============================] - 0s 30ms/step - loss: 0.0040 - val_loss: 0.0421\n",
+      "Epoch 84/150\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 0.0040 - val_loss: 0.0423\n",
+      "Epoch 85/150\n",
+      "1/1 [==============================] - 0s 44ms/step - loss: 0.0040 - val_loss: 0.0419\n",
+      "Epoch 86/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 0.0040 - val_loss: 0.0411\n",
+      "Epoch 87/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0039 - val_loss: 0.0399\n",
+      "Epoch 88/150\n",
+      "1/1 [==============================] - 0s 45ms/step - loss: 0.0038 - val_loss: 0.0385\n",
+      "Epoch 89/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 0.0037 - val_loss: 0.0370\n",
+      "Epoch 90/150\n",
+      "1/1 [==============================] - 0s 41ms/step - loss: 0.0036 - val_loss: 0.0355\n",
+      "Epoch 91/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0036 - val_loss: 0.0340\n",
+      "Epoch 92/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 0.0035 - val_loss: 0.0327\n",
+      "Epoch 93/150\n",
+      "1/1 [==============================] - 0s 24ms/step - loss: 0.0035 - val_loss: 0.0317\n",
+      "Epoch 94/150\n",
+      "1/1 [==============================] - 0s 23ms/step - loss: 0.0035 - val_loss: 0.0309\n",
+      "Epoch 95/150\n",
+      "1/1 [==============================] - 0s 34ms/step - loss: 0.0035 - val_loss: 0.0304\n",
+      "Epoch 96/150\n",
+      "1/1 [==============================] - 0s 41ms/step - loss: 0.0034 - val_loss: 0.0302\n",
+      "Epoch 97/150\n",
+      "1/1 [==============================] - 0s 51ms/step - loss: 0.0034 - val_loss: 0.0303\n",
+      "Epoch 98/150\n",
+      "1/1 [==============================] - 0s 44ms/step - loss: 0.0034 - val_loss: 0.0305\n",
+      "Epoch 99/150\n",
+      "1/1 [==============================] - 0s 45ms/step - loss: 0.0033 - val_loss: 0.0310\n",
+      "Epoch 100/150\n",
+      "1/1 [==============================] - 0s 45ms/step - loss: 0.0033 - val_loss: 0.0316\n",
+      "Epoch 101/150\n",
+      "1/1 [==============================] - 0s 44ms/step - loss: 0.0032 - val_loss: 0.0322\n",
+      "Epoch 102/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.0032 - val_loss: 0.0329\n",
+      "Epoch 103/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 0.0031 - val_loss: 0.0335\n",
+      "Epoch 104/150\n",
+      "1/1 [==============================] - 0s 44ms/step - loss: 0.0031 - val_loss: 0.0340\n",
+      "Epoch 105/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.0031 - val_loss: 0.0344\n",
+      "Epoch 106/150\n",
+      "1/1 [==============================] - 0s 41ms/step - loss: 0.0030 - val_loss: 0.0346\n",
+      "Epoch 107/150\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 0.0030 - val_loss: 0.0347\n",
+      "Epoch 108/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0030 - val_loss: 0.0345\n",
+      "Epoch 109/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 0.0030 - val_loss: 0.0342\n",
+      "Epoch 110/150\n",
+      "1/1 [==============================] - 0s 44ms/step - loss: 0.0029 - val_loss: 0.0338\n",
+      "Epoch 111/150\n",
+      "1/1 [==============================] - 0s 41ms/step - loss: 0.0029 - val_loss: 0.0332\n",
+      "Epoch 112/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0029 - val_loss: 0.0327\n",
+      "Epoch 113/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0028 - val_loss: 0.0321\n",
+      "Epoch 114/150\n",
+      "1/1 [==============================] - 0s 39ms/step - loss: 0.0028 - val_loss: 0.0315\n",
+      "Epoch 115/150\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 0.0028 - val_loss: 0.0309\n",
+      "Epoch 116/150\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 0.0027 - val_loss: 0.0305\n",
+      "Epoch 117/150\n",
+      "1/1 [==============================] - 0s 43ms/step - loss: 0.0027 - val_loss: 0.0301\n",
+      "Epoch 118/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.0027 - val_loss: 0.0298\n",
+      "Epoch 119/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 0.0027 - val_loss: 0.0297\n",
+      "Epoch 120/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 0.0026 - val_loss: 0.0296\n",
+      "Epoch 121/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0026 - val_loss: 0.0295\n",
+      "Epoch 122/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.0026 - val_loss: 0.0296\n",
+      "Epoch 123/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.0025 - val_loss: 0.0296\n",
+      "Epoch 124/150\n",
+      "1/1 [==============================] - 0s 43ms/step - loss: 0.0025 - val_loss: 0.0297\n",
+      "Epoch 125/150\n",
+      "1/1 [==============================] - 0s 44ms/step - loss: 0.0025 - val_loss: 0.0297\n",
+      "Epoch 126/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0025 - val_loss: 0.0297\n",
+      "Epoch 127/150\n",
+      "1/1 [==============================] - 0s 45ms/step - loss: 0.0024 - val_loss: 0.0297\n",
+      "Epoch 128/150\n",
+      "1/1 [==============================] - 0s 41ms/step - loss: 0.0024 - val_loss: 0.0296\n",
+      "Epoch 129/150\n",
+      "1/1 [==============================] - 0s 45ms/step - loss: 0.0024 - val_loss: 0.0294\n",
+      "Epoch 130/150\n",
+      "1/1 [==============================] - 0s 37ms/step - loss: 0.0024 - val_loss: 0.0292\n",
+      "Epoch 131/150\n",
+      "1/1 [==============================] - 0s 41ms/step - loss: 0.0023 - val_loss: 0.0290\n",
+      "Epoch 132/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 0.0023 - val_loss: 0.0287\n",
+      "Epoch 133/150\n",
+      "1/1 [==============================] - 0s 43ms/step - loss: 0.0023 - val_loss: 0.0284\n",
+      "Epoch 134/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 0.0023 - val_loss: 0.0280\n",
+      "Epoch 135/150\n",
+      "1/1 [==============================] - 0s 46ms/step - loss: 0.0022 - val_loss: 0.0277\n",
+      "Epoch 136/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 0.0022 - val_loss: 0.0274\n",
+      "Epoch 137/150\n",
+      "1/1 [==============================] - 0s 48ms/step - loss: 0.0022 - val_loss: 0.0271\n",
+      "Epoch 138/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 0.0022 - val_loss: 0.0268\n",
+      "Epoch 139/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 0.0021 - val_loss: 0.0265\n",
+      "Epoch 140/150\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 0.0021 - val_loss: 0.0263\n",
+      "Epoch 141/150\n",
+      "1/1 [==============================] - 0s 49ms/step - loss: 0.0021 - val_loss: 0.0262\n",
+      "Epoch 142/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0021 - val_loss: 0.0260\n",
+      "Epoch 143/150\n",
+      "1/1 [==============================] - 0s 47ms/step - loss: 0.0021 - val_loss: 0.0259\n",
+      "Epoch 144/150\n",
+      "1/1 [==============================] - 0s 42ms/step - loss: 0.0020 - val_loss: 0.0258\n",
+      "Epoch 145/150\n",
+      "1/1 [==============================] - 0s 44ms/step - loss: 0.0020 - val_loss: 0.0257\n",
+      "Epoch 146/150\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 0.0020 - val_loss: 0.0256\n",
+      "Epoch 147/150\n",
+      "1/1 [==============================] - 0s 40ms/step - loss: 0.0020 - val_loss: 0.0254\n",
+      "Epoch 148/150\n",
+      "1/1 [==============================] - 0s 38ms/step - loss: 0.0020 - val_loss: 0.0253\n",
+      "Epoch 149/150\n",
+      "1/1 [==============================] - 0s 34ms/step - loss: 0.0019 - val_loss: 0.0252\n",
+      "Epoch 150/150\n",
+      "1/1 [==============================] - 0s 36ms/step - loss: 0.0019 - val_loss: 0.0250\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time:  9.673238541000046\n"
+     ]
+    }
+   ],
    "source": [
     "def lstm_2layers(length_of_sequences, batch_size = None, stateful = False):\n",
     "    \"\"\"\n",
@@ -2812,6 +3542,14 @@
     "end = timer()\n",
     "print('Time: ', end-start)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d23615b1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {