ml_randomness_testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6fc4f31b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import d2d\n",
    "import importlib\n",
    "importlib.reload(d2d)\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import random\n",
    "import os\n",
    "# os.environ['TF_DETERMINISTIC_OPS'] = '1'\n",
    "\n",
    "SEED = 1\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "tf.random.set_seed(SEED)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "22367da6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys: <KeysViewHDF5 ['DAS Data', 'Discharge', 'Times']>\n"
     ]
    }
   ],
   "source": [
    "#linear_model, lstm_model, dnn_model,  df_all_chan,input_columns  = d2d.import_data(filename = \"/home/jmanos/notebooks/Non_filtered_rhone_v2/Rhone_data_continuous_nofilt.h5\")\n",
    "#linear_model, lstm_model, dnn_model,  df_all_chan,input_columns  = d2d.import_data(filename = \"/home/jmanos/notebooks/Lowpass_data/Rhone_data_continuous_lowpass.h5\")\n",
    "linear_model, lstm_model, dnn_model,  df_all_chan,input_columns  = d2d.import_data(filename = \"/data/fast0/datasets/Rhone_data_continuous_highpass.h5\")\n",
    "\n",
    "da = df_all_chan.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ffef05d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "window_input_width = 200\n",
    "\n",
    "multi_step_window_shuffled = d2d.WindowGenerator(df_all_chan,\n",
    "    input_width=window_input_width, label_width=1, shift=0,\n",
    "    label_columns=['Discharge'],\n",
    "    input_columns=input_columns,\n",
    "    shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "bbfb372a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(<tf.Tensor: shape=(16, 200, 2308), dtype=float32, numpy=\n",
      "array([[[-3.60525012e-01, -3.45753849e-01, -5.14210999e-01, ...,\n",
      "         -1.63725102e+00,  2.46058369e+00,  4.72802371e-01],\n",
      "        [-2.37699561e-02, -3.59966874e-01, -8.49045873e-01, ...,\n",
      "         -1.33512223e+00,  1.78153157e+00,  1.67255834e-01],\n",
      "        [ 9.63524580e-01,  5.52609444e-01, -3.11399370e-01, ...,\n",
      "         -8.38352203e-01,  1.93823814e+00,  3.49320590e-01],\n",
      "        ...,\n",
      "        [-3.69170547e-01,  1.83505625e-01,  1.30118120e+00, ...,\n",
      "         -1.36341238e+00, -1.14519858e+00, -2.26273108e+00],\n",
      "        [-5.31374216e-01, -3.85223001e-01,  6.59879744e-01, ...,\n",
      "         -1.63823533e+00, -1.62033236e+00, -2.32761741e+00],\n",
      "        [ 6.07038379e-01, -1.44148362e+00, -7.05049783e-02, ...,\n",
      "         -1.63051498e+00, -2.05741930e+00, -2.27523661e+00]],\n",
      "\n",
      "       [[ 4.56930071e-01, -2.67685741e-01, -2.38938272e-01, ...,\n",
      "         -6.28128052e-02,  2.95068479e+00,  3.06335258e+00],\n",
      "        [-6.34500027e-01,  4.02108759e-01,  5.04997484e-02, ...,\n",
      "          4.52412158e-01,  2.74077272e+00,  3.75549650e+00],\n",
      "        [ 3.88838619e-01,  1.19578040e+00, -4.49460983e-01, ...,\n",
      "         -2.69348491e-02,  1.40070963e+00,  4.73050070e+00],\n",
      "        ...,\n",
      "        [-1.00385988e+00, -8.53196532e-02, -5.58048666e-01, ...,\n",
      "          8.57789397e-01, -4.17598942e-03,  2.16478181e+00],\n",
      "        [-8.96526992e-01,  7.57393181e-01, -4.77489084e-01, ...,\n",
      "          1.03276312e+00,  1.34130168e+00,  5.03394032e+00],\n",
      "        [-2.18136664e-02,  9.63440776e-01,  9.44980327e-03, ...,\n",
      "          9.70075548e-01,  1.78963757e+00,  3.86245942e+00]],\n",
      "\n",
      "       [[ 4.78070587e-01, -2.34722272e-01, -5.11791885e-01, ...,\n",
      "         -1.12991488e+00,  7.57253647e-01, -1.34882641e+00],\n",
      "        [-1.21441329e+00,  9.17010128e-01, -6.29772663e-01, ...,\n",
      "         -1.80516231e+00,  2.92008054e-02, -1.64986050e+00],\n",
      "        [-6.36782408e-01,  1.25762582e-01, -2.18573943e-01, ...,\n",
      "         -1.33691072e+00,  6.13242745e-01, -1.54588079e+00],\n",
      "        ...,\n",
      "        [-1.19007540e+00,  4.67642039e-01,  4.40474212e-01, ...,\n",
      "          1.36906457e+00,  2.24858570e+00,  2.24351239e+00],\n",
      "        [-3.10376704e-01, -9.80492294e-01,  3.84928286e-01, ...,\n",
      "          4.62581247e-01,  3.12860823e+00,  1.49562156e+00],\n",
      "        [-9.25787151e-01,  7.46391594e-01, -1.43562353e+00, ...,\n",
      "          1.02434492e+00,  2.54872656e+00,  1.22165763e+00]],\n",
      "\n",
      "       ...,\n",
      "\n",
      "       [[-1.21619081e+00, -2.58671045e+00, -2.47307014e+00, ...,\n",
      "          1.59279406e+00,  8.73783648e-01,  4.94280130e-01],\n",
      "        [-1.93217134e+00, -9.48865175e-01, -1.93210676e-01, ...,\n",
      "          2.02198911e+00, -7.21644610e-02,  2.68485159e-01],\n",
      "        [-1.68430120e-01, -1.68443114e-01,  4.41513687e-01, ...,\n",
      "          1.28393114e+00,  9.86763060e-01,  4.86763865e-01],\n",
      "        ...,\n",
      "        [-8.21599066e-01,  2.94164270e-01,  4.17397797e-01, ...,\n",
      "          2.56474167e-01, -3.19824243e+00, -2.36581326e+00],\n",
      "        [-5.46856225e-01,  9.55484807e-01,  4.02882904e-01, ...,\n",
      "         -1.09143198e+00, -2.77654171e+00, -2.25015044e+00],\n",
      "        [-9.07223463e-01,  5.75151406e-02, -4.85408038e-01, ...,\n",
      "         -1.31424594e+00, -2.61451316e+00, -2.14437127e+00]],\n",
      "\n",
      "       [[-1.53263628e-01,  5.65206446e-02,  1.00744352e-01, ...,\n",
      "         -2.10991573e+00, -2.07497787e+00, -2.31071997e+00],\n",
      "        [ 3.54004055e-01,  2.30392069e-01,  1.07561433e+00, ...,\n",
      "         -1.98423433e+00, -2.02428460e+00, -2.36013317e+00],\n",
      "        [ 1.01396739e+00,  8.76670837e-01,  7.35837281e-01, ...,\n",
      "         -2.19586492e+00, -1.91909552e+00, -2.42475152e+00],\n",
      "        ...,\n",
      "        [ 1.26475096e+00,  4.56308901e-01,  1.83445239e+00, ...,\n",
      "         -1.78277981e+00,  5.08260846e-01,  2.56478757e-01],\n",
      "        [-8.50427985e-01, -1.08938985e-01, -3.47006202e-01, ...,\n",
      "         -1.42516625e+00,  7.55567908e-01,  2.23168075e-01],\n",
      "        [ 7.90151179e-01,  1.12067506e-01,  1.21257985e+00, ...,\n",
      "         -1.26136339e+00,  1.26701653e+00,  4.96452481e-01]],\n",
      "\n",
      "       [[ 1.22507823e+00,  6.49966657e-01,  9.67508614e-01, ...,\n",
      "          6.78344220e-02,  1.46466100e+00, -3.99059743e-01],\n",
      "        [-3.78573358e-01,  6.79760218e-01, -2.98377514e-01, ...,\n",
      "          4.97960448e-01,  9.36677754e-01, -7.83363938e-01],\n",
      "        [ 6.18649900e-01, -1.25841308e+00, -1.83912069e-01, ...,\n",
      "          1.85329642e-03,  5.82824498e-02, -1.28019428e+00],\n",
      "        ...,\n",
      "        [-7.23879278e-01,  4.10852045e-01,  2.22183764e-01, ...,\n",
      "          1.42985427e+00,  8.77359271e-01, -1.45411897e+00],\n",
      "        [-8.32137764e-01,  3.22175957e-02, -9.87457156e-01, ...,\n",
      "         -1.42494321e+00,  8.32368314e-01, -1.65015197e+00],\n",
      "        [-3.71210963e-01,  4.71060604e-01, -3.24827522e-01, ...,\n",
      "         -8.12771142e-01, -1.98120400e-01, -1.75865376e+00]]],\n",
      "      dtype=float32)>, <tf.Tensor: shape=(16, 1, 1), dtype=float32, numpy=\n",
      "array([[[5.560602 ]],\n",
      "\n",
      "       [[4.4213986]],\n",
      "\n",
      "       [[4.5113964]],\n",
      "\n",
      "       [[2.4765444]],\n",
      "\n",
      "       [[3.7321327]],\n",
      "\n",
      "       [[2.3689535]],\n",
      "\n",
      "       [[3.4229462]],\n",
      "\n",
      "       [[4.667466 ]],\n",
      "\n",
      "       [[5.060943 ]],\n",
      "\n",
      "       [[3.186994 ]],\n",
      "\n",
      "       [[3.3765948]],\n",
      "\n",
      "       [[2.7216535]],\n",
      "\n",
      "       [[4.6410394]],\n",
      "\n",
      "       [[2.0422118]],\n",
      "\n",
      "       [[3.3972166]],\n",
      "\n",
      "       [[4.3135047]]], dtype=float32)>)\n"
     ]
    }
   ],
   "source": [
    "print(multi_step_window_shuffled.example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f72f8260",
   "metadata": {},
   "outputs": [],
   "source": [
    "history = {}\n",
    "val_performance = {}\n",
    "performance = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb7cea34",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "14/14 [==============================] - 0s 24ms/step - loss: 44261.8750 - mean_absolute_error: 130.3386 - val_loss: 79420.1094 - val_mean_absolute_error: 161.7914\n",
      "Epoch 2/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 82553.3594 - mean_absolute_error: 165.9969 - val_loss: 89245.4688 - val_mean_absolute_error: 242.5399\n",
      "Epoch 3/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 161015.3594 - mean_absolute_error: 218.9681 - val_loss: 118539.5703 - val_mean_absolute_error: 222.0440\n",
      "Epoch 4/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 134763.0469 - mean_absolute_error: 197.4482 - val_loss: 95507.5000 - val_mean_absolute_error: 186.5526\n",
      "Epoch 5/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 173247.2812 - mean_absolute_error: 218.1298 - val_loss: 133515.1250 - val_mean_absolute_error: 213.8328\n",
      "Epoch 6/100\n",
      "14/14 [==============================] - 0s 22ms/step - loss: 190961.7031 - mean_absolute_error: 223.8463 - val_loss: 113266.4453 - val_mean_absolute_error: 211.3311\n",
      "Epoch 7/100\n",
      "14/14 [==============================] - 0s 22ms/step - loss: 180572.7812 - mean_absolute_error: 224.9484 - val_loss: 144958.7188 - val_mean_absolute_error: 225.4234\n",
      "Epoch 8/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 146191.1562 - mean_absolute_error: 189.6253 - val_loss: 119005.8828 - val_mean_absolute_error: 212.1451\n",
      "Epoch 9/100\n",
      "14/14 [==============================] - 0s 24ms/step - loss: 145605.2656 - mean_absolute_error: 198.8591 - val_loss: 136204.4531 - val_mean_absolute_error: 218.9154\n",
      "Epoch 10/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 142070.9844 - mean_absolute_error: 196.2113 - val_loss: 105280.6250 - val_mean_absolute_error: 196.5551\n",
      "Epoch 11/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 118799.9609 - mean_absolute_error: 170.4805 - val_loss: 123433.5625 - val_mean_absolute_error: 202.5271\n",
      "Epoch 1/100\n",
      "14/14 [==============================] - 1s 30ms/step - loss: 17.5538 - mean_absolute_error: 2.8290 - val_loss: 4.7554 - val_mean_absolute_error: 1.6382\n",
      "Epoch 2/100\n",
      "14/14 [==============================] - 0s 24ms/step - loss: 7.2127 - mean_absolute_error: 1.8713 - val_loss: 2.9196 - val_mean_absolute_error: 1.2416\n",
      "Epoch 3/100\n",
      "14/14 [==============================] - 0s 24ms/step - loss: 4.2658 - mean_absolute_error: 1.4260 - val_loss: 1.2625 - val_mean_absolute_error: 0.8550\n",
      "Epoch 4/100\n",
      "14/14 [==============================] - 0s 24ms/step - loss: 1.7398 - mean_absolute_error: 1.0200 - val_loss: 1.2901 - val_mean_absolute_error: 0.9025\n",
      "Epoch 5/100\n",
      "14/14 [==============================] - 0s 22ms/step - loss: 0.9385 - mean_absolute_error: 0.8059 - val_loss: 2.1187 - val_mean_absolute_error: 1.2184\n",
      "Epoch 6/100\n",
      "14/14 [==============================] - 0s 22ms/step - loss: 0.9256 - mean_absolute_error: 0.7362 - val_loss: 2.8501 - val_mean_absolute_error: 1.4978\n",
      "Epoch 7/100\n",
      "14/14 [==============================] - 0s 22ms/step - loss: 1.3690 - mean_absolute_error: 0.9298 - val_loss: 3.9262 - val_mean_absolute_error: 1.6953\n",
      "Epoch 8/100\n",
      "14/14 [==============================] - 0s 22ms/step - loss: 2.2926 - mean_absolute_error: 1.1975 - val_loss: 1.4996 - val_mean_absolute_error: 0.9493\n",
      "Epoch 9/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 2.3162 - mean_absolute_error: 1.1948 - val_loss: 1.7577 - val_mean_absolute_error: 1.1286\n",
      "Epoch 10/100\n",
      "14/14 [==============================] - 0s 22ms/step - loss: 1.3898 - mean_absolute_error: 0.9310 - val_loss: 1.9416 - val_mean_absolute_error: 1.2000\n",
      "Epoch 11/100\n",
      "14/14 [==============================] - 0s 22ms/step - loss: 1.0095 - mean_absolute_error: 0.7975 - val_loss: 1.3973 - val_mean_absolute_error: 0.8929\n",
      "Epoch 12/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 1.1652 - mean_absolute_error: 0.9105 - val_loss: 0.6843 - val_mean_absolute_error: 0.6018\n",
      "Epoch 13/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 1.1206 - mean_absolute_error: 0.8487 - val_loss: 1.8703 - val_mean_absolute_error: 1.0768\n",
      "Epoch 14/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 1.0474 - mean_absolute_error: 0.8639 - val_loss: 0.6756 - val_mean_absolute_error: 0.6299\n",
      "Epoch 15/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 0.8070 - mean_absolute_error: 0.7366 - val_loss: 0.9476 - val_mean_absolute_error: 0.7598\n",
      "Epoch 16/100\n",
      "14/14 [==============================] - 0s 22ms/step - loss: 0.4487 - mean_absolute_error: 0.5157 - val_loss: 0.8085 - val_mean_absolute_error: 0.7035\n",
      "Epoch 17/100\n",
      "14/14 [==============================] - 0s 22ms/step - loss: 0.4775 - mean_absolute_error: 0.5934 - val_loss: 0.5709 - val_mean_absolute_error: 0.5254\n",
      "Epoch 18/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 0.5572 - mean_absolute_error: 0.5913 - val_loss: 0.7205 - val_mean_absolute_error: 0.6792\n",
      "Epoch 19/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 0.4567 - mean_absolute_error: 0.5555 - val_loss: 0.5162 - val_mean_absolute_error: 0.5094\n",
      "Epoch 20/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 0.2194 - mean_absolute_error: 0.3699 - val_loss: 0.5645 - val_mean_absolute_error: 0.5118\n",
      "Epoch 21/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 0.2774 - mean_absolute_error: 0.3359 - val_loss: 0.6922 - val_mean_absolute_error: 0.6236\n",
      "Epoch 22/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 0.3007 - mean_absolute_error: 0.4484 - val_loss: 0.3947 - val_mean_absolute_error: 0.4143\n",
      "Epoch 23/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 0.2386 - mean_absolute_error: 0.3975 - val_loss: 0.6429 - val_mean_absolute_error: 0.5983\n",
      "Epoch 24/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 0.2463 - mean_absolute_error: 0.3807 - val_loss: 0.5480 - val_mean_absolute_error: 0.5820\n",
      "Epoch 25/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 0.3740 - mean_absolute_error: 0.4972 - val_loss: 0.5190 - val_mean_absolute_error: 0.4889\n",
      "Epoch 26/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 0.2118 - mean_absolute_error: 0.3635 - val_loss: 0.6767 - val_mean_absolute_error: 0.5928\n",
      "Epoch 27/100\n",
      "14/14 [==============================] - 0s 22ms/step - loss: 0.3050 - mean_absolute_error: 0.3828 - val_loss: 0.8065 - val_mean_absolute_error: 0.6574\n",
      "Epoch 28/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 0.3509 - mean_absolute_error: 0.5006 - val_loss: 0.5077 - val_mean_absolute_error: 0.5033\n",
      "Epoch 29/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 0.3762 - mean_absolute_error: 0.5089 - val_loss: 0.6841 - val_mean_absolute_error: 0.6532\n",
      "Epoch 30/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 0.3514 - mean_absolute_error: 0.4760 - val_loss: 0.7211 - val_mean_absolute_error: 0.6788\n",
      "Epoch 31/100\n",
      "14/14 [==============================] - 0s 22ms/step - loss: 0.5050 - mean_absolute_error: 0.6029 - val_loss: 0.4989 - val_mean_absolute_error: 0.4965\n",
      "Epoch 32/100\n",
      "14/14 [==============================] - 0s 23ms/step - loss: 0.2471 - mean_absolute_error: 0.4182 - val_loss: 0.8472 - val_mean_absolute_error: 0.6806\n",
      "Epoch 1/100\n",
      "14/14 [==============================] - 2s 88ms/step - loss: 4.7228 - mean_absolute_error: 1.5530 - val_loss: 3.2787 - val_mean_absolute_error: 1.3717\n",
      "Epoch 2/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 1.9674 - mean_absolute_error: 0.9479 - val_loss: 2.1150 - val_mean_absolute_error: 1.0688\n",
      "Epoch 3/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 1.3951 - mean_absolute_error: 0.7765 - val_loss: 1.7127 - val_mean_absolute_error: 0.9263\n",
      "Epoch 4/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 1.1392 - mean_absolute_error: 0.6888 - val_loss: 1.5975 - val_mean_absolute_error: 0.8867\n",
      "Epoch 5/100\n",
      "14/14 [==============================] - 1s 70ms/step - loss: 0.9311 - mean_absolute_error: 0.6012 - val_loss: 1.3803 - val_mean_absolute_error: 0.8043\n",
      "Epoch 6/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.8625 - mean_absolute_error: 0.5702 - val_loss: 1.3036 - val_mean_absolute_error: 0.7724\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.7334 - mean_absolute_error: 0.5095 - val_loss: 1.1011 - val_mean_absolute_error: 0.6986\n",
      "Epoch 8/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.6641 - mean_absolute_error: 0.4659 - val_loss: 0.9438 - val_mean_absolute_error: 0.6517\n",
      "Epoch 9/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.5862 - mean_absolute_error: 0.4281 - val_loss: 0.8369 - val_mean_absolute_error: 0.6158\n",
      "Epoch 10/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.5428 - mean_absolute_error: 0.3973 - val_loss: 0.7568 - val_mean_absolute_error: 0.5774\n",
      "Epoch 11/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.5066 - mean_absolute_error: 0.3671 - val_loss: 0.7098 - val_mean_absolute_error: 0.5510\n",
      "Epoch 12/100\n",
      "14/14 [==============================] - 1s 70ms/step - loss: 0.4845 - mean_absolute_error: 0.3436 - val_loss: 0.6900 - val_mean_absolute_error: 0.5515\n",
      "Epoch 13/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.4525 - mean_absolute_error: 0.3196 - val_loss: 0.6627 - val_mean_absolute_error: 0.5329\n",
      "Epoch 14/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.4276 - mean_absolute_error: 0.3005 - val_loss: 0.6207 - val_mean_absolute_error: 0.5160\n",
      "Epoch 15/100\n",
      "14/14 [==============================] - 1s 91ms/step - loss: 0.4062 - mean_absolute_error: 0.2927 - val_loss: 0.6031 - val_mean_absolute_error: 0.5135\n",
      "Epoch 16/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.3901 - mean_absolute_error: 0.2871 - val_loss: 0.6012 - val_mean_absolute_error: 0.5086\n",
      "Epoch 17/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.3782 - mean_absolute_error: 0.2818 - val_loss: 0.5817 - val_mean_absolute_error: 0.4930\n",
      "Epoch 18/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.3564 - mean_absolute_error: 0.2660 - val_loss: 0.5624 - val_mean_absolute_error: 0.4841\n",
      "Epoch 19/100\n",
      "14/14 [==============================] - 1s 70ms/step - loss: 0.3431 - mean_absolute_error: 0.2596 - val_loss: 0.5722 - val_mean_absolute_error: 0.4870\n",
      "Epoch 20/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.3249 - mean_absolute_error: 0.2549 - val_loss: 0.5172 - val_mean_absolute_error: 0.4669\n",
      "Epoch 21/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.3070 - mean_absolute_error: 0.2405 - val_loss: 0.5229 - val_mean_absolute_error: 0.4688\n",
      "Epoch 22/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.2989 - mean_absolute_error: 0.2389 - val_loss: 0.5139 - val_mean_absolute_error: 0.4747\n",
      "Epoch 23/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.2889 - mean_absolute_error: 0.2388 - val_loss: 0.4754 - val_mean_absolute_error: 0.4513\n",
      "Epoch 24/100\n",
      "14/14 [==============================] - 1s 67ms/step - loss: 0.2759 - mean_absolute_error: 0.2280 - val_loss: 0.4814 - val_mean_absolute_error: 0.4621\n",
      "Epoch 25/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.2772 - mean_absolute_error: 0.2467 - val_loss: 0.4445 - val_mean_absolute_error: 0.4266\n",
      "Epoch 26/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.2664 - mean_absolute_error: 0.2397 - val_loss: 0.4528 - val_mean_absolute_error: 0.4355\n",
      "Epoch 27/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.2567 - mean_absolute_error: 0.2311 - val_loss: 0.4585 - val_mean_absolute_error: 0.4416\n",
      "Epoch 28/100\n",
      "14/14 [==============================] - 1s 67ms/step - loss: 0.2313 - mean_absolute_error: 0.2173 - val_loss: 0.4585 - val_mean_absolute_error: 0.4437\n",
      "Epoch 29/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.2402 - mean_absolute_error: 0.2384 - val_loss: 0.4039 - val_mean_absolute_error: 0.4063\n",
      "Epoch 30/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.2181 - mean_absolute_error: 0.2163 - val_loss: 0.3791 - val_mean_absolute_error: 0.3978\n",
      "Epoch 31/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.2228 - mean_absolute_error: 0.2193 - val_loss: 0.4145 - val_mean_absolute_error: 0.4228\n",
      "Epoch 32/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.2115 - mean_absolute_error: 0.2185 - val_loss: 0.4037 - val_mean_absolute_error: 0.4245\n",
      "Epoch 33/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.2121 - mean_absolute_error: 0.2356 - val_loss: 0.3949 - val_mean_absolute_error: 0.4251\n",
      "Epoch 34/100\n",
      "14/14 [==============================] - 1s 67ms/step - loss: 0.2158 - mean_absolute_error: 0.2270 - val_loss: 0.3589 - val_mean_absolute_error: 0.3896\n",
      "Epoch 35/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.1597 - mean_absolute_error: 0.1948 - val_loss: 0.3330 - val_mean_absolute_error: 0.3765\n",
      "Epoch 36/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.1503 - mean_absolute_error: 0.1918 - val_loss: 0.4247 - val_mean_absolute_error: 0.4236\n",
      "Epoch 37/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.1390 - mean_absolute_error: 0.1796 - val_loss: 0.3748 - val_mean_absolute_error: 0.3812\n",
      "Epoch 38/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.1277 - mean_absolute_error: 0.1692 - val_loss: 0.3567 - val_mean_absolute_error: 0.3730\n",
      "Epoch 39/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.1281 - mean_absolute_error: 0.1612 - val_loss: 0.3631 - val_mean_absolute_error: 0.3925\n",
      "Epoch 40/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.1410 - mean_absolute_error: 0.1807 - val_loss: 0.3342 - val_mean_absolute_error: 0.3676\n",
      "Epoch 41/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.1234 - mean_absolute_error: 0.1601 - val_loss: 0.3909 - val_mean_absolute_error: 0.3826\n",
      "Epoch 42/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.1194 - mean_absolute_error: 0.1591 - val_loss: 0.3913 - val_mean_absolute_error: 0.3870\n",
      "Epoch 43/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.1171 - mean_absolute_error: 0.1606 - val_loss: 0.3223 - val_mean_absolute_error: 0.3577\n",
      "Epoch 44/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.1152 - mean_absolute_error: 0.1581 - val_loss: 0.3267 - val_mean_absolute_error: 0.3593\n",
      "Epoch 45/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.1101 - mean_absolute_error: 0.1542 - val_loss: 0.4021 - val_mean_absolute_error: 0.3827\n",
      "Epoch 46/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.1067 - mean_absolute_error: 0.1511 - val_loss: 0.3343 - val_mean_absolute_error: 0.3512\n",
      "Epoch 47/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.1088 - mean_absolute_error: 0.1568 - val_loss: 0.3290 - val_mean_absolute_error: 0.3567\n",
      "Epoch 48/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.0842 - mean_absolute_error: 0.1329 - val_loss: 0.3474 - val_mean_absolute_error: 0.3439\n",
      "Epoch 49/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.0803 - mean_absolute_error: 0.1234 - val_loss: 0.3385 - val_mean_absolute_error: 0.3352\n",
      "Epoch 50/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.0762 - mean_absolute_error: 0.1181 - val_loss: 0.3189 - val_mean_absolute_error: 0.3209\n",
      "Epoch 51/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.0722 - mean_absolute_error: 0.1064 - val_loss: 0.3243 - val_mean_absolute_error: 0.3199\n",
      "Epoch 52/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.0700 - mean_absolute_error: 0.1021 - val_loss: 0.3166 - val_mean_absolute_error: 0.3145\n",
      "Epoch 53/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.0694 - mean_absolute_error: 0.1014 - val_loss: 0.3216 - val_mean_absolute_error: 0.3116\n",
      "Epoch 54/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.0640 - mean_absolute_error: 0.0905 - val_loss: 0.3162 - val_mean_absolute_error: 0.3056\n",
      "Epoch 55/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.0622 - mean_absolute_error: 0.0892 - val_loss: 0.2936 - val_mean_absolute_error: 0.3057\n",
      "Epoch 56/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.0614 - mean_absolute_error: 0.0923 - val_loss: 0.3008 - val_mean_absolute_error: 0.3024\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 57/100\n",
      "14/14 [==============================] - 1s 67ms/step - loss: 0.0582 - mean_absolute_error: 0.0854 - val_loss: 0.3110 - val_mean_absolute_error: 0.3069\n",
      "Epoch 58/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.0570 - mean_absolute_error: 0.0859 - val_loss: 0.2997 - val_mean_absolute_error: 0.2974\n",
      "Epoch 59/100\n",
      "14/14 [==============================] - 1s 67ms/step - loss: 0.0552 - mean_absolute_error: 0.0796 - val_loss: 0.2911 - val_mean_absolute_error: 0.3023\n",
      "Epoch 60/100\n",
      "14/14 [==============================] - 1s 67ms/step - loss: 0.0544 - mean_absolute_error: 0.0840 - val_loss: 0.2992 - val_mean_absolute_error: 0.2953\n",
      "Epoch 61/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.0534 - mean_absolute_error: 0.0839 - val_loss: 0.3105 - val_mean_absolute_error: 0.3120\n",
      "Epoch 62/100\n",
      "14/14 [==============================] - 1s 67ms/step - loss: 0.0516 - mean_absolute_error: 0.0786 - val_loss: 0.2937 - val_mean_absolute_error: 0.2958\n",
      "Epoch 63/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.0514 - mean_absolute_error: 0.0794 - val_loss: 0.2962 - val_mean_absolute_error: 0.3057\n",
      "Epoch 64/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.0506 - mean_absolute_error: 0.0817 - val_loss: 0.3068 - val_mean_absolute_error: 0.3052\n",
      "Epoch 65/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.0501 - mean_absolute_error: 0.0852 - val_loss: 0.2993 - val_mean_absolute_error: 0.3074\n",
      "Epoch 66/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.0492 - mean_absolute_error: 0.0827 - val_loss: 0.2817 - val_mean_absolute_error: 0.2972\n",
      "Epoch 67/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.0477 - mean_absolute_error: 0.0789 - val_loss: 0.2949 - val_mean_absolute_error: 0.3126\n",
      "Epoch 68/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.0470 - mean_absolute_error: 0.0768 - val_loss: 0.2920 - val_mean_absolute_error: 0.2965\n",
      "Epoch 69/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.0536 - mean_absolute_error: 0.0946 - val_loss: 0.3157 - val_mean_absolute_error: 0.3213\n",
      "Epoch 70/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.0641 - mean_absolute_error: 0.1297 - val_loss: 0.3521 - val_mean_absolute_error: 0.3398\n",
      "Epoch 71/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.0640 - mean_absolute_error: 0.1341 - val_loss: 0.3389 - val_mean_absolute_error: 0.3244\n",
      "Epoch 72/100\n",
      "14/14 [==============================] - 1s 67ms/step - loss: 0.0642 - mean_absolute_error: 0.1352 - val_loss: 0.3251 - val_mean_absolute_error: 0.3210\n",
      "Epoch 73/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.0687 - mean_absolute_error: 0.1398 - val_loss: 0.2944 - val_mean_absolute_error: 0.3130\n",
      "Epoch 74/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.0656 - mean_absolute_error: 0.1381 - val_loss: 0.3007 - val_mean_absolute_error: 0.3321\n",
      "Epoch 75/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.0726 - mean_absolute_error: 0.1472 - val_loss: 0.2640 - val_mean_absolute_error: 0.3088\n",
      "Epoch 76/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.0868 - mean_absolute_error: 0.1546 - val_loss: 0.4102 - val_mean_absolute_error: 0.3943\n",
      "Epoch 77/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.0914 - mean_absolute_error: 0.1775 - val_loss: 0.4719 - val_mean_absolute_error: 0.4372\n",
      "Epoch 78/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.1143 - mean_absolute_error: 0.2130 - val_loss: 0.3968 - val_mean_absolute_error: 0.4232\n",
      "Epoch 79/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.1257 - mean_absolute_error: 0.2220 - val_loss: 0.4047 - val_mean_absolute_error: 0.4376\n",
      "Epoch 80/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.1279 - mean_absolute_error: 0.2263 - val_loss: 0.4842 - val_mean_absolute_error: 0.4772\n",
      "Epoch 81/100\n",
      "14/14 [==============================] - 1s 68ms/step - loss: 0.1295 - mean_absolute_error: 0.2174 - val_loss: 0.4402 - val_mean_absolute_error: 0.4483\n",
      "Epoch 82/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.1195 - mean_absolute_error: 0.2116 - val_loss: 0.4920 - val_mean_absolute_error: 0.4540\n",
      "Epoch 83/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.1299 - mean_absolute_error: 0.2286 - val_loss: 0.4075 - val_mean_absolute_error: 0.4281\n",
      "Epoch 84/100\n",
      "14/14 [==============================] - ETA: 0s - loss: 0.1481 - mean_absolute_error: 0.2543"
     ]
    }
   ],
   "source": [
    "history['linear_'+str(window_input_width)] = d2d.compile_and_fit(linear_model, multi_step_window_shuffled, learning_rate = 0.001)\n",
    "history['dnn_'+str(window_input_width)] = d2d.compile_and_fit(dnn_model, multi_step_window_shuffled, learning_rate = 0.001)\n",
    "history['lstm_'+str(window_input_width)] = d2d.compile_and_fit(lstm_model, multi_step_window_shuffled, learning_rate = 0.001)\n",
    "#history['conv_'+str(width)] = d2d.compile_and_fit(conv_model, multi_step_window_shuffled, learning_rate = 0.001)\n",
    "\n",
    "val_performance['lstm_'+str(window_input_width)] = lstm_model.evaluate(multi_step_window_shuffled.val)\n",
    "performance['lstm_'+str(window_input_width)] = lstm_model.evaluate(multi_step_window_shuffled.test, verbose=0)\n",
    "\n",
    "val_performance['linear_'+str(window_input_width)] = linear_model.evaluate(multi_step_window_shuffled.val)\n",
    "performance['linear_'+str(window_input_width)] = linear_model.evaluate(multi_step_window_shuffled.test, verbose=0)\n",
    "\n",
    "val_performance['dnn_'+str(window_input_width)] = dnn_model.evaluate(multi_step_window_shuffled.val)\n",
    "performance['dnn_'+str(window_input_width)] = dnn_model.evaluate(multi_step_window_shuffled.test, verbose=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd25bea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 22})\n",
    "\n",
    "fig, (ax0,ax1,ax2) = plt.subplots(nrows=1, \n",
    "                                                     ncols=3, \n",
    "                                                     sharey=False, \n",
    "                                                     sharex=False,\n",
    "                                                     figsize=(36,20))\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "for ax, hist, title in zip((ax0,ax1,ax2), ('linear_200','lstm_200','dnn_200'),\n",
    "                           ('Linear (200 Timesteps)','LSTM (200 Timesteps)','DNN (200 Timesteps)')):\n",
    "    ax.plot(history[hist].history['loss'], label='Train Loss',linewidth=3)\n",
    "    ax.plot(history[hist].history['val_loss'], label='Validation Loss',linewidth=3)\n",
    "    ax.grid(True)\n",
    "    ax.legend()\n",
    "    ax.set_title(title,size=50)\n",
    "    ax.set_ylabel('Loss', size=30)\n",
    "    ax.set_xlabel('Epoch', size=30)\n",
    "    \n",
    "fig.suptitle('Model Training', fontsize = 80)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "08aebbf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2592x1440 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 22})\n",
    "\n",
    "fig, (ax0,ax1,ax2) = plt.subplots(nrows=1, \n",
    "                                                     ncols=3, \n",
    "                                                     sharey=False, \n",
    "                                                     sharex=False,\n",
    "                                                     figsize=(36,20))\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "for ax, hist, title in zip((ax0,ax1,ax2), ('linear_400','lstm_400','dnn_400'),\n",
    "                           ('Linear (400 Timesteps)','LSTM (400 Timesteps)','DNN (400 Timesteps)')):\n",
    "    ax.plot(history[hist].history['loss'], label='Train Loss',linewidth=3)\n",
    "    ax.plot(history[hist].history['val_loss'], label='Validation Loss',linewidth=3)\n",
    "    ax.grid(True)\n",
    "    ax.legend()\n",
    "    ax.set_title(title,size=50)\n",
    "    ax.set_ylabel('Loss', size=30)\n",
    "    ax.set_xlabel('Epoch', size=30)\n",
    "    ax.set_ylim((0,1))\n",
    "    \n",
    "fig.suptitle('Model Training', fontsize = 80)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7357018a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "2e6811fb",
   "metadata": {},
   "source": [
    "# Randomness tests - only using Highpass and LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a2aa9318",
   "metadata": {},
   "outputs": [],
   "source": [
    "import d2d\n",
    "import importlib\n",
    "importlib.reload(d2d)\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import random\n",
    "import os\n",
    "# os.environ['TF_DETERMINISTIC_OPS'] = '1'\n",
    "\n",
    "SEED = 1\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "tf.random.set_seed(SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f1dd8f08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys: <KeysViewHDF5 ['DAS Data', 'Discharge', 'Times']>\n"
     ]
    }
   ],
   "source": [
    "linear_model, lstm_model, dnn_model,  df_all_chan,input_columns  = d2d.import_data(filename = \"/data/fast0/datasets/Rhone_data_continuous_highpass.h5\")\n",
    "\n",
    "da = df_all_chan.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d6419e42",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jmanos/.local/lib/python3.9/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    }
   ],
   "source": [
    "window_input_width = 200\n",
    "\n",
    "multi_step_window_shuffled = d2d.WindowGenerator(df_all_chan,\n",
    "    input_width=window_input_width, label_width=1, shift=0,\n",
    "    label_columns=['Discharge'],\n",
    "    input_columns=input_columns,\n",
    "    shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3871f9cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "history = {}\n",
    "val_performance = {}\n",
    "performance = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c706aa7a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "14/14 [==============================] - 3s 101ms/step - loss: 3.7466 - mean_absolute_error: 1.4477 - val_loss: 2.2450 - val_mean_absolute_error: 1.1939\n",
      "Epoch 2/100\n",
      "14/14 [==============================] - 1s 78ms/step - loss: 1.4934 - mean_absolute_error: 0.8843 - val_loss: 1.5943 - val_mean_absolute_error: 0.9345\n",
      "Epoch 3/100\n",
      "14/14 [==============================] - 1s 81ms/step - loss: 1.0279 - mean_absolute_error: 0.7115 - val_loss: 1.3336 - val_mean_absolute_error: 0.8513\n",
      "Epoch 4/100\n",
      "14/14 [==============================] - 1s 87ms/step - loss: 0.8359 - mean_absolute_error: 0.6392 - val_loss: 1.2331 - val_mean_absolute_error: 0.7852\n",
      "Epoch 5/100\n",
      "14/14 [==============================] - 1s 76ms/step - loss: 0.7911 - mean_absolute_error: 0.6003 - val_loss: 1.0578 - val_mean_absolute_error: 0.7484\n",
      "Epoch 6/100\n",
      "14/14 [==============================] - 1s 76ms/step - loss: 0.7355 - mean_absolute_error: 0.5821 - val_loss: 1.1497 - val_mean_absolute_error: 0.7726\n",
      "Epoch 7/100\n",
      "14/14 [==============================] - 1s 78ms/step - loss: 0.6582 - mean_absolute_error: 0.5585 - val_loss: 0.8955 - val_mean_absolute_error: 0.7035\n",
      "Epoch 8/100\n",
      "14/14 [==============================] - 1s 80ms/step - loss: 0.5510 - mean_absolute_error: 0.5083 - val_loss: 0.7750 - val_mean_absolute_error: 0.6486\n",
      "Epoch 9/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.4314 - mean_absolute_error: 0.4429 - val_loss: 0.7581 - val_mean_absolute_error: 0.6432\n",
      "Epoch 10/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.4476 - mean_absolute_error: 0.4605 - val_loss: 0.7264 - val_mean_absolute_error: 0.6365\n",
      "Epoch 11/100\n",
      "14/14 [==============================] - 1s 76ms/step - loss: 0.3911 - mean_absolute_error: 0.4232 - val_loss: 0.7098 - val_mean_absolute_error: 0.5964\n",
      "Epoch 12/100\n",
      "14/14 [==============================] - 1s 77ms/step - loss: 0.3672 - mean_absolute_error: 0.3896 - val_loss: 0.6603 - val_mean_absolute_error: 0.5773\n",
      "Epoch 13/100\n",
      "14/14 [==============================] - 1s 77ms/step - loss: 0.3497 - mean_absolute_error: 0.3812 - val_loss: 0.7501 - val_mean_absolute_error: 0.6359\n",
      "Epoch 14/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.3786 - mean_absolute_error: 0.3945 - val_loss: 0.6818 - val_mean_absolute_error: 0.5986\n",
      "Epoch 15/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.3199 - mean_absolute_error: 0.3627 - val_loss: 0.6619 - val_mean_absolute_error: 0.5800\n",
      "Epoch 16/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.2753 - mean_absolute_error: 0.3298 - val_loss: 0.6018 - val_mean_absolute_error: 0.5488\n",
      "Epoch 17/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.2263 - mean_absolute_error: 0.2952 - val_loss: 0.5865 - val_mean_absolute_error: 0.5388\n",
      "Epoch 18/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.2058 - mean_absolute_error: 0.2765 - val_loss: 0.5922 - val_mean_absolute_error: 0.5448\n",
      "Epoch 19/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.1819 - mean_absolute_error: 0.2616 - val_loss: 0.5561 - val_mean_absolute_error: 0.5173\n",
      "Epoch 20/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.1659 - mean_absolute_error: 0.2461 - val_loss: 0.5473 - val_mean_absolute_error: 0.5002\n",
      "Epoch 21/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.1588 - mean_absolute_error: 0.2365 - val_loss: 0.5689 - val_mean_absolute_error: 0.5195\n",
      "Epoch 22/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.1506 - mean_absolute_error: 0.2456 - val_loss: 0.5218 - val_mean_absolute_error: 0.4954\n",
      "Epoch 23/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.1348 - mean_absolute_error: 0.2313 - val_loss: 0.5281 - val_mean_absolute_error: 0.4885\n",
      "Epoch 24/100\n",
      "14/14 [==============================] - 1s 78ms/step - loss: 0.1245 - mean_absolute_error: 0.2267 - val_loss: 0.4880 - val_mean_absolute_error: 0.4662\n",
      "Epoch 25/100\n",
      "14/14 [==============================] - 1s 78ms/step - loss: 0.1076 - mean_absolute_error: 0.2028 - val_loss: 0.5160 - val_mean_absolute_error: 0.4735\n",
      "Epoch 26/100\n",
      "14/14 [==============================] - 1s 77ms/step - loss: 0.1004 - mean_absolute_error: 0.1907 - val_loss: 0.5584 - val_mean_absolute_error: 0.4820\n",
      "Epoch 27/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0903 - mean_absolute_error: 0.1747 - val_loss: 0.5098 - val_mean_absolute_error: 0.4681\n",
      "Epoch 28/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.0865 - mean_absolute_error: 0.1715 - val_loss: 0.4910 - val_mean_absolute_error: 0.4585\n",
      "Epoch 29/100\n",
      "14/14 [==============================] - 1s 80ms/step - loss: 0.0794 - mean_absolute_error: 0.1582 - val_loss: 0.4923 - val_mean_absolute_error: 0.4584\n",
      "Epoch 30/100\n",
      "14/14 [==============================] - 1s 76ms/step - loss: 0.0758 - mean_absolute_error: 0.1552 - val_loss: 0.4963 - val_mean_absolute_error: 0.4544\n",
      "Epoch 31/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.0770 - mean_absolute_error: 0.1559 - val_loss: 0.4832 - val_mean_absolute_error: 0.4489\n",
      "Epoch 32/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.0723 - mean_absolute_error: 0.1511 - val_loss: 0.4759 - val_mean_absolute_error: 0.4620\n",
      "Epoch 33/100\n",
      "14/14 [==============================] - 1s 77ms/step - loss: 0.0716 - mean_absolute_error: 0.1518 - val_loss: 0.4500 - val_mean_absolute_error: 0.4454\n",
      "Epoch 34/100\n",
      "14/14 [==============================] - 1s 79ms/step - loss: 0.0795 - mean_absolute_error: 0.1801 - val_loss: 0.4991 - val_mean_absolute_error: 0.4637\n",
      "Epoch 35/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.0705 - mean_absolute_error: 0.1667 - val_loss: 0.5154 - val_mean_absolute_error: 0.4731\n",
      "Epoch 36/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.0716 - mean_absolute_error: 0.1702 - val_loss: 0.5130 - val_mean_absolute_error: 0.4590\n",
      "Epoch 37/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0695 - mean_absolute_error: 0.1650 - val_loss: 0.5070 - val_mean_absolute_error: 0.4664\n",
      "Epoch 38/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.0603 - mean_absolute_error: 0.1401 - val_loss: 0.4525 - val_mean_absolute_error: 0.4305\n",
      "Epoch 39/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0608 - mean_absolute_error: 0.1446 - val_loss: 0.5088 - val_mean_absolute_error: 0.4427\n",
      "Epoch 40/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0581 - mean_absolute_error: 0.1379 - val_loss: 0.4946 - val_mean_absolute_error: 0.4423\n",
      "Epoch 41/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0629 - mean_absolute_error: 0.1455 - val_loss: 0.4713 - val_mean_absolute_error: 0.4169\n",
      "Epoch 42/100\n",
      "14/14 [==============================] - 1s 70ms/step - loss: 0.0578 - mean_absolute_error: 0.1285 - val_loss: 0.4593 - val_mean_absolute_error: 0.4343\n",
      "Epoch 43/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.0556 - mean_absolute_error: 0.1344 - val_loss: 0.4820 - val_mean_absolute_error: 0.4224\n",
      "4/4 [==============================] - 0s 30ms/step - loss: 0.4820 - mean_absolute_error: 0.4224\n"
     ]
    }
   ],
   "source": [
    "history['lstm_'+str(window_input_width)] = d2d.compile_and_fit(lstm_model, multi_step_window_shuffled, learning_rate = 0.001)\n",
    "\n",
    "\n",
    "val_performance['lstm_'+str(window_input_width)] = lstm_model.evaluate(multi_step_window_shuffled.val)\n",
    "performance['lstm_'+str(window_input_width)] = lstm_model.evaluate(multi_step_window_shuffled.test, verbose=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f26f2762",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2592x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting one graph\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 22})\n",
    "\n",
    "fig, ax0 = plt.subplots(nrows=1, \n",
    "                         ncols=1, \n",
    "                         sharey=False, \n",
    "                         sharex=False,\n",
    "                         figsize=(36,20))\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "\n",
    "ax0.plot(history['lstm_200'].history['loss'], label='Train Loss',linewidth=3)\n",
    "ax0.plot(history['lstm_200'].history['val_loss'], label='Validation Loss',linewidth=3)\n",
    "ax0.grid(True)\n",
    "ax0.legend()\n",
    "ax0.set_title('lstm_200',size=50)\n",
    "ax0.set_ylabel('Loss', size=30)\n",
    "ax0.set_xlabel('Epoch', size=30)\n",
    "#ax0.set_ylim([0,1])\n",
    "    \n",
    "fig.suptitle('Model Training', fontsize = 80)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "930b53c1",
   "metadata": {},
   "source": [
    "# OK individual runs are reproduceable, what about a For loop?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "16ba8a94",
   "metadata": {},
   "outputs": [],
   "source": [
    "import d2d\n",
    "import importlib\n",
    "importlib.reload(d2d)\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import random\n",
    "import os\n",
    "# os.environ['TF_DETERMINISTIC_OPS'] = '1'\n",
    "\n",
    "SEED = 1\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "tf.random.set_seed(SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad53eba7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dfa119b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "history = {}\n",
    "val_performance = {}\n",
    "performance = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "09af7104",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys: <KeysViewHDF5 ['DAS Data', 'Discharge', 'Times']>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jmanos/.local/lib/python3.9/site-packages/numpy/core/_asarray.py:83: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "14/14 [==============================] - 2s 92ms/step - loss: 3.7466 - mean_absolute_error: 1.4477 - val_loss: 2.2450 - val_mean_absolute_error: 1.1939\n",
      "Epoch 2/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 1.4934 - mean_absolute_error: 0.8843 - val_loss: 1.5943 - val_mean_absolute_error: 0.9345\n",
      "Epoch 3/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 1.0279 - mean_absolute_error: 0.7115 - val_loss: 1.3336 - val_mean_absolute_error: 0.8513\n",
      "Epoch 4/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.8359 - mean_absolute_error: 0.6392 - val_loss: 1.2331 - val_mean_absolute_error: 0.7852\n",
      "Epoch 5/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.7911 - mean_absolute_error: 0.6003 - val_loss: 1.0578 - val_mean_absolute_error: 0.7484\n",
      "Epoch 6/100\n",
      "14/14 [==============================] - 1s 70ms/step - loss: 0.7355 - mean_absolute_error: 0.5821 - val_loss: 1.1497 - val_mean_absolute_error: 0.7726\n",
      "Epoch 7/100\n",
      "14/14 [==============================] - 1s 70ms/step - loss: 0.6582 - mean_absolute_error: 0.5585 - val_loss: 0.8955 - val_mean_absolute_error: 0.7035\n",
      "Epoch 8/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.5510 - mean_absolute_error: 0.5083 - val_loss: 0.7750 - val_mean_absolute_error: 0.6486\n",
      "Epoch 9/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.4314 - mean_absolute_error: 0.4429 - val_loss: 0.7581 - val_mean_absolute_error: 0.6432\n",
      "Epoch 10/100\n",
      "14/14 [==============================] - 1s 70ms/step - loss: 0.4476 - mean_absolute_error: 0.4605 - val_loss: 0.7264 - val_mean_absolute_error: 0.6365\n",
      "Epoch 11/100\n",
      "14/14 [==============================] - 1s 70ms/step - loss: 0.3911 - mean_absolute_error: 0.4232 - val_loss: 0.7098 - val_mean_absolute_error: 0.5964\n",
      "Epoch 12/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.3672 - mean_absolute_error: 0.3896 - val_loss: 0.6603 - val_mean_absolute_error: 0.5773\n",
      "Epoch 13/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.3497 - mean_absolute_error: 0.3812 - val_loss: 0.7501 - val_mean_absolute_error: 0.6359\n",
      "Epoch 14/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.3786 - mean_absolute_error: 0.3945 - val_loss: 0.6818 - val_mean_absolute_error: 0.5986\n",
      "Epoch 15/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.3199 - mean_absolute_error: 0.3627 - val_loss: 0.6619 - val_mean_absolute_error: 0.5800\n",
      "Epoch 16/100\n",
      "14/14 [==============================] - 1s 77ms/step - loss: 0.2753 - mean_absolute_error: 0.3298 - val_loss: 0.6018 - val_mean_absolute_error: 0.5488\n",
      "Epoch 17/100\n",
      "14/14 [==============================] - 1s 76ms/step - loss: 0.2263 - mean_absolute_error: 0.2952 - val_loss: 0.5865 - val_mean_absolute_error: 0.5388\n",
      "Epoch 18/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.2058 - mean_absolute_error: 0.2765 - val_loss: 0.5922 - val_mean_absolute_error: 0.5448\n",
      "Epoch 19/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.1819 - mean_absolute_error: 0.2616 - val_loss: 0.5561 - val_mean_absolute_error: 0.5173\n",
      "Epoch 20/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.1659 - mean_absolute_error: 0.2461 - val_loss: 0.5473 - val_mean_absolute_error: 0.5002\n",
      "Epoch 21/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.1588 - mean_absolute_error: 0.2365 - val_loss: 0.5689 - val_mean_absolute_error: 0.5195\n",
      "Epoch 22/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.1506 - mean_absolute_error: 0.2456 - val_loss: 0.5218 - val_mean_absolute_error: 0.4954\n",
      "Epoch 23/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.1348 - mean_absolute_error: 0.2313 - val_loss: 0.5281 - val_mean_absolute_error: 0.4885\n",
      "Epoch 24/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.1245 - mean_absolute_error: 0.2267 - val_loss: 0.4880 - val_mean_absolute_error: 0.4662\n",
      "Epoch 25/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.1076 - mean_absolute_error: 0.2028 - val_loss: 0.5160 - val_mean_absolute_error: 0.4735\n",
      "Epoch 26/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.1004 - mean_absolute_error: 0.1907 - val_loss: 0.5584 - val_mean_absolute_error: 0.4820\n",
      "Epoch 27/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0903 - mean_absolute_error: 0.1747 - val_loss: 0.5098 - val_mean_absolute_error: 0.4681\n",
      "Epoch 28/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0865 - mean_absolute_error: 0.1715 - val_loss: 0.4910 - val_mean_absolute_error: 0.4585\n",
      "Epoch 29/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0794 - mean_absolute_error: 0.1582 - val_loss: 0.4923 - val_mean_absolute_error: 0.4584\n",
      "Epoch 30/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0758 - mean_absolute_error: 0.1552 - val_loss: 0.4963 - val_mean_absolute_error: 0.4544\n",
      "Epoch 31/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.0770 - mean_absolute_error: 0.1559 - val_loss: 0.4832 - val_mean_absolute_error: 0.4489\n",
      "Epoch 32/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0723 - mean_absolute_error: 0.1511 - val_loss: 0.4759 - val_mean_absolute_error: 0.4620\n",
      "Epoch 33/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0716 - mean_absolute_error: 0.1518 - val_loss: 0.4500 - val_mean_absolute_error: 0.4454\n",
      "Epoch 34/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.0795 - mean_absolute_error: 0.1801 - val_loss: 0.4991 - val_mean_absolute_error: 0.4637\n",
      "Epoch 35/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0705 - mean_absolute_error: 0.1667 - val_loss: 0.5154 - val_mean_absolute_error: 0.4731\n",
      "Epoch 36/100\n",
      "14/14 [==============================] - 1s 70ms/step - loss: 0.0716 - mean_absolute_error: 0.1702 - val_loss: 0.5130 - val_mean_absolute_error: 0.4590\n",
      "Epoch 37/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.0695 - mean_absolute_error: 0.1650 - val_loss: 0.5070 - val_mean_absolute_error: 0.4664\n",
      "Epoch 38/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0603 - mean_absolute_error: 0.1401 - val_loss: 0.4525 - val_mean_absolute_error: 0.4305\n",
      "Epoch 39/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0608 - mean_absolute_error: 0.1446 - val_loss: 0.5088 - val_mean_absolute_error: 0.4427\n",
      "Epoch 40/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0581 - mean_absolute_error: 0.1379 - val_loss: 0.4946 - val_mean_absolute_error: 0.4423\n",
      "Epoch 41/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0629 - mean_absolute_error: 0.1455 - val_loss: 0.4713 - val_mean_absolute_error: 0.4169\n",
      "Epoch 42/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0578 - mean_absolute_error: 0.1285 - val_loss: 0.4593 - val_mean_absolute_error: 0.4343\n",
      "Epoch 43/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0556 - mean_absolute_error: 0.1344 - val_loss: 0.4820 - val_mean_absolute_error: 0.4224\n",
      "4/4 [==============================] - 0s 29ms/step - loss: 0.4820 - mean_absolute_error: 0.4224\n",
      "Keys: <KeysViewHDF5 ['DAS Data', 'Discharge', 'Times']>\n",
      "Epoch 1/100\n",
      "14/14 [==============================] - 2s 93ms/step - loss: 3.7466 - mean_absolute_error: 1.4477 - val_loss: 2.2450 - val_mean_absolute_error: 1.1939\n",
      "Epoch 2/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 1.4934 - mean_absolute_error: 0.8843 - val_loss: 1.5943 - val_mean_absolute_error: 0.9345\n",
      "Epoch 3/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 1.0279 - mean_absolute_error: 0.7115 - val_loss: 1.3336 - val_mean_absolute_error: 0.8513\n",
      "Epoch 4/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.8359 - mean_absolute_error: 0.6392 - val_loss: 1.2331 - val_mean_absolute_error: 0.7852\n",
      "Epoch 5/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.7911 - mean_absolute_error: 0.6003 - val_loss: 1.0578 - val_mean_absolute_error: 0.7484\n",
      "Epoch 6/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.7355 - mean_absolute_error: 0.5821 - val_loss: 1.1497 - val_mean_absolute_error: 0.7726\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.6582 - mean_absolute_error: 0.5585 - val_loss: 0.8955 - val_mean_absolute_error: 0.7035\n",
      "Epoch 8/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.5510 - mean_absolute_error: 0.5083 - val_loss: 0.7750 - val_mean_absolute_error: 0.6486\n",
      "Epoch 9/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.4314 - mean_absolute_error: 0.4429 - val_loss: 0.7581 - val_mean_absolute_error: 0.6432\n",
      "Epoch 10/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.4476 - mean_absolute_error: 0.4605 - val_loss: 0.7264 - val_mean_absolute_error: 0.6365\n",
      "Epoch 11/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.3911 - mean_absolute_error: 0.4232 - val_loss: 0.7098 - val_mean_absolute_error: 0.5964\n",
      "Epoch 12/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.3672 - mean_absolute_error: 0.3896 - val_loss: 0.6603 - val_mean_absolute_error: 0.5773\n",
      "Epoch 13/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.3497 - mean_absolute_error: 0.3812 - val_loss: 0.7501 - val_mean_absolute_error: 0.6359\n",
      "Epoch 14/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.3786 - mean_absolute_error: 0.3945 - val_loss: 0.6818 - val_mean_absolute_error: 0.5986\n",
      "Epoch 15/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.3199 - mean_absolute_error: 0.3627 - val_loss: 0.6619 - val_mean_absolute_error: 0.5800\n",
      "Epoch 16/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.2753 - mean_absolute_error: 0.3298 - val_loss: 0.6018 - val_mean_absolute_error: 0.5488\n",
      "Epoch 17/100\n",
      "14/14 [==============================] - 1s 70ms/step - loss: 0.2263 - mean_absolute_error: 0.2952 - val_loss: 0.5865 - val_mean_absolute_error: 0.5388\n",
      "Epoch 18/100\n",
      "14/14 [==============================] - 1s 70ms/step - loss: 0.2058 - mean_absolute_error: 0.2765 - val_loss: 0.5922 - val_mean_absolute_error: 0.5448\n",
      "Epoch 19/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.1819 - mean_absolute_error: 0.2616 - val_loss: 0.5561 - val_mean_absolute_error: 0.5173\n",
      "Epoch 20/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.1659 - mean_absolute_error: 0.2461 - val_loss: 0.5473 - val_mean_absolute_error: 0.5002\n",
      "Epoch 21/100\n",
      "14/14 [==============================] - 1s 70ms/step - loss: 0.1588 - mean_absolute_error: 0.2365 - val_loss: 0.5689 - val_mean_absolute_error: 0.5195\n",
      "Epoch 22/100\n",
      "14/14 [==============================] - 1s 70ms/step - loss: 0.1506 - mean_absolute_error: 0.2456 - val_loss: 0.5218 - val_mean_absolute_error: 0.4954\n",
      "Epoch 23/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.1348 - mean_absolute_error: 0.2313 - val_loss: 0.5281 - val_mean_absolute_error: 0.4885\n",
      "Epoch 24/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.1245 - mean_absolute_error: 0.2267 - val_loss: 0.4880 - val_mean_absolute_error: 0.4662\n",
      "Epoch 25/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.1076 - mean_absolute_error: 0.2028 - val_loss: 0.5160 - val_mean_absolute_error: 0.4735\n",
      "Epoch 26/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.1004 - mean_absolute_error: 0.1907 - val_loss: 0.5584 - val_mean_absolute_error: 0.4820\n",
      "Epoch 27/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.0903 - mean_absolute_error: 0.1747 - val_loss: 0.5098 - val_mean_absolute_error: 0.4681\n",
      "Epoch 28/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.0865 - mean_absolute_error: 0.1715 - val_loss: 0.4910 - val_mean_absolute_error: 0.4585\n",
      "Epoch 29/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0794 - mean_absolute_error: 0.1582 - val_loss: 0.4923 - val_mean_absolute_error: 0.4584\n",
      "Epoch 30/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0758 - mean_absolute_error: 0.1552 - val_loss: 0.4963 - val_mean_absolute_error: 0.4544\n",
      "Epoch 31/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0770 - mean_absolute_error: 0.1559 - val_loss: 0.4832 - val_mean_absolute_error: 0.4489\n",
      "Epoch 32/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0723 - mean_absolute_error: 0.1511 - val_loss: 0.4759 - val_mean_absolute_error: 0.4620\n",
      "Epoch 33/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.0716 - mean_absolute_error: 0.1518 - val_loss: 0.4500 - val_mean_absolute_error: 0.4454\n",
      "Epoch 34/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0795 - mean_absolute_error: 0.1801 - val_loss: 0.4991 - val_mean_absolute_error: 0.4637\n",
      "Epoch 35/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.0705 - mean_absolute_error: 0.1667 - val_loss: 0.5154 - val_mean_absolute_error: 0.4731\n",
      "Epoch 36/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.0716 - mean_absolute_error: 0.1702 - val_loss: 0.5130 - val_mean_absolute_error: 0.4590\n",
      "Epoch 37/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.0695 - mean_absolute_error: 0.1650 - val_loss: 0.5070 - val_mean_absolute_error: 0.4664\n",
      "Epoch 38/100\n",
      "14/14 [==============================] - 1s 70ms/step - loss: 0.0603 - mean_absolute_error: 0.1401 - val_loss: 0.4525 - val_mean_absolute_error: 0.4305\n",
      "Epoch 39/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.0608 - mean_absolute_error: 0.1446 - val_loss: 0.5088 - val_mean_absolute_error: 0.4427\n",
      "Epoch 40/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.0581 - mean_absolute_error: 0.1379 - val_loss: 0.4946 - val_mean_absolute_error: 0.4423\n",
      "Epoch 41/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0629 - mean_absolute_error: 0.1455 - val_loss: 0.4713 - val_mean_absolute_error: 0.4169\n",
      "Epoch 42/100\n",
      "14/14 [==============================] - 1s 70ms/step - loss: 0.0578 - mean_absolute_error: 0.1285 - val_loss: 0.4593 - val_mean_absolute_error: 0.4343\n",
      "Epoch 43/100\n",
      "14/14 [==============================] - 1s 69ms/step - loss: 0.0556 - mean_absolute_error: 0.1344 - val_loss: 0.4820 - val_mean_absolute_error: 0.4224\n",
      "4/4 [==============================] - 0s 27ms/step - loss: 0.4820 - mean_absolute_error: 0.4224\n",
      "Keys: <KeysViewHDF5 ['DAS Data', 'Discharge', 'Times']>\n",
      "Epoch 1/100\n",
      "14/14 [==============================] - 2s 94ms/step - loss: 3.7466 - mean_absolute_error: 1.4477 - val_loss: 2.2450 - val_mean_absolute_error: 1.1939\n",
      "Epoch 2/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 1.4934 - mean_absolute_error: 0.8843 - val_loss: 1.5943 - val_mean_absolute_error: 0.9345\n",
      "Epoch 3/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 1.0279 - mean_absolute_error: 0.7115 - val_loss: 1.3336 - val_mean_absolute_error: 0.8513\n",
      "Epoch 4/100\n",
      "14/14 [==============================] - 1s 91ms/step - loss: 0.8359 - mean_absolute_error: 0.6392 - val_loss: 1.2331 - val_mean_absolute_error: 0.7852\n",
      "Epoch 5/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.7911 - mean_absolute_error: 0.6003 - val_loss: 1.0578 - val_mean_absolute_error: 0.7484\n",
      "Epoch 6/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.7355 - mean_absolute_error: 0.5821 - val_loss: 1.1497 - val_mean_absolute_error: 0.7726\n",
      "Epoch 7/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.6582 - mean_absolute_error: 0.5585 - val_loss: 0.8955 - val_mean_absolute_error: 0.7035\n",
      "Epoch 8/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.5510 - mean_absolute_error: 0.5083 - val_loss: 0.7750 - val_mean_absolute_error: 0.6486\n",
      "Epoch 9/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.4314 - mean_absolute_error: 0.4429 - val_loss: 0.7581 - val_mean_absolute_error: 0.6432\n",
      "Epoch 10/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.4476 - mean_absolute_error: 0.4605 - val_loss: 0.7264 - val_mean_absolute_error: 0.6365\n",
      "Epoch 11/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.3911 - mean_absolute_error: 0.4232 - val_loss: 0.7098 - val_mean_absolute_error: 0.5964\n",
      "Epoch 12/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.3672 - mean_absolute_error: 0.3896 - val_loss: 0.6603 - val_mean_absolute_error: 0.5773\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 13/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.3497 - mean_absolute_error: 0.3812 - val_loss: 0.7501 - val_mean_absolute_error: 0.6359\n",
      "Epoch 14/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.3786 - mean_absolute_error: 0.3945 - val_loss: 0.6818 - val_mean_absolute_error: 0.5986\n",
      "Epoch 15/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.3199 - mean_absolute_error: 0.3627 - val_loss: 0.6619 - val_mean_absolute_error: 0.5800\n",
      "Epoch 16/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.2753 - mean_absolute_error: 0.3298 - val_loss: 0.6018 - val_mean_absolute_error: 0.5488\n",
      "Epoch 17/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.2263 - mean_absolute_error: 0.2952 - val_loss: 0.5865 - val_mean_absolute_error: 0.5388\n",
      "Epoch 18/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.2058 - mean_absolute_error: 0.2765 - val_loss: 0.5922 - val_mean_absolute_error: 0.5448\n",
      "Epoch 19/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.1819 - mean_absolute_error: 0.2616 - val_loss: 0.5561 - val_mean_absolute_error: 0.5173\n",
      "Epoch 20/100\n",
      "14/14 [==============================] - 1s 77ms/step - loss: 0.1659 - mean_absolute_error: 0.2461 - val_loss: 0.5473 - val_mean_absolute_error: 0.5002\n",
      "Epoch 21/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.1588 - mean_absolute_error: 0.2365 - val_loss: 0.5689 - val_mean_absolute_error: 0.5195\n",
      "Epoch 22/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.1506 - mean_absolute_error: 0.2456 - val_loss: 0.5218 - val_mean_absolute_error: 0.4954\n",
      "Epoch 23/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.1348 - mean_absolute_error: 0.2313 - val_loss: 0.5281 - val_mean_absolute_error: 0.4885\n",
      "Epoch 24/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.1245 - mean_absolute_error: 0.2267 - val_loss: 0.4880 - val_mean_absolute_error: 0.4662\n",
      "Epoch 25/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.1076 - mean_absolute_error: 0.2028 - val_loss: 0.5160 - val_mean_absolute_error: 0.4735\n",
      "Epoch 26/100\n",
      "14/14 [==============================] - 1s 71ms/step - loss: 0.1004 - mean_absolute_error: 0.1907 - val_loss: 0.5584 - val_mean_absolute_error: 0.4820\n",
      "Epoch 27/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.0903 - mean_absolute_error: 0.1747 - val_loss: 0.5098 - val_mean_absolute_error: 0.4681\n",
      "Epoch 28/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0865 - mean_absolute_error: 0.1715 - val_loss: 0.4910 - val_mean_absolute_error: 0.4585\n",
      "Epoch 29/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.0794 - mean_absolute_error: 0.1582 - val_loss: 0.4923 - val_mean_absolute_error: 0.4584\n",
      "Epoch 30/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0758 - mean_absolute_error: 0.1552 - val_loss: 0.4963 - val_mean_absolute_error: 0.4544\n",
      "Epoch 31/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.0770 - mean_absolute_error: 0.1559 - val_loss: 0.4832 - val_mean_absolute_error: 0.4489\n",
      "Epoch 32/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.0723 - mean_absolute_error: 0.1511 - val_loss: 0.4759 - val_mean_absolute_error: 0.4620\n",
      "Epoch 33/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.0716 - mean_absolute_error: 0.1518 - val_loss: 0.4500 - val_mean_absolute_error: 0.4454\n",
      "Epoch 34/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0795 - mean_absolute_error: 0.1801 - val_loss: 0.4991 - val_mean_absolute_error: 0.4637\n",
      "Epoch 35/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.0705 - mean_absolute_error: 0.1667 - val_loss: 0.5154 - val_mean_absolute_error: 0.4731\n",
      "Epoch 36/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.0716 - mean_absolute_error: 0.1702 - val_loss: 0.5130 - val_mean_absolute_error: 0.4590\n",
      "Epoch 37/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.0695 - mean_absolute_error: 0.1650 - val_loss: 0.5070 - val_mean_absolute_error: 0.4664\n",
      "Epoch 38/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.0603 - mean_absolute_error: 0.1401 - val_loss: 0.4525 - val_mean_absolute_error: 0.4305\n",
      "Epoch 39/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0608 - mean_absolute_error: 0.1446 - val_loss: 0.5088 - val_mean_absolute_error: 0.4427\n",
      "Epoch 40/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0581 - mean_absolute_error: 0.1379 - val_loss: 0.4946 - val_mean_absolute_error: 0.4423\n",
      "Epoch 41/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0629 - mean_absolute_error: 0.1455 - val_loss: 0.4713 - val_mean_absolute_error: 0.4169\n",
      "Epoch 42/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.0578 - mean_absolute_error: 0.1285 - val_loss: 0.4593 - val_mean_absolute_error: 0.4343\n",
      "Epoch 43/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0556 - mean_absolute_error: 0.1344 - val_loss: 0.4820 - val_mean_absolute_error: 0.4224\n",
      "4/4 [==============================] - 0s 31ms/step - loss: 0.4820 - mean_absolute_error: 0.4224\n"
     ]
    }
   ],
   "source": [
    "window_size_array = [200, 200, 200]\n",
    "run = [1,2,3]\n",
    "\n",
    "\n",
    "\n",
    "for window_input_width, i in zip(window_size_array, run):\n",
    "    \n",
    "    SEED = 1\n",
    "    random.seed(SEED)\n",
    "    np.random.seed(SEED)\n",
    "    tf.random.set_seed(SEED)\n",
    "    \n",
    "    linear_model, lstm_model, dnn_model,  df_all_chan,input_columns  = d2d.import_data(filename = \"/data/fast0/datasets/Rhone_data_continuous_highpass.h5\")\n",
    "\n",
    "    da = df_all_chan.to_numpy()\n",
    "    \n",
    "    multi_step_window_shuffled = d2d.WindowGenerator(df_all_chan,\n",
    "        input_width=window_input_width, label_width=1, shift=0,\n",
    "        label_columns=['Discharge'],\n",
    "        input_columns=input_columns,\n",
    "        shuffle=True)\n",
    "    \n",
    "    history['lstm_'+str(window_input_width)+'_'+str(i)] = d2d.compile_and_fit(lstm_model, multi_step_window_shuffled, learning_rate = 0.001)\n",
    "\n",
    "\n",
    "    val_performance['lstm_'+str(window_input_width)+'_'+str(i)] = lstm_model.evaluate(multi_step_window_shuffled.val)\n",
    "    performance['lstm_'+str(window_input_width)+'_'+str(i)] = lstm_model.evaluate(multi_step_window_shuffled.test, verbose=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34966ac1",
   "metadata": {},
   "source": [
    "### For the for loop to work properly and be repeatable, must rebuild the model each time in the loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48b67121",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fc8f65f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "eaaf4bd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2592x1440 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 22})\n",
    "\n",
    "fig, (ax0,ax1,ax2) = plt.subplots(nrows=1, \n",
    "                                                     ncols=3, \n",
    "                                                     sharey=False, \n",
    "                                                     sharex=False,\n",
    "                                                     figsize=(36,20))\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "for ax, hist, title in zip((ax0,ax1,ax2), ('lstm_200_1','lstm_200_2','lstm_200_3'),\n",
    "                           ('LSTM (200 Timesteps) Run 1','LSTM (200 Timesteps) Run 2','LSTM (200 Timesteps) Run 3')):\n",
    "    ax.plot(history[hist].history['loss'], label='Train Loss',linewidth=3)\n",
    "    ax.plot(history[hist].history['val_loss'], label='Validation Loss',linewidth=3)\n",
    "    ax.grid(True)\n",
    "    ax.legend()\n",
    "    ax.set_title(title,size=50)\n",
    "    ax.set_ylabel('Loss', size=30)\n",
    "    ax.set_xlabel('Epoch', size=30)\n",
    "    #ax.set_ylim([0,1])\n",
    "    #ax.set_xlim([0,45])\n",
    "    \n",
    "fig.suptitle('Model Training', fontsize = 80)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "996aa5b7",
   "metadata": {},
   "source": [
    "### OK above code prodcuces repeatable resutls now in a loop, must invoke the seeds and rebuild models from within the loops"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a3a2aea",
   "metadata": {},
   "source": [
    "# What about a for loop with different window sizes with 200 included."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "be09abbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import d2d\n",
    "import importlib\n",
    "importlib.reload(d2d)\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import random\n",
    "import os\n",
    "# os.environ['TF_DETERMINISTIC_OPS'] = '1'\n",
    "\n",
    "SEED = 1\n",
    "random.seed(SEED)\n",
    "np.random.seed(SEED)\n",
    "tf.random.set_seed(SEED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a77583f7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "94095f76",
   "metadata": {},
   "outputs": [],
   "source": [
    "history = {}\n",
    "val_performance = {}\n",
    "performance = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "fb6cf367",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keys: <KeysViewHDF5 ['DAS Data', 'Discharge', 'Times']>\n",
      "Epoch 1/100\n",
      "18/18 [==============================] - 2s 67ms/step - loss: 5.1004 - mean_absolute_error: 1.7461 - val_loss: 3.1323 - val_mean_absolute_error: 1.3451\n",
      "Epoch 2/100\n",
      "18/18 [==============================] - 1s 55ms/step - loss: 2.4799 - mean_absolute_error: 1.1537 - val_loss: 2.0606 - val_mean_absolute_error: 1.0821\n",
      "Epoch 3/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 1.6635 - mean_absolute_error: 0.9135 - val_loss: 1.4437 - val_mean_absolute_error: 0.8727\n",
      "Epoch 4/100\n",
      "18/18 [==============================] - 1s 54ms/step - loss: 1.1636 - mean_absolute_error: 0.7669 - val_loss: 1.0983 - val_mean_absolute_error: 0.7544\n",
      "Epoch 5/100\n",
      "18/18 [==============================] - 1s 55ms/step - loss: 0.8890 - mean_absolute_error: 0.6567 - val_loss: 0.8638 - val_mean_absolute_error: 0.6604\n",
      "Epoch 6/100\n",
      "18/18 [==============================] - 1s 54ms/step - loss: 0.7518 - mean_absolute_error: 0.5765 - val_loss: 0.7833 - val_mean_absolute_error: 0.6185\n",
      "Epoch 7/100\n",
      "18/18 [==============================] - 1s 54ms/step - loss: 0.6057 - mean_absolute_error: 0.5070 - val_loss: 0.7370 - val_mean_absolute_error: 0.5847\n",
      "Epoch 8/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.5034 - mean_absolute_error: 0.4458 - val_loss: 0.7200 - val_mean_absolute_error: 0.5628\n",
      "Epoch 9/100\n",
      "18/18 [==============================] - 1s 52ms/step - loss: 0.4543 - mean_absolute_error: 0.4210 - val_loss: 0.6809 - val_mean_absolute_error: 0.5445\n",
      "Epoch 10/100\n",
      "18/18 [==============================] - 1s 52ms/step - loss: 0.3939 - mean_absolute_error: 0.3742 - val_loss: 0.6478 - val_mean_absolute_error: 0.5225\n",
      "Epoch 11/100\n",
      "18/18 [==============================] - 1s 54ms/step - loss: 0.3510 - mean_absolute_error: 0.3408 - val_loss: 0.6269 - val_mean_absolute_error: 0.5040\n",
      "Epoch 12/100\n",
      "18/18 [==============================] - 1s 52ms/step - loss: 0.3289 - mean_absolute_error: 0.3221 - val_loss: 0.6490 - val_mean_absolute_error: 0.5148\n",
      "Epoch 13/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.3362 - mean_absolute_error: 0.3014 - val_loss: 0.6187 - val_mean_absolute_error: 0.4898\n",
      "Epoch 14/100\n",
      "18/18 [==============================] - 1s 54ms/step - loss: 0.3149 - mean_absolute_error: 0.2856 - val_loss: 0.5964 - val_mean_absolute_error: 0.4696\n",
      "Epoch 15/100\n",
      "18/18 [==============================] - 1s 52ms/step - loss: 0.3023 - mean_absolute_error: 0.2717 - val_loss: 0.5864 - val_mean_absolute_error: 0.4559\n",
      "Epoch 16/100\n",
      "18/18 [==============================] - 1s 52ms/step - loss: 0.2848 - mean_absolute_error: 0.2535 - val_loss: 0.5784 - val_mean_absolute_error: 0.4621\n",
      "Epoch 17/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.2769 - mean_absolute_error: 0.2461 - val_loss: 0.6067 - val_mean_absolute_error: 0.4901\n",
      "Epoch 18/100\n",
      "18/18 [==============================] - 1s 54ms/step - loss: 0.2761 - mean_absolute_error: 0.2520 - val_loss: 0.5889 - val_mean_absolute_error: 0.4878\n",
      "Epoch 19/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.2669 - mean_absolute_error: 0.2558 - val_loss: 0.6054 - val_mean_absolute_error: 0.4922\n",
      "Epoch 20/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.2680 - mean_absolute_error: 0.2475 - val_loss: 0.5821 - val_mean_absolute_error: 0.4771\n",
      "Epoch 21/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.2682 - mean_absolute_error: 0.2588 - val_loss: 0.5669 - val_mean_absolute_error: 0.4539\n",
      "Epoch 22/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.2666 - mean_absolute_error: 0.2587 - val_loss: 0.6087 - val_mean_absolute_error: 0.4892\n",
      "Epoch 23/100\n",
      "18/18 [==============================] - 1s 54ms/step - loss: 0.2806 - mean_absolute_error: 0.2945 - val_loss: 0.6665 - val_mean_absolute_error: 0.5300\n",
      "Epoch 24/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.3264 - mean_absolute_error: 0.3366 - val_loss: 0.5989 - val_mean_absolute_error: 0.4983\n",
      "Epoch 25/100\n",
      "18/18 [==============================] - 1s 55ms/step - loss: 0.3215 - mean_absolute_error: 0.3475 - val_loss: 0.6232 - val_mean_absolute_error: 0.4995\n",
      "Epoch 26/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.3012 - mean_absolute_error: 0.3138 - val_loss: 0.5820 - val_mean_absolute_error: 0.4598\n",
      "Epoch 27/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.2509 - mean_absolute_error: 0.2637 - val_loss: 0.5444 - val_mean_absolute_error: 0.4223\n",
      "Epoch 28/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.2329 - mean_absolute_error: 0.2415 - val_loss: 0.5435 - val_mean_absolute_error: 0.4328\n",
      "Epoch 29/100\n",
      "18/18 [==============================] - 1s 54ms/step - loss: 0.2271 - mean_absolute_error: 0.2327 - val_loss: 0.4746 - val_mean_absolute_error: 0.3760\n",
      "Epoch 30/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.2149 - mean_absolute_error: 0.2130 - val_loss: 0.4799 - val_mean_absolute_error: 0.4074\n",
      "Epoch 31/100\n",
      "18/18 [==============================] - 1s 54ms/step - loss: 0.1924 - mean_absolute_error: 0.2107 - val_loss: 0.4332 - val_mean_absolute_error: 0.3691\n",
      "Epoch 32/100\n",
      "18/18 [==============================] - 1s 54ms/step - loss: 0.1811 - mean_absolute_error: 0.1938 - val_loss: 0.4245 - val_mean_absolute_error: 0.3572\n",
      "Epoch 33/100\n",
      "18/18 [==============================] - 1s 55ms/step - loss: 0.1902 - mean_absolute_error: 0.2112 - val_loss: 0.4248 - val_mean_absolute_error: 0.3705\n",
      "Epoch 34/100\n",
      "18/18 [==============================] - 1s 54ms/step - loss: 0.1968 - mean_absolute_error: 0.2259 - val_loss: 0.5576 - val_mean_absolute_error: 0.4312\n",
      "Epoch 35/100\n",
      "18/18 [==============================] - 1s 54ms/step - loss: 0.2299 - mean_absolute_error: 0.2641 - val_loss: 0.4795 - val_mean_absolute_error: 0.3898\n",
      "Epoch 36/100\n",
      "18/18 [==============================] - 1s 55ms/step - loss: 0.1791 - mean_absolute_error: 0.2156 - val_loss: 0.4507 - val_mean_absolute_error: 0.3838\n",
      "Epoch 37/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.1722 - mean_absolute_error: 0.2078 - val_loss: 0.3980 - val_mean_absolute_error: 0.3746\n",
      "Epoch 38/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.1511 - mean_absolute_error: 0.1959 - val_loss: 0.3782 - val_mean_absolute_error: 0.3712\n",
      "Epoch 39/100\n",
      "18/18 [==============================] - 1s 54ms/step - loss: 0.1569 - mean_absolute_error: 0.2052 - val_loss: 0.3657 - val_mean_absolute_error: 0.3466\n",
      "Epoch 40/100\n",
      "18/18 [==============================] - 1s 55ms/step - loss: 0.1341 - mean_absolute_error: 0.1751 - val_loss: 0.3529 - val_mean_absolute_error: 0.3402\n",
      "Epoch 41/100\n",
      "18/18 [==============================] - 1s 54ms/step - loss: 0.1263 - mean_absolute_error: 0.1644 - val_loss: 0.3557 - val_mean_absolute_error: 0.3403\n",
      "Epoch 42/100\n",
      "18/18 [==============================] - 1s 54ms/step - loss: 0.1158 - mean_absolute_error: 0.1481 - val_loss: 0.3515 - val_mean_absolute_error: 0.3311\n",
      "Epoch 43/100\n",
      "18/18 [==============================] - 1s 54ms/step - loss: 0.1104 - mean_absolute_error: 0.1374 - val_loss: 0.3678 - val_mean_absolute_error: 0.3354\n",
      "Epoch 44/100\n",
      "18/18 [==============================] - 1s 52ms/step - loss: 0.1062 - mean_absolute_error: 0.1273 - val_loss: 0.3672 - val_mean_absolute_error: 0.3252\n",
      "Epoch 45/100\n",
      "18/18 [==============================] - 1s 54ms/step - loss: 0.1034 - mean_absolute_error: 0.1296 - val_loss: 0.3653 - val_mean_absolute_error: 0.3253\n",
      "Epoch 46/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.1036 - mean_absolute_error: 0.1321 - val_loss: 0.3684 - val_mean_absolute_error: 0.3409\n",
      "Epoch 47/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.1083 - mean_absolute_error: 0.1391 - val_loss: 0.3609 - val_mean_absolute_error: 0.3365\n",
      "Epoch 48/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.1092 - mean_absolute_error: 0.1519 - val_loss: 0.3615 - val_mean_absolute_error: 0.3460\n",
      "Epoch 49/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.1100 - mean_absolute_error: 0.1574 - val_loss: 0.3855 - val_mean_absolute_error: 0.3533\n",
      "Epoch 50/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18/18 [==============================] - 1s 54ms/step - loss: 0.1026 - mean_absolute_error: 0.1483 - val_loss: 0.3699 - val_mean_absolute_error: 0.3262\n",
      "Epoch 51/100\n",
      "18/18 [==============================] - 1s 53ms/step - loss: 0.1037 - mean_absolute_error: 0.1494 - val_loss: 0.3926 - val_mean_absolute_error: 0.3551\n",
      "Epoch 52/100\n",
      "18/18 [==============================] - 1s 52ms/step - loss: 0.1147 - mean_absolute_error: 0.1602 - val_loss: 0.3709 - val_mean_absolute_error: 0.3444\n",
      "5/5 [==============================] - 0s 21ms/step - loss: 0.3709 - mean_absolute_error: 0.3444\n",
      "Keys: <KeysViewHDF5 ['DAS Data', 'Discharge', 'Times']>\n",
      "Epoch 1/100\n",
      "14/14 [==============================] - 2s 90ms/step - loss: 3.7466 - mean_absolute_error: 1.4477 - val_loss: 2.2450 - val_mean_absolute_error: 1.1939\n",
      "Epoch 2/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 1.4934 - mean_absolute_error: 0.8843 - val_loss: 1.5943 - val_mean_absolute_error: 0.9345\n",
      "Epoch 3/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 1.0279 - mean_absolute_error: 0.7115 - val_loss: 1.3336 - val_mean_absolute_error: 0.8513\n",
      "Epoch 4/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.8359 - mean_absolute_error: 0.6392 - val_loss: 1.2331 - val_mean_absolute_error: 0.7852\n",
      "Epoch 5/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.7911 - mean_absolute_error: 0.6003 - val_loss: 1.0578 - val_mean_absolute_error: 0.7484\n",
      "Epoch 6/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.7355 - mean_absolute_error: 0.5821 - val_loss: 1.1497 - val_mean_absolute_error: 0.7726\n",
      "Epoch 7/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.6582 - mean_absolute_error: 0.5585 - val_loss: 0.8955 - val_mean_absolute_error: 0.7035\n",
      "Epoch 8/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.5510 - mean_absolute_error: 0.5083 - val_loss: 0.7750 - val_mean_absolute_error: 0.6486\n",
      "Epoch 9/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.4314 - mean_absolute_error: 0.4429 - val_loss: 0.7581 - val_mean_absolute_error: 0.6432\n",
      "Epoch 10/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.4476 - mean_absolute_error: 0.4605 - val_loss: 0.7264 - val_mean_absolute_error: 0.6365\n",
      "Epoch 11/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.3911 - mean_absolute_error: 0.4232 - val_loss: 0.7098 - val_mean_absolute_error: 0.5964\n",
      "Epoch 12/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.3672 - mean_absolute_error: 0.3896 - val_loss: 0.6603 - val_mean_absolute_error: 0.5773\n",
      "Epoch 13/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.3497 - mean_absolute_error: 0.3812 - val_loss: 0.7501 - val_mean_absolute_error: 0.6359\n",
      "Epoch 14/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.3786 - mean_absolute_error: 0.3945 - val_loss: 0.6818 - val_mean_absolute_error: 0.5986\n",
      "Epoch 15/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.3199 - mean_absolute_error: 0.3627 - val_loss: 0.6619 - val_mean_absolute_error: 0.5800\n",
      "Epoch 16/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.2753 - mean_absolute_error: 0.3298 - val_loss: 0.6018 - val_mean_absolute_error: 0.5488\n",
      "Epoch 17/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.2263 - mean_absolute_error: 0.2952 - val_loss: 0.5865 - val_mean_absolute_error: 0.5388\n",
      "Epoch 18/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.2058 - mean_absolute_error: 0.2765 - val_loss: 0.5922 - val_mean_absolute_error: 0.5448\n",
      "Epoch 19/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.1819 - mean_absolute_error: 0.2616 - val_loss: 0.5561 - val_mean_absolute_error: 0.5173\n",
      "Epoch 20/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.1659 - mean_absolute_error: 0.2461 - val_loss: 0.5473 - val_mean_absolute_error: 0.5002\n",
      "Epoch 21/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.1588 - mean_absolute_error: 0.2365 - val_loss: 0.5689 - val_mean_absolute_error: 0.5195\n",
      "Epoch 22/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.1506 - mean_absolute_error: 0.2456 - val_loss: 0.5218 - val_mean_absolute_error: 0.4954\n",
      "Epoch 23/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.1348 - mean_absolute_error: 0.2313 - val_loss: 0.5281 - val_mean_absolute_error: 0.4885\n",
      "Epoch 24/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.1245 - mean_absolute_error: 0.2267 - val_loss: 0.4880 - val_mean_absolute_error: 0.4662\n",
      "Epoch 25/100\n",
      "14/14 [==============================] - 1s 76ms/step - loss: 0.1076 - mean_absolute_error: 0.2028 - val_loss: 0.5160 - val_mean_absolute_error: 0.4735\n",
      "Epoch 26/100\n",
      "14/14 [==============================] - 1s 76ms/step - loss: 0.1004 - mean_absolute_error: 0.1907 - val_loss: 0.5584 - val_mean_absolute_error: 0.4820\n",
      "Epoch 27/100\n",
      "14/14 [==============================] - 1s 76ms/step - loss: 0.0903 - mean_absolute_error: 0.1747 - val_loss: 0.5098 - val_mean_absolute_error: 0.4681\n",
      "Epoch 28/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0865 - mean_absolute_error: 0.1715 - val_loss: 0.4910 - val_mean_absolute_error: 0.4585\n",
      "Epoch 29/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.0794 - mean_absolute_error: 0.1582 - val_loss: 0.4923 - val_mean_absolute_error: 0.4584\n",
      "Epoch 30/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0758 - mean_absolute_error: 0.1552 - val_loss: 0.4963 - val_mean_absolute_error: 0.4544\n",
      "Epoch 31/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0770 - mean_absolute_error: 0.1559 - val_loss: 0.4832 - val_mean_absolute_error: 0.4489\n",
      "Epoch 32/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0723 - mean_absolute_error: 0.1511 - val_loss: 0.4759 - val_mean_absolute_error: 0.4620\n",
      "Epoch 33/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.0716 - mean_absolute_error: 0.1518 - val_loss: 0.4500 - val_mean_absolute_error: 0.4454\n",
      "Epoch 34/100\n",
      "14/14 [==============================] - 1s 76ms/step - loss: 0.0795 - mean_absolute_error: 0.1801 - val_loss: 0.4991 - val_mean_absolute_error: 0.4637\n",
      "Epoch 35/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.0705 - mean_absolute_error: 0.1667 - val_loss: 0.5154 - val_mean_absolute_error: 0.4731\n",
      "Epoch 36/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0716 - mean_absolute_error: 0.1702 - val_loss: 0.5130 - val_mean_absolute_error: 0.4590\n",
      "Epoch 37/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0695 - mean_absolute_error: 0.1650 - val_loss: 0.5070 - val_mean_absolute_error: 0.4664\n",
      "Epoch 38/100\n",
      "14/14 [==============================] - 1s 72ms/step - loss: 0.0603 - mean_absolute_error: 0.1401 - val_loss: 0.4525 - val_mean_absolute_error: 0.4305\n",
      "Epoch 39/100\n",
      "14/14 [==============================] - 1s 76ms/step - loss: 0.0608 - mean_absolute_error: 0.1446 - val_loss: 0.5088 - val_mean_absolute_error: 0.4427\n",
      "Epoch 40/100\n",
      "14/14 [==============================] - 1s 74ms/step - loss: 0.0581 - mean_absolute_error: 0.1379 - val_loss: 0.4946 - val_mean_absolute_error: 0.4423\n",
      "Epoch 41/100\n",
      "14/14 [==============================] - 1s 75ms/step - loss: 0.0629 - mean_absolute_error: 0.1455 - val_loss: 0.4713 - val_mean_absolute_error: 0.4169\n",
      "Epoch 42/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0578 - mean_absolute_error: 0.1285 - val_loss: 0.4593 - val_mean_absolute_error: 0.4343\n",
      "Epoch 43/100\n",
      "14/14 [==============================] - 1s 73ms/step - loss: 0.0556 - mean_absolute_error: 0.1344 - val_loss: 0.4820 - val_mean_absolute_error: 0.4224\n",
      "4/4 [==============================] - 0s 29ms/step - loss: 0.4820 - mean_absolute_error: 0.4224\n",
      "Keys: <KeysViewHDF5 ['DAS Data', 'Discharge', 'Times']>\n",
      "Epoch 1/100\n",
      "11/11 [==============================] - 2s 112ms/step - loss: 6.1349 - mean_absolute_error: 1.9595 - val_loss: 5.0297 - val_mean_absolute_error: 1.7263\n",
      "Epoch 2/100\n",
      "11/11 [==============================] - 1s 94ms/step - loss: 3.4969 - mean_absolute_error: 1.4059 - val_loss: 3.8747 - val_mean_absolute_error: 1.5308\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/100\n",
      "11/11 [==============================] - 1s 93ms/step - loss: 2.6754 - mean_absolute_error: 1.1837 - val_loss: 3.5284 - val_mean_absolute_error: 1.4617\n",
      "Epoch 4/100\n",
      "11/11 [==============================] - 1s 93ms/step - loss: 2.3623 - mean_absolute_error: 1.0955 - val_loss: 3.0926 - val_mean_absolute_error: 1.3407\n",
      "Epoch 5/100\n",
      "11/11 [==============================] - 1s 94ms/step - loss: 2.0692 - mean_absolute_error: 1.0145 - val_loss: 2.6644 - val_mean_absolute_error: 1.2490\n",
      "Epoch 6/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 1.7377 - mean_absolute_error: 0.9215 - val_loss: 2.3612 - val_mean_absolute_error: 1.1554\n",
      "Epoch 7/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 1.5648 - mean_absolute_error: 0.8661 - val_loss: 1.9344 - val_mean_absolute_error: 1.0476\n",
      "Epoch 8/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 1.3155 - mean_absolute_error: 0.7839 - val_loss: 1.7925 - val_mean_absolute_error: 0.9778\n",
      "Epoch 9/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 1.1488 - mean_absolute_error: 0.7021 - val_loss: 1.6568 - val_mean_absolute_error: 0.9193\n",
      "Epoch 10/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 1.0068 - mean_absolute_error: 0.6309 - val_loss: 1.4799 - val_mean_absolute_error: 0.8276\n",
      "Epoch 11/100\n",
      "11/11 [==============================] - 1s 93ms/step - loss: 0.8861 - mean_absolute_error: 0.5801 - val_loss: 1.3087 - val_mean_absolute_error: 0.7558\n",
      "Epoch 12/100\n",
      "11/11 [==============================] - 1s 90ms/step - loss: 0.7823 - mean_absolute_error: 0.5267 - val_loss: 1.1880 - val_mean_absolute_error: 0.7116\n",
      "Epoch 13/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.6954 - mean_absolute_error: 0.4839 - val_loss: 1.0772 - val_mean_absolute_error: 0.6607\n",
      "Epoch 14/100\n",
      "11/11 [==============================] - 1s 91ms/step - loss: 0.6203 - mean_absolute_error: 0.4472 - val_loss: 1.0073 - val_mean_absolute_error: 0.6393\n",
      "Epoch 15/100\n",
      "11/11 [==============================] - 1s 94ms/step - loss: 0.5624 - mean_absolute_error: 0.4204 - val_loss: 0.9407 - val_mean_absolute_error: 0.6084\n",
      "Epoch 16/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.5012 - mean_absolute_error: 0.3865 - val_loss: 0.8892 - val_mean_absolute_error: 0.5777\n",
      "Epoch 17/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.4524 - mean_absolute_error: 0.3657 - val_loss: 0.8536 - val_mean_absolute_error: 0.5725\n",
      "Epoch 18/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.4186 - mean_absolute_error: 0.3406 - val_loss: 0.8169 - val_mean_absolute_error: 0.5459\n",
      "Epoch 19/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.3881 - mean_absolute_error: 0.3165 - val_loss: 0.7914 - val_mean_absolute_error: 0.5396\n",
      "Epoch 20/100\n",
      "11/11 [==============================] - 1s 93ms/step - loss: 0.3588 - mean_absolute_error: 0.3018 - val_loss: 0.7654 - val_mean_absolute_error: 0.5231\n",
      "Epoch 21/100\n",
      "11/11 [==============================] - 1s 91ms/step - loss: 0.3406 - mean_absolute_error: 0.2891 - val_loss: 0.7192 - val_mean_absolute_error: 0.5056\n",
      "Epoch 22/100\n",
      "11/11 [==============================] - 1s 93ms/step - loss: 0.3126 - mean_absolute_error: 0.2743 - val_loss: 0.7028 - val_mean_absolute_error: 0.5105\n",
      "Epoch 23/100\n",
      "11/11 [==============================] - 1s 91ms/step - loss: 0.2978 - mean_absolute_error: 0.2819 - val_loss: 0.6854 - val_mean_absolute_error: 0.4958\n",
      "Epoch 24/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.2902 - mean_absolute_error: 0.2828 - val_loss: 0.6609 - val_mean_absolute_error: 0.4948\n",
      "Epoch 25/100\n",
      "11/11 [==============================] - 1s 91ms/step - loss: 0.2843 - mean_absolute_error: 0.2906 - val_loss: 0.6103 - val_mean_absolute_error: 0.4718\n",
      "Epoch 26/100\n",
      "11/11 [==============================] - 1s 91ms/step - loss: 0.2654 - mean_absolute_error: 0.2760 - val_loss: 0.6338 - val_mean_absolute_error: 0.4855\n",
      "Epoch 27/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.2577 - mean_absolute_error: 0.2731 - val_loss: 0.5858 - val_mean_absolute_error: 0.4662\n",
      "Epoch 28/100\n",
      "11/11 [==============================] - 1s 91ms/step - loss: 0.2435 - mean_absolute_error: 0.2725 - val_loss: 0.5649 - val_mean_absolute_error: 0.4671\n",
      "Epoch 29/100\n",
      "11/11 [==============================] - 1s 93ms/step - loss: 0.2550 - mean_absolute_error: 0.2935 - val_loss: 0.6577 - val_mean_absolute_error: 0.5235\n",
      "Epoch 30/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.3298 - mean_absolute_error: 0.4191 - val_loss: 0.5502 - val_mean_absolute_error: 0.5172\n",
      "Epoch 31/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.2688 - mean_absolute_error: 0.3409 - val_loss: 0.5367 - val_mean_absolute_error: 0.4878\n",
      "Epoch 32/100\n",
      "11/11 [==============================] - 1s 90ms/step - loss: 0.2361 - mean_absolute_error: 0.3068 - val_loss: 0.5428 - val_mean_absolute_error: 0.4888\n",
      "Epoch 33/100\n",
      "11/11 [==============================] - 1s 93ms/step - loss: 0.2189 - mean_absolute_error: 0.2819 - val_loss: 0.5371 - val_mean_absolute_error: 0.4758\n",
      "Epoch 34/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.1987 - mean_absolute_error: 0.2617 - val_loss: 0.5069 - val_mean_absolute_error: 0.4597\n",
      "Epoch 35/100\n",
      "11/11 [==============================] - 1s 93ms/step - loss: 0.1960 - mean_absolute_error: 0.2449 - val_loss: 0.5267 - val_mean_absolute_error: 0.4488\n",
      "Epoch 36/100\n",
      "11/11 [==============================] - 1s 91ms/step - loss: 0.1878 - mean_absolute_error: 0.2333 - val_loss: 0.5303 - val_mean_absolute_error: 0.4454\n",
      "Epoch 37/100\n",
      "11/11 [==============================] - 1s 91ms/step - loss: 0.1815 - mean_absolute_error: 0.2306 - val_loss: 0.5222 - val_mean_absolute_error: 0.4576\n",
      "Epoch 38/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.1721 - mean_absolute_error: 0.2186 - val_loss: 0.5331 - val_mean_absolute_error: 0.4509\n",
      "Epoch 39/100\n",
      "11/11 [==============================] - 1s 93ms/step - loss: 0.1657 - mean_absolute_error: 0.2070 - val_loss: 0.5067 - val_mean_absolute_error: 0.4322\n",
      "Epoch 40/100\n",
      "11/11 [==============================] - 1s 94ms/step - loss: 0.1601 - mean_absolute_error: 0.1950 - val_loss: 0.4873 - val_mean_absolute_error: 0.4164\n",
      "Epoch 41/100\n",
      "11/11 [==============================] - 1s 91ms/step - loss: 0.1573 - mean_absolute_error: 0.1891 - val_loss: 0.4659 - val_mean_absolute_error: 0.4094\n",
      "Epoch 42/100\n",
      "11/11 [==============================] - 1s 93ms/step - loss: 0.1500 - mean_absolute_error: 0.1869 - val_loss: 0.4650 - val_mean_absolute_error: 0.4087\n",
      "Epoch 43/100\n",
      "11/11 [==============================] - 1s 91ms/step - loss: 0.1544 - mean_absolute_error: 0.1885 - val_loss: 0.4766 - val_mean_absolute_error: 0.4134\n",
      "Epoch 44/100\n",
      "11/11 [==============================] - 1s 93ms/step - loss: 0.1530 - mean_absolute_error: 0.1832 - val_loss: 0.4752 - val_mean_absolute_error: 0.4012\n",
      "Epoch 45/100\n",
      "11/11 [==============================] - 1s 91ms/step - loss: 0.1525 - mean_absolute_error: 0.1860 - val_loss: 0.4766 - val_mean_absolute_error: 0.4117\n",
      "Epoch 46/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.1454 - mean_absolute_error: 0.1891 - val_loss: 0.4701 - val_mean_absolute_error: 0.4148\n",
      "Epoch 47/100\n",
      "11/11 [==============================] - 1s 91ms/step - loss: 0.1409 - mean_absolute_error: 0.1799 - val_loss: 0.4606 - val_mean_absolute_error: 0.4042\n",
      "Epoch 48/100\n",
      "11/11 [==============================] - 1s 90ms/step - loss: 0.1389 - mean_absolute_error: 0.1761 - val_loss: 0.4676 - val_mean_absolute_error: 0.4079\n",
      "Epoch 49/100\n",
      "11/11 [==============================] - 1s 91ms/step - loss: 0.1378 - mean_absolute_error: 0.1776 - val_loss: 0.4604 - val_mean_absolute_error: 0.3949\n",
      "Epoch 50/100\n",
      "11/11 [==============================] - 1s 90ms/step - loss: 0.1370 - mean_absolute_error: 0.1762 - val_loss: 0.4509 - val_mean_absolute_error: 0.4045\n",
      "Epoch 51/100\n",
      "11/11 [==============================] - 1s 94ms/step - loss: 0.1299 - mean_absolute_error: 0.1715 - val_loss: 0.4478 - val_mean_absolute_error: 0.3836\n",
      "Epoch 52/100\n",
      "11/11 [==============================] - 1s 93ms/step - loss: 0.1353 - mean_absolute_error: 0.1747 - val_loss: 0.4519 - val_mean_absolute_error: 0.3986\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 53/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.1348 - mean_absolute_error: 0.1816 - val_loss: 0.4402 - val_mean_absolute_error: 0.3796\n",
      "Epoch 54/100\n",
      "11/11 [==============================] - 1s 93ms/step - loss: 0.1232 - mean_absolute_error: 0.1687 - val_loss: 0.4265 - val_mean_absolute_error: 0.3843\n",
      "Epoch 55/100\n",
      "11/11 [==============================] - 1s 90ms/step - loss: 0.1236 - mean_absolute_error: 0.1693 - val_loss: 0.4334 - val_mean_absolute_error: 0.3831\n",
      "Epoch 56/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.1308 - mean_absolute_error: 0.1773 - val_loss: 0.4452 - val_mean_absolute_error: 0.3887\n",
      "Epoch 57/100\n",
      "11/11 [==============================] - 1s 91ms/step - loss: 0.1284 - mean_absolute_error: 0.1727 - val_loss: 0.4391 - val_mean_absolute_error: 0.3931\n",
      "Epoch 58/100\n",
      "11/11 [==============================] - 1s 89ms/step - loss: 0.1268 - mean_absolute_error: 0.1688 - val_loss: 0.4335 - val_mean_absolute_error: 0.3753\n",
      "Epoch 59/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.1273 - mean_absolute_error: 0.1669 - val_loss: 0.4337 - val_mean_absolute_error: 0.3770\n",
      "Epoch 60/100\n",
      "11/11 [==============================] - 1s 89ms/step - loss: 0.1251 - mean_absolute_error: 0.1685 - val_loss: 0.4303 - val_mean_absolute_error: 0.3866\n",
      "Epoch 61/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.1253 - mean_absolute_error: 0.1676 - val_loss: 0.4202 - val_mean_absolute_error: 0.3832\n",
      "Epoch 62/100\n",
      "11/11 [==============================] - 1s 91ms/step - loss: 0.1294 - mean_absolute_error: 0.1745 - val_loss: 0.4434 - val_mean_absolute_error: 0.3981\n",
      "Epoch 63/100\n",
      "11/11 [==============================] - 1s 93ms/step - loss: 0.1293 - mean_absolute_error: 0.1786 - val_loss: 0.4265 - val_mean_absolute_error: 0.3782\n",
      "Epoch 64/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.1240 - mean_absolute_error: 0.1721 - val_loss: 0.4148 - val_mean_absolute_error: 0.3844\n",
      "Epoch 65/100\n",
      "11/11 [==============================] - 1s 94ms/step - loss: 0.1218 - mean_absolute_error: 0.1643 - val_loss: 0.4209 - val_mean_absolute_error: 0.3728\n",
      "Epoch 66/100\n",
      "11/11 [==============================] - 1s 94ms/step - loss: 0.1207 - mean_absolute_error: 0.1589 - val_loss: 0.4281 - val_mean_absolute_error: 0.3714\n",
      "Epoch 67/100\n",
      "11/11 [==============================] - 1s 91ms/step - loss: 0.1175 - mean_absolute_error: 0.1576 - val_loss: 0.4212 - val_mean_absolute_error: 0.3844\n",
      "Epoch 68/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.1176 - mean_absolute_error: 0.1589 - val_loss: 0.4114 - val_mean_absolute_error: 0.3627\n",
      "Epoch 69/100\n",
      "11/11 [==============================] - 1s 93ms/step - loss: 0.1613 - mean_absolute_error: 0.2024 - val_loss: 0.5207 - val_mean_absolute_error: 0.4558\n",
      "Epoch 70/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.2007 - mean_absolute_error: 0.2488 - val_loss: 0.4975 - val_mean_absolute_error: 0.4495\n",
      "Epoch 71/100\n",
      "11/11 [==============================] - 1s 91ms/step - loss: 0.2281 - mean_absolute_error: 0.2682 - val_loss: 0.4914 - val_mean_absolute_error: 0.4402\n",
      "Epoch 72/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.2259 - mean_absolute_error: 0.2741 - val_loss: 0.5150 - val_mean_absolute_error: 0.4654\n",
      "Epoch 73/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.2021 - mean_absolute_error: 0.2649 - val_loss: 0.4717 - val_mean_absolute_error: 0.4378\n",
      "Epoch 74/100\n",
      "11/11 [==============================] - 1s 93ms/step - loss: 0.1866 - mean_absolute_error: 0.2509 - val_loss: 0.4654 - val_mean_absolute_error: 0.4322\n",
      "Epoch 75/100\n",
      "11/11 [==============================] - 1s 94ms/step - loss: 0.1846 - mean_absolute_error: 0.2470 - val_loss: 0.4541 - val_mean_absolute_error: 0.4204\n",
      "Epoch 76/100\n",
      "11/11 [==============================] - 1s 94ms/step - loss: 0.1831 - mean_absolute_error: 0.2363 - val_loss: 0.4807 - val_mean_absolute_error: 0.4154\n",
      "Epoch 77/100\n",
      "11/11 [==============================] - 1s 92ms/step - loss: 0.1670 - mean_absolute_error: 0.2207 - val_loss: 0.4527 - val_mean_absolute_error: 0.3904\n",
      "Epoch 78/100\n",
      "11/11 [==============================] - 1s 94ms/step - loss: 0.1553 - mean_absolute_error: 0.1977 - val_loss: 0.4125 - val_mean_absolute_error: 0.3870\n",
      "3/3 [==============================] - 0s 44ms/step - loss: 0.4125 - mean_absolute_error: 0.3870\n"
     ]
    }
   ],
   "source": [
    "window_size_array = [150, 200, 250]\n",
    "run = [1,2,3]\n",
    "\n",
    "\n",
    "\n",
    "for window_input_width, i in zip(window_size_array, run):\n",
    "    \n",
    "    SEED = 1\n",
    "    random.seed(SEED)\n",
    "    np.random.seed(SEED)\n",
    "    tf.random.set_seed(SEED)\n",
    "    \n",
    "    linear_model, lstm_model, dnn_model,  df_all_chan,input_columns  = d2d.import_data(filename = \"/data/fast0/datasets/Rhone_data_continuous_highpass.h5\")\n",
    "\n",
    "    da = df_all_chan.to_numpy()\n",
    "\n",
    "    multi_step_window_shuffled = d2d.WindowGenerator(df_all_chan,\n",
    "        input_width=window_input_width, label_width=1, shift=0,\n",
    "        label_columns=['Discharge'],\n",
    "        input_columns=input_columns,\n",
    "        shuffle=True)\n",
    "    \n",
    "    history['lstm_'+str(window_input_width)+'_'+str(i)] = d2d.compile_and_fit(lstm_model, multi_step_window_shuffled, learning_rate = 0.001)\n",
    "\n",
    "\n",
    "    val_performance['lstm_'+str(window_input_width)+'_'+str(i)] = lstm_model.evaluate(multi_step_window_shuffled.val)\n",
    "    performance['lstm_'+str(window_input_width)+'_'+str(i)] = lstm_model.evaluate(multi_step_window_shuffled.test, verbose=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e4b5b5a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2592x1440 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "matplotlib.rcParams.update({'font.size': 22})\n",
    "\n",
    "fig, (ax0,ax1,ax2) = plt.subplots(nrows=1, \n",
    "                                                     ncols=3, \n",
    "                                                     sharey=False, \n",
    "                                                     sharex=False,\n",
    "                                                     figsize=(36,20))\n",
    "fig.patch.set_facecolor('white')\n",
    "\n",
    "for ax, hist, title in zip((ax0,ax1,ax2), ('lstm_150_1','lstm_200_2','lstm_250_3'),\n",
    "                           ('LSTM (200 Timesteps) Run 1','LSTM (200 Timesteps) Run 2','LSTM (200 Timesteps) Run 3')):\n",
    "    ax.plot(history[hist].history['loss'], label='Train Loss',linewidth=3)\n",
    "    ax.plot(history[hist].history['val_loss'], label='Validation Loss',linewidth=3)\n",
    "    ax.grid(True)\n",
    "    ax.legend()\n",
    "    ax.set_title(title,size=50)\n",
    "    ax.set_ylabel('Loss', size=30)\n",
    "    ax.set_xlabel('Epoch', size=30)\n",
    "    ax.set_ylim([0,1])\n",
    "    \n",
    "fig.suptitle('Model Training', fontsize = 80)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55e7bd82",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fda2bc81",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4469b15",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}