Generate Artificial Vector.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "45eb8779-7c20-4c0c-a717-a301dee2be3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import datetime\n",
    "\n",
    "import IPython\n",
    "import IPython.display\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "\n",
    "mpl.rcParams['figure.figsize'] = (8, 6)\n",
    "mpl.rcParams['axes.grid'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7e19ea53-97fe-4eb2-bb91-84084670514f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, LSTM, Dropout, GRU, Bidirectional\n",
    "from keras.optimizers import SGD\n",
    "import math\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5ab82362-8bbf-40a0-866d-10e1287beeb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_predictions(test,predicted, title='horizontal_motion[pixel]'):\n",
    "    plt.plot(test, color='red',label='Real')\n",
    "    plt.plot(predicted, color='blue',label='Predicted')\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Time')\n",
    "    plt.ylabel(title)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "def return_rmse(test,predicted):\n",
    "    rmse = math.sqrt(mean_squared_error(test, predicted))\n",
    "    print(\"The root mean squared error is {}.\".format(rmse))\n",
    "\n",
    "def plot_colored_line(values, X):\n",
    "    \"\"\"\n",
    "    Plots a line graph with the first X values in red and the rest in blue.\n",
    "\n",
    "    :param values: List of values to plot.\n",
    "    :param X: Number of initial values to plot in red.\n",
    "    \"\"\"\n",
    "    # Splitting the data\n",
    "    x_values = range(len(values))\n",
    "    red_x = x_values[:X]\n",
    "    red_y = values[:X]\n",
    "    blue_x = x_values[X-1:]  # Overlap at the point of color change\n",
    "    blue_y = values[X-1:]\n",
    "\n",
    "    # Plotting\n",
    "    plt.plot(red_x, red_y, color='red')\n",
    "    plt.plot(blue_x, blue_y, color='blue')\n",
    "\n",
    "    # Adding titles and labels (optional)\n",
    "    plt.title(\"Line Plot with Different Color Segments\")\n",
    "    plt.xlabel(\"Time\")\n",
    "    plt.ylabel(\"Values\")\n",
    "\n",
    "    # Show plot\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9d91f81d-caee-48e8-826b-560eb618aa53",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_variables = ['time[s]',\n",
    "       'horizontal_motion[deg]', 'vertical_motion[deg]']\n",
    "\n",
    "dir = './MCI/eye_motion_trace'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "426dbcf3-c180-4317-9c22-0d818dd5cf45",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_x_train = []\n",
    "all_y_train = []\n",
    "\n",
    "for col in all_variables:\n",
    "    col_x_train = []\n",
    "    col_y_train = []\n",
    "\n",
    "    for i in os.listdir(dir + '/'):\n",
    "        df = pd.read_csv(dir + '/' + i)\n",
    "        df = df[[col]]\n",
    "    \n",
    "        df_arr = np.array(df)\n",
    "        divider = int(0.8 * len(df))\n",
    "        training_set = df_arr[:divider]\n",
    "        test_set = df_arr[divider:]\n",
    "\n",
    "        sc = MinMaxScaler(feature_range=(0,1)) #Transform to 0-1 domain\n",
    "        training_set_scaled = sc.fit_transform(training_set)\n",
    "        \n",
    "        for i in range(60,len(training_set_scaled)):\n",
    "            col_x_train.append(training_set_scaled[i-60:i,0])\n",
    "            col_y_train.append(training_set_scaled[i,0])\n",
    "\n",
    "        if(len(col_x_train) >= 5000):\n",
    "            break\n",
    "\n",
    "    col_x_train, col_y_train = np.array(col_x_train), np.array(col_y_train)\n",
    "    all_x_train.append(col_x_train)\n",
    "    all_y_train.append(col_y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ef80309f-0e5d-4347-a9f9-139979b01670",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We don't need this much data\n",
    "# print(len(all_x_train[0])) --> 294840"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f23ce5e7-69ff-4729-8bc8-60c11d522a75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7560\n"
     ]
    }
   ],
   "source": [
    "print(len(all_x_train[0])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9617c6de-1240-402f-8454-688226501d23",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "119/119 [==============================] - 11s 74ms/step - loss: 0.0227\n",
      "Epoch 2/30\n",
      "119/119 [==============================] - 9s 78ms/step - loss: 0.0084\n",
      "Epoch 3/30\n",
      "119/119 [==============================] - 9s 73ms/step - loss: 0.0063\n",
      "Epoch 4/30\n",
      "119/119 [==============================] - 9s 77ms/step - loss: 0.0049\n",
      "Epoch 5/30\n",
      "119/119 [==============================] - 9s 79ms/step - loss: 0.0042\n",
      "Epoch 6/30\n",
      "119/119 [==============================] - 9s 74ms/step - loss: 0.0036\n",
      "Epoch 7/30\n",
      "119/119 [==============================] - 9s 75ms/step - loss: 0.0033\n",
      "Epoch 8/30\n",
      "119/119 [==============================] - 9s 77ms/step - loss: 0.0029\n",
      "Epoch 9/30\n",
      "119/119 [==============================] - 9s 73ms/step - loss: 0.0028\n",
      "Epoch 10/30\n",
      "119/119 [==============================] - 9s 77ms/step - loss: 0.0025\n",
      "Epoch 11/30\n",
      "119/119 [==============================] - 9s 75ms/step - loss: 0.0024\n",
      "Epoch 12/30\n",
      "119/119 [==============================] - 9s 78ms/step - loss: 0.0023\n",
      "Epoch 13/30\n",
      "119/119 [==============================] - 8s 71ms/step - loss: 0.0022\n",
      "Epoch 14/30\n",
      "119/119 [==============================] - 8s 70ms/step - loss: 0.0021\n",
      "Epoch 15/30\n",
      "119/119 [==============================] - 8s 65ms/step - loss: 0.0020\n",
      "Epoch 16/30\n",
      "119/119 [==============================] - 8s 65ms/step - loss: 0.0020\n",
      "Epoch 17/30\n",
      "119/119 [==============================] - 8s 65ms/step - loss: 0.0018\n",
      "Epoch 18/30\n",
      "119/119 [==============================] - 7s 63ms/step - loss: 0.0017\n",
      "Epoch 19/30\n",
      "119/119 [==============================] - 7s 62ms/step - loss: 0.0017\n",
      "Epoch 20/30\n",
      "119/119 [==============================] - 7s 60ms/step - loss: 0.0016\n",
      "Epoch 21/30\n",
      "119/119 [==============================] - 8s 63ms/step - loss: 0.0016\n",
      "Epoch 22/30\n",
      "119/119 [==============================] - 7s 62ms/step - loss: 0.0016\n",
      "Epoch 23/30\n",
      "119/119 [==============================] - 7s 62ms/step - loss: 0.0015\n",
      "Epoch 24/30\n",
      "119/119 [==============================] - 7s 62ms/step - loss: 0.0014\n",
      "Epoch 25/30\n",
      "119/119 [==============================] - 7s 58ms/step - loss: 0.0015\n",
      "Epoch 26/30\n",
      "119/119 [==============================] - 7s 58ms/step - loss: 0.0014\n",
      "Epoch 27/30\n",
      "119/119 [==============================] - 7s 59ms/step - loss: 0.0013\n",
      "Epoch 28/30\n",
      "119/119 [==============================] - 7s 59ms/step - loss: 0.0013\n",
      "Epoch 29/30\n",
      "119/119 [==============================] - 7s 61ms/step - loss: 0.0012\n",
      "Epoch 30/30\n",
      "119/119 [==============================] - 7s 61ms/step - loss: 0.0013\n",
      "Epoch 1/30\n",
      "119/119 [==============================] - 10s 62ms/step - loss: 0.0229\n",
      "Epoch 2/30\n",
      "119/119 [==============================] - 7s 62ms/step - loss: 0.0099\n",
      "Epoch 3/30\n",
      "119/119 [==============================] - 7s 62ms/step - loss: 0.0072\n",
      "Epoch 4/30\n",
      "119/119 [==============================] - 7s 62ms/step - loss: 0.0055\n",
      "Epoch 5/30\n",
      "119/119 [==============================] - 7s 57ms/step - loss: 0.0050\n",
      "Epoch 6/30\n",
      "119/119 [==============================] - 7s 55ms/step - loss: 0.0041\n",
      "Epoch 7/30\n",
      "119/119 [==============================] - 7s 59ms/step - loss: 0.0035\n",
      "Epoch 8/30\n",
      "119/119 [==============================] - 7s 59ms/step - loss: 0.0033\n",
      "Epoch 9/30\n",
      "119/119 [==============================] - 7s 59ms/step - loss: 0.0030\n",
      "Epoch 10/30\n",
      "119/119 [==============================] - 7s 57ms/step - loss: 0.0028\n",
      "Epoch 11/30\n",
      "119/119 [==============================] - 7s 58ms/step - loss: 0.0026\n",
      "Epoch 12/30\n",
      "119/119 [==============================] - 7s 56ms/step - loss: 0.0024\n",
      "Epoch 13/30\n",
      "119/119 [==============================] - 7s 56ms/step - loss: 0.0023\n",
      "Epoch 14/30\n",
      "119/119 [==============================] - 7s 55ms/step - loss: 0.0022\n",
      "Epoch 15/30\n",
      "119/119 [==============================] - 7s 55ms/step - loss: 0.0020\n",
      "Epoch 16/30\n",
      "119/119 [==============================] - 7s 56ms/step - loss: 0.0020\n",
      "Epoch 17/30\n",
      "119/119 [==============================] - 6s 54ms/step - loss: 0.0019\n",
      "Epoch 18/30\n",
      "119/119 [==============================] - 7s 55ms/step - loss: 0.0018\n",
      "Epoch 19/30\n",
      "119/119 [==============================] - 6s 53ms/step - loss: 0.0017\n",
      "Epoch 20/30\n",
      "119/119 [==============================] - 6s 53ms/step - loss: 0.0017\n",
      "Epoch 21/30\n",
      "119/119 [==============================] - 6s 54ms/step - loss: 0.0016\n",
      "Epoch 22/30\n",
      "119/119 [==============================] - 7s 55ms/step - loss: 0.0015\n",
      "Epoch 23/30\n",
      "119/119 [==============================] - 6s 54ms/step - loss: 0.0015\n",
      "Epoch 24/30\n",
      "119/119 [==============================] - 6s 53ms/step - loss: 0.0014\n",
      "Epoch 25/30\n",
      "119/119 [==============================] - 6s 53ms/step - loss: 0.0013\n",
      "Epoch 26/30\n",
      "119/119 [==============================] - 6s 53ms/step - loss: 0.0013\n",
      "Epoch 27/30\n",
      "119/119 [==============================] - 6s 53ms/step - loss: 0.0013\n",
      "Epoch 28/30\n",
      "119/119 [==============================] - 6s 53ms/step - loss: 0.0012\n",
      "Epoch 29/30\n",
      "119/119 [==============================] - 6s 53ms/step - loss: 0.0012\n",
      "Epoch 30/30\n",
      "119/119 [==============================] - 6s 53ms/step - loss: 0.0011\n",
      "Epoch 1/30\n",
      "119/119 [==============================] - 8s 53ms/step - loss: 0.0200\n",
      "Epoch 2/30\n",
      "119/119 [==============================] - 6s 54ms/step - loss: 0.0081\n",
      "Epoch 3/30\n",
      "119/119 [==============================] - 6s 54ms/step - loss: 0.0056\n",
      "Epoch 4/30\n",
      "119/119 [==============================] - 7s 56ms/step - loss: 0.0043\n",
      "Epoch 5/30\n",
      "119/119 [==============================] - 6s 54ms/step - loss: 0.0036\n",
      "Epoch 6/30\n",
      "119/119 [==============================] - 6s 54ms/step - loss: 0.0031\n",
      "Epoch 7/30\n",
      "119/119 [==============================] - 6s 54ms/step - loss: 0.0028\n",
      "Epoch 8/30\n",
      "119/119 [==============================] - 7s 55ms/step - loss: 0.0025\n",
      "Epoch 9/30\n",
      "119/119 [==============================] - 7s 55ms/step - loss: 0.0024\n",
      "Epoch 10/30\n",
      "119/119 [==============================] - 7s 56ms/step - loss: 0.0022\n",
      "Epoch 11/30\n",
      "119/119 [==============================] - 7s 56ms/step - loss: 0.0020\n",
      "Epoch 12/30\n",
      "119/119 [==============================] - 6s 54ms/step - loss: 0.0019\n",
      "Epoch 13/30\n",
      "119/119 [==============================] - 7s 56ms/step - loss: 0.0017\n",
      "Epoch 14/30\n",
      "119/119 [==============================] - 6s 52ms/step - loss: 0.0018\n",
      "Epoch 15/30\n",
      "119/119 [==============================] - 6s 54ms/step - loss: 0.0016\n",
      "Epoch 16/30\n",
      "119/119 [==============================] - 6s 53ms/step - loss: 0.0016\n",
      "Epoch 17/30\n",
      "119/119 [==============================] - 6s 54ms/step - loss: 0.0015\n",
      "Epoch 18/30\n",
      "119/119 [==============================] - 6s 54ms/step - loss: 0.0014\n",
      "Epoch 19/30\n",
      "119/119 [==============================] - 7s 55ms/step - loss: 0.0014\n",
      "Epoch 20/30\n",
      "119/119 [==============================] - 6s 53ms/step - loss: 0.0013\n",
      "Epoch 21/30\n",
      "119/119 [==============================] - 7s 55ms/step - loss: 0.0013\n",
      "Epoch 22/30\n",
      "119/119 [==============================] - 6s 54ms/step - loss: 0.0012\n",
      "Epoch 23/30\n",
      "119/119 [==============================] - 7s 55ms/step - loss: 0.0012\n",
      "Epoch 24/30\n",
      "119/119 [==============================] - 6s 53ms/step - loss: 0.0012\n",
      "Epoch 25/30\n",
      "119/119 [==============================] - 6s 54ms/step - loss: 0.0011\n",
      "Epoch 26/30\n",
      "119/119 [==============================] - 6s 53ms/step - loss: 0.0012\n",
      "Epoch 27/30\n",
      "119/119 [==============================] - 6s 53ms/step - loss: 0.0011\n",
      "Epoch 28/30\n",
      "119/119 [==============================] - 6s 53ms/step - loss: 0.0010\n",
      "Epoch 29/30\n",
      "119/119 [==============================] - 6s 53ms/step - loss: 0.0011\n",
      "Epoch 30/30\n",
      "119/119 [==============================] - 6s 53ms/step - loss: 0.0010\n"
     ]
    }
   ],
   "source": [
    "all_regressors = []\n",
    "for i in range(len(all_variables)):\n",
    "    # The LSTM architecture\n",
    "    regressor = Sequential()\n",
    "    # First LSTM layer with Dropout regularisation\n",
    "    regressor.add(LSTM(units=50, return_sequences=True, input_shape=(60, 1)))\n",
    "    regressor.add(Dropout(0.2))\n",
    "    # Second LSTM layer\n",
    "    regressor.add(LSTM(units=50, return_sequences=True))\n",
    "    regressor.add(Dropout(0.2))\n",
    "    # Third LSTM layer\n",
    "    regressor.add(LSTM(units=50, return_sequences=True))\n",
    "    regressor.add(Dropout(0.2))\n",
    "    # Fourth LSTM layer\n",
    "    regressor.add(LSTM(units=50))\n",
    "    regressor.add(Dropout(0.2))\n",
    "    # The output layer\n",
    "    regressor.add(Dense(units=1))\n",
    "    \n",
    "    # regressor.summary()\n",
    "    regressor.compile(optimizer='rmsprop',loss='mean_squared_error')\n",
    "    regressor.fit(all_x_train[i],all_y_train[i],epochs=30,batch_size=64)\n",
    "    all_regressors.append(regressor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aceb9737-48b5-46f7-aba9-1a81097a9e77",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.17405765]\n",
      " [0.1759054 ]\n",
      " [0.17738359]\n",
      " ...\n",
      " [0.87004219]\n",
      " [0.86891702]\n",
      " [0.86835443]]\n"
     ]
    }
   ],
   "source": [
    "all_sc = []\n",
    "for i in range(len(all_y_train)):\n",
    "    sc = MinMaxScaler(feature_range=(0,1)) #Transform to 0-1 domain\n",
    "    inputs = sc.fit(all_y_train[i])\n",
    "    inputs = sc.transform(all_y_train[i])\n",
    "    all_y_train[i] = inputs\n",
    "    all_sc.append(sc)\n",
    "\n",
    "print(all_y_train[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1f3681ff-f2a3-4411-b225-483904db3660",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 60, 1)\n"
     ]
    }
   ],
   "source": [
    "starting_windows = []\n",
    "for i in range(len(all_variables)):\n",
    "    cur = all_y_train[i]\n",
    "    starting_windows.append(cur[len(cur) - 60 : len(cur)])\n",
    "\n",
    "starting_windows = np.array(starting_windows)\n",
    "print(starting_windows.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6f53f038-5a4f-432b-a64e-288f36f22c75",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[19], line 17\u001b[0m\n\u001b[1;32m     15\u001b[0m cur_frames \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(starting_windows[i])\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(generated_frames):\n\u001b[0;32m---> 17\u001b[0m     pred \u001b[38;5;241m=\u001b[39m \u001b[43mall_regressors\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcur_frames\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcur_frames\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m60\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcur_frames\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     18\u001b[0m     cur_frames\u001b[38;5;241m.\u001b[39mappend(pred[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m     20\u001b[0m all_frames\u001b[38;5;241m.\u001b[39mappend(np\u001b[38;5;241m.\u001b[39marray(cur_frames))\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/utils/traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/training.py:2521\u001b[0m, in \u001b[0;36mModel.predict\u001b[0;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   2512\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n\u001b[1;32m   2513\u001b[0m         warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m   2514\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUsing Model.predict with MultiWorkerMirroredStrategy \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2515\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mor TPUStrategy and AutoShardPolicy.FILE might lead to \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2518\u001b[0m             stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,\n\u001b[1;32m   2519\u001b[0m         )\n\u001b[0;32m-> 2521\u001b[0m data_handler \u001b[38;5;241m=\u001b[39m \u001b[43mdata_adapter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_data_handler\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2522\u001b[0m \u001b[43m    \u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2523\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2524\u001b[0m \u001b[43m    \u001b[49m\u001b[43msteps_per_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msteps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2525\u001b[0m \u001b[43m    \u001b[49m\u001b[43minitial_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2526\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2527\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_queue_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_queue_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2528\u001b[0m \u001b[43m    \u001b[49m\u001b[43mworkers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mworkers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2529\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_multiprocessing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_multiprocessing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2530\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2531\u001b[0m \u001b[43m    \u001b[49m\u001b[43msteps_per_execution\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_steps_per_execution\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2532\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2534\u001b[0m \u001b[38;5;66;03m# Container that configures and calls `tf.keras.Callback`s.\u001b[39;00m\n\u001b[1;32m   2535\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(callbacks, callbacks_module\u001b[38;5;241m.\u001b[39mCallbackList):\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/data_adapter.py:1678\u001b[0m, in \u001b[0;36mget_data_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1676\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m _ClusterCoordinatorExactEvalDataHandler(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1677\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m _ClusterCoordinatorDataHandler(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m-> 1678\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mDataHandler\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/data_adapter.py:1285\u001b[0m, in \u001b[0;36mDataHandler.__init__\u001b[0;34m(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution, distribute, pss_evaluation_shards)\u001b[0m\n\u001b[1;32m   1282\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_steps_per_execution \u001b[38;5;241m=\u001b[39m steps_per_execution\n\u001b[1;32m   1284\u001b[0m adapter_cls \u001b[38;5;241m=\u001b[39m select_data_adapter(x, y)\n\u001b[0;32m-> 1285\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_adapter \u001b[38;5;241m=\u001b[39m \u001b[43madapter_cls\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1286\u001b[0m \u001b[43m    \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1287\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1288\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mbatch_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1289\u001b[0m \u001b[43m    \u001b[49m\u001b[43msteps\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msteps_per_epoch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1290\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mepochs\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43minitial_epoch\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1291\u001b[0m \u001b[43m    \u001b[49m\u001b[43msample_weights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msample_weight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1292\u001b[0m \u001b[43m    \u001b[49m\u001b[43mshuffle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshuffle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1293\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmax_queue_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmax_queue_size\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1294\u001b[0m \u001b[43m    \u001b[49m\u001b[43mworkers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mworkers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1295\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_multiprocessing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43muse_multiprocessing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1296\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdistribution_strategy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdistribute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_strategy\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1297\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1298\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpss_evaluation_shards\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpss_evaluation_shards\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1299\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1301\u001b[0m strategy \u001b[38;5;241m=\u001b[39m tf\u001b[38;5;241m.\u001b[39mdistribute\u001b[38;5;241m.\u001b[39mget_strategy()\n\u001b[1;32m   1303\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_current_step \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/keras/src/engine/data_adapter.py:314\u001b[0m, in \u001b[0;36mTensorLikeDataAdapter.__init__\u001b[0;34m(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs)\u001b[0m\n\u001b[1;32m    307\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m indices\n\u001b[1;32m    309\u001b[0m \u001b[38;5;66;03m# We prefetch a single element. Computing large permutations can take\u001b[39;00m\n\u001b[1;32m    310\u001b[0m \u001b[38;5;66;03m# quite a while so we don't want to wait for prefetching over an epoch\u001b[39;00m\n\u001b[1;32m    311\u001b[0m \u001b[38;5;66;03m# boundary to trigger the next permutation. On the other hand, too many\u001b[39;00m\n\u001b[1;32m    312\u001b[0m \u001b[38;5;66;03m# simultaneous shuffles can contend on a hardware level and degrade all\u001b[39;00m\n\u001b[1;32m    313\u001b[0m \u001b[38;5;66;03m# performance.\u001b[39;00m\n\u001b[0;32m--> 314\u001b[0m indices_dataset \u001b[38;5;241m=\u001b[39m \u001b[43mindices_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpermutation\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mprefetch(\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m    316\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mslice_batch_indices\u001b[39m(indices):\n\u001b[1;32m    317\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Convert a Tensor of indices into a dataset of batched indices.\u001b[39;00m\n\u001b[1;32m    318\u001b[0m \n\u001b[1;32m    319\u001b[0m \u001b[38;5;124;03m    This step can be accomplished in several ways. The most natural is\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    331\u001b[0m \u001b[38;5;124;03m      A Dataset of batched indices.\u001b[39;00m\n\u001b[1;32m    332\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/data/ops/dataset_ops.py:2278\u001b[0m, in \u001b[0;36mDatasetV2.map\u001b[0;34m(self, map_func, num_parallel_calls, deterministic, name)\u001b[0m\n\u001b[1;32m   2274\u001b[0m \u001b[38;5;66;03m# Loaded lazily due to a circular dependency (dataset_ops -> map_op ->\u001b[39;00m\n\u001b[1;32m   2275\u001b[0m \u001b[38;5;66;03m# dataset_ops).\u001b[39;00m\n\u001b[1;32m   2276\u001b[0m \u001b[38;5;66;03m# pylint: disable=g-import-not-at-top,protected-access\u001b[39;00m\n\u001b[1;32m   2277\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpython\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mops\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m map_op\n\u001b[0;32m-> 2278\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mmap_op\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_map_v2\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2279\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2280\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmap_func\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2281\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnum_parallel_calls\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_parallel_calls\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2282\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdeterministic\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdeterministic\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2283\u001b[0m \u001b[43m    \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/data/ops/map_op.py:37\u001b[0m, in \u001b[0;36m_map_v2\u001b[0;34m(input_dataset, map_func, num_parallel_calls, deterministic, name)\u001b[0m\n\u001b[1;32m     34\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m deterministic \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m debug_mode\u001b[38;5;241m.\u001b[39mDEBUG_MODE:\n\u001b[1;32m     35\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe `deterministic` argument has no effect unless the \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     36\u001b[0m                   \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`num_parallel_calls` argument is specified.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 37\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_MapDataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     38\u001b[0m \u001b[43m      \u001b[49m\u001b[43minput_dataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmap_func\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpreserve_cardinality\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     39\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     40\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m _ParallelMapDataset(\n\u001b[1;32m     41\u001b[0m       input_dataset,\n\u001b[1;32m     42\u001b[0m       map_func,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     45\u001b[0m       preserve_cardinality\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m     46\u001b[0m       name\u001b[38;5;241m=\u001b[39mname)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/data/ops/map_op.py:115\u001b[0m, in \u001b[0;36m_MapDataset.__init__\u001b[0;34m(self, input_dataset, map_func, use_inter_op_parallelism, preserve_cardinality, use_legacy_function, name)\u001b[0m\n\u001b[1;32m    107\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_map_func \u001b[38;5;241m=\u001b[39m structured_function\u001b[38;5;241m.\u001b[39mStructuredFunctionWrapper(\n\u001b[1;32m    108\u001b[0m     map_func,\n\u001b[1;32m    109\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_transformation_name(),\n\u001b[1;32m    110\u001b[0m     dataset\u001b[38;5;241m=\u001b[39minput_dataset,\n\u001b[1;32m    111\u001b[0m     use_legacy_function\u001b[38;5;241m=\u001b[39muse_legacy_function)\n\u001b[1;32m    112\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_name \u001b[38;5;241m=\u001b[39m name\n\u001b[1;32m    113\u001b[0m variant_tensor \u001b[38;5;241m=\u001b[39m gen_dataset_ops\u001b[38;5;241m.\u001b[39mmap_dataset(\n\u001b[1;32m    114\u001b[0m     input_dataset\u001b[38;5;241m.\u001b[39m_variant_tensor,  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[0;32m--> 115\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_map_func\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241m.\u001b[39mcaptured_inputs,\n\u001b[1;32m    116\u001b[0m     f\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_map_func\u001b[38;5;241m.\u001b[39mfunction,\n\u001b[1;32m    117\u001b[0m     use_inter_op_parallelism\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_use_inter_op_parallelism,\n\u001b[1;32m    118\u001b[0m     preserve_cardinality\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_preserve_cardinality,\n\u001b[1;32m    119\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_common_args)\n\u001b[1;32m    120\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(input_dataset, variant_tensor)\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.11/lib/python3.11/site-packages/tensorflow/python/data/ops/structured_function.py:314\u001b[0m, in \u001b[0;36mStructuredFunctionWrapper.function\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    308\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    309\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21moutput_types\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    310\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m nest\u001b[38;5;241m.\u001b[39mmap_structure(\n\u001b[1;32m    311\u001b[0m       \u001b[38;5;28;01mlambda\u001b[39;00m component_spec: component_spec\u001b[38;5;241m.\u001b[39m_to_legacy_output_types(),  \u001b[38;5;66;03m# pylint: disable=protected-access\u001b[39;00m\n\u001b[1;32m    312\u001b[0m       \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_output_structure)\n\u001b[0;32m--> 314\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[1;32m    315\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfunction\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    316\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_function\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "# X_test = []\n",
    "# for i in range(60,len(inputs)):\n",
    "#     X_test.append(inputs[i-60:i,0])\n",
    "    \n",
    "# X_test = np.array(X_test)\n",
    "# X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1))\n",
    "\n",
    "# pred = regressor.predict(X_test)\n",
    "# pred = sc.inverse_transform(pred)\n",
    "\n",
    "generated_frames = 10000\n",
    "all_frames = []\n",
    "\n",
    "for i in range(len(all_variables)):\n",
    "    cur_frames = list(starting_windows[i])\n",
    "    for j in range(generated_frames):\n",
    "        pred = all_regressors[i].predict(np.array(cur_frames[len(cur_frames)-60:len(cur_frames)]))\n",
    "        cur_frames.append(pred[0])\n",
    "    \n",
    "    all_frames.append(np.array(cur_frames))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e8ef9c14-0e1c-4e4d-a744-bca8ef0fa67b",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(all_frames)):\n",
    "    all_frames[i] = all_sc[i].inverse_transform(all_frames[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2147b265-7bf7-4869-8c0b-337f6e91b423",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "testing = list(starting_windows[0])\n",
    "for i in range(1000):\n",
    "    pred = regressor.predict(np.array(testing[len(testing)-60:len(testing)]))\n",
    "    testing.append(pred[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e6d8317b-717e-407f-b4a9-54f90f91760f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " array([0.34043822], dtype=float32),\n",
       " ...]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "956743bd-119d-4330-81a3-88024860a54e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1060, 1)\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2e66a88a-e4fd-44d8-879a-ec88128a51c0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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