Untitled1 (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "27f91760-b463-4788-8c65-c4163a321754",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: yfinance in c:\\users\\nasta\\anaconda3\\lib\\site-packages (0.2.37)\n",
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     ]
    }
   ],
   "source": [
    "!pip install yfinance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c42b9783-261a-434c-94cf-51932d566f0f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%%**********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  Open        High         Low       Close   Adj Close  \\\n",
      "Date                                                                     \n",
      "2022-05-18  261.100006  269.420013  261.000000  264.839996  264.839996   \n",
      "2022-05-19  268.000000  269.779999  262.649994  266.679993  266.679993   \n",
      "2022-05-20  266.679993  266.679993  266.679993  266.679993  266.679993   \n",
      "2022-05-23  266.679993  266.679993  266.679993  266.679993  266.679993   \n",
      "2022-05-24  266.679993  266.679993  266.679993  266.679993  266.679993   \n",
      "\n",
      "              Volume  \n",
      "Date                  \n",
      "2022-05-18  51227600  \n",
      "2022-05-19  24980910  \n",
      "2022-05-20         0  \n",
      "2022-05-23         0  \n",
      "2022-05-24         0  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "import numpy as np\n",
    "\n",
    "# Загрузка данных\n",
    "ticker = 'GAZP.ME'\n",
    "data = yf.download(ticker, start=\"2018-01-01\", end=\"2023-01-01\")\n",
    "\n",
    "# Проверяем, что данные были успешно загружены\n",
    "print(data.tail())\n",
    "\n",
    "# Выборка данных о цене закрытия\n",
    "close_prices = data['Close'].values.reshape(-1, 1)\n",
    "\n",
    "# Масштабирование данных\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "scaled_data = scaler.fit_transform(close_prices)\n",
    "\n",
    "# Функция для создания временных последовательностей\n",
    "def create_sequences(data, sequence_length):\n",
    "    xs = []\n",
    "    ys = []\n",
    "    for i in range(len(data) - sequence_length):\n",
    "        sequence = data[i:(i + sequence_length)]\n",
    "        label = data[i + sequence_length]\n",
    "        xs.append(sequence)\n",
    "        ys.append(label)\n",
    "    return np.array(xs), np.array(ys)\n",
    "\n",
    "# Длина временной последовательности\n",
    "sequence_length = 60\n",
    "\n",
    "# Создание временных последовательностей\n",
    "X, y = create_sequences(scaled_data, sequence_length)\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "train_size = int(len(X) * 0.8)\n",
    "X_train, X_test = X[:train_size], X[train_size:]\n",
    "y_train, y_test = y[:train_size], y[train_size:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7e3d5f5-fe8b-4999-b127-f968d0331528",
   "metadata": {},
   "outputs": [
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     ]
    }
   ],
   "source": [
    "!pip install --upgrade tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3ea81cf-6a7d-4516-b78f-7292a1a32392",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nasta\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 89ms/step - loss: 0.0241 - val_loss: 0.0026\n",
      "Epoch 2/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 0.0030 - val_loss: 5.3895e-04\n",
      "Epoch 3/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 0.0020 - val_loss: 0.0014\n",
      "Epoch 4/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 0.0016 - val_loss: 0.0015\n",
      "Epoch 5/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 0.0015 - val_loss: 6.5092e-04\n",
      "Epoch 6/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 0.0013 - val_loss: 0.0015\n",
      "Epoch 7/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0016 - val_loss: 4.7876e-04\n",
      "Epoch 8/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0016 - val_loss: 0.0011\n",
      "Epoch 9/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 0.0013 - val_loss: 0.0013\n",
      "Epoch 10/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0011 - val_loss: 4.8173e-04\n",
      "Epoch 11/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 0.0012 - val_loss: 6.1468e-04\n",
      "Epoch 12/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 32ms/step - loss: 0.0012 - val_loss: 9.5659e-04\n",
      "Epoch 13/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0011 - val_loss: 6.1543e-04\n",
      "Epoch 14/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 9.6880e-04 - val_loss: 0.0020\n",
      "Epoch 15/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 0.0010 - val_loss: 0.0013\n",
      "Epoch 16/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0012 - val_loss: 0.0025\n",
      "Epoch 17/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 0.0014 - val_loss: 3.7131e-04\n",
      "Epoch 18/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 43ms/step - loss: 9.5436e-04 - val_loss: 4.2908e-04\n",
      "Epoch 19/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 8.6539e-04 - val_loss: 0.0036\n",
      "Epoch 20/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 0.0014 - val_loss: 5.8644e-04\n",
      "Epoch 21/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - loss: 0.0011 - val_loss: 9.3031e-04\n",
      "Epoch 22/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 9.0162e-04 - val_loss: 0.0017\n",
      "Epoch 23/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - loss: 0.0014 - val_loss: 3.2669e-04\n",
      "Epoch 24/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0010 - val_loss: 4.6506e-04\n",
      "Epoch 25/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 8.9461e-04 - val_loss: 4.0053e-04\n",
      "Epoch 26/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - loss: 8.5502e-04 - val_loss: 3.5315e-04\n",
      "Epoch 27/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 8.4244e-04 - val_loss: 0.0022\n",
      "Epoch 28/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 8.8638e-04 - val_loss: 6.5723e-04\n",
      "Epoch 29/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 6.6161e-04 - val_loss: 9.5467e-04\n",
      "Epoch 30/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 7.5828e-04 - val_loss: 3.2357e-04\n",
      "Epoch 31/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 7.8525e-04 - val_loss: 0.0017\n",
      "Epoch 32/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 7.9210e-04 - val_loss: 3.5319e-04\n",
      "Epoch 33/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 6.6746e-04 - val_loss: 6.3963e-04\n",
      "Epoch 34/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 6.6021e-04 - val_loss: 7.8061e-04\n",
      "Epoch 35/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 43ms/step - loss: 6.2679e-04 - val_loss: 8.4125e-04\n",
      "Epoch 36/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 9.9884e-04 - val_loss: 0.0012\n",
      "Epoch 37/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 7.0920e-04 - val_loss: 6.6588e-04\n",
      "Epoch 38/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 5.7192e-04 - val_loss: 4.6512e-04\n",
      "Epoch 39/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 6.3358e-04 - val_loss: 4.0873e-04\n",
      "Epoch 40/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 5.9024e-04 - val_loss: 6.6798e-04\n",
      "Epoch 41/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 6.5466e-04 - val_loss: 4.9149e-04\n",
      "Epoch 42/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 5.1497e-04 - val_loss: 8.7126e-04\n",
      "Epoch 43/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 7.3110e-04 - val_loss: 3.3027e-04\n",
      "Epoch 44/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 6.5482e-04 - val_loss: 5.1464e-04\n",
      "Epoch 45/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 5.8199e-04 - val_loss: 2.5210e-04\n",
      "Epoch 46/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - loss: 6.0547e-04 - val_loss: 2.7875e-04\n",
      "Epoch 47/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 45ms/step - loss: 5.7435e-04 - val_loss: 3.6293e-04\n",
      "Epoch 48/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 43ms/step - loss: 5.1786e-04 - val_loss: 8.2020e-04\n",
      "Epoch 49/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 6.1498e-04 - val_loss: 2.5065e-04\n",
      "Epoch 50/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - loss: 5.2321e-04 - val_loss: 2.4792e-04\n",
      "Epoch 51/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 46ms/step - loss: 5.3423e-04 - val_loss: 4.3371e-04\n",
      "Epoch 52/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 5.2548e-04 - val_loss: 3.8367e-04\n",
      "Epoch 53/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 7.0692e-04 - val_loss: 2.4751e-04\n",
      "Epoch 54/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 5.4672e-04 - val_loss: 3.1411e-04\n",
      "Epoch 55/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 4.7770e-04 - val_loss: 5.2223e-04\n",
      "Epoch 56/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 4.3146e-04 - val_loss: 2.1964e-04\n",
      "Epoch 57/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - loss: 4.8715e-04 - val_loss: 5.0461e-04\n",
      "Epoch 58/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - loss: 5.0665e-04 - val_loss: 6.4953e-04\n",
      "Epoch 59/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 5.5661e-04 - val_loss: 4.1544e-04\n",
      "Epoch 60/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 5.2786e-04 - val_loss: 4.8375e-04\n",
      "Epoch 61/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 4.9792e-04 - val_loss: 8.1012e-04\n",
      "Epoch 62/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 5.5218e-04 - val_loss: 2.3645e-04\n",
      "Epoch 63/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 5.2047e-04 - val_loss: 6.1938e-04\n",
      "Epoch 64/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 5.7869e-04 - val_loss: 2.1128e-04\n",
      "Epoch 65/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 6.3076e-04 - val_loss: 7.7497e-04\n",
      "Epoch 66/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - loss: 0.0010 - val_loss: 3.3184e-04\n",
      "Epoch 67/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 45ms/step - loss: 6.4540e-04 - val_loss: 2.1505e-04\n",
      "Epoch 68/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 5.5655e-04 - val_loss: 3.2000e-04\n",
      "Epoch 69/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 6.0072e-04 - val_loss: 6.0913e-04\n",
      "Epoch 70/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 5.9880e-04 - val_loss: 7.0754e-04\n",
      "Epoch 71/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 5.6949e-04 - val_loss: 3.8932e-04\n",
      "Epoch 72/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 5.5081e-04 - val_loss: 7.0397e-04\n",
      "Epoch 73/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 6.2241e-04 - val_loss: 3.4410e-04\n",
      "Epoch 74/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 43ms/step - loss: 4.9053e-04 - val_loss: 1.8788e-04\n",
      "Epoch 75/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 5.2255e-04 - val_loss: 4.2127e-04\n",
      "Epoch 76/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 4.3863e-04 - val_loss: 1.9991e-04\n",
      "Epoch 77/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 5.3335e-04 - val_loss: 3.3860e-04\n",
      "Epoch 78/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 4.5935e-04 - val_loss: 3.5463e-04\n",
      "Epoch 79/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 5.4656e-04 - val_loss: 2.3751e-04\n",
      "Epoch 80/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 4.5628e-04 - val_loss: 3.9942e-04\n",
      "Epoch 81/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 43ms/step - loss: 7.8213e-04 - val_loss: 2.2500e-04\n",
      "Epoch 82/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 43ms/step - loss: 5.7144e-04 - val_loss: 2.7534e-04\n",
      "Epoch 83/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 5.9970e-04 - val_loss: 5.0287e-04\n",
      "Epoch 84/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 5.4152e-04 - val_loss: 3.2899e-04\n",
      "Epoch 85/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 5.4002e-04 - val_loss: 5.9612e-04\n",
      "Epoch 86/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 5.9809e-04 - val_loss: 2.5034e-04\n",
      "Epoch 87/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - loss: 4.2842e-04 - val_loss: 1.9668e-04\n",
      "Epoch 88/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 4.9620e-04 - val_loss: 4.0872e-04\n",
      "Epoch 89/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 4.3529e-04 - val_loss: 6.6215e-04\n",
      "Epoch 90/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 6.3919e-04 - val_loss: 5.9987e-04\n",
      "Epoch 91/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 44ms/step - loss: 5.8956e-04 - val_loss: 2.2136e-04\n",
      "Epoch 92/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 3.8992e-04 - val_loss: 5.8370e-04\n",
      "Epoch 93/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 5.1718e-04 - val_loss: 1.9026e-04\n",
      "Epoch 94/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 4.4416e-04 - val_loss: 1.6907e-04\n",
      "Epoch 95/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 54ms/step - loss: 4.7143e-04 - val_loss: 2.7547e-04\n",
      "Epoch 96/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 69ms/step - loss: 4.7594e-04 - val_loss: 3.8885e-04\n",
      "Epoch 97/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 55ms/step - loss: 5.2879e-04 - val_loss: 3.0081e-04\n",
      "Epoch 98/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 77ms/step - loss: 3.9113e-04 - val_loss: 3.7092e-04\n",
      "Epoch 99/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 60ms/step - loss: 3.8665e-04 - val_loss: 6.8424e-04\n",
      "Epoch 100/100\n",
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 82ms/step - loss: 4.4241e-04 - val_loss: 1.7109e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x2bf7f6aff50>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense, Dropout\n",
    "\n",
    "# Размеры входных данных\n",
    "input_shape = (X_train.shape[1], X_train.shape[2])\n",
    "\n",
    "# Создание модели\n",
    "model = Sequential([\n",
    "    LSTM(units=50, return_sequences=True, input_shape=input_shape),\n",
    "    Dropout(0.2),\n",
    "    LSTM(units=50, return_sequences=False),\n",
    "    Dropout(0.2),\n",
    "    Dense(units=25),\n",
    "    Dense(units=1)\n",
    "])\n",
    "\n",
    "# Компиляция модели\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Обучение модели\n",
    "model.fit(X_train, y_train, batch_size=32, epochs=100, validation_split=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "10dc2682-7eb7-4171-b701-acba166f093f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 56ms/step - loss: 0.0200 - val_loss: 0.0068\n",
      "Epoch 2/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 0.0028 - val_loss: 0.0068\n",
      "Epoch 3/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0021 - val_loss: 0.0095\n",
      "Epoch 4/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0020 - val_loss: 0.0064\n",
      "Epoch 5/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0018 - val_loss: 0.0053\n",
      "Epoch 6/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 58ms/step - loss: 0.0017 - val_loss: 0.0050\n",
      "Epoch 7/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0016 - val_loss: 0.0057\n",
      "Epoch 8/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 0.0014 - val_loss: 0.0064\n",
      "Epoch 9/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 46ms/step - loss: 0.0013 - val_loss: 0.0062\n",
      "Epoch 10/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - loss: 0.0013 - val_loss: 0.0041\n",
      "Epoch 11/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0010 - val_loss: 0.0046\n",
      "Epoch 12/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - loss: 0.0012 - val_loss: 0.0043\n",
      "Epoch 13/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 0.0012 - val_loss: 0.0037\n",
      "Epoch 14/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 0.0011 - val_loss: 0.0036\n",
      "Epoch 15/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 0.0010 - val_loss: 0.0034\n",
      "Epoch 16/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 0.0011 - val_loss: 0.0034\n",
      "Epoch 17/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 44ms/step - loss: 9.1950e-04 - val_loss: 0.0040\n",
      "Epoch 18/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 44ms/step - loss: 8.8748e-04 - val_loss: 0.0032\n",
      "Epoch 19/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 8.8487e-04 - val_loss: 0.0031\n",
      "Epoch 20/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 8.3976e-04 - val_loss: 0.0034\n",
      "Epoch 21/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 7.5263e-04 - val_loss: 0.0030\n",
      "Epoch 22/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 7.1492e-04 - val_loss: 0.0029\n",
      "Epoch 23/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 7.0343e-04 - val_loss: 0.0036\n",
      "Epoch 24/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 46ms/step - loss: 7.1238e-04 - val_loss: 0.0035\n",
      "Epoch 25/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 83ms/step - loss: 8.8518e-04 - val_loss: 0.0028\n",
      "Epoch 26/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 6.2432e-04 - val_loss: 0.0028\n",
      "Epoch 27/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 6.9250e-04 - val_loss: 0.0029\n",
      "Epoch 28/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 7.9630e-04 - val_loss: 0.0030\n",
      "Epoch 29/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 6.5535e-04 - val_loss: 0.0026\n",
      "Epoch 30/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 6.4079e-04 - val_loss: 0.0026\n",
      "Epoch 31/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 6.2096e-04 - val_loss: 0.0023\n",
      "Epoch 32/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 6.4408e-04 - val_loss: 0.0023\n",
      "Epoch 33/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 43ms/step - loss: 5.8798e-04 - val_loss: 0.0028\n",
      "Epoch 34/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 6.0324e-04 - val_loss: 0.0022\n",
      "Epoch 35/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - loss: 5.4478e-04 - val_loss: 0.0036\n",
      "Epoch 36/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 45ms/step - loss: 5.4178e-04 - val_loss: 0.0022\n",
      "Epoch 37/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 6.2103e-04 - val_loss: 0.0026\n",
      "Epoch 38/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 63ms/step - loss: 5.0765e-04 - val_loss: 0.0023\n",
      "Epoch 39/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 36ms/step - loss: 5.4601e-04 - val_loss: 0.0021\n",
      "Epoch 40/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 56ms/step - loss: 6.1840e-04 - val_loss: 0.0023\n",
      "Epoch 41/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 5.5977e-04 - val_loss: 0.0022\n",
      "Epoch 42/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 6.0732e-04 - val_loss: 0.0023\n",
      "Epoch 43/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - loss: 5.8708e-04 - val_loss: 0.0022\n",
      "Epoch 44/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 5.3135e-04 - val_loss: 0.0021\n",
      "Epoch 45/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 60ms/step - loss: 5.2734e-04 - val_loss: 0.0022\n",
      "Epoch 46/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - loss: 5.6818e-04 - val_loss: 0.0020\n",
      "Epoch 47/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 49ms/step - loss: 4.9629e-04 - val_loss: 0.0021\n",
      "Epoch 48/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 5.0601e-04 - val_loss: 0.0023\n",
      "Epoch 49/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 6.1416e-04 - val_loss: 0.0029\n",
      "Epoch 50/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 75ms/step - loss: 5.5319e-04 - val_loss: 0.0020\n",
      "Epoch 51/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - loss: 5.1515e-04 - val_loss: 0.0021\n",
      "Epoch 52/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 33ms/step - loss: 5.9490e-04 - val_loss: 0.0024\n",
      "Epoch 53/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 4.8068e-04 - val_loss: 0.0020\n",
      "Epoch 54/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 5.6683e-04 - val_loss: 0.0020\n",
      "Epoch 55/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 4.9055e-04 - val_loss: 0.0019\n",
      "Epoch 56/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 47ms/step - loss: 5.3938e-04 - val_loss: 0.0022\n",
      "Epoch 57/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - loss: 4.7503e-04 - val_loss: 0.0020\n",
      "Epoch 58/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 4.3847e-04 - val_loss: 0.0021\n",
      "Epoch 59/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 44ms/step - loss: 4.6069e-04 - val_loss: 0.0020\n",
      "Epoch 60/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 4.2772e-04 - val_loss: 0.0019\n",
      "Epoch 61/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 4.8210e-04 - val_loss: 0.0019\n",
      "Epoch 62/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 4.3468e-04 - val_loss: 0.0021\n",
      "Epoch 63/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 49ms/step - loss: 4.9014e-04 - val_loss: 0.0019\n",
      "Epoch 64/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - loss: 5.1458e-04 - val_loss: 0.0019\n",
      "Epoch 65/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 35ms/step - loss: 4.3206e-04 - val_loss: 0.0019\n",
      "Epoch 66/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 4.8880e-04 - val_loss: 0.0019\n",
      "Epoch 67/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 48ms/step - loss: 4.6292e-04 - val_loss: 0.0019\n",
      "Epoch 68/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 45ms/step - loss: 4.4283e-04 - val_loss: 0.0019\n",
      "Epoch 69/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 96ms/step - loss: 4.7573e-04 - val_loss: 0.0020\n",
      "Epoch 70/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 4.4002e-04 - val_loss: 0.0023\n",
      "Epoch 71/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 48ms/step - loss: 4.2087e-04 - val_loss: 0.0019\n",
      "Epoch 72/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 4.0194e-04 - val_loss: 0.0021\n",
      "Epoch 73/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 5.5171e-04 - val_loss: 0.0031\n",
      "Epoch 74/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 49ms/step - loss: 5.1324e-04 - val_loss: 0.0020\n",
      "Epoch 75/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 59ms/step - loss: 4.1413e-04 - val_loss: 0.0018\n",
      "Epoch 76/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 50ms/step - loss: 5.1221e-04 - val_loss: 0.0024\n",
      "Epoch 77/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 106ms/step - loss: 4.3637e-04 - val_loss: 0.0018\n",
      "Epoch 78/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 38ms/step - loss: 4.2055e-04 - val_loss: 0.0018\n",
      "Epoch 79/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 35ms/step - loss: 4.5750e-04 - val_loss: 0.0025\n",
      "Epoch 80/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 40ms/step - loss: 4.4360e-04 - val_loss: 0.0024\n",
      "Epoch 81/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 4.4380e-04 - val_loss: 0.0018\n",
      "Epoch 82/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 42ms/step - loss: 4.8501e-04 - val_loss: 0.0022\n",
      "Epoch 83/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 43ms/step - loss: 4.8928e-04 - val_loss: 0.0018\n",
      "Epoch 84/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 45ms/step - loss: 4.1686e-04 - val_loss: 0.0020\n",
      "Epoch 85/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 62ms/step - loss: 4.3073e-04 - val_loss: 0.0018\n",
      "Epoch 86/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 4.9110e-04 - val_loss: 0.0018\n",
      "Epoch 87/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 43ms/step - loss: 4.6031e-04 - val_loss: 0.0020\n",
      "Epoch 88/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 111ms/step - loss: 5.0272e-04 - val_loss: 0.0018\n",
      "Epoch 89/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 4.3820e-04 - val_loss: 0.0018\n",
      "Epoch 90/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 51ms/step - loss: 4.2005e-04 - val_loss: 0.0018\n",
      "Epoch 91/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 54ms/step - loss: 4.5181e-04 - val_loss: 0.0018\n",
      "Epoch 92/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 4.3038e-04 - val_loss: 0.0018\n",
      "Epoch 93/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 4.0152e-04 - val_loss: 0.0018\n",
      "Epoch 94/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 37ms/step - loss: 5.1418e-04 - val_loss: 0.0019\n",
      "Epoch 95/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 39ms/step - loss: 4.5923e-04 - val_loss: 0.0017\n",
      "Epoch 96/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 41ms/step - loss: 4.3838e-04 - val_loss: 0.0017\n",
      "Epoch 97/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 52ms/step - loss: 4.5795e-04 - val_loss: 0.0018\n",
      "Epoch 98/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 34ms/step - loss: 4.1335e-04 - val_loss: 0.0022\n",
      "Epoch 99/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 49ms/step - loss: 4.6546e-04 - val_loss: 0.0017\n",
      "Epoch 100/100\n",
      "\u001b[1m26/26\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 38ms/step - loss: 4.9579e-04 - val_loss: 0.0019\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "history = model.fit(X_train, y_train, batch_size=32, epochs=100, validation_data=(X_test, y_test))\n",
    "\n",
    "# Визуализация истории обучения\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(history.history['loss'], label='Обучение - потери')\n",
    "plt.plot(history.history['val_loss'], label='Валидация - потери')\n",
    "plt.title('История обучения модели')\n",
    "plt.ylabel('Потери')\n",
    "plt.xlabel('Эпоха')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "96106b3c-0b6e-4544-a3eb-8f9eb345ba56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m7/7\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 102ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(X_test)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4d7867fb-aa1a-4e0b-8fc8-0c43176399e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Обратное масштабирование предсказаний\n",
    "predicted_prices = scaler.inverse_transform(predictions)\n",
    "\n",
    "# Обратное масштабирование фактических значений\n",
    "actual_prices = scaler.inverse_transform(y_test.reshape(-1, 1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9ce1ee2f-1a90-4cf4-8784-c95b030d9268",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE: 11.283587683100905\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "rmse = sqrt(mean_squared_error(actual_prices, predicted_prices))\n",
    "print(f\"RMSE: {rmse}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "710fc7f0-77d0-4f86-b42f-5309e19f2e29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Визуализация фактических и предсказанных цен\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(actual_prices, color='blue', label='Фактические цены')\n",
    "plt.plot(predicted_prices, color='red', linestyle='--', label='Предсказанные цены')\n",
    "plt.title('Сравнение фактических и предсказанных цен')\n",
    "plt.xlabel('Время')\n",
    "plt.ylabel('Цена')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5f18a066-7625-4123-81bf-68786304a044",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('my_model.keras')\n",
    "# сохраняю модель"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6c0b03eb-9252-44af-b4f5-5046ad1bfb9d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\nasta\\anaconda3\\Lib\\site-packages\\keras\\src\\saving\\saving_lib.py:396: UserWarning: Skipping variable loading for optimizer 'rmsprop', because it has 12 variables whereas the saved optimizer has 22 variables. \n",
      "  trackable.load_own_variables(weights_store.get(inner_path))\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.models.load_model('my_model.keras')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eba6b029-f5a4-4a9c-9ece-a1689119e3c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting python-telegram-botNote: you may need to restart the kernel to use updated packages.\n",
      "\n",
      "  Downloading python_telegram_bot-21.0.1-py3-none-any.whl.metadata (15 kB)\n",
      "Requirement already satisfied: tensorflow in c:\\users\\nasta\\anaconda3\\lib\\site-packages (2.16.1)\n",
      "Collecting httpx~=0.27 (from python-telegram-bot)\n",
      "  Downloading httpx-0.27.0-py3-none-any.whl.metadata (7.2 kB)\n",
      "Requirement already satisfied: tensorflow-intel==2.16.1 in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from tensorflow) (2.16.1)\n",
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      "Requirement already satisfied: google-pasta>=0.1.1 in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (0.2.0)\n",
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      "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from tensorflow-intel==2.16.1->tensorflow) (1.26.4)\n",
      "Requirement already satisfied: anyio in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from httpx~=0.27->python-telegram-bot) (4.2.0)\n",
      "Requirement already satisfied: certifi in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from httpx~=0.27->python-telegram-bot) (2024.2.2)\n",
      "Collecting httpcore==1.* (from httpx~=0.27->python-telegram-bot)\n",
      "  Downloading httpcore-1.0.5-py3-none-any.whl.metadata (20 kB)\n",
      "Requirement already satisfied: idna in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from httpx~=0.27->python-telegram-bot) (3.4)\n",
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      "Collecting h11<0.15,>=0.13 (from httpcore==1.*->httpx~=0.27->python-telegram-bot)\n",
      "  Downloading h11-0.14.0-py3-none-any.whl.metadata (8.2 kB)\n",
      "Requirement already satisfied: wheel<1.0,>=0.23.0 in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from astunparse>=1.6.0->tensorflow-intel==2.16.1->tensorflow) (0.41.2)\n",
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      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from requests<3,>=2.21.0->tensorflow-intel==2.16.1->tensorflow) (2.0.7)\n",
      "Requirement already satisfied: markdown>=2.6.8 in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (3.4.1)\n",
      "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from tensorboard<2.17,>=2.16->tensorflow-intel==2.16.1->tensorflow) (0.7.2)\n",
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      "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (2.15.1)\n",
      "Requirement already satisfied: mdurl~=0.1 in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from markdown-it-py<3.0.0,>=2.2.0->rich->keras>=3.0.0->tensorflow-intel==2.16.1->tensorflow) (0.1.0)\n",
      "Downloading python_telegram_bot-21.0.1-py3-none-any.whl (604 kB)\n",
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      "   ---------------------------------------- 0.0/75.6 kB ? eta -:--:--\n",
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      "Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
      "   ---------------------------------------- 0.0/58.3 kB ? eta -:--:--\n",
      "   ---------------------------------------- 58.3/58.3 kB 3.0 MB/s eta 0:00:00\n",
      "Installing collected packages: h11, httpcore, httpx, python-telegram-bot\n",
      "Successfully installed h11-0.14.0 httpcore-1.0.5 httpx-0.27.0 python-telegram-bot-21.0.1\n"
     ]
    }
   ],
   "source": [
    "pip install python-telegram-bot tensorflow\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ff97f7bc-fcfb-4ede-8e2e-b44ef8a49dc5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: python-telegram-bot in c:\\users\\nasta\\anaconda3\\lib\\site-packages (21.0.1)\n",
      "Requirement already satisfied: httpx~=0.27 in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from python-telegram-bot) (0.27.0)\n",
      "Requirement already satisfied: anyio in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from httpx~=0.27->python-telegram-bot) (4.2.0)\n",
      "Requirement already satisfied: certifi in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from httpx~=0.27->python-telegram-bot) (2024.2.2)\n",
      "Requirement already satisfied: httpcore==1.* in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from httpx~=0.27->python-telegram-bot) (1.0.5)\n",
      "Requirement already satisfied: idna in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from httpx~=0.27->python-telegram-bot) (3.4)\n",
      "Requirement already satisfied: sniffio in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from httpx~=0.27->python-telegram-bot) (1.3.0)\n",
      "Requirement already satisfied: h11<0.15,>=0.13 in c:\\users\\nasta\\anaconda3\\lib\\site-packages (from httpcore==1.*->httpx~=0.27->python-telegram-bot) (0.14.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install python-telegram-bot --upgrade\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f5c0f11f-3a2f-43b1-8ace-7bfc7cf75e00",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Updater.__init__() missing 1 required positional argument: 'update_queue'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[29], line 23\u001b[0m\n\u001b[0;32m     20\u001b[0m     updater\u001b[38;5;241m.\u001b[39midle()\n\u001b[0;32m     22\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m__main__\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m---> 23\u001b[0m     main()\n",
      "Cell \u001b[1;32mIn[29], line 12\u001b[0m, in \u001b[0;36mmain\u001b[1;34m()\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mmain\u001b[39m():\n\u001b[0;32m     11\u001b[0m     bot_token \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1234567890:ABCDEFgHiJKLmnop-QRsTUVWXYZ1234567890\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m---> 12\u001b[0m     updater \u001b[38;5;241m=\u001b[39m Updater(bot_token)\n\u001b[0;32m     13\u001b[0m     dispatcher \u001b[38;5;241m=\u001b[39m updater\u001b[38;5;241m.\u001b[39mdispatcher\n\u001b[0;32m     15\u001b[0m     dispatcher\u001b[38;5;241m.\u001b[39madd_handler(CommandHandler(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mstart\u001b[39m\u001b[38;5;124m\"\u001b[39m, start))\n",
      "\u001b[1;31mTypeError\u001b[0m: Updater.__init__() missing 1 required positional argument: 'update_queue'"
     ]
    }
   ],
   "source": [
    "from telegram import Update\n",
    "from telegram.ext import Updater, CommandHandler, MessageHandler, CallbackContext\n",
    "\n",
    "def start(update: Update, context: CallbackContext) -> None:\n",
    "    update.message.reply_text('Привет! Я эхо-бот. Отправь мне сообщение.')\n",
    "\n",
    "def echo(update: Update, context: CallbackContext) -> None:\n",
    "    update.message.reply_text(update.message.text)\n",
    "\n",
    "def main():\n",
    "    bot_token = '1234567890:ABCDEFgHiJKLmnop-QRsTUVWXYZ1234567890'\n",
    "    updater = Updater(bot_token)\n",
    "    dispatcher = updater.dispatcher\n",
    "\n",
    "    dispatcher.add_handler(CommandHandler(\"start\", start))\n",
    "    # Создаём обработчик сообщений без явного указания фильтра, что позволяет отвечать на все текстовые сообщения\n",
    "    dispatcher.add_handler(MessageHandler(None, echo))\n",
    "\n",
    "    updater.start_polling()\n",
    "    updater.idle()\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "a54a9635-7572-4535-a471-20c550e627e1",
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'Filters' from 'telegram.ext' (C:\\Users\\nasta\\anaconda3\\Lib\\site-packages\\telegram\\ext\\__init__.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[32], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtelegram\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Update\n\u001b[1;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtelegram\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mext\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Updater, CommandHandler, MessageHandler, Filters, CallbackContext\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mtf\u001b[39;00m  \u001b[38;5;66;03m# или import joblib для sklearn моделей\u001b[39;00m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m# Загрузите вашу модель (пример для TensorFlow)\u001b[39;00m\n",
      "\u001b[1;31mImportError\u001b[0m: cannot import name 'Filters' from 'telegram.ext' (C:\\Users\\nasta\\anaconda3\\Lib\\site-packages\\telegram\\ext\\__init__.py)"
     ]
    }
   ],
   "source": [
    "from telegram import Update\n",
    "from telegram.ext import Updater, CommandHandler, MessageHandler, Filters, CallbackContext\n",
    "import tensorflow as tf  # или import joblib для sklearn моделей\n",
    "\n",
    "# Загрузите вашу модель (пример для TensorFlow)\n",
    "model = tf.keras.models.load_model('path/to/your/model')\n",
    "\n",
    "def start(update: Update, context: CallbackContext) -> None:\n",
    "    update.message.reply_text('Привет! Отправь мне что-нибудь, и я обработаю это с помощью модели.')\n",
    "\n",
    "def analyze(update: Update, context: CallbackContext) -> None:\n",
    "    input_text = update.message.text\n",
    "    # Обработайте input_text с помощью вашей модели и подготовьте ответ\n",
    "    # Например: prediction = model.predict([input_text])\n",
    "    prediction = \"пример ответа\"  # Замените это на реальный вызов вашей модели\n",
    "    update.message.reply_text(f'Результат анализа: {prediction}')\n",
    "\n",
    "def main():\n",
    "    # Замените \"YOUR_TOKEN\" на токен вашего бота\n",
    "    updater = Updater('1234567890:ABCDEFgHiJKLmnop-QRsTUVWXYZ1234567890')\n",
    "\n",
    "    dispatcher = updater.dispatcher\n",
    "    dispatcher.add_handler(CommandHandler(\"start\", start))\n",
    "    dispatcher.add_handler(MessageHandler(Filters.text & ~Filters.command, analyze))\n",
    "\n",
    "    updater.start_polling()\n",
    "    updater.idle()\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bc13229-ba20-4d3e-9f69-34bddf4d8756",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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