predict1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num GPUs Available:  1\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "print(\"Num GPUs Available: \", len(tf.config.list_physical_devices(\"GPU\")))\n",
    "# fix random seed for reproducibility\n",
    "tf.random.set_seed(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyperparameter\n",
    "TRAIN_TEST_RATIO = 0.8\n",
    "TIME_STEP = 8\n",
    "MODEL_TYPE = \"CNN_LSTM\"\n",
    "EPOCH_SIZE = 200\n",
    "BATCH_SIZE = 16 \n",
    "MODEL_JSON = {}\n",
    "def to_string():\n",
    "    print_list = [\n",
    "        f\"Time step: {TIME_STEP}\",\n",
    "        f\"Ratio: {TRAIN_TEST_RATIO}\",\n",
    "        f\"Model type: {MODEL_TYPE}\",\n",
    "        f\"Epoch size: {EPOCH_SIZE}\",\n",
    "        f\"Batch size: {BATCH_SIZE}\",\n",
    "        f\"Model json: {MODEL_JSON}\",\n",
    "    ]\n",
    "    return \"\\n\".join(print_list)\n",
    "VERSION = 17\n",
    "model = tf.keras.models.load_model(f\"./Model/{VERSION}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15\n",
      "[10166   312   823  1184  1394  1660  1459  1064   750   511   375   255\n",
      "   172   107     8]\n",
      "[ 0.      2.1432  4.2864  6.4296  8.5728 10.716  12.8592 15.0024 17.1456\n",
      " 19.2888 21.432  23.5752 25.7184 27.8616 30.0048 32.148 ]\n",
      "[5.02272727e-01 1.54150198e-02 4.06620553e-02 5.84980237e-02\n",
      " 6.88735178e-02 8.20158103e-02 7.20849802e-02 5.25691700e-02\n",
      " 3.70553360e-02 2.52470356e-02 1.85276680e-02 1.25988142e-02\n",
      " 8.49802372e-03 5.28656126e-03 3.95256917e-04]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataframe20 = pd.read_excel('Data/Data_1h/Data_1h/Solar_System_2.xlsx', sheet_name=\"2020\", header=1, usecols=[11])\n",
    "dataframe21 = pd.read_excel('Data/Data_1h/Data_1h/Solar_System_2.xlsx', sheet_name=\"2021\", header=1, usecols=[11])\n",
    "dataframe22 = pd.read_excel('Data/Data_1h/Data_1h/Solar_System_2.xlsx', sheet_name=\"2022\", header=1, usecols=[11])\n",
    "dataframe23 = pd.read_excel('Data/Data_1h/Data_1h/Solar_System_2.xlsx', sheet_name=\"2023\", header=1, usecols=[11])\n",
    "dataset = dataframe20.values\n",
    "dataset = np.append(dataset, dataframe21.values)\n",
    "dataset = np.append(dataset, dataframe22.values)\n",
    "dataset = np.append(dataset, dataframe23.values)\n",
    "dataset = dataset.astype('float64')\n",
    "dataset = np.reshape(dataset, (-1, 1))\n",
    "\n",
    "#Preprocessing\n",
    "dataset[dataset<0] = 0\n",
    "dataset[np.isnan(dataset)] = 0\n",
    "bins_size = int(1 + 3.3*np.log10(len(dataset)))\n",
    "print(bins_size)\n",
    "counts, bin_dataset = np.histogram(dataset, bins=bins_size, density=True)\n",
    "counts_number, bin_dataset = np.histogram(dataset, bins=bins_size)\n",
    "rel_freq =counts_number/len(dataset)\n",
    "print(counts_number)\n",
    "print(bin_dataset)\n",
    "print(rel_freq)\n",
    "plt.plot()\n",
    "plt.plot(bin_dataset[1:],counts,label=\"actual_pdf\")\n",
    "# plt.clf()\n",
    "# plt.figure(figsize=(100,60))\n",
    "# plt.rcParams.update({'font.size': 100})\n",
    "# plt.plot(dataset)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normalize the dataset, when working with sigmoid or tanh function as activation function, normalize data to range 0, 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "dataset = scaler.fit_transform(dataset)\n",
    "def create_dataset(dataset, look_back=1):\n",
    "\tdataX, dataY = [], []\n",
    "\tfor i in range(len(dataset)-look_back-1):\n",
    "\t\ta = dataset[i:(i+look_back), 0]\n",
    "\t\tdataX.append(a)\n",
    "\t\tdataY.append(dataset[i + look_back, 0])\n",
    "\treturn np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Model was constructed with shape (None, 10, 1) for input KerasTensor(type_spec=TensorSpec(shape=(None, 10, 1), dtype=tf.float32, name='conv1d_6_input'), name='conv1d_6_input', description=\"created by layer 'conv1d_6_input'\"), but it was called on an input with incompatible shape (None, 1, 1).\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "in user code:\n\n    File \"d:\\Bon\\Programing\\Miniconda\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 2041, in predict_function  *\n        return step_function(self, iterator)\n    File \"d:\\Bon\\Programing\\Miniconda\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 2027, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"d:\\Bon\\Programing\\Miniconda\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 2015, in run_step  **\n        outputs = model.predict_step(data)\n    File \"d:\\Bon\\Programing\\Miniconda\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 1983, in predict_step\n        return self(x, training=False)\n    File \"d:\\Bon\\Programing\\Miniconda\\envs\\tf\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n\n    ValueError: Exception encountered when calling layer \"conv1d_6\" \"                 f\"(type Conv1D).\n    \n    Negative dimension size caused by subtracting 3 from 1 for '{{node sequential_5/conv1d_6/Conv1D}} = Conv2D[T=DT_FLOAT, data_format=\"NHWC\", dilations=[1, 1, 1, 1], explicit_paddings=[], padding=\"VALID\", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true](sequential_5/conv1d_6/Conv1D/ExpandDims, sequential_5/conv1d_6/Conv1D/ExpandDims_1)' with input shapes: [?,1,1,1], [1,3,1,32].\n    \n    Call arguments received by layer \"conv1d_6\" \"                 f\"(type Conv1D):\n      • inputs=tf.Tensor(shape=(None, 1, 1), dtype=float32)\n",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[5], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m model \u001b[39m=\u001b[39m tf\u001b[39m.\u001b[39mkeras\u001b[39m.\u001b[39mmodels\u001b[39m.\u001b[39mload_model(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m./Model/\u001b[39m\u001b[39m{\u001b[39;00mVERSION\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m      2\u001b[0m datasetX, datasetY \u001b[39m=\u001b[39m create_dataset(dataset)\n\u001b[1;32m----> 3\u001b[0m result \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mpredict(dataset)\n\u001b[0;32m      4\u001b[0m plt\u001b[39m.\u001b[39mplot(dataset)\n\u001b[0;32m      5\u001b[0m plt\u001b[39m.\u001b[39mplot(result)\n",
      "File \u001b[1;32md:\\Bon\\Programing\\Miniconda\\envs\\tf\\lib\\site-packages\\keras\\utils\\traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n\u001b[0;32m     68\u001b[0m     \u001b[39m# To get the full stack trace, call:\u001b[39;00m\n\u001b[0;32m     69\u001b[0m     \u001b[39m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[1;32m---> 70\u001b[0m     \u001b[39mraise\u001b[39;00m e\u001b[39m.\u001b[39mwith_traceback(filtered_tb) \u001b[39mfrom\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m     71\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m     72\u001b[0m     \u001b[39mdel\u001b[39;00m filtered_tb\n",
      "File \u001b[1;32m~\\AppData\\Local\\Temp\\__autograph_generated_fileyz61xi4r.py:15\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__predict_function\u001b[1;34m(iterator)\u001b[0m\n\u001b[0;32m     13\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m     14\u001b[0m     do_return \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m---> 15\u001b[0m     retval_ \u001b[39m=\u001b[39m ag__\u001b[39m.\u001b[39mconverted_call(ag__\u001b[39m.\u001b[39mld(step_function), (ag__\u001b[39m.\u001b[39mld(\u001b[39mself\u001b[39m), ag__\u001b[39m.\u001b[39mld(iterator)), \u001b[39mNone\u001b[39;00m, fscope)\n\u001b[0;32m     16\u001b[0m \u001b[39mexcept\u001b[39;00m:\n\u001b[0;32m     17\u001b[0m     do_return \u001b[39m=\u001b[39m \u001b[39mFalse\u001b[39;00m\n",
      "\u001b[1;31mValueError\u001b[0m: in user code:\n\n    File \"d:\\Bon\\Programing\\Miniconda\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 2041, in predict_function  *\n        return step_function(self, iterator)\n    File \"d:\\Bon\\Programing\\Miniconda\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 2027, in step_function  **\n        outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"d:\\Bon\\Programing\\Miniconda\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 2015, in run_step  **\n        outputs = model.predict_step(data)\n    File \"d:\\Bon\\Programing\\Miniconda\\envs\\tf\\lib\\site-packages\\keras\\engine\\training.py\", line 1983, in predict_step\n        return self(x, training=False)\n    File \"d:\\Bon\\Programing\\Miniconda\\envs\\tf\\lib\\site-packages\\keras\\utils\\traceback_utils.py\", line 70, in error_handler\n        raise e.with_traceback(filtered_tb) from None\n\n    ValueError: Exception encountered when calling layer \"conv1d_6\" \"                 f\"(type Conv1D).\n    \n    Negative dimension size caused by subtracting 3 from 1 for '{{node sequential_5/conv1d_6/Conv1D}} = Conv2D[T=DT_FLOAT, data_format=\"NHWC\", dilations=[1, 1, 1, 1], explicit_paddings=[], padding=\"VALID\", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true](sequential_5/conv1d_6/Conv1D/ExpandDims, sequential_5/conv1d_6/Conv1D/ExpandDims_1)' with input shapes: [?,1,1,1], [1,3,1,32].\n    \n    Call arguments received by layer \"conv1d_6\" \"                 f\"(type Conv1D):\n      • inputs=tf.Tensor(shape=(None, 1, 1), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "model = tf.keras.models.load_model(f\"./Model/{VERSION}\")\n",
    "datasetX, datasetY = create_dataset(dataset)\n",
    "result = model.predict(dataset)\n",
    "plt.plot(dataset)\n",
    "plt.plot(result)\n"
   ]
  }
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