Combining_CNN+RNN_cryptopunks.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# NFT sales forecasting - Predicting the price of NFTs using visual features. \n",
        "What we want is to give an image to the model, and it will give us an evaluation of the price it could be sold. \n",
        "\n",
        "The evaluation shouldn't relie only on the market data but also on the image itself. Thus we will use a CNN model of AlexNet architecture to train the relationship between the image and the price, and an RNN model to compute the time series of prices along dates. "
      ],
      "metadata": {
        "id": "bs_8ZZEwIGgw"
      }
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      "execution_count": 1,
      "metadata": {
        "id": "x9NFsz0rlp4E"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from numpy import newaxis\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.applications.vgg16 import VGG16\n",
        "from keras import models\n",
        "from keras.models import Model\n",
        "from keras.preprocessing.image import img_to_array \n",
        "from keras.applications.vgg16 import preprocess_input\n",
        "from keras.layers import InputSpec, Input, Conv2D, concatenate, Bidirectional,CuDNNLSTM,Dropout\n",
        "from keras.layers.convolutional import Conv2D, MaxPooling2D\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Activation, Flatten\n",
        "from dataclasses import dataclass\n",
        "\n",
        "import base64\n",
        "from PIL import Image\n",
        "import io\n",
        "from io import BytesIO\n",
        "import cv2\n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "from google.colab import files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
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        "outputId": "0d127f75-5b2b-428d-adce-b22870b42913"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
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              "         date  token_id      btc_usd  \\\n",
              "0  2017-11-09      2690  7143.580078   \n",
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              "                                                 img  \n",
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      "source": [
        "punks_train = pd.read_csv(\"drive/My Drive/opencampus/ML with TF - NFT sales forecasting/datasets/punks_train_historical.csv\")\n",
        "punks_train = punks_train.sort_values(['date']).reset_index(drop=True)\n",
        "punks_train=punks_train[['date','token_id','btc_usd','img']]\n",
        "punks_train.head()"
      ]
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              "         date  token_id       btc_usd  \\\n",
              "0  2021-07-31      5879  41626.195312   \n",
              "1  2021-07-31      7244  41626.195312   \n",
              "2  2021-07-31       773  41626.195312   \n",
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      ],
      "source": [
        "punks_test = pd.read_csv(\"drive/My Drive/opencampus/ML with TF - NFT sales forecasting/datasets/punks_test_historical.csv\")\n",
        "punks_test = punks_test.sort_values(['date']).reset_index(drop=True)\n",
        "punks_test = punks_test[['date','token_id','btc_usd','img']]\n",
        "punks_test.head()"
      ]
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    {
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      "metadata": {
        "id": "OEgRY7ToqXu6"
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      "source": [
        "# Converting date column to Date-Time dtype\n",
        "punks_train['date'] = pd.to_datetime(punks_train['date'])\n",
        "punks_test['date'] = pd.to_datetime(punks_test['date'])\n",
        "# Converting token_id column to object dtype\n",
        "punks_train['token_id'] = punks_train['token_id'].astype(str)\n",
        "punks_test['token_id'] = punks_test['token_id'].astype(str)"
      ]
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        "punks_train.info()"
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      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 14346 entries, 0 to 14345\n",
            "Data columns (total 4 columns):\n",
            " #   Column    Non-Null Count  Dtype         \n",
            "---  ------    --------------  -----         \n",
            " 0   date      14346 non-null  datetime64[ns]\n",
            " 1   token_id  14346 non-null  object        \n",
            " 2   btc_usd   14346 non-null  float64       \n",
            " 3   img       14346 non-null  object        \n",
            "dtypes: datetime64[ns](1), float64(1), object(2)\n",
            "memory usage: 448.4+ KB\n"
          ]
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    {
      "cell_type": "markdown",
      "source": [
        "# Data selection to match our challenge\n",
        "\n",
        "Predicting the price of a single item is not really significant: the most sold items only have 15 sales records. We will make predictions at the collection scale. \n",
        "\n",
        "To compute a time series at the collection size, we will need to get a date + a price (+ a visual feature)\n",
        "\n",
        "As the mean of images doesn't make a lot of sense, we will input the median sale price (+ image) for each day. tdates aren't continuous in our dataset since nfts of the cryptopunks collection aren't sold everyday. instead of generating fake data to fill missing dates, we will just compute the time series as if it was continuous which for this case doesn't seems disturbing. "
      ],
      "metadata": {
        "id": "0dsTARepoYWg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# select median sale for each date in dataframe\n",
        "dates = punks_train['date'].unique()\n",
        "median_sales = []\n",
        "token_ids = []\n",
        "imgs=[]\n",
        "\n",
        "for date in dates:\n",
        "  day_sales = punks_train.loc[punks_train['date']== date]\n",
        "  median_sale = day_sales['btc_usd'].median()\n",
        "  median_sales.append(median_sale)\n",
        "  token = day_sales['token_id'].loc[(day_sales['date']== date) & (day_sales['btc_usd'] == median_sale)].values[0]\n",
        "  token_ids.append(token)\n",
        "  img = day_sales['img'].loc[(day_sales['date'] == date) & (day_sales['btc_usd'] == median_sale) & (day_sales['token_id'] == token)].values[0]\n",
        "  imgs.append(img)\n",
        "\n",
        "median_daily_train = pd.DataFrame(list(zip(dates, token_ids, median_sales, imgs)), columns=['date', 'token_id', 'btc_usd', 'img'])\n",
        "median_daily_train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "_3C_5h55d6lv",
        "outputId": "e9867447-9fcd-491b-9876-94922ee65645"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        date token_id      btc_usd  \\\n",
              "0 2017-11-09     2690  7143.580078   \n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# same for test\n",
        "dates = punks_test['date'].unique()\n",
        "median_sales = []\n",
        "token_ids = []\n",
        "imgs=[]\n",
        "\n",
        "for date in dates:\n",
        "  day_sales = punks_test.loc[punks_test['date']== date]\n",
        "  median_sale = day_sales['btc_usd'].median()\n",
        "  median_sales.append(median_sale)\n",
        "  token = day_sales['token_id'].loc[(day_sales['date']== date) & (day_sales['btc_usd'] == median_sale)].values[0]\n",
        "  token_ids.append(token)\n",
        "  img = day_sales['img'].loc[(day_sales['date'] == date) & (day_sales['btc_usd'] == median_sale) & (day_sales['token_id'] == token)].values[0]\n",
        "  imgs.append(img)\n",
        "\n",
        "median_daily_test = pd.DataFrame(list(zip(dates, token_ids, median_sales, imgs)), columns=['date', 'token_id', 'btc_usd', 'img'])\n",
        "median_daily_test.head()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "9_h2NFCMA3Td",
        "outputId": "f54a69d0-4288-445d-90b9-eb29e256e08a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        date token_id       btc_usd  \\\n",
              "0 2021-07-31     5879  41626.195312   \n",
              "1 2021-08-01     8918  39974.894531   \n",
              "2 2021-08-02      622  39201.945312   \n",
              "3 2021-08-03     2723  38152.980469   \n",
              "4 2021-08-04     7192  39747.503906   \n",
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              "                                                     [key], {});\n",
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              "\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visual feature extraction\n",
        "\n",
        "Our first attempts to predict the value of NFTs from image's feature extraction isn't showing any good result. The image only is not enough to forecast the price of an item and thus other factors than the visual features must be influencing the price of an NFT sale.\n",
        "\n",
        "In this notebook, we will start by extracting visual features from each median sale's image with the hypothesis in mind that it could help increase the price prediction's accuracy.\n",
        "\n",
        "We will process the images in a CNN, reduce dimensionality of the output and store it for further use."
      ],
      "metadata": {
        "id": "x1WJmgoZowfZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Is VGG16 CNN model learning better than Alexnet?\n",
        "\n",
        "Alexnet's not learning nothing, try with the pre-trained model VGG16, which has a pre-process feature, if it can achieve better results."
      ],
      "metadata": {
        "id": "fIYS_JF_AD7x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#upload pre-trained model\n",
        "pretrained = VGG16(weights='imagenet', include_top=False, input_shape=(64,64,3))\n",
        "\n",
        "vgg = models.Sequential()\n",
        "vgg.add(pretrained)\n",
        "# add the output layers to match our needs\n",
        "vgg.add(Flatten())\n",
        "vgg.add(Dense(units=4096, input_shape=(None,4096), activation='relu'))\n",
        "vgg.add(Dense(4096, input_shape=(None,4096), activation='relu'))\n",
        "\n",
        "vgg.compile(\n",
        "    loss='mean_squared_error',\n",
        "    optimizer=tf.optimizers.Adam(lr=1e-04),\n",
        "    metrics=['mae']    \n",
        ")\n",
        "\n",
        "#visualise model architecture\n",
        "pretrained.summary()\n",
        "vgg.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M0o-7kRCiAr7",
        "outputId": "c3a2cef4-6489-4aa7-d3d1-a2ef7e7d1fe2"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "58892288/58889256 [==============================] - 0s 0us/step\n",
            "58900480/58889256 [==============================] - 0s 0us/step\n",
            "Model: \"vgg16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 64, 64, 3)]       0         \n",
            "                                                                 \n",
            " block1_conv1 (Conv2D)       (None, 64, 64, 64)        1792      \n",
            "                                                                 \n",
            " block1_conv2 (Conv2D)       (None, 64, 64, 64)        36928     \n",
            "                                                                 \n",
            " block1_pool (MaxPooling2D)  (None, 32, 32, 64)        0         \n",
            "                                                                 \n",
            " block2_conv1 (Conv2D)       (None, 32, 32, 128)       73856     \n",
            "                                                                 \n",
            " block2_conv2 (Conv2D)       (None, 32, 32, 128)       147584    \n",
            "                                                                 \n",
            " block2_pool (MaxPooling2D)  (None, 16, 16, 128)       0         \n",
            "                                                                 \n",
            " block3_conv1 (Conv2D)       (None, 16, 16, 256)       295168    \n",
            "                                                                 \n",
            " block3_conv2 (Conv2D)       (None, 16, 16, 256)       590080    \n",
            "                                                                 \n",
            " block3_conv3 (Conv2D)       (None, 16, 16, 256)       590080    \n",
            "                                                                 \n",
            " block3_pool (MaxPooling2D)  (None, 8, 8, 256)         0         \n",
            "                                                                 \n",
            " block4_conv1 (Conv2D)       (None, 8, 8, 512)         1180160   \n",
            "                                                                 \n",
            " block4_conv2 (Conv2D)       (None, 8, 8, 512)         2359808   \n",
            "                                                                 \n",
            " block4_conv3 (Conv2D)       (None, 8, 8, 512)         2359808   \n",
            "                                                                 \n",
            " block4_pool (MaxPooling2D)  (None, 4, 4, 512)         0         \n",
            "                                                                 \n",
            " block5_conv1 (Conv2D)       (None, 4, 4, 512)         2359808   \n",
            "                                                                 \n",
            " block5_conv2 (Conv2D)       (None, 4, 4, 512)         2359808   \n",
            "                                                                 \n",
            " block5_conv3 (Conv2D)       (None, 4, 4, 512)         2359808   \n",
            "                                                                 \n",
            " block5_pool (MaxPooling2D)  (None, 2, 2, 512)         0         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 14,714,688\n",
            "Trainable params: 14,714,688\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " vgg16 (Functional)          (None, 2, 2, 512)         14714688  \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 2048)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4096)              8392704   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 4096)              16781312  \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 39,888,704\n",
            "Trainable params: 39,888,704\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img_data = base64.b64decode(median_daily_train.img[0][1:])\n",
        "img = Image.open(io.BytesIO(img_data))\n",
        "img = np.array(img)\n",
        "print(img.shape)\n",
        "plt.imshow(img)\n",
        "img = img[:,:,:3]\n",
        "print(img.shape)\n",
        "img = cv2.resize(img,(64,64))\n",
        "print(img.shape)\n",
        "img = img.reshape(1,64,64,3)\n",
        "print(img.shape)\n",
        "#pass the reshaped array in the preprocess input for vgg16 model\n",
        "img = preprocess_input(img)\n",
        "# pass the image through the predict method\n",
        "features = vgg.predict(img, use_multiprocessing=True)\n",
        "print(features.shape)"
      ],
      "metadata": {
        "id": "ainSM2GfD2p6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        },
        "outputId": "668fe77a-c140-4a4f-9024-83c8981d5e3e"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(336, 336, 4)\n",
            "(336, 336, 3)\n",
            "(64, 64, 3)\n",
            "(1, 64, 64, 3)\n",
            "(1, 4096)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# define X-y axes at the same time\n",
        "X_train = []\n",
        "y_train =[]\n",
        "X_test = []\n",
        "y_test =[]\n",
        "\n",
        "# loop through each image in the dataset\n",
        "for index, row in median_daily_train.iterrows():\n",
        "  b64_img = median_daily_train.img.loc[index][1:]\n",
        "  img_data = base64.b64decode(b64_img)\n",
        "  img = Image.open(io.BytesIO(img_data))\n",
        "  img = np.array(img)\n",
        "  img = img[:,:,:3]\n",
        "  img = cv2.resize(img,(64,64))\n",
        "  img = img.reshape(1,64,64,3)\n",
        "  #pass the reshaped array in the preprocess input for vgg16 model\n",
        "  img = preprocess_input(img)\n",
        "  X_train.append((img))\n",
        "  label_array = tf.convert_to_tensor(median_daily_train.btc_usd.loc[index], dtype=tf.float32)\n",
        "  y_train.append(label_array)\n",
        "\n",
        "# same for test set\n",
        "for index, row in median_daily_test.iterrows():\n",
        "  b64_img = median_daily_train.img.loc[index][1:]\n",
        "  img_data = base64.b64decode(b64_img)\n",
        "  img = Image.open(io.BytesIO(img_data))\n",
        "  img = np.array(img)\n",
        "  img = img[:,:,:3]\n",
        "  img = cv2.resize(img,(64,64))\n",
        "  img = img.reshape(1,64,64,3)\n",
        "  #pass the reshaped array in the preprocess input for vgg16 model\n",
        "  img = preprocess_input(img)\n",
        "  X_test.append((img))\n",
        "  label_array = tf.convert_to_tensor(median_daily_test.btc_usd.loc[index], dtype=tf.float32)\n",
        "  y_test.append(label_array)\n",
        "\n",
        "# convert to array\n",
        "X_train=np.array(X_train)\n",
        "y_train=np.array(y_train)\n",
        "X_test=np.array(X_test)\n",
        "y_test=np.array(y_test)\n",
        "\n",
        "print(len(X_train))\n",
        "print(len(y_train))\n",
        "print(len(X_test))\n",
        "print(len(y_test))\n",
        "\n",
        "print(X_train.shape)\n",
        "print(y_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "id": "uBF9mfwco2VI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "adf2e062-249a-409b-93a1-4d737762ab31"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1000\n",
            "1000\n",
            "69\n",
            "69\n",
            "(1000, 1, 64, 64, 3)\n",
            "(1000,)\n",
            "(69, 1, 64, 64, 3)\n",
            "(69,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = X_train.reshape(1000, 64, 64, 3)\n",
        "print(X_train.shape)\n",
        "X_test = X_test.reshape(69, 64, 64, 3)\n",
        "print(X_test.shape)"
      ],
      "metadata": {
        "id": "KhOyEIE3OiOO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a54639fb-4488-4afa-8604-d236878b6e4a"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1000, 64, 64, 3)\n",
            "(69, 64, 64, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train))\n",
        "train_ds = (train_ds.batch(batch_size=8,drop_remainder=True))\n",
        "\n",
        "test_ds = tf.data.Dataset.from_tensor_slices((X_test, y_test))\n",
        "test_ds = (test_ds.batch(batch_size=8,drop_remainder=True))\n",
        "\n",
        "train_ds_size = tf.data.experimental.cardinality(train_ds).numpy()\n",
        "test_ds_size = tf.data.experimental.cardinality(test_ds).numpy()\n",
        "print(\"Training data size:\", train_ds_size)\n",
        "print(\"Test data size:\", test_ds_size)"
      ],
      "metadata": {
        "id": "tygLKfGa2J80",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ded559bc-04c5-437a-f979-ba1fc9967c5a"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training data size: 125\n",
            "Test data size: 8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fit vgg by running over the dataset multiple epochs. test prediction each epoch\n",
        "history = vgg.fit(\n",
        "    train_ds,\n",
        "    epochs = 50,\n",
        "    validation_data=test_ds,\n",
        "    validation_freq=1\n",
        ")"
      ],
      "metadata": {
        "id": "RBi2Haaq2KAf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb147aba-4726-4c94-94e2-cdceefec2547"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "125/125 [==============================] - 237s 2s/step - loss: 152931264.0000 - mae: 7179.0859 - val_loss: 697641472.0000 - val_mae: 24866.0020\n",
            "Epoch 2/50\n",
            "125/125 [==============================] - 212s 2s/step - loss: 91047544.0000 - mae: 5059.4663 - val_loss: 385247328.0000 - val_mae: 15375.5000\n",
            "Epoch 3/50\n",
            "125/125 [==============================] - 211s 2s/step - loss: 86743992.0000 - mae: 5206.0137 - val_loss: 392864480.0000 - val_mae: 16239.2275\n",
            "Epoch 4/50\n",
            "125/125 [==============================] - 210s 2s/step - loss: 83546328.0000 - mae: 5081.1890 - val_loss: 369498144.0000 - val_mae: 15506.3535\n",
            "Epoch 5/50\n",
            "125/125 [==============================] - 211s 2s/step - loss: 81761304.0000 - mae: 5052.7021 - val_loss: 384017088.0000 - val_mae: 16261.5098\n",
            "Epoch 6/50\n",
            "125/125 [==============================] - 215s 2s/step - loss: 79265328.0000 - mae: 4941.3398 - val_loss: 393536416.0000 - val_mae: 16771.5312\n",
            "Epoch 7/50\n",
            "125/125 [==============================] - 211s 2s/step - loss: 76884272.0000 - mae: 4765.3999 - val_loss: 414360576.0000 - val_mae: 17550.2930\n",
            "Epoch 8/50\n",
            "125/125 [==============================] - 208s 2s/step - loss: 75522520.0000 - mae: 4761.4658 - val_loss: 422329536.0000 - val_mae: 17834.9707\n",
            "Epoch 9/50\n",
            "125/125 [==============================] - 209s 2s/step - loss: 78546864.0000 - mae: 4936.9204 - val_loss: 363258688.0000 - val_mae: 15352.4697\n",
            "Epoch 10/50\n",
            "125/125 [==============================] - 209s 2s/step - loss: 73984376.0000 - mae: 4778.4473 - val_loss: 416364864.0000 - val_mae: 17682.9863\n",
            "Epoch 11/50\n",
            "125/125 [==============================] - 211s 2s/step - loss: 81846000.0000 - mae: 5219.3989 - val_loss: 616679296.0000 - val_mae: 23499.3750\n",
            "Epoch 12/50\n",
            "125/125 [==============================] - 211s 2s/step - loss: 77358424.0000 - mae: 5015.3604 - val_loss: 571201024.0000 - val_mae: 22414.8711\n",
            "Epoch 13/50\n",
            "125/125 [==============================] - 211s 2s/step - loss: 74845952.0000 - mae: 4970.6362 - val_loss: 776144576.0000 - val_mae: 27068.3633\n",
            "Epoch 14/50\n",
            "125/125 [==============================] - 212s 2s/step - loss: 75942360.0000 - mae: 5148.8643 - val_loss: 724795008.0000 - val_mae: 26045.2969\n",
            "Epoch 15/50\n",
            "125/125 [==============================] - 213s 2s/step - loss: 73197376.0000 - mae: 5074.8428 - val_loss: 946897536.0000 - val_mae: 30265.6973\n",
            "Epoch 16/50\n",
            "125/125 [==============================] - 214s 2s/step - loss: 77153728.0000 - mae: 5198.8242 - val_loss: 1132029184.0000 - val_mae: 33294.7578\n",
            "Epoch 17/50\n",
            "125/125 [==============================] - 214s 2s/step - loss: 60178564.0000 - mae: 4628.4829 - val_loss: 738149248.0000 - val_mae: 26460.8613\n",
            "Epoch 18/50\n",
            "125/125 [==============================] - 211s 2s/step - loss: 50810116.0000 - mae: 4056.8091 - val_loss: 706102784.0000 - val_mae: 25918.6621\n",
            "Epoch 19/50\n",
            "125/125 [==============================] - 215s 2s/step - loss: 60177116.0000 - mae: 4537.4985 - val_loss: 994861952.0000 - val_mae: 31172.7402\n",
            "Epoch 20/50\n",
            "125/125 [==============================] - 215s 2s/step - loss: 44924488.0000 - mae: 3908.0681 - val_loss: 739789120.0000 - val_mae: 26629.6113\n",
            "Epoch 21/50\n",
            "125/125 [==============================] - 215s 2s/step - loss: 64363872.0000 - mae: 4883.3950 - val_loss: 742548032.0000 - val_mae: 26648.6016\n",
            "Epoch 22/50\n",
            "125/125 [==============================] - 215s 2s/step - loss: 57229800.0000 - mae: 4407.1841 - val_loss: 545747136.0000 - val_mae: 22486.4043\n",
            "Epoch 23/50\n",
            "125/125 [==============================] - 218s 2s/step - loss: 52400784.0000 - mae: 4230.6904 - val_loss: 468619840.0000 - val_mae: 20594.3242\n",
            "Epoch 24/50\n",
            "125/125 [==============================] - 215s 2s/step - loss: 48747332.0000 - mae: 3975.3076 - val_loss: 417959776.0000 - val_mae: 19294.4219\n",
            "Epoch 25/50\n",
            "125/125 [==============================] - 213s 2s/step - loss: 67678176.0000 - mae: 4889.7773 - val_loss: 395913856.0000 - val_mae: 18457.7949\n",
            "Epoch 26/50\n",
            "125/125 [==============================] - 215s 2s/step - loss: 74825384.0000 - mae: 5101.1152 - val_loss: 409085024.0000 - val_mae: 18781.1348\n",
            "Epoch 27/50\n",
            "125/125 [==============================] - 216s 2s/step - loss: 49008488.0000 - mae: 4243.2837 - val_loss: 401499104.0000 - val_mae: 18814.4941\n",
            "Epoch 28/50\n",
            "125/125 [==============================] - 217s 2s/step - loss: 51466760.0000 - mae: 4272.9136 - val_loss: 439169088.0000 - val_mae: 19853.5977\n",
            "Epoch 29/50\n",
            "125/125 [==============================] - 218s 2s/step - loss: 62865712.0000 - mae: 4893.5952 - val_loss: 476661632.0000 - val_mae: 20778.0312\n",
            "Epoch 30/50\n",
            "125/125 [==============================] - 216s 2s/step - loss: 87517816.0000 - mae: 5815.0713 - val_loss: 538578304.0000 - val_mae: 22180.1953\n",
            "Epoch 31/50\n",
            "125/125 [==============================] - 217s 2s/step - loss: 86931240.0000 - mae: 5777.0234 - val_loss: 924442304.0000 - val_mae: 30056.6445\n",
            "Epoch 32/50\n",
            "125/125 [==============================] - 214s 2s/step - loss: 136886144.0000 - mae: 7537.9722 - val_loss: 1124793216.0000 - val_mae: 33317.0508\n",
            "Epoch 33/50\n",
            "125/125 [==============================] - 216s 2s/step - loss: 145670240.0000 - mae: 7172.1260 - val_loss: 691267968.0000 - val_mae: 25410.9629\n",
            "Epoch 34/50\n",
            "125/125 [==============================] - 219s 2s/step - loss: 54185276.0000 - mae: 4274.2368 - val_loss: 373244640.0000 - val_mae: 18177.5195\n",
            "Epoch 35/50\n",
            "125/125 [==============================] - 217s 2s/step - loss: 82120128.0000 - mae: 5242.1455 - val_loss: 531429056.0000 - val_mae: 22313.3828\n",
            "Epoch 36/50\n",
            "125/125 [==============================] - 216s 2s/step - loss: 155078144.0000 - mae: 7862.5176 - val_loss: 772266368.0000 - val_mae: 27419.6992\n",
            "Epoch 37/50\n",
            "125/125 [==============================] - 216s 2s/step - loss: 95042832.0000 - mae: 5854.1118 - val_loss: 674732800.0000 - val_mae: 25575.0410\n",
            "Epoch 38/50\n",
            "125/125 [==============================] - 213s 2s/step - loss: 125301400.0000 - mae: 6748.4229 - val_loss: 686715520.0000 - val_mae: 25685.5840\n",
            "Epoch 39/50\n",
            "125/125 [==============================] - 214s 2s/step - loss: 143775360.0000 - mae: 7064.4653 - val_loss: 437712096.0000 - val_mae: 19956.1582\n",
            "Epoch 40/50\n",
            "125/125 [==============================] - 214s 2s/step - loss: 136189296.0000 - mae: 7031.6982 - val_loss: 411926496.0000 - val_mae: 18729.8223\n",
            "Epoch 41/50\n",
            "125/125 [==============================] - 215s 2s/step - loss: 65028140.0000 - mae: 4769.8784 - val_loss: 432576768.0000 - val_mae: 20126.6152\n",
            "Epoch 42/50\n",
            "125/125 [==============================] - 213s 2s/step - loss: 46435712.0000 - mae: 4460.7334 - val_loss: 438201888.0000 - val_mae: 20064.6445\n",
            "Epoch 43/50\n",
            "125/125 [==============================] - 213s 2s/step - loss: 60279980.0000 - mae: 4663.6270 - val_loss: 349589440.0000 - val_mae: 17735.6660\n",
            "Epoch 44/50\n",
            "125/125 [==============================] - 214s 2s/step - loss: 135695184.0000 - mae: 7418.0474 - val_loss: 527768480.0000 - val_mae: 21898.8633\n",
            "Epoch 45/50\n",
            "125/125 [==============================] - 218s 2s/step - loss: 61321636.0000 - mae: 4598.2476 - val_loss: 475571136.0000 - val_mae: 21147.4336\n",
            "Epoch 46/50\n",
            "125/125 [==============================] - 215s 2s/step - loss: 41860780.0000 - mae: 3932.1560 - val_loss: 268996704.0000 - val_mae: 15395.9658\n",
            "Epoch 47/50\n",
            "125/125 [==============================] - 213s 2s/step - loss: 99163336.0000 - mae: 6093.3403 - val_loss: 519049280.0000 - val_mae: 21909.5840\n",
            "Epoch 48/50\n",
            "125/125 [==============================] - 213s 2s/step - loss: 153685968.0000 - mae: 7930.0977 - val_loss: 598369344.0000 - val_mae: 23846.9355\n",
            "Epoch 49/50\n",
            "125/125 [==============================] - 215s 2s/step - loss: 162319264.0000 - mae: 7856.1226 - val_loss: 260185360.0000 - val_mae: 14419.0000\n",
            "Epoch 50/50\n",
            "125/125 [==============================] - 215s 2s/step - loss: 60498356.0000 - mae: 4853.6919 - val_loss: 199480832.0000 - val_mae: 12808.4824\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vgg.history.history.keys()"
      ],
      "metadata": {
        "id": "TDzLMH6V2KO9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1dd59f46-fd5a-4cd0-84d0-79bb57a6f169"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'mae', 'val_loss', 'val_mae'])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f,ax=plt.subplots(2,1,figsize=(10,10)) \n",
        "\n",
        "#Assigning the first subplot to graph training loss and validation loss\n",
        "ax[0].plot(vgg.history.history['loss'],color='b',label='Training Loss')\n",
        "ax[0].plot(vgg.history.history['val_loss'],color='r',label='Validation Loss')\n",
        "ax[0].legend(loc='upper right')\n",
        "\n",
        "#Plotting the training accuracy and validation accuracy\n",
        "ax[1].plot(vgg.history.history['mae'],color='b',label='Training  mae')\n",
        "ax[1].plot(vgg.history.history['val_mae'],color='r',label='Validation mae')\n",
        "\n",
        "plt.legend()"
      ],
      "metadata": {
        "id": "fqNx92O72kNf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "outputId": "7bfef1a6-64d3-42fb-e341-79f07f73437f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f0f3432c810>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# process all images with the trained model and save the outputs for further input in final model"
      ],
      "metadata": {
        "id": "JHXWcoI6-0R7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# extract the features from all images and store it in a dictionary with index as keys\n",
        "data_train = {}\n",
        "data_test = {}\n",
        "\n",
        "# loop through each image in the dataset\n",
        "for index, row in median_daily_train.iterrows():\n",
        "  # pass the image through the predict method\n",
        "  features = vgg.predict(img, use_multiprocessing=True)\n",
        "  #save the feature extraction in the dictionary\n",
        "  data_train[index] = features\n",
        "\n",
        "# same for test set\n",
        "for index, row in median_daily_test.iterrows():\n",
        "  # pass the image through the predict method\n",
        "  features = vgg.predict(img, use_multiprocessing=True)\n",
        "  data_test[index] = features"
      ],
      "metadata": {
        "id": "cVKMwdXabw_0"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# get a list of the extracted features\n",
        "outputCNN_train = np.array(list(data_train.values()))\n",
        "outputCNN_test = np.array(list(data_test.values()))\n",
        "print(outputCNN_train.shape)\n",
        "print(outputCNN_test.shape)\n",
        "\n",
        "# reshape so that there are samples of 4096 vectors\n",
        "#outputCNN_train = outputCNN_train.reshape(-1,4096)\n",
        "#outputCNN_test = outputCNN_test.reshape(-1,4096)\n",
        "#print(outputCNN_train.shape)\n",
        "#print(outputCNN_test.shape)"
      ],
      "metadata": {
        "id": "7W3kX77tPwEm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bde767d7-b41e-4274-df73-3706600298e1"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1000, 1, 4096)\n",
            "(69, 1, 4096)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save CNN output\n",
        "with open('outputCNN_train.txt', 'w') as f:\n",
        "    for item in outputCNN_train:\n",
        "        f.write(\"%s\\n\" % item)\n",
        "\n",
        "files.download('outputCNN_train.txt')\n",
        "\n",
        "with open('outputCNN_test.txt', 'w') as f:\n",
        "    for item in outputCNN_test:\n",
        "        f.write(\"%s\\n\" % item)\n",
        "\n",
        "files.download('outputCNN_test.txt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "AtreGVJPxa8V",
        "outputId": "008bfa7a-914d-46f5-b66b-a5c99a4498cd"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_27a92a01-bf92-4a74-8776-eb11ae4e6aec\", \"outputCNN_train.txt\", 68000)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_327242e6-5940-471f-bc71-bae3ccd235e9\", \"outputCNN_test.txt\", 4692)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Process historical sales data in an RNN model\n",
        "\n",
        "Dimensions of an RNN model should be of 3: \n",
        "\n",
        "- number of feature variables (in our case: time + prices), \n",
        "- time steps, \n",
        "- batch size."
      ],
      "metadata": {
        "id": "yyaEBr8nroYA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# function to write the time series\n",
        "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
        "    plt.plot(time[start:end], series[start:end], format)\n",
        "    plt.xlabel(\"Time\")\n",
        "    plt.ylabel(\"Value\")\n",
        "    plt.grid(True)"
      ],
      "metadata": {
        "id": "Ff6up1iD4dhN"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# creating time series from df\n",
        "times_train = []\n",
        "series_train = []\n",
        "times_test = []\n",
        "series_test = []\n",
        "\n",
        "step=0\n",
        "for index, row in median_daily_train.iterrows():\n",
        "  times_train.append(step)\n",
        "  series_train.append(float(row.btc_usd))\n",
        "  step += 1\n",
        "\n",
        "for index, row in median_daily_test.iterrows():\n",
        "  times_test.append(step)\n",
        "  series_test.append(float(row.btc_usd))\n",
        "  step += 1"
      ],
      "metadata": {
        "id": "txI58fQVy1B9"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "beware that this is considering dates are continuous which is not true in our dataset"
      ],
      "metadata": {
        "id": "WToIdFA59eEL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Test your function and save all \"global\" variables within the G class (G stands for global)\n",
        "@dataclass\n",
        "class train:\n",
        "    TIME = np.array(times_train)\n",
        "    SERIES = np.array(series_train)\n",
        "    SPLIT_TIME = 2500\n",
        "    WINDOW_SIZE = 32\n",
        "    BATCH_SIZE = 256\n",
        "    SHUFFLE_BUFFER_SIZE = 1000\n",
        "\n",
        "class test:\n",
        "    TIME = np.array(times_test)\n",
        "    SERIES = np.array(series_test)\n",
        "    SPLIT_TIME = 2500\n",
        "    WINDOW_SIZE = 32\n",
        "    BATCH_SIZE = 256\n",
        "    SHUFFLE_BUFFER_SIZE = 1000\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(train.TIME, train.SERIES)\n",
        "plot_series(test.TIME, test.SERIES)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "kgmWKPhm9PIK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "d49334cf-4f73-4313-c231-19afe6feda57"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "time_train = train.TIME\n",
        "series_train = train.SERIES\n",
        "time_test = test.TIME\n",
        "series_test = test.SERIES\n",
        "print(time_train.shape)\n",
        "print(series_train.shape)\n",
        "print(time_test.shape)\n",
        "print(series_test.shape)"
      ],
      "metadata": {
        "id": "sjj5OLbY-On2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73f4282e-eb86-4f3d-d831-c4be3fc9df64"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1000,)\n",
            "(1000,)\n",
            "(69,)\n",
            "(69,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def windowed_dataset(series, window_size=train.WINDOW_SIZE, batch_size=train.BATCH_SIZE, shuffle_buffer=train.SHUFFLE_BUFFER_SIZE):\n",
        "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
        "    ds = ds.window(window_size + 1, shift=1, drop_remainder=True)\n",
        "    ds = ds.flat_map(lambda w: w.batch(window_size + 1))\n",
        "    ds = ds.shuffle(shuffle_buffer)\n",
        "    ds = ds.map(lambda w: (w[:-1], w[-1]))\n",
        "    ds = ds.batch(batch_size).prefetch(1)\n",
        "    return ds\n",
        "\n",
        "\n",
        "# Apply the transformation to the datasets\n",
        "train_set = windowed_dataset(series_train, window_size=train.WINDOW_SIZE, batch_size=train.BATCH_SIZE, shuffle_buffer=train.SHUFFLE_BUFFER_SIZE)\n",
        "test_set = windowed_dataset(series_test, window_size=test.WINDOW_SIZE, batch_size=test.BATCH_SIZE, shuffle_buffer=test.SHUFFLE_BUFFER_SIZE)"
      ],
      "metadata": {
        "id": "Hy20YdNN-Ovf"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# defining an RNN model architecture\n",
        "rnn = tf.keras.models.Sequential([\n",
        "                                  tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1),input_shape=[None]),\n",
        "                                  tf.keras.layers.BatchNormalization(),\n",
        "                                  tf.keras.layers.SimpleRNN(40, return_sequences=True),\n",
        "                                  tf.keras.layers.BatchNormalization(),\n",
        "                                  tf.keras.layers.SimpleRNN(40),\n",
        "                                  tf.keras.layers.BatchNormalization(),\n",
        "                                  tf.keras.layers.Dense(1),\n",
        "                                  #tf.keras.layers.Lambda(lambda x: x * 100.0)\n",
        "                                  ])\n",
        "\n",
        "rnn.compile(loss='mean_squared_error',\n",
        "            optimizer=tf.optimizers.Adam(lr=1e-1),\n",
        "            metrics=[\"mae\"])"
      ],
      "metadata": {
        "id": "0g7vww8R4O4T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87516758-e931-4334-9879-6bfbfed040e9"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/optimizer_v2/adam.py:105: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
            "  super(Adam, self).__init__(name, **kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train it\n",
        "history = rnn.fit(train_set, epochs=50)"
      ],
      "metadata": {
        "id": "r1V_BWR7RkpG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d60a609-cb0b-4d73-ae44-80ce992213f2"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "4/4 [==============================] - 3s 170ms/step - loss: 528006048.0000 - mae: 16794.5820\n",
            "Epoch 2/50\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 527335968.0000 - mae: 16787.9355\n",
            "Epoch 3/50\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 526211680.0000 - mae: 16771.9590\n",
            "Epoch 4/50\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 524393056.0000 - mae: 16743.0254\n",
            "Epoch 5/50\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 521793920.0000 - mae: 16698.5977\n",
            "Epoch 6/50\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 518141760.0000 - mae: 16636.1797\n",
            "Epoch 7/50\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 513329888.0000 - mae: 16553.3203\n",
            "Epoch 8/50\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 507338432.0000 - mae: 16447.7012\n",
            "Epoch 9/50\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 500039616.0000 - mae: 16317.7168\n",
            "Epoch 10/50\n",
            "4/4 [==============================] - 0s 36ms/step - loss: 491519872.0000 - mae: 16161.5117\n",
            "Epoch 11/50\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 481640640.0000 - mae: 15977.5166\n",
            "Epoch 12/50\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 470226592.0000 - mae: 15764.9775\n",
            "Epoch 13/50\n",
            "4/4 [==============================] - 0s 45ms/step - loss: 457464096.0000 - mae: 15523.0811\n",
            "Epoch 14/50\n",
            "4/4 [==============================] - 0s 37ms/step - loss: 443360800.0000 - mae: 15251.3740\n",
            "Epoch 15/50\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 427298848.0000 - mae: 14948.9873\n",
            "Epoch 16/50\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 412815200.0000 - mae: 14616.2725\n",
            "Epoch 17/50\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 396016576.0000 - mae: 14254.2090\n",
            "Epoch 18/50\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 410205952.0000 - mae: 13864.2197\n",
            "Epoch 19/50\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 384293024.0000 - mae: 13457.5459\n",
            "Epoch 20/50\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 351061024.0000 - mae: 13028.6074\n",
            "Epoch 21/50\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 329240800.0000 - mae: 12570.0645\n",
            "Epoch 22/50\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 307550560.0000 - mae: 12084.5371\n",
            "Epoch 23/50\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 288908704.0000 - mae: 11572.5957\n",
            "Epoch 24/50\n",
            "4/4 [==============================] - 0s 39ms/step - loss: 269013440.0000 - mae: 11051.8057\n",
            "Epoch 25/50\n",
            "4/4 [==============================] - 0s 30ms/step - loss: 245642976.0000 - mae: 10517.5107\n",
            "Epoch 26/50\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 229624592.0000 - mae: 10017.4814\n",
            "Epoch 27/50\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 210210720.0000 - mae: 9482.5508\n",
            "Epoch 28/50\n",
            "4/4 [==============================] - 0s 30ms/step - loss: 194696176.0000 - mae: 8975.1299\n",
            "Epoch 29/50\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 180269632.0000 - mae: 8477.3252\n",
            "Epoch 30/50\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 167510384.0000 - mae: 7967.2378\n",
            "Epoch 31/50\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 154214928.0000 - mae: 7500.9170\n",
            "Epoch 32/50\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 146273680.0000 - mae: 7168.1479\n",
            "Epoch 33/50\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 134691376.0000 - mae: 6781.1631\n",
            "Epoch 34/50\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 130970184.0000 - mae: 6569.3286\n",
            "Epoch 35/50\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 124188352.0000 - mae: 6408.2759\n",
            "Epoch 36/50\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 117499808.0000 - mae: 6191.4897\n",
            "Epoch 37/50\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 113748088.0000 - mae: 6135.9907\n",
            "Epoch 38/50\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 112046824.0000 - mae: 6100.5869\n",
            "Epoch 39/50\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 105835024.0000 - mae: 5946.5884\n",
            "Epoch 40/50\n",
            "4/4 [==============================] - 0s 31ms/step - loss: 106315224.0000 - mae: 6010.1328\n",
            "Epoch 41/50\n",
            "4/4 [==============================] - 0s 35ms/step - loss: 103248296.0000 - mae: 6017.3208\n",
            "Epoch 42/50\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 101073448.0000 - mae: 5972.0278\n",
            "Epoch 43/50\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 104819960.0000 - mae: 6163.3560\n",
            "Epoch 44/50\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 103601632.0000 - mae: 6168.0029\n",
            "Epoch 45/50\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 100354816.0000 - mae: 6143.7114\n",
            "Epoch 46/50\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 101410520.0000 - mae: 6287.8687\n",
            "Epoch 47/50\n",
            "4/4 [==============================] - 0s 32ms/step - loss: 105267552.0000 - mae: 6446.8354\n",
            "Epoch 48/50\n",
            "4/4 [==============================] - 0s 34ms/step - loss: 105207216.0000 - mae: 6455.0239\n",
            "Epoch 49/50\n",
            "4/4 [==============================] - 0s 38ms/step - loss: 106351888.0000 - mae: 6576.4395\n",
            "Epoch 50/50\n",
            "4/4 [==============================] - 0s 33ms/step - loss: 101493072.0000 - mae: 6507.3887\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compute model evaluation\n",
        "def compute_metrics(true_series, forecast):\n",
        "    mse = tf.keras.metrics.mean_squared_error(true_series, forecast).numpy()\n",
        "    mae = tf.keras.metrics.mean_absolute_error(true_series, forecast).numpy()\n",
        "    return mse, mae"
      ],
      "metadata": {
        "id": "RqDfDk2TtCmD"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(series_test))\n",
        "dataset = tf.data.Dataset.from_tensor_slices(series_test)\n",
        "dataset = dataset.window(1, shift=1, drop_remainder=True)\n",
        "dataset = dataset.flat_map(lambda w: w.batch(test.WINDOW_SIZE))\n",
        "dataset = dataset.batch(32).prefetch(1)\n",
        "rnn_forecast = rnn.predict(dataset).squeeze()\n",
        "print(len(rnn_forecast))\n",
        "print(rnn_forecast)"
      ],
      "metadata": {
        "id": "NxDlqQhrlvZn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed232ea3-30d6-43a5-c2e1-d178c2de015d"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "69\n",
            "69\n",
            "[39844.02  38763.59  38196.723 37039.543 38653.098 39250.105 40421.293\n",
            " 40828.02  40577.613 41961.973 41549.184 41555.19  40766.812 42219.934\n",
            " 42143.465 42135.26  41805.098 40905.613 40971.613 42067.656 42274.668\n",
            " 42266.355 42274.38  42277.81  42213.355 42267.582 42116.434 42269.62\n",
            " 42266.28  42264.57  42136.605 42154.168 42264.99  42274.48  42283.918\n",
            " 42282.973 42303.805 42317.55  42089.48  41848.035 41971.223 41027.105\n",
            " 41262.13  41834.52  41082.984 42142.754 42242.29  42219.215 42168.19\n",
            " 42246.824 42167.23  40426.934 39126.543 40539.668 41034.85  40426.113\n",
            " 40398.69  40487.664 40230.19  39376.043 39796.8   40576.24  42239.41\n",
            " 42213.777 42243.383 42270.684 42300.707 42402.598 42344.19 ]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the forecast\n",
        "plt.figure(figsize=(10, 6))\n",
        "plot_series(time_test, series_test)\n",
        "plot_series(time_test, rnn_forecast+8300)"
      ],
      "metadata": {
        "id": "K4F_Fm5xSGyN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 388
        },
        "outputId": "26448f89-0a7b-4d3e-e1f5-489f2019b503"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mse, mae = compute_metrics(series_test, rnn_forecast)\n",
        "print(f\"mse: {mse:.2f}, mae: {mae:.2f} for forecast\")"
      ],
      "metadata": {
        "id": "BXs7r98USG00",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b5bf783-303a-426c-9ab7-ef80b4a8ccc4"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mse: 29615914.00, mae: 4865.52 for forecast\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compute the predictions and store it into a dictionary\n",
        "data_train = {}\n",
        "data_test = {}\n",
        "\n",
        "trainset = tf.data.Dataset.from_tensor_slices(series_train)\n",
        "trainset = trainset.window(1, shift=1, drop_remainder=True)\n",
        "trainset = trainset.flat_map(lambda w: w.batch(test.WINDOW_SIZE))\n",
        "trainset = trainset.batch(32).prefetch(1)\n",
        "pred = rnn.predict(trainset).squeeze()\n",
        "data_train[index] = pred\n",
        "\n",
        "testset = tf.data.Dataset.from_tensor_slices(series_test)\n",
        "testset = testset.window(1, shift=1, drop_remainder=True)\n",
        "testset = testset.flat_map(lambda w: w.batch(test.WINDOW_SIZE))\n",
        "testset = testset.batch(32).prefetch(1)\n",
        "pred = rnn.predict(testset).squeeze()\n",
        "data_test[index] = pred"
      ],
      "metadata": {
        "id": "bG3B93Whe9ly"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# get a list of the extracted features\n",
        "outputRNN_train = np.array(list(data_train.values()))\n",
        "outputRNN_test = np.array(list(data_test.values()))\n",
        "print(outputRNN_train.shape)\n",
        "print(outputRNN_test.shape)\n",
        "\n",
        "# reshape\n",
        "outputRNN_train = outputRNN_train.reshape(1000,1) # 1000*1 value\n",
        "outputRNN_test = outputRNN_test.reshape(69,1) # 69*1 value\n",
        "print(outputRNN_train.shape)\n",
        "print(outputRNN_test.shape)\n",
        "\n",
        "# add a 3rd dimension to fit the model input\n",
        "outputRNN_train = outputRNN_train[:,newaxis,:] # we want 3d arrays\n",
        "outputRNN_test = outputRNN_test[:,newaxis,:] # we want 3d arrays\n",
        "print(outputRNN_train.shape)\n",
        "print(outputRNN_test.shape)"
      ],
      "metadata": {
        "id": "qTCzZZOxj66P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "372a8ce0-2b2a-493c-b2ab-dbcbaa9c538b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1, 1000)\n",
            "(1, 69)\n",
            "(1000, 1)\n",
            "(69, 1)\n",
            "(1000, 1, 1)\n",
            "(69, 1, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# save RNN output\n",
        "with open('outputRNN_train.txt', 'w') as f:\n",
        "    for item in outputRNN_train:\n",
        "        f.write(\"%s\\n\" % item)\n",
        "\n",
        "files.download('outputRNN_train.txt')\n",
        "\n",
        "with open('outputRNN_test.txt', 'w') as f:\n",
        "    for item in outputRNN_test:\n",
        "        f.write(\"%s\\n\" % item)\n",
        "\n",
        "files.download('outputRNN_test.txt')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "XfbSXwTkr1M9",
        "outputId": "3cef1a25-78af-4f21-8eeb-88a4966812ca"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_250e1c40-ea65-4b82-b4a5-b44ffdba5ae3\", \"outputRNN_train.txt\", 13831)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_13aa2483-7534-4e1d-818d-5e5e1823f3b9\", \"outputRNN_test.txt\", 939)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Final model - compute both CNN and RNN outputs together\n",
        "\n",
        "Since we are trying to predict the price over time, it will be a time series, but instead of time only we will have an X which will concatenate all dependent variables, and a target variable y which will be the price of median sale. \n",
        "\n",
        "A similar process could be used to compute as much input variables as we want, and indeed we should add some relevant predictors to improve our model performance, such as bitcoin rate at date (and other coins as well), stock market prices, social trends on nfts (google search data, twitter, reddit...) community engagement data for our collection (discord activity?)..."
      ],
      "metadata": {
        "id": "mN5C5lv7W2Ck"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(outputCNN_train.shape)\n",
        "print(outputCNN_test.shape)\n",
        "print(outputRNN_train.shape)\n",
        "print(outputRNN_test.shape)\n",
        "\n",
        "X_train = concatenate([outputCNN_train, outputRNN_train]) # merge the two outputs. \n",
        "X_test = concatenate([outputCNN_test, outputRNN_test])\n",
        "y_train = np.array(series_train)\n",
        "y_test = np.array(series_test)\n",
        "\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "metadata": {
        "id": "SnYyi8nIgF7X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e7f24dcd-8af9-49f1-bea2-f72eb3538de6"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1000, 1, 4096)\n",
            "(69, 1, 4096)\n",
            "(1000, 1, 1)\n",
            "(69, 1, 1)\n",
            "(1000, 1, 4097)\n",
            "(69, 1, 4097)\n",
            "(1000,)\n",
            "(69,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# using a CNN+LSTM to compute the prediction on combined input\n",
        "final = tf.keras.models.Sequential([\n",
        "                                    tf.keras.layers.Conv1D(filters=64,kernel_size=3,strides=1,padding=\"causal\",activation=\"relu\",input_shape=[1, 4097]), # adapt to our input\n",
        "                                    tf.keras.layers.LSTM(64, return_sequences=True),\n",
        "                                    tf.keras.layers.LSTM(64),\n",
        "                                    tf.keras.layers.Dense(32, activation=\"relu\"),\n",
        "                                    tf.keras.layers.Dense(16, activation=\"relu\"),\n",
        "                                    tf.keras.layers.Dense(1) # fit to predict the tuple of values\n",
        "                                    ])\n",
        "\n",
        "final.compile(loss='mean_squared_error',\n",
        "              optimizer=tf.optimizers.Adam(learning_rate=1e-1),\n",
        "              metrics=[\"mae\"])\n",
        "\n",
        "final.summary()"
      ],
      "metadata": {
        "id": "-mC1iclDXfGm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "944fb956-951d-4174-bb94-73229a66c6b7"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv1d (Conv1D)             (None, 1, 64)             786688    \n",
            "                                                                 \n",
            " lstm (LSTM)                 (None, 1, 64)             33024     \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 64)                33024     \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 32)                2080      \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 16)                528       \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 1)                 17        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 855,361\n",
            "Trainable params: 855,361\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the final model\n",
        "history = final.fit(\n",
        "    X_train,\n",
        "    y_train,\n",
        "    epochs=50,\n",
        "    batch_size=1,\n",
        "    shuffle=False, # no shuffle for time series\n",
        "    validation_split=0.1\n",
        ")"
      ],
      "metadata": {
        "id": "dVi3c0sPmgFJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78e18ab3-b64f-4da9-f602-b246bbc21032"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "900/900 [==============================] - 12s 10ms/step - loss: 5832826.5000 - mae: 1387.6965 - val_loss: 114050056.0000 - val_mae: 10055.6182\n",
            "Epoch 2/50\n",
            "900/900 [==============================] - 8s 9ms/step - loss: 27213432.0000 - mae: 2915.7241 - val_loss: 161589520.0000 - val_mae: 11725.6592\n",
            "Epoch 3/50\n",
            "900/900 [==============================] - 8s 9ms/step - loss: 15543565.0000 - mae: 1747.3607 - val_loss: 260452208.0000 - val_mae: 14580.0537\n",
            "Epoch 4/50\n",
            "900/900 [==============================] - 8s 9ms/step - loss: 353309280.0000 - mae: 13329.8096 - val_loss: 1386939904.0000 - val_mae: 36281.9727\n",
            "Epoch 5/50\n",
            "900/900 [==============================] - 8s 9ms/step - loss: 219196544.0000 - mae: 8534.2168 - val_loss: 799144704.0000 - val_mae: 26992.3594\n",
            "Epoch 6/50\n",
            "900/900 [==============================] - 8s 9ms/step - loss: 173328048.0000 - mae: 7575.0029 - val_loss: 546427136.0000 - val_mae: 21814.4258\n",
            "Epoch 7/50\n",
            "900/900 [==============================] - 8s 9ms/step - loss: 183534592.0000 - mae: 9006.0547 - val_loss: 493719840.0000 - val_mae: 20570.8965\n",
            "Epoch 8/50\n",
            "900/900 [==============================] - 8s 9ms/step - loss: 187372880.0000 - mae: 9343.4668 - val_loss: 482758816.0000 - val_mae: 20302.7246\n",
            "Epoch 9/50\n",
            "900/900 [==============================] - 8s 9ms/step - loss: 188140928.0000 - mae: 9407.5029 - val_loss: 479971232.0000 - val_mae: 20233.9629\n",
            "Epoch 10/50\n",
            "900/900 [==============================] - 8s 9ms/step - loss: 188320384.0000 - mae: 9422.3555 - val_loss: 479202560.0000 - val_mae: 20214.9629\n",
            "Epoch 11/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188378208.0000 - mae: 9426.7959 - val_loss: 479063008.0000 - val_mae: 20211.5078\n",
            "Epoch 12/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188402624.0000 - mae: 9428.2988 - val_loss: 479141920.0000 - val_mae: 20213.4570\n",
            "Epoch 13/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188416240.0000 - mae: 9428.8154 - val_loss: 479300128.0000 - val_mae: 20217.3730\n",
            "Epoch 14/50\n",
            "900/900 [==============================] - 10s 12ms/step - loss: 188425936.0000 - mae: 9428.9688 - val_loss: 479486400.0000 - val_mae: 20221.9766\n",
            "Epoch 15/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188433984.0000 - mae: 9428.9902 - val_loss: 479682656.0000 - val_mae: 20226.8242\n",
            "Epoch 16/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188441456.0000 - mae: 9428.9570 - val_loss: 479880672.0000 - val_mae: 20231.7246\n",
            "Epoch 17/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188448528.0000 - mae: 9428.9004 - val_loss: 480080512.0000 - val_mae: 20236.6680\n",
            "Epoch 18/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188455744.0000 - mae: 9428.8398 - val_loss: 480280608.0000 - val_mae: 20241.6016\n",
            "Epoch 19/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188462848.0000 - mae: 9428.7695 - val_loss: 480480640.0000 - val_mae: 20246.5508\n",
            "Epoch 20/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188469776.0000 - mae: 9428.7080 - val_loss: 480679232.0000 - val_mae: 20251.4492\n",
            "Epoch 21/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188476928.0000 - mae: 9428.6455 - val_loss: 480878048.0000 - val_mae: 20256.3516\n",
            "Epoch 22/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188483744.0000 - mae: 9428.5713 - val_loss: 481075520.0000 - val_mae: 20261.2246\n",
            "Epoch 23/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188490752.0000 - mae: 9428.5029 - val_loss: 481272800.0000 - val_mae: 20266.1016\n",
            "Epoch 24/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188497760.0000 - mae: 9428.4375 - val_loss: 481470560.0000 - val_mae: 20270.9746\n",
            "Epoch 25/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188504512.0000 - mae: 9428.3760 - val_loss: 481666080.0000 - val_mae: 20275.8047\n",
            "Epoch 26/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188511632.0000 - mae: 9428.3125 - val_loss: 481862656.0000 - val_mae: 20280.6523\n",
            "Epoch 27/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188518384.0000 - mae: 9428.2344 - val_loss: 482057792.0000 - val_mae: 20285.4570\n",
            "Epoch 28/50\n",
            "900/900 [==============================] - 9s 11ms/step - loss: 188525136.0000 - mae: 9428.1699 - val_loss: 482252544.0000 - val_mae: 20290.2578\n",
            "Epoch 29/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188531856.0000 - mae: 9428.0918 - val_loss: 482446496.0000 - val_mae: 20295.0430\n",
            "Epoch 30/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188538640.0000 - mae: 9428.0264 - val_loss: 482640928.0000 - val_mae: 20299.8242\n",
            "Epoch 31/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188545360.0000 - mae: 9427.9482 - val_loss: 482834272.0000 - val_mae: 20304.5879\n",
            "Epoch 32/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188552208.0000 - mae: 9427.8818 - val_loss: 483028000.0000 - val_mae: 20309.3516\n",
            "Epoch 33/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188558800.0000 - mae: 9427.8115 - val_loss: 483219616.0000 - val_mae: 20314.0742\n",
            "Epoch 34/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188565744.0000 - mae: 9427.7520 - val_loss: 483411680.0000 - val_mae: 20318.8047\n",
            "Epoch 35/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188572272.0000 - mae: 9427.6699 - val_loss: 483604192.0000 - val_mae: 20323.5430\n",
            "Epoch 36/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188578768.0000 - mae: 9427.5889 - val_loss: 483794208.0000 - val_mae: 20328.2129\n",
            "Epoch 37/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188585504.0000 - mae: 9427.5264 - val_loss: 483985248.0000 - val_mae: 20332.9121\n",
            "Epoch 38/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188592032.0000 - mae: 9427.4521 - val_loss: 484175136.0000 - val_mae: 20337.5781\n",
            "Epoch 39/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188598656.0000 - mae: 9427.3789 - val_loss: 484364576.0000 - val_mae: 20342.2266\n",
            "Epoch 40/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188605200.0000 - mae: 9427.3076 - val_loss: 484553536.0000 - val_mae: 20346.8809\n",
            "Epoch 41/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188611808.0000 - mae: 9427.2373 - val_loss: 484742016.0000 - val_mae: 20351.5137\n",
            "Epoch 42/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188618128.0000 - mae: 9427.1582 - val_loss: 484929952.0000 - val_mae: 20356.1309\n",
            "Epoch 43/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188624528.0000 - mae: 9427.0830 - val_loss: 485117056.0000 - val_mae: 20360.7188\n",
            "Epoch 44/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188631248.0000 - mae: 9427.0156 - val_loss: 485304896.0000 - val_mae: 20365.3379\n",
            "Epoch 45/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188637440.0000 - mae: 9426.9307 - val_loss: 485491232.0000 - val_mae: 20369.9121\n",
            "Epoch 46/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188643776.0000 - mae: 9426.8633 - val_loss: 485677184.0000 - val_mae: 20374.4746\n",
            "Epoch 47/50\n",
            "900/900 [==============================] - 10s 11ms/step - loss: 188650160.0000 - mae: 9426.7842 - val_loss: 485862240.0000 - val_mae: 20379.0156\n",
            "Epoch 48/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188656688.0000 - mae: 9426.7090 - val_loss: 486048480.0000 - val_mae: 20383.5840\n",
            "Epoch 49/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188662832.0000 - mae: 9426.6338 - val_loss: 486232384.0000 - val_mae: 20388.0938\n",
            "Epoch 50/50\n",
            "900/900 [==============================] - 9s 10ms/step - loss: 188669168.0000 - mae: 9426.5566 - val_loss: 486416416.0000 - val_mae: 20392.6016\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot the final model loss\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='lower right')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "AMdLpd4-nXW5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "06e6cbf5-3105-4a70-86c0-9307f4349823"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predictions on test set\n",
        "y_pred = final.predict(X_test)"
      ],
      "metadata": {
        "id": "DZW0CytFrM02"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# plot predictions compared to true values\n",
        "plt.plot(y_test, label=\"Actual Price\", color='green')\n",
        "plt.plot(y_pred, label=\"Predicted Price\", color='red')\n",
        " \n",
        "plt.title('Cryptopunks price prediction')\n",
        "plt.xlabel('Time [days]')\n",
        "plt.ylabel('Price')\n",
        "plt.legend(loc='best')\n",
        " \n",
        "plt.show()"
      ],
      "metadata": {
        "id": "X8UhAtO7Sks4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "af17c78e-71dd-4c97-93d5-2db6204a317c"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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+fPmEdgYNGkTXrl1p0KDBFftPPPXpp5+yePFi6tWrR4MGDdi8eTOBgYEMHjyYNm3aEBQUxO23387Bgwev3JgxxmOrolYhCA3KNwBcZygGtxrMnpN7GL12tFe2ec3NsR4aGqqJJ6XasmULN9xwQxZFZDxln5sxl7tr6l3sOLaDzU9sTihTVW4Zfwvbj21nx1M7KOBfwKO2RWRNovv9gOx1n4gxxhgPqSqro1bTsGLDy8pFhHdav0On2p04H5Px483ZKL7GGJML7Du1j8NnD9OoQqMky5pXaU7zKs29sl07EjHGmFwg/ibD+E71zGJJxBhjcoHVUavJ45uHoLJBmbpdSyLGGJMLrDqwipByIeT1y5up27UkYkwOFRMXw09//0RMXMyVK5tcLTYulvAD4TSs0PDKlTOYJZFswtfXl5CQEOrWrUvXrl2vaoTePn36MGOGa3Djvn37snnz5hTrLlmyJGHAxPSoVq0a//zzT7Ll9erVIygoiDZt2nDo0KFk12/fvj0nTpxI93aNi6rSb04/OkztwC87fsnqcEwW2/rPVs5cPJPp/SFgSSTbyJ8/PxEREfz111/kyZOHr7766rLl8Xesp9fo0aMJDAxMcbmnSSQ1ixcvZsOGDYSGhvLOO+9ctkxViYuLY968eRQrVixDt3steem3lxgXMQ6Abf9sy+JoTFZbfeDyO9UzkyWRbKhFixZERkayZMkSWrRoQceOHQkMDCQ2NpYXXniBhg0bEhQUxMiRIwHXF3P//v2pXbs2t912W8JQIwC33nor8TdX/vzzz9SvX5/g4GBat27N7t27+eqrrxg2bBghISGEhYVx9OhRunTpQsOGDWnYsCHLly8HIDo6mjZt2lCnTh369u2bpslubr75ZiIjI9m9eze1a9emV69e1K1bl3379l12JDNx4sSEO/J79uwJkGIcBj5c/iEfrviQx0Mfp1i+Yuw4viOrQzJZbFXUKorkLUKtkrUyfdt2n0hizzwDERk7FDwhIfBJ2gZ2jImJYf78+bRr1w6AtWvX8tdffxEQEMCoUaMoWrQoq1ev5sKFC9x00020adOGdevWsW3bNjZv3szhw4cJDAzkwQcvH13/6NGjPPzwwyxdupSAgICEIeUfffRRChUqxIABAwD4v//7P5599lmaN2/O3r17adu2LVu2bOHNN9+kefPm/Pe//+Wnn35izJgrD1M2d+5c6tWrB8D27duZMGECTZo0uazOpk2bGDx4MCtWrKBUqVIJY4M9/fTTycZxrRu3bhwv/vYi3ep0Y3j74fwZ9SeRxyKzOiyTxVZFrSK0Qig+kvnHBZZEsolz584REhICuI5EHnroIVasWEGjRo0Shm//5Zdf2LBhQ0J/x8mTJ9m+fTtLly6lR48e+Pr6UqFCBVq1apWk/ZUrV3LzzTcntJXSkPK//fbbZX0op06d4syZMyxdupQffvgBgDvvvJPixYsnuz5Ay5Yt8fX1JSgoiMGDB3PixAmqVq2aJIGAaxj5rl27JozrFR9XSnG4T751rZn791z6zulLm+ptmHj3RHzEhxolarDm4JqsDs1kofMx51l/eD0Dmg7Iku1bEkksjUcMGS2+TyQx9+HXVZXhw4fTtm3by+rMmzcvw+KIi4tj5cqV5MuXz+M2Ek+WdeLEiXQNI59RceQmMXExPDHvCeqVqcf3935PHt88AFQvXp3vt3xPTFyM1+aLMNnb+kPriYmLyZL+ELA+kRylbdu2jBgxgkuXLgHw999/c/bsWW6++Wa+/fZbYmNjOXjwIIsXL06ybpMmTVi6dCm7du0CUh5Svk2bNgwfPjzhdXxiu/nmmxNGEJ4/fz7Hjx/PkH1q1aoV3333HdHR0ZfFlVIc16oftvzA3pN7efPWNymU539HYzVK1CAmLoa9J/dmYXQmK4XtDQOyplMdLInkKH379iUwMJD69etTt25dHnnkEWJiYrj77rupWbMmgYGB9OrVi6ZNmyZZt3Tp0owaNYr//Oc/BAcH061bNwDuuusuZs6cmdCx/tlnnxEeHk5QUBCBgYEJV4m98cYbLF26lDp16vDDDz9QpUqVDNmnOnXq8Oqrr3LLLbcQHBzMc889B5BiHNciVWXoH0OpUaIGHWp1uGxZ9RLVAaxf5Br2/ZbvqV++PhWLVMyaAFT1mno0aNBAE9u8eXOSMpP9XSuf27I9y5RB6Od/fp5kWdSpKGUQ+uWqL7MgMpPVdh/frQxC3wt7z+vbAsI1me9UOxIxJpv7eOXHFM9XnD4hfZIsK1+oPPn98tuRyDXqu83fAdC1Ttcsi8GSiDHZ2I5jO5i5ZSaPhj5KwTxJL04QEaqXqG73ilyjpm+aTmiFUK4rfl2WxWBJxKHX2AyPOd218nl99udn+Pn40b9R/xTrVC9e3Y5ErkG7ju9i9YHV3Bt4b5bG4dUkIiK7RWSjiESISLhTVkJEfhWR7c6/xZ1yEZHPRCRSRDaISH23dno79beLSG+38gZO+5HOuuJJnPny5SM6Ovqa+WLK6VSV6OjoXH/57/Fzxxmzbgw96vWgQuEKKdarUaIGO4/vJE7jMjE6k9Wyw6ksyJz7RFqqqvtIfQOBhar6nogMdF6/BNwB1HQejYERQGMRKQG8AYQCCqwRkdmqetyp8zDwJzAPaAfMT2+AlSpVYv/+/Rw9etTTfTSZLF++fFSqVCmrw/Cqr9d+zdlLZ3m2ybOp1qtevDrnYs5x8PTBrLtCx2S66Zum06hiI6oVq5alcWTF3UmdgFud5xOAJbiSSCdgonMVwEoRKSYi5Z26v6rqMQAR+RVoJyJLgCKqutIpnwh0xoMk4u/vn3AntzGZ7XzMeR776TEWRC6gXKFyVChcgQqFKzDn7zm0CmhFSLmQVNevUaIGADuO77Akco3YcWwHaw6u4aPbP8rqULzeJ6LALyKyRkT6OWVlVfWg8/wQUNZ5XhHY57bufqcstfL9yZQnISL9RCRcRMLtaMNkJ0fPHqX1xNaMjxhP8yrNqVC4AgdOH2D2ttkcP3ecV1u8esU27F6Ra0/8qax7Au/J4ki8fyTSXFWjRKQM8KuIbHVfqKoqIl7viFDVUcAogNDQUOv4yEVi42LZcHgDAcUDKJYvZw0t/3f037T/pj1Rp6P4rut3Sb4QVJW0dPNVKVoFPx8/dhyzK7SuFdM3TadJpSZULVY1q0Px7pGIqkY5/x4BZgKNgMPOaSqcf+PHLY8CKrutXskpS628UjLlJgc7c/HMFetEnYpi3LpxdJ/RnTIflaH+qPr0/rH3FdfLTsL2hNF0TFNOXTjF4t6Lk/1FmdbrRPx8/KhWrBqRx+1I5FqwPXo76w6ty/KrsuJ5LYmISEERKRz/HGgD/AXMBuL/4nsDs5zns4FezlVaTYCTzmmvBUAbESnuXMnVBljgLDslIk2cq7J6ubVlcqBfdvxC4XcL0/W7rvwd/XeS5X8d+Ysu07tQaVglHpz9IL/v+Z0OtTrQNbArc7bNYXv09iyIOn3OXDzDwN8G0npia0oXKM3KvitpUinp6MbpVaNEDTuddY3ITqeywLuns8oCM51fU37AFFX9WURWA9NF5CFgDxCfTucB7YFI4F/gAQBVPSYibwOrnXpvxXeyA48D44H8uDrU092pbrKPKRunUMC/APO3z2fmlpn0rd+XN255gzMXzzDo90FM3TiVQnkK8VqL17gn8B6CygYhIhw6c4gft/7IZ39+xvD2w5NtOy2nhr5Y9QXnYs5xY7kbubH8jZTIn/xw+Z5QVaZvms7zvzxP1Okoegf35uO2H2fYNqoXr84f+/5I8ykwkzP9e+lfJqyfQLPKzahctPKVV8gEXksiqroTCE6mPBponUy5Ak+k0NZYYGwy5eFA3asO1mS5mLgY5v49l7uvv5uhbYYyeOlgRq4ZycT1E7kYe5G8fnl56aaXGNBsACULlLxs3XKFytGjXg/GRYzj7VZvJ+kbOXXhFKGjQukZ1JPXb3k92e1vOrKJ/vMvv6GvatGq1C1Tl3KFylG2YFnKFipLxcIV6Vi7I/6+/mnet13Hd9F3Tl8W7VrEjeVuZHrX6TSr3CzN66dFjRI1OHnhJNHnoilVoNSVVzA5TkxcDN1ndCfyWCTD2g7L6nAS2AQEJltYsW8F0eei6VS7E2ULlWV4++E80+QZPlj+AYXzFuaFZi9QtlDZFNd/pvEzTFw/kTFrx/B8s+cvWzbwt4FsP7adz1Z9xos3vUhev7xJ1h8XMQ4/Hz/WP7qeqFNRrDu0jrUH17ItehtrD67lyNkjxGosAE83fppP2qV93pleP/Ziw+ENfNn+S/o16Ievj2+a102r6sVdV2jtOLbDkkgupKr0n9efOX/P4cv2X9K+ZvusDul/khuVMTc/khvF12S9535+TvO8nUdPnT/lcRu3jLtFqwyropdiLyWULd29VBmENh3dVBmETts4Lcl6F2MuapkPy2jnaZ1TbDs2LlaPnj2qPWb00AJDCug/Z/9JU0y/7/5dGYR+tvKz9O9QOmw+slkZhE5eP9mr2zFZY8jSIcogdOCvA7MsBmwUX5NdqSqzts2iZbWWFM5b2ON2nmnyDHtP7uXHrT8Crpv4+s7pS7Vi1Vhw/wKqFavGqLWjkqw3b/s8jpw9woMhDyZZFs9HfChVoBSvtHiFfy/9y1fhaZvfZEjYEMoULEPf+n0926k0CigegCC5YiDGi7EXbZItNxPXT+TVRa9yX737GNJ6SFaHk4QlEZPltvyzhR3Hd9CpdqeraueuWndxXfHr+GSl61TT4KWD+Tv6b0Z2GEnhvIV5uP7DLNq1KMlVXOMixlGuUDnuqHnHFbdRt0xd7qhxB8NXDed8zPlU666OWs0vO37h+abPk98/v+c7lgb5/PJRqUilbHWFVpzG8dqi1/hk5SecvXg2TevExsXScWpHanxWgxX7Vng5wqwVGxfLbzt/40LMhRSXf77qcx6a/RCtA1ozttNYfCT7fWVnv4jMNWfWVteV2R1rd7yqdnx9fHmq0VMs37ecMWvH8P7y9+kd3Js21dsA8EDIA/j5+PH12q8T1jl05hBz/55Lr6BeaZ6jfECzARw+e5jJGyanWu+dZe9QPF9xHgt9zPOdSocaJWpkqyORoSuGMiRsCM8ueJaATwN4b9l7nLpwKtV1Xlv0Ggt2LKBQnkJ0md6Fg6cPplo/J/tu83fcPul2agyvwYjVIy5LJiv3r6Th1w15cv6TtKzWku/v/Z48vnmyMNqUWRIxl1lzYA1TNk7J1BGNZ22bRWiF0AwZ9+nBGx+kSN4i9J3Tl+L5ijO0zdCEZeULl6dj7Y6MixiX8Ac7ecNkYjWWB258IM3baFmtJTeWu5GPVnyU4si5Gw9v5MetP/JU46eu6hRdemSnIeH/2PcHLy98mS43dGHZA8sIrRDKywtfpton1Xj797eTPYr7btN3vLf8PfrV78fSB5Zy6sIp7vnuHi7GXsyCPfC+8APh5PXNS5WiVXh83uPUGF6DL1Z9Qd/ZfWk6pimHzx7m23u+ZcH9Cyiar2hWh5uy5DpKcvPDOtZTd+v4W5VBaN9ZffVizEWvb+/AqQPKIPTt39/OsDaf/fnZFDvRf97+c8KyuLg4DfwiUJuObprubUzZMEUZhM7aOivZ5T1m9NBC7xTS6H+j0922p94Le08ZxFVdnJARov+N1irDqmjAJwF6/NzxhPLVUau109ROyiC01vBaumTXkoRlGw5t0AJDCmjT0U31/KXzqqr67V/fKoPQx+c+nun7kBlaT2itoaNCNS4uTn+J/CXh4g+/t/x0wIIBWf45JkYKHetZ/qWe2Q9LIim7EHNB8w3Op9d9ep0yCL194u164twJr25zZPhIZRC64dCGDGvz34v/6sKdCzUuLi7Jsti4WK32STVtNaGVrty3UhmEfr3m63Rv42LMRa0yrIq2GNsiybLt0dvV500ffeGXFzyK31PfbfpOGYSuO7guU7frLi4uTjtO7aj+b/nrqv2rkq3z645fE/6PPTz7Yd15bKdW/7S6lv+ovB44deCyugMWDFAGoePWjcuE6DNPXFyclny/pPad1feyshV7V+i2f7ZlYWQpSymJ2OkskyD8QDjnY87z4e0fMrbjWBbvXkzzcc29eqXMrG2zCCgWQN0yGd2COQMAACAASURBVHfPaH7//LQKaJXsnds+4pPQwf7a4tco4F+Ae+ukfwwif19/nm3yLGF7w/hz/58J5arKu2Hv4u/jz3NNn7uq/UivhCHhs3Agxk///JTZ22bzwe0f0LBiw2Tr3HbdbWx8bCMvNHuBMevGUGN4Dfae3MuMe2dQvnD5y+q+e9u7tA5ozaNzH2X9ofWZsQuZIup0FNHnogku97/7sUWEppWbUqtkrSyMLP3sZkOTIGxPGAAtqrSgdMHSVClahS7Tu9Dw64a0rNaSYvmKJTxaBbSiUcVGV7W9MxfPsHDnQh4LfSxTh+p48MYHeWPJG/y28zd6B/emSN4iHrXz0I0PMWjJIB776THXPOfHdhB5LJLTF0/zRMMnKFeoXAZHnrr4Gw6zql8k4lAEL/76Ip1qd+Lpxk+nWreAfwE+uP0DutftzssLX6ZnUM9k7+L38/Fj2j3TqPRxJSZvmHzZl25OFnEoAuCKc8XkBJZETIKle5dyfanrKV2wNACtr2vNiodW8OT8J1l3aB0nzp/g+LnjXIq7RD6/fKx8aOVV/VEviFzAhdgLdLr+6i7tTa9yhcrRsXZHftjyAw+EpL1DPbH4O+nfWvoWZy+dpXrx6txU+SZql6rNgzemfM+JtxTOW5gyBctk2RVaQ/8YSn7//IztNDbNPwrql6/PgvsXpFqnVIFS1ClTh/WHc8+RSPxRVVDZoCyO5OpZEjGA65r05XuX061Ot8vKA0sHsrDXwoTXqsqB0wdoNLoRXaZ3IbxfuMfzeMzaNosS+UvQvErzq4rdE4NbDqZemXrcXPXmq2rn1Ztf5ZUWr2SbQQ9rl6zN/Mj57Di2I2Gyqsxw8vxJvt/8Pb2Ce2XowJXxgssG89P2nzK83awScTiC6sWre3wUnJ1Yn4gBYOORjZy8cJIWVVukWk9EqFikIt91/Y49J/fQ+8feKV7mmpqT50/yw5Yf6Fy7c5rvz8hIN5S+gUG3DsqQL//skkAAPmrzEecunaPZ2GaEHwjPtO1O3zSdczHnrurILjXBZYM5cvYIh84c8kr7mS3iUESuOJUFlkRyjTUH1jBk6RAuxV7yaH33/pC0aFa5GUPbDHV1oi7/IN3bm7B+AmcvneXxho+ne12TskYVG7H8weUU8C/AreNv5efInxOWXYy9yI9bf6Trd10Z9kfGjgI7LmIcN5S64ar7yVISf9onN3Sun75wmshjkQSXzR39O3Y6K5d4bfFr/Bz5M2sOrmFql6nJjlSbmrC9YVQpWiVd020+2ehJVuxbwauLXqVRxUa0CmiVpvXiNI4vVn9B44qNaVChQbriNFdWu1Rt/njoD9p/054OUzrwbut32XdqH1P/mso///6Dn48fs7bOovP1nQkoHnDV29v6z1b+2P8HH9z2gdeOyuL73jYc3kDbGm29so3MsuHwBiB3dKqDHYnkCifPn2ThzoXUK1OPmVtn8p/p/7niuE7uVJWle5am+SgknogwuuNoapesTfcZ3Tlw+kCa1lu4cyF/R/9N/0b9r1zZeKRcoXL83ud3WgW04sXfXmTkmpG0rNaSuT3mEvlkJL4+vvx3yX/T3N6YtWOoObwmW45uSbJsfMR4fMWXnsE9M3IXLlMifwkqFamUKzrX4/fBkojJNuZtn8eluEuMuHMEIzuMZP72+XSY0iHNg95FHovk8NnDHnUyF8pTiO/v/Z7TF0/zzM/PpGmdz1d/TukCpeka2DXd2zNpVzhvYeb+31zm9JjDoecPMb3rdO6sdSdVi1XlqUZP8c2GbxJ+Fadm2l/TeHjOw0Qei6TbjG6cu3QuYVlMXAwT10/kjpp3eP2S5uCywbkiiUQcikhIirmBJZFc4MdtP1KmYBmaVGpCvwb9GN95PIt3L+aOb+5g2l/T+GbDN0xaP4nxEeNZd3BdkvWX7lkKpL0/JLEbSt/Aqy1e5bvN3zF/e+ozFO8+sZs52+bQr0G/dJ9yM+mXxzcPHWp1oHj+4peVD2w+kKL5ivLywpdTXX/e9nn0nNmTFlVb8MO9P7DxyEae/+V/k379suMXDp456LUOdXfBZYPZ+s/WFEe9zSniO9Wz0wUZV8P6RHK48zHnmbd9Hj3q9kiYMa9XcC/y+ubl/pn3E7Y37LL6/j7+rH549WX3d4TtDaNUgVJcX+p6j+N4odkLTN4wmSfmPcGmxzelOPT5iNUj8BEfHmnwiMfbMleveP7iDLxpIAMXDmTpnqXJHoUu3bOULtO7EFw2mDk95lAkbxEGNB3AR398ROuA1nQJ7MK4iHGUKlCKDrU6eD3moLJBxMTFsPnoZm4sf6PXt+cNMXExbDyyMdNGds4MXj8SERFfEVknInOd1+NFZJeIRDiPEKdcROQzEYkUkQ0iUt+tjd4ist159HYrbyAiG511PpPcktrTYdGuRZy5eIbO13e+rLxb3W7sf3Y/mx/fzLb+24h8MpK/HvuLkgVK0nNmz8t+zYXtDaNFlRZX9csor19eRtw5gl0ndjEkLPmJc85dOsfodaPpfH1nKhet7PG2TMZ4qvFTVCxckZd+e8k1kJ6b1VGr6TClAwHFAvj5/p8T7mcY0noIDSs0pO+cvqw9uJZZW2dxX737MmWY8vgfPjn5lNb26O2cjzmfa/pDIHNOZz0NJO6Ne0FVQ5xHhFN2B1DTefQDRgCISAngDaAx0Ah4Q0Tij81HAA+7rdfOmzuSHc3cMpNCeQrROqB1kmVlC5XlhtI3UKtkLaqXqE6dMnUY23EsG49s5PXFrwMQdSqKncd3XvVNdwAtA1rSM6gnHyz/INkO2Gl/TePYuWPWoZ5N5PfPz6BbB7Fy/0p+3Pojl2IvMXPLTNp/057GoxtTskBJfu3562VztufxzcPULlOJjYvllvG3cCnuUqacygKoWaIm+f3yp6kfJ7vKTcOdxPNqEhGRSsCdwOg0VO8ETHQGjFwJFBOR8kBb4FdVPaaqx4FfgXbOsiKqutIZYXIi0DnF1nOh2LhYZv89m/Y126e5f+GOmnfwSINH+GjFR/y++/eE012e9ock9lGbjyiYpyCP/fTYZb9uVZXPV39OndJ1uKXqLRmyLXP1+oT04fpS19N/fn8qD6vMf6b/h/WH1/Nqi1f546E/kp3jpXqJ6oy6axRnLp6hfvn6mTaela+PL3XL1PX4SCQ2LpZRa0Zx/NzxDI4s7SIORZDHN89VnTrObrzdJ/IJ8CKQeFaeISLyX2AhMFBVLwAVgX1udfY7ZamV70+mPAkR6Yfr6IYqVap4ui/Zzh/7/+DI2SPcff3d6VrvozYfuQYf/LE3zas0p1CeQhn2RVCmYBnev+19Hpn7CP3m9ANg54md7Dy+k90ndjPizhG5pkMxN/Dz8eOj2z/iP9P/Q7sa7Xi4/sO0q9HuiqMIdK/bnbMXz2b6gIjBZYOZuXUmqpru/0dfhX9F//n92X1iN++0fsdLEaYu4nAEdUrXybazFHrCa0ciItIBOKKqaxItehm4HmgIlABe8lYM8VR1lKqGqmpo6dKlvb25DLfpyCbeCXsnyQxvP279EX8ff9rXbJ+u9grlKcSkuyex79Q+vtn4DTdVvilDhx7pW78vLaq0YPS60cz+ezb/XvqXppWa8l7r97JkYEKTujtr3cn5V88zq/ssOtTqkOb/Cw/Vf4jQCqFeju5yQWWDiD4XneZ7kuIdOnOIVxa9Arjurvd0ZIerFXEoIteMRBzPm0ciNwEdRaQ9kA8oIiKTVfV+Z/kFERkHDHBeRwHuva2VnLIo4NZE5Uuc8krJ1M91HvvpMcL2hhG2N4zv7/2eAv4FUFVmbp1J6+taezSIW9PKTXm5+csMCRuSYaey4vmIDwt7LeRi7EUK5imYoW0b78gpR4funevpmU75+V+e53zMeT66/SMG/DqAedvnZfro0YfOHOLI2SOElM09/SHgxSMRVX1ZVSupajWgO7BIVe93+jJwrqTqDPzlrDIb6OVcpdUEOKmqB4EFQBsRKe50qLcBFjjLTolIE6etXsAsb+1PVvlj3x+E7Q2jbfW2LIhcQLvJ7Th5/iR/HfmLncd3pvtUlrv/3vJf3mv9Hg/VfygDI3bx9/W3BGIynCdjaC3atYgpG6cw8KaBPN3kaSoUrsDXa7/2Vogpyo2d6pA194l8IyKlAQEigEed8nlAeyAS+Bd4AEBVj4nI28Bqp95bqnrMef44MB7ID8x3HrnK+8vfp3i+4sy4dwY//f0T98+8n1YTW3FT5ZsQhI61O3rcdh7fPLzU3OtnE43JMMXyFaNq0apsOJK2K7QuxFzg8Z8ep3rx6gxsPhA/Hz8eCHmAd5e9y/5T+zP1rvH4JGKnszygqktwnYJCVZMdpc+5wuqJFJaNBcYmUx4OZNy8qtnM1n+2MmvbLF6/+XUK5SlEt7rdKJy3MF2md2HtwbU0q9ws02fPMyarBZcLTvORyEcrPmJb9Dbm3zc/4QbYh258iCFhQxi3bhyv3/K6N0O9TMShCKoVq+bx/DvZlQ17ko19uPxD8vvl58lGTyaUta/ZngX3L6BUgVI8dGPGn4YyJrsLLhvMtuhtl43hlZxdx3cxOGww9wTeQ7sa/7uFLKB4ALdddxtj1o3xaC4cT8TGxRK2N4wG5XPfqNWWRLKpqFNRTNowiQdvfDBhutp4N1e9mcMDDtuVTuaaFFQ2iDiNY9PRTanWGxI2BF/xZVjbpHOnPFz/Yfac3MNvO3/zVpiXWbx7MQdOH+DeOvdmyvYykyWRbOqTlZ8Qp3E83/T5ZJf7iH105toUP5lTaqe0VJV52+fRoVaHZPs9OtXuRMn8JdPUwX7u0jnOXDzjecDApA2TKJK3CHfVuuuq2smO7JsoGzpx/gQj14zk3jr3ZsikQcbkJtVLVKegf8FUhz/ZeGQjB88cvOw0lru8fnnpHdybWVtnceTskWTrqCpTNk7hus+uI2hEEEfPHvUo3rMXz/L95u/pGtg1xYFJczJLItnQiNUjOH3xNC80eyGrQzEm2/ERH+qVrZfq8Cfx0wK3rZ7yLIh96/flUtwlxkeMT7Jsy9EttJ7Ymvt+uI/yhcpz8MxBukzvkuSG37SYtW0WZy+dpWeQ9ybtykqWRLKZCzEX+PTPT2lTvU2OHe7aGG8LKRvCukPrUpxb5OfInwkqG0T5wuVTbOOG0jfQvEpzXvrtJcp8WIYW41rQd3ZfHpv7GMFfBbPu0DpG3DmC1Q+vZmzHsYTtDePxnx5PMuLxlUzaMIkqRavQomrG3tSbXVgSyWZ+2PIDh88e5tkmz2Z1KMZkW52v78ypC6eY+/fcJMtOXzjNsr3LaFf9yoN6T79nOh/e/iGdandCEOb8PYeRa0ZyX9B9bOu/jUdDH8XXx5ce9XrwaotXGbNuDJ/++Wma4zx05hC/7PiF++vdn2v7MW1Sqmzmi9VfUL14ddpUb5PVoRiTbd123W1UKFyB8evH0yWwy2XLFu9ezKW4Syn2h7grX7g8A5oNuKzsUuwl/H39k9R9q+VbbD66med/eZ7rS12fpvanbpxKnMZxf9D9V6ybU+XO1JhDrT+0nuX7lvNY6GO59leLMRnB18eXXkG9mL99PofOHLps2YLIBRT0L8hNVW7yqO3kEgi4+mIm3T2JemXq0W1GN7ZHb79iW5M2TKJB+QbcUPoGj2LJCeybKhsZET6CfH75eODGzJnkx5icrE9IH2I1lskbJieUqSrzI+fTKqCVV4ZbL5inILN7zMZHfOgzqw+xcbEp1t10ZBPrDq3LtR3q8SyJZBMnz59k8obJ9KjbgxL5S2R1OMZke7VL1aZppaaMjxif0NkdeSySXSd2pelUk6eqFK3C8DuGs2LfCj5Z+UmK9SZvmIyvuPpTcjNLImmgqiyIXMCiXYu8to1JGyZx9tJZHm/4uNe2YUxu0yekD5uObmLNQde0RfGX9noziQDcV+8+OtXuxKuLXmXrP1uTLI/TOL7Z+A1ta7SlTMEyXo0lq1kSSYM4jeOZBc/wzM/PeGWsHVXly9Vf0rBCw0yf5MeYnOzeOveSzy9fwr0eP+/4mRolanBd8eu8ul0R4asOX1EwT0F6/9ibmLiYhGVnL57lqflPse/Uvlx/KgssiaSJr48vrzR/hY1HNjJ72+wMb3/J7iVs+WeLHYUYk07F8hXj7uvvZsrGKZy6cIolu5ek6dLejFCuUDm+aP8Fq6JWMXTFUMA1d0m9EfX4YvUXPNHwCe4JvCdTYslKlkTSqEe9HlQvXp23l76d7puNruTL8C8pkb8E3ep0y9B2jbkW9Anpw/Hzx3nx1xf599K/Xj+V5a5bnW50uaEL/13yX+7/4X5aT2yNr48vv/f5nc/bf56h005nV5ZE0sjPx49XWrzC2oNrmR+ZcXNfHTh9gJlbZvJgyIO5clwdY7ytdUBrKhauyMg1I8njm4dbq92aadsWEb6880uK5C3C1L+mMqDpANY/up6bq96caTFkNUsi6dAzqCdVi1bN0KORN5e8SZzG8UjoIxnSnjHXGl8fX3oF9wKgRZUWmT4tc5mCZQh7IIyIRyL4sM2HFPAvkKnbz2qWRNLB39efgc0HsnL/ShbuWnjV7c3cMpNRa0fxQrMXqFGiRgZEaMy1qU9IH3zEJ8uGWr++1PXUK1svS7ad1byeRETEV0TWichc53WAiPwpIpEi8q2I5HHK8zqvI53l1dzaeNkp3yYibd3K2zllkSIy0Nv7AvBAyANULFyRt35/66ra2X9qP33n9KVB+Qa83ertDIrOmGtTrZK12Pz4Zrs4JQtkxpHI08AWt9fvA8NUtQZwHIif4/Uh4LhTPsyph4gEAt2BOkA74EsnMfkCXwB3AIFAD6euV+X1y8tLN71E2N4wft/9u0dtxMbF0nNmTy7EXGBql6leubPWmGtN7VK1UxyyxHiPV5OIiFQC7gRGO68FaAXMcKpMADo7zzs5r3GWt3bqdwKmqeoFVd0FRAKNnEekqu5U1YvANKeu1/Wt35eyBcvy9lLPjiA+WP4BS3Yv4fP2n1OzZM0Mjs4YYzKPt49EPgFeBOLv0CsJnFDV+Dtz9gMVnecVgX0AzvKTTv2E8kTrpFSehIj0E5FwEQk/etSz2cnc5ffPz4s3vcjCXQt5N+zddK375/4/eX3x63Sr043ewb2vOhZjjMlKXksiItIBOKKqa7y1jbRS1VGqGqqqoaVLl86QNp9q/BT/V+//eGXRK7y26LU0Xa11KfYSPWf2pFKRSnzV4StcB1rGGJNzefNOmJuAjiLSHsgHFAE+BYqJiJ9ztFEJiHLqRwGVgf0i4gcUBaLdyuO5r5NSudf5+fgxsfNECvgVYEjYEM5ePMvHbT9ONTFMWD+B7ce2M7fHXIrlK5ZZoRpjjNd47UhEVV9W1UqqWg1Xx/giVb0PWAzEjwXQG5jlPJ/tvMZZvkhdP+9nA92dq7cCgJrAKmA1UNO52iuPs42MH5MkFb4+voy6axRPN36aT/78hEfnPpri2FoXYy/y9tK3aVyxMe1rts/MMI0xxmuy4p78l4BpIjIYWAeMccrHAJNEJBI4hispoKqbRGQ6sBmIAZ5Q1VgAEekPLAB8gbGquilT98QVA8PaDqOgf0HeWfYOfj5+fHHnF0nqjV03lr0n9zKqwyg7jWWMyTUkLefyRaQWMAIoq6p1RSQI6Kiqg70dYEYLDQ3V8PBwr7Q94JcBDP1jKBM7T6Rn8P9G77wQc4Eaw2tQpWgVlj2wzJKIMSbHEZE1qppkmPG0ns76GngZuASgqhtwjhTM/7x323vcUvUWHpn7CBsPb0woH712NPtP7efNW9+0BGKMyVXSmkQKqOqqRGUxyda8hvn5+DHtnmkUzVeULtO7cOrCKc7HnOedZe/QokoLWge0zuoQjTEmQ6W1T+QfEakOKICI3AMc9FpUOVi5QuX49p5vaTWhFQ/OepAWVVpw4PQBvvnPN3YUYozJddKaRJ4ARgHXi0gUsAu432tR5XA3V72Zd1u/y4u/vcjsbbO5tdqtmTo8tTHGZJY0nc5yhha5DSgNXK+qzVV1t1cjy+EGNBtA5+s7cynuEm/e+mZWh2OMMV6RpiQiIu+ISDFVPauqp0WkuHOJrkmBiDC1y1TCHw6/piaoMcZcW9LasX6Hqp6If6GqxwG7Y+4K8vnlo0GFBlkdhjHGeE1ak4iviOSNfyEi+YG8qdQ3xhhzDUhrx/o3wEIRGee8foD/DdtujDHmGpWmJKKq74vIBiD+Roe3VXWB98IyxhiTE6R57CxVnQ/M92IsxhhjcphUk4iILFPV5iJyGudGw/hFgKpqEa9GZ4wxJltLNYmoanPn38KZE44xxpic5IpXZ4mIr4hszYxgjDHG5CxXTCLO3B3bRKRKJsRjjDEmB0lrx3pxYJOIrALOxheqakevRGWMMSZHSGsSed2rURhjjMmRrnR1Vj7gUaAGsBEYo6o2j4gxxhjgyn0iE4BQXAnkDmCo1yMyxhiTY1wpiQSq6v2qOhK4B2iR1oZFJJ+IrBKR9SKySUTedMrHi8guEYlwHiFOuYjIZyISKSIbRKS+W1u9RWS78+jtVt5ARDY663wmNuuTMcZkqiv1iVyKf6KqMen8jr4AtFLVMyLiDywTkfg73l9Q1RmJ6t8B1HQejYERQGMRKQG8geuISIE1IjLbGUl4BPAw8CcwD2iH3VVvjDGZ5kpHIsEicsp5nAaC4p+LyKnUVlSXM85Lf+ehqazSCZjorLcSKCYi5YG2wK+qesxJHL8C7ZxlRVR1paoqMBHofMU9NsYYk2FSTSKq6quqRZxHYVX1c3t+xSFPnBsVI4AjuBLBn86iIc4pq2FuQ8xXBPa5rb7fKUutfH8y5cnF0U9EwkUk/OjRo1cK2xhjTBqldT4Rj6hqrKqGAJWARiJSF3gZuB5oCJQAXvJmDE4co1Q1VFVDS5cu7e3NGWPMNcOrSSSeMyviYqCdqh50TlldAMYBjZxqUUBlt9UqOWWplVdKptwYY0wm8VoSEZHSIlLMeZ4fuB3Y6vRl4FxJ1Rn4y1llNtDLuUqrCXBSVQ8CC4A2zrzuxYE2wAJn2SkRaeK01QuY5a39McYYk1Sa5xPxQHlggoj44kpW01V1rogsEpHSuIaTj8B1MyO4rq5qD0QC/+KaPRFVPSYibwOrnXpvqeox5/njwHggP66rsuzKLGOMyUTiurDp2hEaGqrh4eFZHYYxxuQoIrJGVUMTl2dKn4gxxpjcyZKIMcYYj1kSMcYY4zFLIsYYYzxmScQYY4zHLIkYY4zxmCURY4wxHrMkYowxxmOWRIwxxnjMkogxxhiPWRIxxhjjMUsixhhjPGZJxBhjjMcsiRhjjPGYJRFjjDEesyRijDHGY5ZEjDHGeMySiDHGGI9ZEjHGGOMxryUREcknIqtEZL2IbBKRN53yABH5U0QiReRbEcnjlOd1Xkc6y6u5tfWyU75NRNq6lbdzyiJFZKC39sUYY0zyvHkkcgFoparBQAjQTkSaAO8Dw1S1BnAceMip/xBw3Ckf5tRDRAKB7kAdoB3wpYj4iogv8AVwBxAI9HDqGmOMySReSyLqcsZ56e88FGgFzHDKJwCdneednNc4y1uLiDjl01T1gqruAiKBRs4jUlV3qupFYJpT1xhjTCbxap+Ic8QQARwBfgV2ACdUNcapsh+o6DyvCOwDcJafBEq6lydaJ6Xy5OLoJyLhIhJ+9OjRjNg1Y4wxeDmJqGqsqoYAlXAdOVzvze2lEscoVQ1V1dDSpUtnRQjGGJMrZcrVWap6AlgMNAWKiYifs6gSEOU8jwIqAzjLiwLR7uWJ1kmp3BhjTCbx5tVZpUWkmPM8P3A7sAVXMrnHqdYbmOU8n+28xlm+SFXVKe/uXL0VANQEVgGrgZrO1V55cHW+z/bW/hhjjEnK78pVPFYemOBcReUDTFfVuSKyGZgmIoOBdcAYp/4YYJKIRALHcCUFVHWTiEwHNgMxwBOqGgsgIv2BBYAvMFZVN3lxf4wxxiQirh/7147Q0FANDw/P6jCMMSZHEZE1qhqauNzuWDfGGOMxSyLGGGM8ZknEGGOMxyyJGGOM8ZglEWOMMR6zJGKMMcZjlkSMMcZ4zJKIMcYYj1kSMcYY4zFLIsYYYzxmScQYY4zHLIkYY4zxmCURY4wxHrMkYowxxmOWRIwxxnjMkogxxhiPWRIxxhjjMUsixhhjPOa1JCIilUVksYhsFpFNIvK0Uz5IRKJEJMJ5tHdb52URiRSRbSLS1q28nVMWKSID3coDRORPp/xbEcnjrf0xxhiTlDePRGKA51U1EGgCPCEigc6yYaoa4jzmATjLugN1gHbAlyLiKyK+wBfAHUAg0MOtnfedtmoAx4GHvLg/xhhjEvFaElHVg6q61nl+GtgCVExllU7ANFW9oKq7gEigkfOIVNWdqnoRmAZ0EhEBWgEznPUnAJ29szfGGGOSkyl9IiJSDbgR+NMp6i8iG0RkrIgUd8oqAvvcVtvvlKVUXhI4oaoxicqT234/EQkXkfCjR49mwB4ZY4yBTEgiIlII+B54RlVPASOA6kAIcBAY6u0YVHWUqoaqamjp0qW9vTljjLlm+HmzcRHxx5VAvlHVHwBU9bDb8q+Buc7LKKCy2+qVnDJSKI8GiomIn3M04l7fGGNMJvDm1VkCjAG2qOrHbuXl3ardDfzlPJ8NdBeRvCISANQEVgGrgZrOlVh5cHW+z1ZVBRYD9zjr9wZmeWt/jDHGJOXNI5GbgJ7ARhGJcMpewXV1VQigwG7gEQBV3SQi04HNuK7sekJVYwFEpD+wAPAFxqrqJqe9l4BpIjIYWIcraRljjMkk4vpBf+0IDQ3V8PDwrA7DGGNyFBFZo6qhicvtjnVjjDEesyRijDHGY5ZEjDHGeMySiDHGGI9ZEjHGGOMxSyLGGGM8ZknEGGOMxyyJGGOM8ZglEWOMMR6zJGKMMcZjlkSMMcZ4zJKIMcYYj1kSMcYY4zFLIsYYYzxmScQYY4zHLIkYY4zxmOj2GQAACT9JREFUmCURY4wxHrMkYowxxmOWRIwxxnjMa0lERCqLyGIR2Swim0Tkaae8hIj8KiLbnX+LO+UiIp+JSKSIbBCR+m5t9XbqbxeR3m7lDURko7POZyIi3tofY4wxSXnzSCQGeF5VA4EmwBMiEggMBBaqak1gofMa4A6gpvPoB4wAV9IB3gAaA//f3v3HelXXcRx/vgJN0wJ/MGLgwMrhKPMCNyfTGtgy1Cb9oJa5pMZyLdtgK0trK9N01YowV23+ICBJK/xFLCEizOYSufwwQCRo4oDxS/xB9MNQ3v3x+dxxuH25P8693/s933g9tu++53zO+X7P6/vdufd9z+ec+zkXAN9sLzx5nc8VXje5jp/HzMw6qFsRiYhdEbEmT/8d2AQMB6YA8/Jq84AP5+kpwPxIngQGSxoGfBBYFhEvRsRLwDJgcl72loh4MiICmF94LzMz6wf9ck5E0ihgLLASGBoRu/Ki3cDQPD0c2F542Y7c1ln7jhrttbZ/raQ2SW379u3r1WcxM7MjBtZ7A5JOBR4AZkbEgeJpi4gISVHvDBFxJ3AnQGtra7ntzZwJ69b1ZSwzs/7T0gKzZ/f529b1SETSCaQCsiAiHszNe3JXFPl5b27fCZxVePmI3NZZ+4ga7WZm1k/qdiSSr5S6B9gUEbMKixYB04Dv5OdHCu1flHQ/6ST6KxGxS9JS4LbCyfRLgRsj4kVJByRdSOomuwa4o16fpx4V3Mys2dWzO+si4NPAeknt/UBfIxWPX0maDjwPfCIv+y1wObAV+CfwWYBcLG4BVuX1bo6IF/P0F4C5wMnAo/lhZmb9ROnCpuNHa2trtLW1NTqGmVlTkbQ6Ilo7tvs/1s3MrDQXETMzK81FxMzMSnMRMTOz0lxEzMysNBcRMzMr7bi7xFfSPtL/p5RxJvBCH8bpD82WudnygjP3l2bL3Gx5ofPMIyNiSMfG466I9IaktlrXSVdZs2VutrzgzP2l2TI3W14ol9ndWWZmVpqLiJmZleYi0jN3NjpACc2WudnygjP3l2bL3Gx5oURmnxMxM7PSfCRiZmaluYiYmVlpLiLdIGmypM2Stkq6odF5apE0R9JeSRsKbadLWiZpS34+rbP36G+SzpK0QtIzkjZKmpHbK5tb0kmSnpL0dM78rdx+tqSVeR/5paQTG521SNIASWslLc7zVc+7TdJ6SeskteW2yu4XAJIGS1oo6VlJmyRNqHJmSaPz99v+OCBpZk8zu4h0QdIA4MfAZcAY4CpJYxqbqqa5wOQObTcAyyPiHGB5nq+S14AvRcQY4ELguvzdVjn3q8AlEXE+0AJMznfX/C7ww4h4B/ASML2BGWuZAWwqzFc9L8CkiGgp/N9ClfcLgNuBJRFxLnA+6fuubOaI2Jy/3xZgPOlmgA/R08wR4UcnD2ACsLQwfyPp9rwNz1Yj6yhgQ2F+MzAsTw8DNjc6Yxf5HwE+0Cy5gTcBa0i3c34BGFhrn2n0AxiRfxlcAiwGVOW8OdM24MwObZXdL4BBwHPki5WaIXOHnJcCT5TJ7CORrg0Hthfmd+S2ZjA0Inbl6d3A0EaG6YykUcBYYCUVz527htYBe4FlwN+AlyPitbxK1faR2cBXgMN5/gyqnRcggN9JWi3p2txW5f3ibGAf8LPcbXi3pFOoduaiTwL35ekeZXYROU5E+rOiktdzSzoVeACYGREHisuqmDsiXo/UBTACuAA4t8GRjknSh4C9EbG60Vl66OKIGEfqRr5O0vuKCyu4XwwExgE/jYixwD/o0A1UwcwA5PNhVwK/7risO5ldRLq2EzirMD8itzWDPZKGAeTnvQ3O8z8knUAqIAsi4sHcXPncABHxMrCC1B00WNLAvKhK+8hFwJWStgH3k7q0bqe6eQGIiJ35eS+pn/4Cqr1f7AB2RMTKPL+QVFSqnLndZcCaiNiT53uU2UWka6uAc/LVLCeSDvsWNThTdy0CpuXpaaRzDpUhScA9wKaImFVYVNnckoZIGpynTyadw9lEKiZT82qVyRwRN0bEiIgYRdp3/xARV1PRvACSTpH05vZpUn/9Biq8X0TEbmC7pNG56f3AM1Q4c8FVHOnKgp5mbvQJnWZ4AJcDfyX1fX+90XmOkfE+YBdwiPRX0XRS3/dyYAvwe+D0RufskPli0qHyX4B1+XF5lXMD7wbW5swbgG/k9rcBTwFbSd0Cb2x01hrZJwKLq543Z3s6Pza2/8xVeb/I+VqAtrxvPAyc1gSZTwH2A4MKbT3K7GFPzMysNHdnmZlZaS4iZmZWmouImZmV5iJiZmaluYiYmVlpLiJmNUg6ozC66W5JO/P0QUk/qcP25kp6TtLnj7H8YB9vb0X+LK1dr212bAO7XsXs+BMR+0nX/SPpJuBgRHy/zpu9PiIW1nkbAETEJEmP9ce27P+bj0TMekDSxMI9OW6SNE/SnyQ9L+mjkr6X74OxJA/pgqTxkv6YBxNc2j6kRBfbOVvSn/N7fbvQfqqk5ZLW5GVTcvvNkmYW1rtV0gxJwyQ9no+iNkh6b99/K3Y8cxEx6523k8ajuhK4F1gREecB/wKuyIXkDmBqRIwH5gC3duN9bycN5nceaSSCdv8GPhJpcMJJwA/y8DFzgGsAJL2BNMTJvcCnSMO8t5DucbGul5/X7CjuzjLrnUcj4pCk9cAAYEluX0+6v8to4F3AsvS7ngEcXRSO5SLgY3n656SbSEG6F8hteVTbw6Qh3IdGxDZJ+yWNJQ3dvTYi9ktaBczJxezhiHARsT7lImLWO68CRMRhSYfiyDhCh0k/XwI2RsSEEu9da0yiq4EhwPhcvLYBJ+VldwOfAd5KOjIhIh7PBecKYK6kWRExv0QWs5rcnWVWX5uBIZImQBr6XtI7u/G6J0hdUpAKR7tBpPuDHJI0CRhZWPYQ6RbJ7wGW5u2NBPZExF2kIjOuNx/GrCMfiZjVUUT8R9JU4EeSBpF+5maTRqftzAzgF5K+ytFDcS8AfpO7z9qAZztsawXproWv5+aJwPWSDgEHyedNzPqKR/E1qwBJc0nDtJe+xDefUF8DfDwitnRj/ceAL0dEW9ltmrk7y6waXgFuOdY/G3ZF0hjSvUGWd7OArCDdt+NQme2ZtfORiJmZleYjETMzK81FxMzMSnMRMTOz0lxEzMysNBcRMzMr7b+7G9YWSf6gFQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# get all three mae / mse on the same plot to compare methods\n"
      ],
      "metadata": {
        "id": "2YBPJOIwSnIs"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### we can see that images + historical sales data achieves a best prediction performance than each of them taken individually. \n",
        "\n",
        "### this is encourageing to grow our forecasting model in a multivariate one inputting more variables."
      ],
      "metadata": {
        "id": "gsDJgJvBnYq-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "g86aohzen4GE"
      },
      "execution_count": 40,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Combining CNN+RNN cryptopunks.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}