Windspeed-Predictions-with-TPU.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Windspeed Predictions with TPU.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMA3YDPPR66BzgiTEcpHK2e"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "go2l7m97lzPc"
      },
      "source": [
        "####Introduction\n",
        "Example of using TPUs for CNN to predict windspeed from satellite image of hurricane.  Images are in a Google Bucket in TFRecord format (instead of JPG) to reduce model training time.  TFRecord format allows for more efficient streaming to the model.  \n",
        "\n",
        "During training, the model checkpoints are saved for the best MSE.  After traning, the final model is saved.   "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dVsnPc7Vdz5M"
      },
      "source": [
        "####**Initial Setup**\n",
        "Prepare to connect to Google Bucket and Google Drive plus other imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K4Zp5LMMc1UK"
      },
      "source": [
        "from google.colab import drive\n",
        "from google.colab import auth\n",
        "auth.authenticate_user()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zZEUak8BdQEb"
      },
      "source": [
        "import datetime \n",
        "import numpy as np\n",
        "import os\n",
        "\n",
        "\n",
        "import keras as keras\n",
        "\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "from tensorflow.keras.layers import Conv2D, Dense, GlobalAveragePooling2D, MaxPooling2D, \\\n",
        "                                    Flatten, BatchNormalization, Dropout, Input, concatenate\n",
        "from tensorflow.keras.models import Sequential, Model\n",
        "from tensorflow.keras.layers.experimental import preprocessing\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5SliUIwIef7U"
      },
      "source": [
        "####**TPU Setup**\n",
        "Connect to cluster and set distribution strategy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-UrICSxebqk",
        "outputId": "c1b42a95-e16f-44a5-8434-2deafff393a0"
      },
      "source": [
        "resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])\n",
        "tf.config.experimental_connect_to_cluster(resolver)\n",
        "tf.tpu.experimental.initialize_tpu_system(resolver)\n",
        "print(\"All devices: \", tf.config.list_logical_devices('TPU'))\n",
        "\n",
        "strategy = tf.distribute.TPUStrategy(resolver)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.57.227.186:8470\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Initializing the TPU system: grpc://10.57.227.186:8470\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Clearing out eager caches\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Clearing out eager caches\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Finished initializing TPU system.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "All devices:  [LogicalDevice(name='/job:worker/replica:0/task:0/device:TPU:7', device_type='TPU'), LogicalDevice(name='/job:worker/replica:0/task:0/device:TPU:6', device_type='TPU'), LogicalDevice(name='/job:worker/replica:0/task:0/device:TPU:5', device_type='TPU'), LogicalDevice(name='/job:worker/replica:0/task:0/device:TPU:4', device_type='TPU'), LogicalDevice(name='/job:worker/replica:0/task:0/device:TPU:3', device_type='TPU'), LogicalDevice(name='/job:worker/replica:0/task:0/device:TPU:0', device_type='TPU'), LogicalDevice(name='/job:worker/replica:0/task:0/device:TPU:1', device_type='TPU'), LogicalDevice(name='/job:worker/replica:0/task:0/device:TPU:2', device_type='TPU')]\n",
            "INFO:tensorflow:Found TPU system:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Found TPU system:\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores: 8\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Workers: 1\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Num TPU Cores Per Worker: 8\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lspXVReNg55U"
      },
      "source": [
        "####**Data Files**\n",
        "Connect to Google Bucket and verify TFRecord file entries look OK. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eA7CUODihRQ3"
      },
      "source": [
        "def read_one_whole_rec(data):\n",
        "  tfrecord_format = (\n",
        "        {\n",
        "            \"x\": tf.io.FixedLenFeature([], tf.string),\n",
        "            \"y\": tf.io.FixedLenFeature([], tf.float32)\n",
        "        }\n",
        "   )\n",
        "  \n",
        "  data = tf.io.parse_example(data, tfrecord_format)\n",
        "  img = tf.io.decode_raw(data[\"x\"], tf.uint8)\n",
        "  #expecting this original imgage size \n",
        "  img = tf.reshape(img, [366,366,1])\n",
        "  lab = data[\"y\"]\n",
        "  return img, lab\n",
        "\n",
        "# Train set and validation set were decided outside of this notebook.  \n",
        "# Data was split by storm instead of a random split to keep a storms' images together.  \n",
        "train_images = tf.data.TFRecordDataset(\n",
        "    \"gs://eye-speed/data/tfrecs/train.tfrecords\"\n",
        ")\n",
        "ds_train = train_images.map(read_one_whole_rec)\n",
        "\n",
        "val_images = tf.data.TFRecordDataset(\n",
        "    \"gs://eye-speed/data/tfrecs/val.tfrecords\"\n",
        ")\n",
        "ds_val = val_images.map(read_one_whole_rec)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "rg8p6khyhrcZ",
        "outputId": "94d44a25-2a6e-4cb0-d910-35b13d200850"
      },
      "source": [
        "from PIL import Image \n",
        "\n",
        "for example in ds_train.take(1):\n",
        "  im, lb = example\n",
        "  print(\"Windspeed is\", str(lb.numpy())) \n",
        "  display(Image.fromarray(tf.squeeze(im).numpy())) \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Windspeed is 50.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=366x366 at 0x7F2E740B3CF8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400
        },
        "id": "tjBQR5l5iBch",
        "outputId": "7a12a927-d25e-46d1-c22c-42c4cb48f712"
      },
      "source": [
        "for example in ds_val.take(1):\n",
        "  im, lb = example\n",
        "  print(\"Windspeed is\", str(lb.numpy())) \n",
        "  display(Image.fromarray(tf.squeeze(im).numpy())) \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Windspeed is 15.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=L size=366x366 at 0x7F2E73FB9710>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8sTfMdeYkyXk"
      },
      "source": [
        "####**Calculations and Batching**\n",
        "Determine the number of records to send to the model per batch (batch size) and the number of batches for each epoch (steps per epoch)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FkLBlJlblDyK",
        "outputId": "fa4d994b-2a4d-4957-df8e-7c407a72c200"
      },
      "source": [
        "#to improve streaming performance\n",
        "AUTO = tf.data.experimental.AUTOTUNE\n",
        "\n",
        "TOTAL_RECORDS = ds_train.reduce(np.int64(0), lambda x, _: x + 1)\n",
        "BATCH_SIZE_PER_REPLICA = 24\n",
        "GLOBAL_BATCH_SIZE = BATCH_SIZE_PER_REPLICA * strategy.num_replicas_in_sync\n",
        "STEPS_PER_EPOCH = (TOTAL_RECORDS // GLOBAL_BATCH_SIZE).numpy()\n",
        "print(\"Steps per epoch\", str(STEPS_PER_EPOCH))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Steps per epoch 295\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4DThuKWhoShr",
        "outputId": "b184ff19-086a-4e7e-de5d-daab3021f629"
      },
      "source": [
        "# to ensure all batches are the same size, use drop_remainder=True\n",
        "batched_ds_train = ds_train.batch(GLOBAL_BATCH_SIZE, drop_remainder=True).prefetch(AUTO)\n",
        "print(batched_ds_train) \n",
        "\n",
        "batched_ds_val = ds_val.batch(GLOBAL_BATCH_SIZE, drop_remainder=True).prefetch(AUTO)\n",
        "print(batched_ds_val)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<PrefetchDataset shapes: ((192, 366, 366, 1), (192,)), types: (tf.uint8, tf.float32)>\n",
            "<PrefetchDataset shapes: ((192, 366, 366, 1), (192,)), types: (tf.uint8, tf.float32)>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JCb-TzR0gm_G"
      },
      "source": [
        "####Model\n",
        "Create model, associate it with the TPU strategy, then fit.  "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ce_dhciKpT4D"
      },
      "source": [
        "IMG_SIZE = 366\n",
        "EPOCHS = 100\n",
        "\n",
        "def create_compile_model_with_bn_dropout_366():\n",
        "  model = Sequential(\n",
        "      [\n",
        "        #seeing OK performance with fullsize images,\n",
        "        #but preprocessing is an option if needed to make training faster. \n",
        "        #preprocessing.Resizing(IMG_SIZE, IMG_SIZE, interpolation=\"nearest\"),\n",
        "        preprocessing.Rescaling(1./255)\n",
        "      ]\n",
        "  )\n",
        "  model.add(Conv2D(32, (7,7), padding='same', activation='selu', input_shape=(IMG_SIZE, IMG_SIZE, 1)))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Conv2D(64, (3,3), padding='same', activation='selu'))\n",
        "  model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Conv2D(64, (3,3), padding='same', activation='selu'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Conv2D(64, (3,3), padding='same', activation='selu'))\n",
        "  model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Conv2D(64, (3,3), padding='same', activation='selu'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Conv2D(32, (3,3), padding='same', activation='selu'))\n",
        "  model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Conv2D(32, (3,3), padding='same', activation='selu'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Conv2D(32, (3,3), padding='same', activation='selu'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "  model.add(Dropout(0.1))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Conv2D(32, (3,3), padding='same', activation='selu'))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Conv2D(32, (3,3), padding='same', activation='selu'))\n",
        "  model.add(MaxPooling2D(pool_size=(2,2)))\n",
        "  model.add(Dropout(0.1))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Conv2D(32, (3,3), padding='same', activation='selu'))\n",
        "  model.add(Flatten())\n",
        "  model.add(Dropout(0.1))\n",
        "  model.add(BatchNormalization())\n",
        "  model.add(Dense(32, activation='selu'))\n",
        "  model.add(Dense(1, activation='linear'))\n",
        "\n",
        "  model.compile(loss='mse', optimizer='adam', metrics=['mse', 'mae', 'mape']) \n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2uDLBzX3ptwc",
        "outputId": "304de236-baf0-4031-c1d7-034d4a668aed"
      },
      "source": [
        "#associate with TPU strategy\n",
        "with strategy.scope():\n",
        "  model = create_compile_model_with_bn_dropout_366()\n",
        "\n",
        "#setup to save the best model because the last is not necessarily the best. \n",
        "d = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n",
        "checkpoint_filename = '/content/drive/MyDrive/TeamHurricane/models/best-' + d +'.h5'\n",
        "checkpoint = ModelCheckpoint(checkpoint_filename, monitor='val_mse', verbose=1, save_best_only=True, mode='min') \n",
        "\n",
        "#extra logging to know when fitting starts \n",
        "print(datetime.datetime.strptime(d, \"%Y-%m-%d %H:%M:%S\").strftime(\"%Y-%m-%d %I:%M:%S %p\"))\n",
        "\n",
        "history = model.fit(batched_ds_train.repeat(), epochs=EPOCHS, steps_per_epoch=STEPS_PER_EPOCH, verbose=1, \\\n",
        "                    validation_data=batched_ds_val, callbacks=[checkpoint])\n",
        "\n",
        "final_filename = '/content/drive/MyDrive/TeamHurricane/models/final-' + d + '.h5'\n",
        "model.save(final_filename)\n",
        "print(\"just saved model\" )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2021-02-19 08:07:49 PM\n",
            "Epoch 1/100\n",
            "295/295 [==============================] - 144s 434ms/step - loss: 965.9577 - mse: 965.9577 - mae: 23.0601 - mape: 49.1337 - val_loss: 466.4637 - val_mse: 466.4637 - val_mae: 17.5974 - val_mape: 43.7568\n",
            "\n",
            "Epoch 00001: val_mse improved from inf to 466.46371, saving model to /content/drive/MyDrive/TeamHurricane/models/best-2021-02-19 20:07:49.h5\n",
            "Epoch 2/100\n",
            "295/295 [==============================] - 90s 305ms/step - loss: 184.4748 - mse: 184.4748 - mae: 10.3451 - mape: 23.1781 - val_loss: 145.6429 - val_mse: 145.6429 - val_mae: 9.1601 - val_mape: 21.1889\n",
            "\n",
            "Epoch 00002: val_mse improved from 466.46371 to 145.64290, saving model to /content/drive/MyDrive/TeamHurricane/models/best-2021-02-19 20:07:49.h5\n",
            "Epoch 3/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 142.7137 - mse: 142.7137 - mae: 9.0990 - mape: 20.4291 - val_loss: 128.2822 - val_mse: 128.2822 - val_mae: 8.5285 - val_mape: 20.5153\n",
            "\n",
            "Epoch 00003: val_mse improved from 145.64290 to 128.28223, saving model to /content/drive/MyDrive/TeamHurricane/models/best-2021-02-19 20:07:49.h5\n",
            "Epoch 4/100\n",
            "295/295 [==============================] - 89s 303ms/step - loss: 121.8789 - mse: 121.8789 - mae: 8.4112 - mape: 18.8695 - val_loss: 118.2469 - val_mse: 118.2469 - val_mae: 8.2104 - val_mape: 19.6334\n",
            "\n",
            "Epoch 00004: val_mse improved from 128.28223 to 118.24695, saving model to /content/drive/MyDrive/TeamHurricane/models/best-2021-02-19 20:07:49.h5\n",
            "Epoch 5/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 108.0493 - mse: 108.0493 - mae: 7.9345 - mape: 17.7607 - val_loss: 135.3226 - val_mse: 135.3226 - val_mae: 8.6939 - val_mape: 19.9935\n",
            "\n",
            "Epoch 00005: val_mse did not improve from 118.24695\n",
            "Epoch 6/100\n",
            "295/295 [==============================] - 89s 303ms/step - loss: 96.4601 - mse: 96.4601 - mae: 7.5054 - mape: 16.8763 - val_loss: 129.0140 - val_mse: 129.0140 - val_mae: 8.4842 - val_mape: 19.7862\n",
            "\n",
            "Epoch 00006: val_mse did not improve from 118.24695\n",
            "Epoch 7/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 87.0018 - mse: 87.0018 - mae: 7.1455 - mape: 16.0548 - val_loss: 148.0100 - val_mse: 148.0100 - val_mae: 9.1435 - val_mape: 20.8677\n",
            "\n",
            "Epoch 00007: val_mse did not improve from 118.24695\n",
            "Epoch 8/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 80.4811 - mse: 80.4811 - mae: 6.8643 - mape: 15.4320 - val_loss: 116.2254 - val_mse: 116.2254 - val_mae: 8.1271 - val_mape: 19.2859\n",
            "\n",
            "Epoch 00008: val_mse improved from 118.24695 to 116.22540, saving model to /content/drive/MyDrive/TeamHurricane/models/best-2021-02-19 20:07:49.h5\n",
            "Epoch 9/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 73.9270 - mse: 73.9270 - mae: 6.5863 - mape: 14.8422 - val_loss: 105.5149 - val_mse: 105.5149 - val_mae: 7.7013 - val_mape: 18.2750\n",
            "\n",
            "Epoch 00009: val_mse improved from 116.22540 to 105.51492, saving model to /content/drive/MyDrive/TeamHurricane/models/best-2021-02-19 20:07:49.h5\n",
            "Epoch 10/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 68.2898 - mse: 68.2898 - mae: 6.3415 - mape: 14.2774 - val_loss: 141.0480 - val_mse: 141.0480 - val_mae: 9.1845 - val_mape: 22.0916\n",
            "\n",
            "Epoch 00010: val_mse did not improve from 105.51492\n",
            "Epoch 11/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 63.3579 - mse: 63.3579 - mae: 6.1112 - mape: 13.8181 - val_loss: 121.4265 - val_mse: 121.4265 - val_mae: 8.3032 - val_mape: 19.9365\n",
            "\n",
            "Epoch 00011: val_mse did not improve from 105.51492\n",
            "Epoch 12/100\n",
            "295/295 [==============================] - 89s 303ms/step - loss: 60.5182 - mse: 60.5182 - mae: 5.9766 - mape: 13.5310 - val_loss: 121.8492 - val_mse: 121.8492 - val_mae: 8.1484 - val_mape: 18.7607\n",
            "\n",
            "Epoch 00012: val_mse did not improve from 105.51492\n",
            "Epoch 13/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 57.6536 - mse: 57.6536 - mae: 5.8377 - mape: 13.2551 - val_loss: 120.4133 - val_mse: 120.4133 - val_mae: 8.4038 - val_mape: 20.3683\n",
            "\n",
            "Epoch 00013: val_mse did not improve from 105.51492\n",
            "Epoch 14/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 56.4601 - mse: 56.4601 - mae: 5.7651 - mape: 13.0351 - val_loss: 127.7435 - val_mse: 127.7435 - val_mae: 8.6286 - val_mape: 20.6197\n",
            "\n",
            "Epoch 00014: val_mse did not improve from 105.51492\n",
            "Epoch 15/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 51.7168 - mse: 51.7168 - mae: 5.5287 - mape: 12.5064 - val_loss: 140.9926 - val_mse: 140.9926 - val_mae: 8.9829 - val_mape: 21.3719\n",
            "\n",
            "Epoch 00015: val_mse did not improve from 105.51492\n",
            "Epoch 16/100\n",
            "295/295 [==============================] - 89s 303ms/step - loss: 51.4434 - mse: 51.4434 - mae: 5.5202 - mape: 12.5193 - val_loss: 161.9867 - val_mse: 161.9867 - val_mae: 9.6080 - val_mape: 22.7236\n",
            "\n",
            "Epoch 00016: val_mse did not improve from 105.51492\n",
            "Epoch 17/100\n",
            "295/295 [==============================] - 90s 305ms/step - loss: 52.9105 - mse: 52.9105 - mae: 5.6198 - mape: 12.7177 - val_loss: 118.7853 - val_mse: 118.7853 - val_mae: 8.2183 - val_mape: 19.0920\n",
            "\n",
            "Epoch 00017: val_mse did not improve from 105.51492\n",
            "Epoch 18/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 48.9512 - mse: 48.9512 - mae: 5.3803 - mape: 12.1544 - val_loss: 119.6397 - val_mse: 119.6397 - val_mae: 8.1675 - val_mape: 18.7031\n",
            "\n",
            "Epoch 00018: val_mse did not improve from 105.51492\n",
            "Epoch 19/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 44.0064 - mse: 44.0064 - mae: 5.1012 - mape: 11.5298 - val_loss: 123.9431 - val_mse: 123.9431 - val_mae: 8.4703 - val_mape: 20.1413\n",
            "\n",
            "Epoch 00019: val_mse did not improve from 105.51492\n",
            "Epoch 20/100\n",
            "295/295 [==============================] - 89s 303ms/step - loss: 41.4172 - mse: 41.4172 - mae: 4.9574 - mape: 11.2141 - val_loss: 137.7346 - val_mse: 137.7346 - val_mae: 8.8314 - val_mape: 20.5407\n",
            "\n",
            "Epoch 00020: val_mse did not improve from 105.51492\n",
            "Epoch 21/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 44.4626 - mse: 44.4626 - mae: 5.1109 - mape: 11.4989 - val_loss: 135.9256 - val_mse: 135.9256 - val_mae: 8.7099 - val_mape: 20.4504\n",
            "\n",
            "Epoch 00021: val_mse did not improve from 105.51492\n",
            "Epoch 22/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 56.3745 - mse: 56.3745 - mae: 5.7124 - mape: 12.6056 - val_loss: 137.0096 - val_mse: 137.0096 - val_mae: 8.7496 - val_mape: 19.9161\n",
            "\n",
            "Epoch 00022: val_mse did not improve from 105.51492\n",
            "Epoch 23/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 56.8880 - mse: 56.8880 - mae: 5.7061 - mape: 12.4476 - val_loss: 120.1998 - val_mse: 120.1998 - val_mae: 8.3374 - val_mape: 19.7517\n",
            "\n",
            "Epoch 00023: val_mse did not improve from 105.51492\n",
            "Epoch 24/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 42.5813 - mse: 42.5813 - mae: 4.9831 - mape: 11.0965 - val_loss: 129.0289 - val_mse: 129.0289 - val_mae: 8.6726 - val_mape: 20.6769\n",
            "\n",
            "Epoch 00024: val_mse did not improve from 105.51492\n",
            "Epoch 25/100\n",
            "295/295 [==============================] - 90s 305ms/step - loss: 37.4231 - mse: 37.4231 - mae: 4.6992 - mape: 10.5589 - val_loss: 118.7216 - val_mse: 118.7216 - val_mae: 8.2917 - val_mape: 19.7195\n",
            "\n",
            "Epoch 00025: val_mse did not improve from 105.51492\n",
            "Epoch 26/100\n",
            "295/295 [==============================] - 90s 305ms/step - loss: 34.1684 - mse: 34.1684 - mae: 4.4963 - mape: 10.1125 - val_loss: 120.8392 - val_mse: 120.8392 - val_mae: 8.4415 - val_mape: 20.5138\n",
            "\n",
            "Epoch 00026: val_mse did not improve from 105.51492\n",
            "Epoch 27/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 32.1114 - mse: 32.1114 - mae: 4.3787 - mape: 9.9379 - val_loss: 130.3882 - val_mse: 130.3882 - val_mae: 8.7511 - val_mape: 20.6545\n",
            "\n",
            "Epoch 00027: val_mse did not improve from 105.51492\n",
            "Epoch 28/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 30.9770 - mse: 30.9770 - mae: 4.2968 - mape: 9.6869 - val_loss: 124.4082 - val_mse: 124.4082 - val_mae: 8.4770 - val_mape: 20.0003\n",
            "\n",
            "Epoch 00028: val_mse did not improve from 105.51492\n",
            "Epoch 29/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 29.8880 - mse: 29.8880 - mae: 4.2327 - mape: 9.5793 - val_loss: 129.5917 - val_mse: 129.5917 - val_mae: 8.7118 - val_mape: 21.3224\n",
            "\n",
            "Epoch 00029: val_mse did not improve from 105.51492\n",
            "Epoch 30/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 29.1708 - mse: 29.1708 - mae: 4.1888 - mape: 9.4741 - val_loss: 154.4309 - val_mse: 154.4309 - val_mae: 9.3246 - val_mape: 21.4949\n",
            "\n",
            "Epoch 00030: val_mse did not improve from 105.51492\n",
            "Epoch 31/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 29.4573 - mse: 29.4573 - mae: 4.1920 - mape: 9.4569 - val_loss: 123.2071 - val_mse: 123.2071 - val_mae: 8.4214 - val_mape: 20.3353\n",
            "\n",
            "Epoch 00031: val_mse did not improve from 105.51492\n",
            "Epoch 32/100\n",
            "295/295 [==============================] - 90s 305ms/step - loss: 28.1917 - mse: 28.1917 - mae: 4.1075 - mape: 9.2755 - val_loss: 128.0162 - val_mse: 128.0162 - val_mae: 8.6491 - val_mape: 20.8076\n",
            "\n",
            "Epoch 00032: val_mse did not improve from 105.51492\n",
            "Epoch 33/100\n",
            "295/295 [==============================] - 90s 305ms/step - loss: 27.4329 - mse: 27.4329 - mae: 4.0534 - mape: 9.1875 - val_loss: 122.1158 - val_mse: 122.1158 - val_mae: 8.4061 - val_mape: 20.2551\n",
            "\n",
            "Epoch 00033: val_mse did not improve from 105.51492\n",
            "Epoch 34/100\n",
            "295/295 [==============================] - 91s 308ms/step - loss: 26.8802 - mse: 26.8802 - mae: 4.0058 - mape: 9.0430 - val_loss: 112.4102 - val_mse: 112.4102 - val_mae: 8.0222 - val_mape: 19.0412\n",
            "\n",
            "Epoch 00034: val_mse did not improve from 105.51492\n",
            "Epoch 35/100\n",
            "295/295 [==============================] - 92s 311ms/step - loss: 26.3662 - mse: 26.3662 - mae: 3.9706 - mape: 8.9627 - val_loss: 129.0659 - val_mse: 129.0659 - val_mae: 8.7231 - val_mape: 21.2829\n",
            "\n",
            "Epoch 00035: val_mse did not improve from 105.51492\n",
            "Epoch 36/100\n",
            "295/295 [==============================] - 90s 305ms/step - loss: 25.3611 - mse: 25.3611 - mae: 3.8939 - mape: 8.7883 - val_loss: 133.0656 - val_mse: 133.0656 - val_mae: 8.7954 - val_mape: 21.3771\n",
            "\n",
            "Epoch 00036: val_mse did not improve from 105.51492\n",
            "Epoch 37/100\n",
            "295/295 [==============================] - 90s 305ms/step - loss: 26.1654 - mse: 26.1654 - mae: 3.9676 - mape: 8.9745 - val_loss: 149.1004 - val_mse: 149.1004 - val_mae: 9.3351 - val_mape: 22.6676\n",
            "\n",
            "Epoch 00037: val_mse did not improve from 105.51492\n",
            "Epoch 38/100\n",
            "295/295 [==============================] - 90s 305ms/step - loss: 28.8115 - mse: 28.8115 - mae: 4.1775 - mape: 9.4724 - val_loss: 117.0374 - val_mse: 117.0374 - val_mae: 8.0750 - val_mape: 18.8725\n",
            "\n",
            "Epoch 00038: val_mse did not improve from 105.51492\n",
            "Epoch 39/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 26.7764 - mse: 26.7764 - mae: 4.0125 - mape: 9.0813 - val_loss: 133.9477 - val_mse: 133.9477 - val_mae: 8.6891 - val_mape: 20.1172\n",
            "\n",
            "Epoch 00039: val_mse did not improve from 105.51492\n",
            "Epoch 40/100\n",
            "295/295 [==============================] - 90s 307ms/step - loss: 24.3038 - mse: 24.3038 - mae: 3.8200 - mape: 8.6551 - val_loss: 144.7501 - val_mse: 144.7501 - val_mae: 8.9664 - val_mape: 20.2548\n",
            "\n",
            "Epoch 00040: val_mse did not improve from 105.51492\n",
            "Epoch 41/100\n",
            "295/295 [==============================] - 90s 305ms/step - loss: 24.1004 - mse: 24.1004 - mae: 3.8010 - mape: 8.5695 - val_loss: 124.0120 - val_mse: 124.0120 - val_mae: 8.2885 - val_mape: 18.7968\n",
            "\n",
            "Epoch 00041: val_mse did not improve from 105.51492\n",
            "Epoch 42/100\n",
            "295/295 [==============================] - 90s 305ms/step - loss: 23.4194 - mse: 23.4194 - mae: 3.7303 - mape: 8.4122 - val_loss: 153.0329 - val_mse: 153.0329 - val_mae: 9.0025 - val_mape: 19.3113\n",
            "\n",
            "Epoch 00042: val_mse did not improve from 105.51492\n",
            "Epoch 43/100\n",
            "295/295 [==============================] - 90s 305ms/step - loss: 25.6755 - mse: 25.6755 - mae: 3.8999 - mape: 8.7468 - val_loss: 247.6869 - val_mse: 247.6869 - val_mae: 11.3003 - val_mape: 23.4169\n",
            "\n",
            "Epoch 00043: val_mse did not improve from 105.51492\n",
            "Epoch 44/100\n",
            "295/295 [==============================] - 90s 305ms/step - loss: 34.2808 - mse: 34.2808 - mae: 4.4263 - mape: 9.5739 - val_loss: 157.9557 - val_mse: 157.9557 - val_mae: 9.2327 - val_mape: 20.4044\n",
            "\n",
            "Epoch 00044: val_mse did not improve from 105.51492\n",
            "Epoch 45/100\n",
            "295/295 [==============================] - 90s 305ms/step - loss: 28.0816 - mse: 28.0816 - mae: 4.0692 - mape: 9.0126 - val_loss: 131.0856 - val_mse: 131.0856 - val_mae: 8.5650 - val_mape: 19.6747\n",
            "\n",
            "Epoch 00045: val_mse did not improve from 105.51492\n",
            "Epoch 46/100\n",
            "295/295 [==============================] - 90s 304ms/step - loss: 23.8640 - mse: 23.8640 - mae: 3.7582 - mape: 8.4216 - val_loss: 161.3125 - val_mse: 161.3125 - val_mae: 9.2854 - val_mape: 20.3113\n",
            "\n",
            "Epoch 00046: val_mse did not improve from 105.51492\n",
            "Epoch 47/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 21.7875 - mse: 21.7875 - mae: 3.6075 - mape: 8.1171 - val_loss: 145.9334 - val_mse: 145.9334 - val_mae: 8.8788 - val_mape: 19.7338\n",
            "\n",
            "Epoch 00047: val_mse did not improve from 105.51492\n",
            "Epoch 48/100\n",
            "295/295 [==============================] - 90s 305ms/step - loss: 21.3870 - mse: 21.3870 - mae: 3.5827 - mape: 8.0610 - val_loss: 118.5112 - val_mse: 118.5112 - val_mae: 8.2393 - val_mape: 19.4260\n",
            "\n",
            "Epoch 00048: val_mse did not improve from 105.51492\n",
            "Epoch 49/100\n",
            "295/295 [==============================] - 91s 307ms/step - loss: 21.7270 - mse: 21.7270 - mae: 3.6115 - mape: 8.0949 - val_loss: 122.2881 - val_mse: 122.2881 - val_mae: 8.2079 - val_mape: 18.4971\n",
            "\n",
            "Epoch 00049: val_mse did not improve from 105.51492\n",
            "Epoch 50/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 20.9125 - mse: 20.9125 - mae: 3.5502 - mape: 8.0201 - val_loss: 118.5063 - val_mse: 118.5063 - val_mae: 8.2072 - val_mape: 19.0797\n",
            "\n",
            "Epoch 00050: val_mse did not improve from 105.51492\n",
            "Epoch 51/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 19.9125 - mse: 19.9125 - mae: 3.4583 - mape: 7.8293 - val_loss: 109.0452 - val_mse: 109.0452 - val_mae: 7.8612 - val_mape: 18.5105\n",
            "\n",
            "Epoch 00051: val_mse did not improve from 105.51492\n",
            "Epoch 52/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 19.4535 - mse: 19.4535 - mae: 3.4201 - mape: 7.7716 - val_loss: 120.4544 - val_mse: 120.4544 - val_mae: 8.2015 - val_mape: 18.8645\n",
            "\n",
            "Epoch 00052: val_mse did not improve from 105.51492\n",
            "Epoch 53/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 19.0723 - mse: 19.0723 - mae: 3.3800 - mape: 7.6102 - val_loss: 120.7429 - val_mse: 120.7429 - val_mae: 8.2585 - val_mape: 19.3152\n",
            "\n",
            "Epoch 00053: val_mse did not improve from 105.51492\n",
            "Epoch 54/100\n",
            "295/295 [==============================] - 90s 307ms/step - loss: 19.0903 - mse: 19.0903 - mae: 3.3909 - mape: 7.6505 - val_loss: 110.8210 - val_mse: 110.8210 - val_mae: 7.8493 - val_mape: 18.0609\n",
            "\n",
            "Epoch 00054: val_mse did not improve from 105.51492\n",
            "Epoch 55/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 18.4044 - mse: 18.4044 - mae: 3.3292 - mape: 7.5564 - val_loss: 109.9225 - val_mse: 109.9225 - val_mae: 7.8854 - val_mape: 18.4643\n",
            "\n",
            "Epoch 00055: val_mse did not improve from 105.51492\n",
            "Epoch 56/100\n",
            "295/295 [==============================] - 91s 307ms/step - loss: 18.3023 - mse: 18.3023 - mae: 3.3151 - mape: 7.5372 - val_loss: 121.0851 - val_mse: 121.0851 - val_mae: 8.2726 - val_mape: 19.1963\n",
            "\n",
            "Epoch 00056: val_mse did not improve from 105.51492\n",
            "Epoch 57/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 18.1836 - mse: 18.1836 - mae: 3.3057 - mape: 7.4799 - val_loss: 120.4465 - val_mse: 120.4465 - val_mae: 8.2419 - val_mape: 19.2139\n",
            "\n",
            "Epoch 00057: val_mse did not improve from 105.51492\n",
            "Epoch 58/100\n",
            "295/295 [==============================] - 90s 305ms/step - loss: 17.4392 - mse: 17.4392 - mae: 3.2462 - mape: 7.3989 - val_loss: 122.9251 - val_mse: 122.9251 - val_mae: 8.2747 - val_mape: 19.0018\n",
            "\n",
            "Epoch 00058: val_mse did not improve from 105.51492\n",
            "Epoch 59/100\n",
            "295/295 [==============================] - 90s 307ms/step - loss: 17.6730 - mse: 17.6730 - mae: 3.2469 - mape: 7.3697 - val_loss: 119.0703 - val_mse: 119.0703 - val_mae: 8.1758 - val_mape: 18.8149\n",
            "\n",
            "Epoch 00059: val_mse did not improve from 105.51492\n",
            "Epoch 60/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 16.6171 - mse: 16.6171 - mae: 3.1655 - mape: 7.2033 - val_loss: 121.4889 - val_mse: 121.4889 - val_mae: 8.1882 - val_mape: 18.5896\n",
            "\n",
            "Epoch 00060: val_mse did not improve from 105.51492\n",
            "Epoch 61/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 17.0535 - mse: 17.0535 - mae: 3.2072 - mape: 7.2923 - val_loss: 110.5359 - val_mse: 110.5359 - val_mae: 7.9161 - val_mape: 18.4839\n",
            "\n",
            "Epoch 00061: val_mse did not improve from 105.51492\n",
            "Epoch 62/100\n",
            "295/295 [==============================] - 91s 307ms/step - loss: 16.3334 - mse: 16.3334 - mae: 3.1426 - mape: 7.1702 - val_loss: 112.4021 - val_mse: 112.4021 - val_mae: 7.9332 - val_mape: 18.4618\n",
            "\n",
            "Epoch 00062: val_mse did not improve from 105.51492\n",
            "Epoch 63/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 16.1558 - mse: 16.1558 - mae: 3.1297 - mape: 7.1632 - val_loss: 112.3765 - val_mse: 112.3765 - val_mae: 7.9522 - val_mape: 18.4889\n",
            "\n",
            "Epoch 00063: val_mse did not improve from 105.51492\n",
            "Epoch 64/100\n",
            "295/295 [==============================] - 91s 309ms/step - loss: 15.8823 - mse: 15.8823 - mae: 3.1023 - mape: 7.1000 - val_loss: 108.2737 - val_mse: 108.2737 - val_mae: 7.8886 - val_mape: 18.6164\n",
            "\n",
            "Epoch 00064: val_mse did not improve from 105.51492\n",
            "Epoch 65/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 15.7532 - mse: 15.7532 - mae: 3.0844 - mape: 7.0653 - val_loss: 107.1688 - val_mse: 107.1688 - val_mae: 7.8115 - val_mape: 18.0804\n",
            "\n",
            "Epoch 00065: val_mse did not improve from 105.51492\n",
            "Epoch 66/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 15.9913 - mse: 15.9913 - mae: 3.1212 - mape: 7.1970 - val_loss: 109.5134 - val_mse: 109.5134 - val_mae: 7.8329 - val_mape: 18.1855\n",
            "\n",
            "Epoch 00066: val_mse did not improve from 105.51492\n",
            "Epoch 67/100\n",
            "295/295 [==============================] - 90s 307ms/step - loss: 15.6813 - mse: 15.6813 - mae: 3.0957 - mape: 7.1143 - val_loss: 110.3983 - val_mse: 110.3983 - val_mae: 7.8391 - val_mape: 17.9161\n",
            "\n",
            "Epoch 00067: val_mse did not improve from 105.51492\n",
            "Epoch 68/100\n",
            "295/295 [==============================] - 90s 307ms/step - loss: 15.3401 - mse: 15.3401 - mae: 3.0527 - mape: 7.0198 - val_loss: 108.4034 - val_mse: 108.4034 - val_mae: 7.8569 - val_mape: 18.5730\n",
            "\n",
            "Epoch 00068: val_mse did not improve from 105.51492\n",
            "Epoch 69/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 15.2168 - mse: 15.2168 - mae: 3.0448 - mape: 6.9933 - val_loss: 110.2729 - val_mse: 110.2729 - val_mae: 7.9955 - val_mape: 19.2926\n",
            "\n",
            "Epoch 00069: val_mse did not improve from 105.51492\n",
            "Epoch 70/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 15.4476 - mse: 15.4476 - mae: 3.0607 - mape: 7.0269 - val_loss: 109.3465 - val_mse: 109.3465 - val_mae: 7.9193 - val_mape: 19.1370\n",
            "\n",
            "Epoch 00070: val_mse did not improve from 105.51492\n",
            "Epoch 71/100\n",
            "295/295 [==============================] - 91s 308ms/step - loss: 16.5354 - mse: 16.5354 - mae: 3.1505 - mape: 7.1080 - val_loss: 119.9368 - val_mse: 119.9368 - val_mae: 8.4337 - val_mape: 20.8101\n",
            "\n",
            "Epoch 00071: val_mse did not improve from 105.51492\n",
            "Epoch 72/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 23.5511 - mse: 23.5511 - mae: 3.7197 - mape: 8.1948 - val_loss: 116.0820 - val_mse: 116.0820 - val_mae: 8.2879 - val_mape: 20.3010\n",
            "\n",
            "Epoch 00072: val_mse did not improve from 105.51492\n",
            "Epoch 73/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 23.5411 - mse: 23.5411 - mae: 3.6981 - mape: 8.1489 - val_loss: 105.1074 - val_mse: 105.1074 - val_mae: 7.8095 - val_mape: 18.7485\n",
            "\n",
            "Epoch 00073: val_mse improved from 105.51492 to 105.10744, saving model to /content/drive/MyDrive/TeamHurricane/models/best-2021-02-19 20:07:49.h5\n",
            "Epoch 74/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 15.6352 - mse: 15.6352 - mae: 3.0757 - mape: 6.9650 - val_loss: 107.0911 - val_mse: 107.0911 - val_mae: 7.9800 - val_mape: 19.4584\n",
            "\n",
            "Epoch 00074: val_mse did not improve from 105.10744\n",
            "Epoch 75/100\n",
            "295/295 [==============================] - 90s 307ms/step - loss: 14.4010 - mse: 14.4010 - mae: 2.9573 - mape: 6.8077 - val_loss: 105.8227 - val_mse: 105.8227 - val_mae: 7.8953 - val_mape: 19.1319\n",
            "\n",
            "Epoch 00075: val_mse did not improve from 105.10744\n",
            "Epoch 76/100\n",
            "295/295 [==============================] - 91s 307ms/step - loss: 13.9577 - mse: 13.9577 - mae: 2.9141 - mape: 6.6815 - val_loss: 105.3926 - val_mse: 105.3926 - val_mae: 7.8727 - val_mape: 19.1545\n",
            "\n",
            "Epoch 00076: val_mse did not improve from 105.10744\n",
            "Epoch 77/100\n",
            "295/295 [==============================] - 91s 307ms/step - loss: 14.1378 - mse: 14.1378 - mae: 2.9303 - mape: 6.7143 - val_loss: 115.4237 - val_mse: 115.4237 - val_mae: 8.3001 - val_mape: 20.4057\n",
            "\n",
            "Epoch 00077: val_mse did not improve from 105.10744\n",
            "Epoch 78/100\n",
            "295/295 [==============================] - 91s 307ms/step - loss: 14.7347 - mse: 14.7347 - mae: 2.9885 - mape: 6.8508 - val_loss: 111.2524 - val_mse: 111.2524 - val_mae: 8.1032 - val_mape: 19.8196\n",
            "\n",
            "Epoch 00078: val_mse did not improve from 105.10744\n",
            "Epoch 79/100\n",
            "295/295 [==============================] - 90s 306ms/step - loss: 15.6928 - mse: 15.6928 - mae: 3.0685 - mape: 7.0057 - val_loss: 118.8573 - val_mse: 118.8573 - val_mae: 8.4541 - val_mape: 21.0343\n",
            "\n",
            "Epoch 00079: val_mse did not improve from 105.10744\n",
            "Epoch 80/100\n",
            "295/295 [==============================] - 91s 308ms/step - loss: 17.9264 - mse: 17.9264 - mae: 3.2502 - mape: 7.2436 - val_loss: 120.3089 - val_mse: 120.3089 - val_mae: 8.4702 - val_mape: 20.5892\n",
            "\n",
            "Epoch 00080: val_mse did not improve from 105.10744\n",
            "Epoch 81/100\n",
            "295/295 [==============================] - 90s 307ms/step - loss: 16.3417 - mse: 16.3417 - mae: 3.1158 - mape: 7.0193 - val_loss: 108.4000 - val_mse: 108.4000 - val_mae: 7.9143 - val_mape: 18.7403\n",
            "\n",
            "Epoch 00081: val_mse did not improve from 105.10744\n",
            "Epoch 82/100\n",
            "295/295 [==============================] - 91s 308ms/step - loss: 14.2525 - mse: 14.2525 - mae: 2.9244 - mape: 6.6696 - val_loss: 110.3446 - val_mse: 110.3446 - val_mae: 8.1076 - val_mape: 19.7020\n",
            "\n",
            "Epoch 00082: val_mse did not improve from 105.10744\n",
            "Epoch 83/100\n",
            "295/295 [==============================] - 91s 307ms/step - loss: 14.5183 - mse: 14.5183 - mae: 2.9641 - mape: 6.7670 - val_loss: 115.9808 - val_mse: 115.9808 - val_mae: 8.1385 - val_mape: 18.8983\n",
            "\n",
            "Epoch 00083: val_mse did not improve from 105.10744\n",
            "Epoch 84/100\n",
            "295/295 [==============================] - 91s 308ms/step - loss: 15.4350 - mse: 15.4350 - mae: 3.0346 - mape: 6.8423 - val_loss: 112.0163 - val_mse: 112.0163 - val_mae: 7.9472 - val_mape: 18.2531\n",
            "\n",
            "Epoch 00084: val_mse did not improve from 105.10744\n",
            "Epoch 85/100\n",
            "295/295 [==============================] - 90s 307ms/step - loss: 16.1730 - mse: 16.1730 - mae: 3.1045 - mape: 6.9535 - val_loss: 118.6486 - val_mse: 118.6486 - val_mae: 8.1055 - val_mape: 18.2645\n",
            "\n",
            "Epoch 00085: val_mse did not improve from 105.10744\n",
            "Epoch 86/100\n",
            "295/295 [==============================] - 90s 307ms/step - loss: 16.0223 - mse: 16.0223 - mae: 3.1220 - mape: 7.0451 - val_loss: 106.5072 - val_mse: 106.5072 - val_mae: 7.8112 - val_mape: 18.3898\n",
            "\n",
            "Epoch 00086: val_mse did not improve from 105.10744\n",
            "Epoch 87/100\n",
            "295/295 [==============================] - 91s 308ms/step - loss: 14.5678 - mse: 14.5678 - mae: 2.9779 - mape: 6.7550 - val_loss: 113.2961 - val_mse: 113.2961 - val_mae: 7.9968 - val_mape: 18.5360\n",
            "\n",
            "Epoch 00087: val_mse did not improve from 105.10744\n",
            "Epoch 88/100\n",
            "295/295 [==============================] - 91s 307ms/step - loss: 13.0386 - mse: 13.0386 - mae: 2.8136 - mape: 6.4348 - val_loss: 109.6258 - val_mse: 109.6258 - val_mae: 7.9013 - val_mape: 18.5677\n",
            "\n",
            "Epoch 00088: val_mse did not improve from 105.10744\n",
            "Epoch 89/100\n",
            "295/295 [==============================] - 91s 307ms/step - loss: 12.4419 - mse: 12.4419 - mae: 2.7586 - mape: 6.3417 - val_loss: 119.1984 - val_mse: 119.1984 - val_mae: 8.3209 - val_mape: 19.6796\n",
            "\n",
            "Epoch 00089: val_mse did not improve from 105.10744\n",
            "Epoch 90/100\n",
            "295/295 [==============================] - 90s 307ms/step - loss: 12.2251 - mse: 12.2251 - mae: 2.7297 - mape: 6.2722 - val_loss: 103.7295 - val_mse: 103.7295 - val_mae: 7.7571 - val_mape: 18.7708\n",
            "\n",
            "Epoch 00090: val_mse improved from 105.10744 to 103.72948, saving model to /content/drive/MyDrive/TeamHurricane/models/best-2021-02-19 20:07:49.h5\n",
            "Epoch 91/100\n",
            "295/295 [==============================] - 91s 307ms/step - loss: 12.3293 - mse: 12.3293 - mae: 2.7455 - mape: 6.3111 - val_loss: 107.4389 - val_mse: 107.4389 - val_mae: 7.9405 - val_mape: 19.2703\n",
            "\n",
            "Epoch 00091: val_mse did not improve from 103.72948\n",
            "Epoch 92/100\n",
            "295/295 [==============================] - 91s 307ms/step - loss: 12.8874 - mse: 12.8874 - mae: 2.7967 - mape: 6.3844 - val_loss: 114.1607 - val_mse: 114.1607 - val_mae: 8.2492 - val_mape: 20.3364\n",
            "\n",
            "Epoch 00092: val_mse did not improve from 103.72948\n",
            "Epoch 93/100\n",
            "295/295 [==============================] - 90s 307ms/step - loss: 15.1390 - mse: 15.1390 - mae: 3.0085 - mape: 6.7967 - val_loss: 121.1651 - val_mse: 121.1651 - val_mae: 8.5664 - val_mape: 21.3701\n",
            "\n",
            "Epoch 00093: val_mse did not improve from 103.72948\n",
            "Epoch 94/100\n",
            "295/295 [==============================] - 91s 307ms/step - loss: 20.9846 - mse: 20.9846 - mae: 3.5226 - mape: 7.8408 - val_loss: 112.7301 - val_mse: 112.7301 - val_mae: 8.2033 - val_mape: 19.9635\n",
            "\n",
            "Epoch 00094: val_mse did not improve from 103.72948\n",
            "Epoch 95/100\n",
            "295/295 [==============================] - 91s 308ms/step - loss: 14.8495 - mse: 14.8495 - mae: 2.9918 - mape: 6.8005 - val_loss: 115.7761 - val_mse: 115.7761 - val_mae: 8.1523 - val_mape: 19.2066\n",
            "\n",
            "Epoch 00095: val_mse did not improve from 103.72948\n",
            "Epoch 96/100\n",
            "295/295 [==============================] - 91s 308ms/step - loss: 12.6273 - mse: 12.6273 - mae: 2.7685 - mape: 6.3512 - val_loss: 106.3574 - val_mse: 106.3574 - val_mae: 7.7966 - val_mape: 18.1133\n",
            "\n",
            "Epoch 00096: val_mse did not improve from 103.72948\n",
            "Epoch 97/100\n",
            "295/295 [==============================] - 91s 307ms/step - loss: 12.5877 - mse: 12.5877 - mae: 2.7553 - mape: 6.3039 - val_loss: 109.6339 - val_mse: 109.6339 - val_mae: 7.9531 - val_mape: 18.7354\n",
            "\n",
            "Epoch 00097: val_mse did not improve from 103.72948\n",
            "Epoch 98/100\n",
            "295/295 [==============================] - 91s 308ms/step - loss: 13.0419 - mse: 13.0419 - mae: 2.7995 - mape: 6.3433 - val_loss: 112.7705 - val_mse: 112.7705 - val_mae: 7.9613 - val_mape: 18.2479\n",
            "\n",
            "Epoch 00098: val_mse did not improve from 103.72948\n",
            "Epoch 99/100\n",
            "295/295 [==============================] - 91s 308ms/step - loss: 14.3158 - mse: 14.3158 - mae: 2.9184 - mape: 6.5851 - val_loss: 116.4943 - val_mse: 116.4943 - val_mae: 8.0344 - val_mape: 18.2811\n",
            "\n",
            "Epoch 00099: val_mse did not improve from 103.72948\n",
            "Epoch 100/100\n",
            "295/295 [==============================] - 91s 308ms/step - loss: 15.1013 - mse: 15.1013 - mae: 3.0037 - mape: 6.7544 - val_loss: 111.9729 - val_mse: 111.9729 - val_mae: 7.9688 - val_mape: 18.3335\n",
            "\n",
            "Epoch 00100: val_mse did not improve from 103.72948\n",
            "just saved model\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Td8o2Wsqq2W3",
        "outputId": "947a200d-4836-4899-cf59-5809ddcb8899"
      },
      "source": [
        "model.save(final_filename)\n",
        "print(\"just saved model\" )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "just saved model\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qtuUi6rVcMd5"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}