weather_LSTM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vscAAfT9EaMs"
      },
      "source": [
        "# Load libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FxT_lpD30Squ"
      },
      "outputs": [],
      "source": [
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import PIL\n",
        "import h5py\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers.convolutional import Conv3D\n",
        "from keras.layers import ConvLSTM2D\n",
        "from keras.layers.convolutional import Conv2D\n",
        "from keras.layers import BatchNormalization\n",
        "from keras.layers import LeakyReLU\n",
        "from PIL import Image\n",
        "import glob\n",
        "from IPython import display\n",
        "import matplotlib.animation as animation\n",
        "matplotlib.use(\"Agg\")\n",
        "import io\n",
        "import imageio\n",
        "from IPython.display import Image, display\n",
        "from ipywidgets import widgets, Layout, HBox\n",
        "from PIL import Image\n",
        "from IPython.display import HTML\n",
        "from PIL import Image, ImageDraw\n",
        "import sklearn.model_selection as sk"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZwlQPyp7EN1K"
      },
      "source": [
        "# Load dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ksTZmPYS4kZs",
        "outputId": "73c89cd3-6bf8-4fe2-a5d0-cbfdc7b0ec9f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ],
      "source": [
        "def create_dataset_from_raw(directory_path, resize_to):\n",
        "    resize_width = resize_to[0]\n",
        "    resize_height = resize_to[1]\n",
        "    batch_names = [directory_path + name for name in os.listdir(directory_path) if os.path.isdir(os.path.join(directory_path, name))]\n",
        "    dataset = np.zeros(shape=(len(batch_names),36,resize_height,resize_width)) # (samples, filters, rows = height, cols = width)\n",
        "\n",
        "    for batch_idx,batch in enumerate(batch_names):\n",
        "        files = [x for x in os.listdir(batch) if x != '.DS_Store']\n",
        "        files.sort()\n",
        "        crn_batch = np.zeros(shape=(36, resize_height, resize_width)) \n",
        "        for (idx,raster) in enumerate(files):\n",
        "            fn = batch + '/' + raster\n",
        "            img = h5py.File(fn)\n",
        "            original_image = np.array(img[\"image1\"][\"image_data\"]).astype(float)\n",
        "            img = Image.fromarray(original_image)\n",
        "            # note that here it is (width, heigh) while in the tensor is in (rows = height, cols = width)\n",
        "            img = img.resize(size=(resize_width, resize_height)) \n",
        "            original_image = np.array(img)\n",
        "            original_image = original_image / 255.0\n",
        "            crn_batch[idx] = original_image\n",
        "        dataset[batch_idx] = crn_batch\n",
        "        print(\"Importing batch: \" + str(batch_idx))\n",
        "    return dataset\n",
        "\n",
        "def create_shifted_frames(data):\n",
        "    x = data[:, 0 : 18, :, :]\n",
        "    y = data[:, 18 : 36, :, :]\n",
        "    return x, y\n",
        "\n",
        "dataset = create_dataset_from_raw('./drive/MyDrive/raw/', resize_to=(315,344))\n",
        "dataset = np.expand_dims(dataset, axis=-1)\n",
        "dataset_x, dataset_y = create_shifted_frames(dataset)\n",
        "X_train, X_val, y_train, y_val = sk.train_test_split(dataset_x,dataset_y,test_size=0.2, random_state = 42)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AfpmKRug4pqW"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nW_hY-vhEX9G",
        "outputId": "ca4449cb-f08f-482a-af04-b71ed0fcd205"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv_lstm2d_10 (ConvLSTM2D)  (None, 18, 344, 315, 64)  815616   \n",
            "                                                                 \n",
            " batch_normalization_10 (Bat  (None, 18, 344, 315, 64)  256      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " conv_lstm2d_11 (ConvLSTM2D)  (None, 18, 344, 315, 64)  819456   \n",
            "                                                                 \n",
            " batch_normalization_11 (Bat  (None, 18, 344, 315, 64)  256      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " conv_lstm2d_12 (ConvLSTM2D)  (None, 18, 344, 315, 64)  295168   \n",
            "                                                                 \n",
            " batch_normalization_12 (Bat  (None, 18, 344, 315, 64)  256      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " conv_lstm2d_13 (ConvLSTM2D)  (None, 18, 344, 315, 64)  33024    \n",
            "                                                                 \n",
            " batch_normalization_13 (Bat  (None, 18, 344, 315, 64)  256      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " conv3d_3 (Conv3D)           (None, 18, 344, 315, 1)   1729      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,966,017\n",
            "Trainable params: 1,965,505\n",
            "Non-trainable params: 512\n",
            "_________________________________________________________________\n",
            "None\n",
            "Epoch 1/25\n",
            "123/123 [==============================] - 129s 984ms/step - loss: 0.5905 - val_loss: 0.7124\n",
            "Epoch 2/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.4310 - val_loss: 0.6991\n",
            "Epoch 3/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.3541 - val_loss: 0.5808\n",
            "Epoch 4/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.3037 - val_loss: 0.4054\n",
            "Epoch 5/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.2656 - val_loss: 0.3379\n",
            "Epoch 6/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.2342 - val_loss: 0.2956\n",
            "Epoch 7/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.2096 - val_loss: 0.2320\n",
            "Epoch 8/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1907 - val_loss: 0.1993\n",
            "Epoch 9/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1763 - val_loss: 0.1806\n",
            "Epoch 10/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1644 - val_loss: 0.1675\n",
            "Epoch 11/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1541 - val_loss: 0.1561\n",
            "Epoch 12/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1461 - val_loss: 0.1480\n",
            "Epoch 13/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1396 - val_loss: 0.1402\n",
            "Epoch 14/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1341 - val_loss: 0.1332\n",
            "Epoch 15/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1295 - val_loss: 0.1289\n",
            "Epoch 16/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1257 - val_loss: 0.1242\n",
            "Epoch 17/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1225 - val_loss: 0.1207\n",
            "Epoch 18/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1199 - val_loss: 0.1182\n",
            "Epoch 19/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1177 - val_loss: 0.1163\n",
            "Epoch 20/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1158 - val_loss: 0.1143\n",
            "Epoch 21/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1141 - val_loss: 0.1127\n",
            "Epoch 22/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1127 - val_loss: 0.1110\n",
            "Epoch 23/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1113 - val_loss: 0.1096\n",
            "Epoch 24/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1103 - val_loss: 0.1087\n",
            "Epoch 25/25\n",
            "123/123 [==============================] - 119s 971ms/step - loss: 0.1093 - val_loss: 0.1077\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, leaky_re_lu_10_layer_call_fn, leaky_re_lu_10_layer_call_and_return_conditional_losses, leaky_re_lu_11_layer_call_fn, leaky_re_lu_11_layer_call_and_return_conditional_losses while saving (showing 5 of 9). These functions will not be directly callable after loading.\n"
          ]
        }
      ],
      "source": [
        "from keras.layers.core.dense import Dense\n",
        "def create_model():\n",
        "    model = Sequential()\n",
        "    model.add(ConvLSTM2D(filters=64, kernel_size=(7, 7),\n",
        "                    input_shape=(18,344,315,1),\n",
        "                    padding='same',activation=LeakyReLU(alpha=0.01), \n",
        "                    return_sequences=True))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(ConvLSTM2D(filters=64, kernel_size=(5, 5),\n",
        "                    padding='same',activation=LeakyReLU(alpha=0.01), \n",
        "                    return_sequences=True))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(ConvLSTM2D(filters=64, kernel_size=(3, 3),\n",
        "                    padding='same',activation=LeakyReLU(alpha=0.01), \n",
        "                    return_sequences=True))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(ConvLSTM2D(filters=64, kernel_size=(1, 1),\n",
        "                    padding='same',activation=LeakyReLU(alpha=0.01), \n",
        "                    return_sequences=True))\n",
        "    model.add(BatchNormalization())\n",
        "    model.add(Conv3D(filters=1, kernel_size=(3,3,3),\n",
        "                activation='sigmoid',\n",
        "                padding='same', data_format='channels_last'))\n",
        "    return model\n",
        "\n",
        "model = create_model()\n",
        "\n",
        "model.compile(loss='binary_crossentropy', optimizer='adadelta')\n",
        "print(model.summary())\n",
        "# Define modifiable training hyperparameters.\n",
        "epochs = 25\n",
        "batch_size = 1\n",
        "\n",
        "#Fit the model to the training data.\n",
        "model.fit(\n",
        "    X_train,\n",
        "    y_train,\n",
        "    batch_size=batch_size,\n",
        "    epochs=epochs,\n",
        "    validation_data=(X_val, y_val),\n",
        "    verbose=1,\n",
        ")\n",
        "model.save('./drive/MyDrive/model_saved')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4896q5LxPLAR"
      },
      "source": [
        "# Predict values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "7AXGYRWNPCoG",
        "outputId": "1f129b34-8d7d-4214-8998-fcdb32cde855"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 262ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x288 with 36 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# reconstructed_model = keras.models.load_model(\"./drive/MyDrive/model_saved\")\n",
        "# pick a random index from validation dataset\n",
        "random_index = np.random.choice(range(len(X_val)), size=1)\n",
        "test_serie_X = X_val[random_index[0]]\n",
        "test_serie_Y = y_val[random_index[0]]\n",
        "\n",
        "first_frames = test_serie_X\n",
        "original_frames = test_serie_Y\n",
        "# predict the next 18 fames\n",
        "new_prediction = model.predict(np.expand_dims(first_frames, axis=0))\n",
        "new_prediction = np.squeeze(new_prediction, axis=0)\n",
        "\n",
        "fig, axes = plt.subplots(2, 18, figsize=(20, 4))\n",
        "\n",
        "# Plot the original frames.\n",
        "for idx, ax in enumerate(axes[0]):\n",
        "    ax.imshow(np.squeeze(original_frames[idx]), cmap=\"viridis\")\n",
        "    ax.set_title(f\"Frame {idx + 18}\")\n",
        "    ax.axis(\"off\")\n",
        "\n",
        "# Plot the predicted frames.\n",
        "for idx, ax in enumerate(axes[1]):\n",
        "    ax.imshow((new_prediction[idx]).reshape((344,315)), cmap=\"viridis\")\n",
        "    ax.set_title(f\"Frame {idx + 18}\")\n",
        "    ax.axis(\"off\")\n",
        "\n",
        "# Display the figure.\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H91LuZM5Wqjj"
      },
      "source": [
        "# Create animated GIFs for original frames"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dWRR28xAWuGW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 557
        },
        "outputId": "e8d33b91-9059-4270-9aa4-3120633f6e6f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
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              "  Your browser does not support the video tag.\n",
              "</video>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, ax = plt.subplots()\n",
        "original_images = []\n",
        "for f in original_frames:\n",
        "    ax.set_title(f\"Ground Truth\")\n",
        "    ax.axis(\"off\")\n",
        "    crn_f = ax.imshow(np.squeeze(f),cmap='viridis', animated=False)\n",
        "    original_images.append([crn_f])\n",
        "animation_originals = animation.ArtistAnimation(fig, original_images, \n",
        "                                                interval=100, blit=False, \n",
        "                                                repeat_delay=1000)\n",
        "animation_originals.save('./drive/MyDrive/ground_truth.gif', \n",
        "                         writer=animation.PillowWriter(), dpi=100)\n",
        "HTML(animation_originals.to_html5_video())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tfz35LsFwUSo"
      },
      "source": [
        "# Create animated GIF for predicted frames"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 576
        },
        "id": "JJJEGHcdwaJ7",
        "outputId": "fd9d8f93-9b1b-49db-88ce-0bf7e397a692"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "18\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
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              "\">\n",
              "  Your browser does not support the video tag.\n",
              "</video>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, ax = plt.subplots()\n",
        "predicted_images = []\n",
        "for f in new_prediction:\n",
        "    ax.set_title(f\"Ground Truth\")\n",
        "    ax.axis(\"off\")\n",
        "    crn_f = ax.imshow(np.squeeze(f),cmap='viridis', animated=False)\n",
        "    ax.set_title(f\"Predicted frames\")\n",
        "    predicted_images.append([crn_f])\n",
        "print(len(predicted_images))\n",
        "animation_predicted = animation.ArtistAnimation(fig, predicted_images, \n",
        "                                                interval=100, blit=False, \n",
        "                                                repeat_delay=1000)\n",
        "animation_predicted.save('./drive/MyDrive/predicted.gif', \n",
        "                         writer=animation.PillowWriter(), dpi=100)\n",
        "HTML(animation_predicted.to_html5_video())"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": []
    },
    "gpuClass": "premium",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}