Morphological_Classification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "accelerator": "TPU",
    "colab": {
      "name": "Morphological_Classification.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LXL6b1irtWlA",
        "outputId": "a96a7664-ab61-4409-9aa9-340a3fc81a8b"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('content/')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at content/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e-WM_FVwoMrl"
      },
      "source": [
        "! pip install -q kaggle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jQXkjzReml0q"
      },
      "source": [
        "#create directory in root dir with name kaggle\n",
        "!mkdir ~/.kaggle\n",
        "#copy kaggle.json file in that dir\n",
        "! cp /content/content/MyDrive/kaggle.json ~/.kaggle/\n",
        "#change user permissions for file kaggle.json, 6 is 110 which is read and write permissions to user and no permissions to other people/groups \n",
        "! chmod 600 ~/.kaggle/kaggle.json\n",
        "#! kaggle datasets list"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LDl6sa_Oo6qM",
        "outputId": "e0818282-64cd-4c95-d229-cfa5fb5638a1"
      },
      "source": [
        "! kaggle competitions download -c galaxy-zoo-the-galaxy-challenge"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: Looks like you're using an outdated API Version, please consider updating (server 1.5.12 / client 1.5.4)\n",
            "Downloading central_pixel_benchmark.zip to /content\n",
            "  0% 0.00/520k [00:00<?, ?B/s]\n",
            "100% 520k/520k [00:00<00:00, 55.2MB/s]\n",
            "Downloading training_solutions_rev1.zip to /content\n",
            " 65% 3.00M/4.63M [00:00<00:00, 29.6MB/s]\n",
            "100% 4.63M/4.63M [00:00<00:00, 29.5MB/s]\n",
            "Downloading images_training_rev1.zip to /content\n",
            " 99% 780M/792M [00:13<00:00, 94.3MB/s]\n",
            "100% 792M/792M [00:13<00:00, 62.5MB/s]\n",
            "Downloading images_test_rev1.zip to /content\n",
            " 99% 1.00G/1.01G [00:19<00:00, 114MB/s]\n",
            "100% 1.01G/1.01G [00:19<00:00, 54.9MB/s]\n",
            "Downloading all_zeros_benchmark.zip to /content\n",
            "  0% 0.00/265k [00:00<?, ?B/s]\n",
            "100% 265k/265k [00:00<00:00, 115MB/s]\n",
            "Downloading all_ones_benchmark.zip to /content\n",
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            "100% 265k/265k [00:00<00:00, 122MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QoXLbsB5qhUi",
        "outputId": "611d60e0-45ad-498c-d0e5-a7d31b9da601"
      },
      "source": [
        "\n",
        "#create directories tp unzip the data\n",
        "!mkdir X_train\n",
        "!mkdir Y_train\n",
        "!mkdir X_test\n",
        "\n",
        "!unzip images_test_rev1.zip -d X_test\n",
        "! unzip images_training_rev1.zip -d X_train\n",
        "! unzip training_solutions_rev1.zip -d Y_train\n",
        "#!mkdir X_test\n",
        "#!mkdir central_pixel_benchmark"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "Archive:  training_solutions_rev1.zip\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "291jFX09s9Rd"
      },
      "source": [
        "train_imgs_path = '/content/X_train/images_training_rev1'\n",
        "test_imgs_path = '/content/X_test/images_test_rev1'\n",
        "train_labels_path = '/content/Y_train/training_solutions_rev1.csv'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IDsYcN19XqUi",
        "outputId": "9081c839-a3ee-49db-9294-b959c65000fe"
      },
      "source": [
        "import os\n",
        "import math\n",
        "import cv2\n",
        "import random\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.models import load_model\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "print(tf.version.VERSION)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.5.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "33IN170DuTKO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a589aee8-3442-4652-de23-c537a8cd14cf"
      },
      "source": [
        "train_img_dir = os.listdir(train_imgs_path)\n",
        "test_img_dir = os.listdir(test_imgs_path)\n",
        "print(len(train_img_dir),len(test_img_dir))\n",
        "train_labels = pd.read_csv(train_labels_path)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "61578 79975\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_eUyeBBW10h",
        "outputId": "b0f4d5ea-370f-4b6b-c7e5-6a8d76e3b1ec"
      },
      "source": [
        "galaxy_dist =train_labels.drop(['GalaxyID'],axis=1)\n",
        "Galaxy_id = train_labels['GalaxyID']\n",
        "print(galaxy_dist.shape)\n",
        "labels = np.array(galaxy_dist)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(61578, 37)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jENpdm1y7ybH"
      },
      "source": [
        "class softmaxConversion(tf.keras.layers.Layer):\n",
        " \n",
        "  def __init__(self, weights=False, units = 37, **kwargs):\n",
        "    super(softmaxConversion, self).__init__(**kwargs)\n",
        "    self.units = units\n",
        "    self.input_dim = 37\n",
        "    if weights:\n",
        "      #output layer dimension and units\n",
        "      weights_init = tf.random_normal_initializer()\n",
        "      self.w = tf.Variable(\n",
        "          initial_value=weights_init(shape=(self.input_dim, self.units), dtype=\"float32\"),\n",
        "          trainable=True,\n",
        "      )\n",
        "      bias_init = tf.zeros_initializer()\n",
        "      self.b = tf.Variable(\n",
        "          initial_value=bias_init(shape=(self.units,), dtype=\"float32\"), trainable=True\n",
        "      )\n",
        "    else:\n",
        "      self.trainable = False\n",
        "\n",
        "  def get_config(self):\n",
        "    config = super(softmaxConversion, self).get_config()\n",
        "    config.update({\"units\": self.units})\n",
        "    return config\n",
        "\n",
        "  @classmethod\n",
        "  def from_config(cls, config):\n",
        "      return cls(**config)\n",
        "\n",
        "  def getTasks(inputs):\n",
        "    task1 = tf.nn.softmax(inputs[:, 0:3],axis=1) # 1.1 - 1.3\n",
        "    task2 = tf.nn.softmax(inputs[:, 3:5],axis=1) # 2.1 - 2.2\n",
        "    task3 = tf.nn.softmax(inputs[:, 5:7],axis=1) # 3.1 - 3.2\n",
        "    task4 = tf.nn.softmax(inputs[:, 7:9], axis=1) # 4.1 - 4.2\n",
        "    task5 = tf.nn.softmax(inputs[:, 9:13],axis=1) # 5.1 - 5.4\n",
        "    task6 = tf.nn.softmax(inputs[:, 13:15],axis=1) # 6.1 - 6.2\n",
        "    task7 = tf.nn.softmax(inputs[:, 15:18],axis=1) # 7.1 - 7.3\n",
        "    task8 = tf.nn.softmax(inputs[:, 18:25],axis=1) # 8.1 - 8.7\n",
        "    task9 = tf.nn.softmax(inputs[:, 25:28], axis=1) # 9.1 - 9.3\n",
        "    task10 = tf.nn.softmax(inputs[:, 28:31],axis= 1) # 10.1 - 10.3\n",
        "    task11 = tf.nn.softmax(inputs[:, 31:37],axis=1) # 11.1 - 11.6\n",
        "\n",
        "    return (task1, task2, task3, task4, task5, task6, task7, task8, task9, task10, task11)\n",
        "\n",
        "  def call(self, inputs):\n",
        "    task1, task2, task3, task4, task5, task6, task7, task8, task9, task10, task11 = getTasks(inputs)     \n",
        "\n",
        "    w1 = 1\n",
        "    w2 = task1[:, 1] # question 1 answer 2\n",
        "    w3 = task2[:, 1] * w2 # question 2 answer 2 * w2\n",
        "    w4 = w3\n",
        "    w7 = task1[:, 0] # question 1 answer 1\n",
        "    w9 = task2[:, 0] * w2 # question 2 answer 1 * w2\n",
        "    w10 = task4[:, 0] * w4 # question 4 answer 1 * w4\n",
        "    w11 = w10\n",
        "    w5 = w4 # THIS WAS WRONG BEFORE\n",
        "    w6 = 1 # THIS SHOULD TECHNICALLY BE w5 + w7 + w9, but as explained on the forums, there was a mistake generating the dataset.\n",
        "    # see http://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge/forums/t/6706/is-question-6-also-answered-for-stars-artifacts-answer-1-3 for more info\n",
        "    w8 = task6[:, 0] * w6 # question 6 answer 1 * w6\n",
        "\n",
        "    inp1 = w1*task1\n",
        "    inp2 = w2*task2\n",
        "    inp3 = w3*task3\n",
        "    inp4 = w4*task4\n",
        "    inp5 = w5*task5\n",
        "    inp6 = w6*task6\n",
        "    inp7 = w7*task7\n",
        "    inp8 = w8*task8\n",
        "    inp9 = w9*task9\n",
        "    inp10 = w10*task10\n",
        "    inp11 = w11*task11\n",
        "\n",
        "    output = tf.concat([inp1,inp2,inp3,inp4,inp5,inp6,inp7,inp8,inp9,inp10,inp11],axis=1)\n",
        "\n",
        "    return output"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5IN722p2zHHc"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_graphs(history, string):\n",
        "  plt.plot(history.history[string])\n",
        "  plt.plot(history.history['val_'+string])\n",
        "  plt.xlabel(\"Epochs\")\n",
        "  plt.ylabel(string)\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q2ke-FdB0nXX"
      },
      "source": [
        "\n",
        "def createDF(test_pred):\n",
        "  print(test_pred.shape)\n",
        "\n",
        "  #Galaxy IDs\n",
        "  index = []\n",
        "  for img in test_img_dir:\n",
        "    img_id = img.split('.')[0]\n",
        "    index.append(img_id)\n",
        "\n",
        "  print(index[:2])\n",
        "\n",
        "  galaxy_ids = pd.Series(index)\n",
        "  galaxy_distributions = pd.DataFrame(test_pred)\n",
        "  test_df = pd.concat([galaxy_ids,galaxy_distributions],axis=1)\n",
        "\n",
        "  test_df.columns = list(train_labels.columns)\n",
        "  print(test_df)\n",
        "  filename = None\n",
        "  filename = input(\"Input Filename with csv extension\")\n",
        "  #filename = 'test_images_predictions.csv'\n",
        "  test_df.to_csv(filename,index=False)\n",
        "\n",
        "  return filename, test_df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ovn9FLkk62mB"
      },
      "source": [
        "\n",
        "def download_file(filename):\n",
        "  from google.colab import files\n",
        "  files.download(filename)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPOyjtsbq-WC"
      },
      "source": [
        "\n",
        "#returns probability and index of maximum subtask of a task\n",
        "def max(y_pred):\n",
        "  pred1 = np.expand_dims(np.max(y_pred[:, 0:3],axis=1),axis=1) # 1.1 - 1.3\n",
        "  pred2 = np.expand_dims(np.max(y_pred[:, 3:5],axis=1),axis=1) # 2.1 - 2.2\n",
        "  pred3 = np.expand_dims(np.max(y_pred[:, 5:7],axis=1),axis=1) # 3.1 - 3.2\n",
        "  pred4 = np.expand_dims(np.max(y_pred[:, 7:9],axis=1),axis=1) # 4.1 - 4.2\n",
        "  pred5 = np.expand_dims(np.max(y_pred[:, 9:13],axis=1),axis=1) # 5.1 - 5.4\n",
        "  pred6 = np.expand_dims(np.max(y_pred[:, 13:15],axis=1),axis=1) # 6.1 - 6.2\n",
        "  pred7 = np.expand_dims(np.max(y_pred[:, 15:18],axis=1),axis=1) # 7.1 - 7.3\n",
        "  pred8 = np.expand_dims(np.max(y_pred[:, 18:25],axis=1),axis=1) # 8.1 - 8.7\n",
        "  pred9 = np.expand_dims(np.max(y_pred[:, 25:28],axis=1),axis=1) # 9.1 - 9.3\n",
        "  pred10 = np.expand_dims(np.max(y_pred[:, 28:31],axis=1),axis=1) # 10.1 - 10.3\n",
        "  pred11 = np.expand_dims(np.max(y_pred[:, 31:37],axis=1),axis=1)\n",
        "\n",
        "  index1 = np.expand_dims(np.argmax(y_pred[:, 0:3],axis=1),axis=1) # 1.1 - 1.3\n",
        "  index2 = np.expand_dims(np.argmax(y_pred[:, 3:5],axis=1),axis=1) # 2.1 - 2.2\n",
        "  index3 = np.expand_dims(np.argmax(y_pred[:, 5:7],axis=1),axis=1) # 3.1 - 3.2\n",
        "  index4 = np.expand_dims(np.argmax(y_pred[:, 7:9],axis=1),axis=1) # 4.1 - 4.2\n",
        "  index5 = np.expand_dims(np.argmax(y_pred[:, 9:13],axis=1),axis=1) # 5.1 - 5.4\n",
        "  index6 = np.expand_dims(np.argmax(y_pred[:, 13:15],axis=1),axis=1) # 6.1 - 6.2\n",
        "  index7 = np.expand_dims(np.argmax(y_pred[:, 15:18],axis=1),axis=1) # 7.1 - 7.3\n",
        "  index8 = np.expand_dims(np.argmax(y_pred[:, 18:25],axis=1),axis=1) # 8.1 - 8.7\n",
        "  index9 = np.expand_dims(np.argmax(y_pred[:, 25:28],axis=1),axis=1) # 9.1 - 9.3\n",
        "  index10 = np.expand_dims(np.argmax(y_pred[:, 28:31],axis=1),axis=1) # 10.1 - 10.3\n",
        "  index11 = np.expand_dims(np.argmax(y_pred[:, 31:37],axis=1),axis=1)\n",
        "\n",
        "  prediction = np.concatenate([pred1,pred2,pred3,pred4,pred5,pred6,pred7,pred8,pred9,pred10,pred11],axis=1)\n",
        "  indices = np.concatenate([index1,index2,index3,index4,index5,index6,index7,index8,index9,index10,index11],axis=1)\n",
        "  \n",
        "  return prediction, indices"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hfbsrrq4rEzr"
      },
      "source": [
        "#Y_pred = model.predict(test_generator,batch_size=32,verbose=1,steps=len(X_test)//32)\n",
        "#prob_pred,ans_pred = max(Y_pred)\n",
        "#prob_true,ans_true = max(Y_test)\n",
        "#ans_pred.shape"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4c7jtyhkyfOc"
      },
      "source": [
        "Questions = {1:\"Is the galaxy simply smooth with no sign of a disk?\",\\\n",
        "             2:\"Could this be a disk viewed edge-on?\",\\\n",
        "             3:\"Is there a sign of a bar feature through the centre of the galaxy?\",\\\n",
        "             4:\"Is there any sign of a spiral arm pattern?\",\\\n",
        "             5:\"How prominent is the central bulge, compared with the rest of the galaxy?\",\\\n",
        "             6:\"Is there anything odd?\",\\\n",
        "             7:\"How rounded is it?\",\\\n",
        "             8:\"Is the odd feature a ring, or is the galaxy disturbed or irregular?\",\\\n",
        "             9:\"Does the galaxy have a bulge at its centre? If so, what shape?\",\\\n",
        "             10:\"How tightly wound do the spiral arms appear?\",\\\n",
        "             11:\"How many spiral arms are there?\"}\n",
        "\n",
        "Answers = {1:[\"smooth\",\"disk\",\"star or artifact\"],\\\n",
        "             2:[\"yes\",\"no\"],\\\n",
        "             3:[\"yes\",\"no\"],\\\n",
        "             4:[\"yes\",\"no\"],\\\n",
        "             5:[\"no bulge\",\"just noticeable bulge\",\"obvious bulge\",\"dominant bulge\"],\\\n",
        "             6:[\"yes\",\"no\"],\\\n",
        "             7:[\"complete roundedness\",\"in-between roundedness\",\"cigar-shaped roundedness\"],\\\n",
        "             8:[\"ring\",\"gravitational lens or arc\",\"disturbed galaxy\",\"irrregular galaxy\",\"other\",\"merger\",\"dust lane galaxy\"],\\\n",
        "             9:[\"rounded bulge\",\"boxy bulge\",\"NO bulge\"],\\\n",
        "             10:[\"tight spiral arms\",\"medium spiral arms\",\"loose spiral arms\"],\\\n",
        "             11:[\"1 spiral arm\",\"2 spiral arms\",\"3 spiral arms\",\"4 spiral arms\",\"more than 4 spiral arms\",\"can't count spiral arms\"]}\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LB5G6TRE4bp1"
      },
      "source": [
        "#print(galaxy_dist.iloc[5])\n",
        "def print_output(path,galaxy,pred,true):\n",
        "  img = plt.imread(os.path.join(path,galaxy))\n",
        "  cropped_img = img[84:341,84:341,:]\n",
        "  plt.imshow(cropped_img,interpolation='nearest')\n",
        "  for i in range(11):\n",
        "    print(\"\\nQuestion:\\t\",Questions[i+1])\n",
        "    print(\"Answer:\\t\\t\",Answers[i+1][pred[i]],prob_pred[75,i])\n",
        "    print(\"True Answer:\\t\",Answers[i+1][true[i]],prob_true[75,i])\n",
        "\n",
        "#print_output(train_imgs_path,X_test[75],ans_pred[75],ans_true[75])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CUv-7CPufd5V"
      },
      "source": [
        "\n",
        "random.seed(42)\n",
        "\n",
        "def dataGenerator(directory,image_paths,labels,batch_size, size ,color_mode='gray',augmentation = True):\n",
        "  \n",
        "  X, Y = list(), list()  \n",
        "  bs = 0\n",
        "  \n",
        "  while 1:\n",
        "    for img_path,label in zip(image_paths,labels):\n",
        "      bs+=1\n",
        "      img = cv2.imread(os.path.join(directory,img_path))\n",
        "      #print(type(img),img.shape)\n",
        "      #crop the image to 224 * 224\n",
        "      cropped_img = img[100:324,100:324,:]\n",
        "      \n",
        "      if color_mode=='gray':\n",
        "        gray = cv2.cvtColor(cropped_img, cv2.COLOR_BGR2GRAY)\n",
        "        resized_img = cv2.resize(gray, size, interpolation = cv2.INTER_AREA)\n",
        "      else:\n",
        "        resized_img = cv2.resize(cropped_img, size, interpolation = cv2.INTER_AREA)\n",
        "\n",
        "      if augmentation==True:\n",
        "        aug_images = ImageAugmentation(resized_img, hflip=True, vflip=True, rot_angle = 180, bright=True, z = True)\n",
        "        [X.append(image/255) for image in aug_images]\n",
        "        [Y.append(label) for i in range(len(aug_images))]\n",
        "\n",
        "      else:\n",
        "        scaled_image = resized_img/255\n",
        "        \n",
        "        X.append(scaled_image)\n",
        "        Y.append(label)\n",
        "        #cv2.destroyAllWindows()\n",
        "\n",
        "      if bs==batch_size or image_paths.index(img_path)==len(image_paths)-1:\n",
        "        dx, dy = np.array(X), np.array(Y)\n",
        "        #print(dx.shape, dy.shape)\n",
        "        yield (dx,dy)\n",
        "        X, Y = list(), list()\n",
        "        bs=0\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aqKdZXMqf-co"
      },
      "source": [
        "def horizontal_flip(image):\n",
        "  return cv2.flip(image, 1)\n",
        "\n",
        "def vertical_flip(image):\n",
        "  return cv2.flip(image, 0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EOh1Sk4Qf8Eg"
      },
      "source": [
        "def rotation(image, angle):\n",
        "  angle = int(random.uniform(-angle, angle))\n",
        "  h, w = image.shape[:2]\n",
        "  M = cv2.getRotationMatrix2D((int(w/2), int(h/2)), angle, 1)\n",
        "  image = cv2.warpAffine(image, M, (w, h))\n",
        "  return image"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GuRMXch5k9Sw"
      },
      "source": [
        "def brightness(img, low, high):\n",
        "    value = random.uniform(low, high)\n",
        "    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n",
        "    hsv = np.array(hsv, dtype = np.float64)\n",
        "    hsv[:,:,1] = hsv[:,:,1]*value\n",
        "    hsv[:,:,1][hsv[:,:,1]>255]  = 255\n",
        "    hsv[:,:,2] = hsv[:,:,2]*value \n",
        "    hsv[:,:,2][hsv[:,:,2]>255]  = 255\n",
        "    hsv = np.array(hsv, dtype = np.uint8)\n",
        "    img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)\n",
        "    return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UB4d5BJtlBmf"
      },
      "source": [
        "def zoom(img, value):\n",
        "    if value > 1 or value < 0:\n",
        "        print('Value for zoom should be less than 1 and greater than 0')\n",
        "        return img\n",
        "    #value = random.uniform(value, 1)\n",
        "    h, w = img.shape[:2]\n",
        "    h_taken = int(value*h)\n",
        "    w_taken = int(value*w)\n",
        "    h_start = random.randint(0, h-h_taken)\n",
        "    w_start = random.randint(0, w-w_taken)\n",
        "    if len(img.shape)==2:\n",
        "      img = img[h_start:h_start+h_taken, w_start:w_start+w_taken]\n",
        "    else:\n",
        "      img = img[h_start:h_start+h_taken, w_start:w_start+w_taken,:]\n",
        "    img = cv2.resize(img, (h, w), cv2.INTER_CUBIC)\n",
        "    return img"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "62s0QMUGCOia"
      },
      "source": [
        "def ImageAugmentation(image, hflip, vflip, rot_angle, bright, z):\n",
        "  aug_images = [image]\n",
        "  if hflip:\n",
        "    aug_images.append(horizontal_flip(image))\n",
        "  \n",
        "  if vflip:\n",
        "    aug_images.append(vertical_flip(image))\n",
        "  \n",
        "  if rot_angle:\n",
        "    aug_images.append(rotation(image, rot_angle))\n",
        "  \n",
        "  if bright:\n",
        "    aug_images.append(brightness(image, low = 1, high = 2))\n",
        "  \n",
        "  if z:\n",
        "    aug_images.append(zoom(image, 0.8))\n",
        "\n",
        "  return aug_images"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "imFGKiKLM3C5"
      },
      "source": [
        "def get_generators(image_shape = (64, 64), batch_size = 32, color_mode = 'gray', augmentation = False):\n",
        "\n",
        "  X_train, X_val, Y_train, Y_val = train_test_split(train_img_dir, labels, test_size = 0.2, shuffle=True, random_state = 42)\n",
        "\n",
        "  assert (len(X_train)==Y_train.shape[0])\n",
        "  assert (len(X_val)==Y_val.shape[0])\n",
        "  print(\"Training data size\", len(X_train))\n",
        "  print(\"Validation data size\",len(X_val))\n",
        "\n",
        "  train_gen = dataGenerator(train_imgs_path, X_train, Y_train, batch_size, image_shape, color_mode, augmentation)\n",
        "  val_gen = dataGenerator(train_imgs_path, X_val, Y_val, batch_size, image_shape, color_mode, augmentation=False)\n",
        "  test_gen = dataGenerator(test_imgs_path, test_img_dir, np.ones((len(test_img_dir),1)) , batch_size, image_shape, color_mode,augmentation = False)\n",
        "\n",
        "  return train_gen, val_gen, test_gen"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mdf5VjAHenPG"
      },
      "source": [
        "class myCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_end(self, epochs, logs = {}):\n",
        "\n",
        "    if logs.get('loss') <.0099:\n",
        "      print(\"\\nloss is less than 0.0099 hence stoping the execution\")\n",
        "      self.model.stop_training = True\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TWU83IXR2DuY"
      },
      "source": [
        "#RESNET MODEL"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n1MjMWf1LLAy"
      },
      "source": [
        "#Resnet50\n",
        "def ResNet(image_shape, opt = 'Adam', loss_func = 'mse'):\n",
        "  model = tf.keras.models.Sequential(\n",
        "      [tf.keras.applications.resnet50.ResNet50(weights = None, input_shape=image_shape, pooling='avg', include_top = False),\n",
        "      tf.keras.layers.Dropout(0.5),\n",
        "      tf.keras.layers.Dense(37, activation='relu'),\n",
        "      tf.keras.layers.Layer(softmaxConversion(weights = False)),\n",
        "      ]\n",
        "  )\n",
        "  model.compile(optimizer= opt, loss= loss_func)\n",
        "  return model\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9X4EFeXWEH7"
      },
      "source": [
        "def get_resnet_model(shape,checkpoint_filepath,checkpoint_filename):\n",
        "  drive_content = os.listdir('/content/content/MyDrive')\n",
        "  #print(drive_content)\n",
        "  if checkpoint_filename in drive_content:\n",
        "    print(\"\\nsaved resnet model loaded successfully\")\n",
        "    model = load_model(checkpoint_filepath, compile= True, custom_objects={\"softmaxConversion\":softmaxConversion})\n",
        "    return model\n",
        "\n",
        "  else:\n",
        "    optimizer = tf.keras.optimizers.Adam()\n",
        "    model = ResNet(image_shape = shape, opt = optimizer, loss_func='mse')\n",
        "    return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yiNFJMspE28P"
      },
      "source": [
        "#Multilayer Perceptron Model\n",
        "#flag decides the weights initialization in output layer\n",
        "def ANN(image_shape, opt = 'Adam', loss_func = 'mse', flag = False):\n",
        "  model = tf.keras.models.Sequential(\n",
        "      [\n",
        "      tf.keras.layers.Flatten(input_shape = image_shape),\n",
        "      tf.keras.layers.Dense(4096,activation='relu'),\n",
        "      tf.keras.layers.Dense(512,activation='relu'),\n",
        "      tf.keras.layers.Dense(256, activation= \"relu\"),\n",
        "      tf.keras.layers.Dropout(0.5),\n",
        "      tf.keras.layers.Dense(37, activation= \"relu\"),\n",
        "      tf.keras.layers.Layer(softmaxConversion(units = 37, weights = flag))\n",
        "      ]   \n",
        "  )\n",
        "\n",
        "  model.compile(optimizer= opt , loss= loss_func)\n",
        "  return model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2gFDBDtEE71q"
      },
      "source": [
        "def get_ann_model(shape,checkpoint_filepath,checkpoint_filename):\n",
        "  drive_content = os.listdir('/content/content/MyDrive')\n",
        "  #print(drive_content)\n",
        "  if checkpoint_filename in drive_content:\n",
        "    print(\"\\nsaved model loaded successfully\")\n",
        "    model = load_model(checkpoint_filepath, compile= True, custom_objects={\"softmaxConversion\":softmaxConversion})\n",
        "    return model\n",
        "\n",
        "  else:\n",
        "    #without explicit learning rate decay\n",
        "    optimizer = tf.keras.optimizers.Adam()\n",
        "    #with explicit learning rate decay\n",
        "    #decayed_lr = tf.keras.optimizers.schedules.ExponentialDecay(\n",
        "    #initial_learning_rate = 0.001, decay_steps = 10000, decay_rate = 0.9, staircase= True, name= 'exponentialDecay')\n",
        "    #optimizer = tf.keras.optimizers.Adam(decayed_lr)\n",
        "    \n",
        "    model = ANN(image_shape = shape, opt = optimizer, loss_func='mse', flag=False)\n",
        "    return model\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IxaGs1APPkf2"
      },
      "source": [
        "#RESNET\n",
        "\n",
        "\n",
        "*   Without Data Augmentation\n",
        "*   RGB Images\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pn-7aOQx2iVQ",
        "outputId": "7be556f7-d1a6-4d87-bbab-714a160522b2"
      },
      "source": [
        "checkpoint_filepath = '/content/content/MyDrive/resnet50_checkpoint_without_aug'\n",
        "checkpoint_filename = 'resnet50_checkpoint_without_aug'\n",
        "\n",
        "#define and compile model\n",
        "resnet_model1 = get_resnet_model((224,224,3), checkpoint_filepath, checkpoint_filename)\n",
        "resnet_model1.summary()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "resnet50 (Functional)        (None, 2048)              23587712  \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 2048)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 37)                75813     \n",
            "_________________________________________________________________\n",
            "layer (Layer)                (None, 37)                0         \n",
            "=================================================================\n",
            "Total params: 23,663,525\n",
            "Trainable params: 23,610,405\n",
            "Non-trainable params: 53,120\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2jZc5FBIO0b"
      },
      "source": [
        "#fit the model\n",
        "num_epochs  = 2\n",
        "bs = 32\n",
        "\n",
        "spe = math.ceil(len(X_train)/bs)\n",
        "val_steps = math.ceil(len(X_val)/bs)\n",
        "\n",
        "stop_callback = myCallback()\n",
        "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_filepath, save_weights_only=False, save_best_only=False)\n",
        "\n",
        "train_gen, val_gen, test_gen = get_generators(image_shape = (224,224), batch_size = bs, color_mode = 'rgb', augmentation= False)\n",
        "\n",
        "history = resnet_model1.fit(train_gen, validation_data = val_gen, epochs= num_epochs, steps_per_epoch= spe, \\\n",
        "                           verbose = 1, callbacks = [stop_callback, savemodel_callback])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HCEJ1CatJjNQ"
      },
      "source": [
        "plot_graphs(history, 'loss')\n",
        "plt.savefig('/content/content/MyDrive/cost_reduction_graph_resnet1.png')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qKBa450ME0F6"
      },
      "source": [
        "steps = math.ceil(len(X_test)/bs)\n",
        "print(\"Total Test Images\",len(X_test))\n",
        "\n",
        "test_pred = resnet_model1.predict(test_gen, batch_size = bs, steps= steps, verbose=1)\n",
        "print(\"Test Prediction Shape\",test_pred.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EcU1OsBIQAfM"
      },
      "source": [
        "#RESNET MODEL\n",
        "\n",
        "\n",
        "*   With data augmentation\n",
        "*   RGB Images\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T8r6xo52QL1F"
      },
      "source": [
        "checkpoint_filepath = '/content/content/MyDrive/resnet50_checkpoint_with_aug'\n",
        "checkpoint_filname = 'resnet50_checkpoint_with_aug'\n",
        "\n",
        "#define and compile model\n",
        "resnet_model2 = get_resnet_model(shape = (224,224,3), checkpoint_filepath, checkpoint_filename)\n",
        "resnet_model2.summary()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GkRtmwjSQbRF"
      },
      "source": [
        "#fit the model\n",
        "num_epochs  = 2\n",
        "bs = 32\n",
        "\n",
        "spe = math.ceil(len(X_train)/bs)\n",
        "val_steps = math.ceil(len(X_val)/bs)\n",
        "\n",
        "stop_callback = myCallback()\n",
        "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_filepath, save_weights_only=False, save_best_only=False)\n",
        "\n",
        "train_gen, val_gen, test_gen = get_generators(image_shape = (224,224), batch_size = bs, color_mode = 'rgb', augmentation= True)\n",
        "\n",
        "history = resnet_model2.fit(train_gen, validation_data = val_gen, epochs= num_epochs, steps_per_epoch= spe, \\\n",
        "                           verbose = 1, callbacks = [stop_callback, savemodel_callback])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqfP-ZcaQi8I"
      },
      "source": [
        "plot_graphs(history, 'loss')\n",
        "plt.savefig('/content/content/MyDrive/cost_reduction_graph_resnet2.png')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zkDxKJoGQkf4"
      },
      "source": [
        "steps = math.ceil(len(X_test)/bs)\n",
        "print(\"Total Test Images\",len(X_test))\n",
        "\n",
        "test_pred = resnet_model2.predict(test_gen, batch_size = bs, steps= steps, verbose=1)\n",
        "print(\"Test Prediction Shape\",test_pred.shape)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1PcbXUM4M3Vx"
      },
      "source": [
        "#Multilayer Perceptron\n",
        "\n",
        "\n",
        "*   Without Data Augmentation\n",
        "*   Grayscale Images\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-QYw0NW1Q8hw",
        "outputId": "60a81ef1-e328-42fd-fd33-68d7bce5153a"
      },
      "source": [
        "mlp_checkpoint_filepath = '/content/content/MyDrive/mlp_gray_model_without_aug2'\n",
        "mlp_checkpoint_filename = 'mlp_gray_model_without_aug2'\n",
        "\n",
        "mlp_rgb_model1 = get_ann_model((64,64) , mlp_checkpoint_filepath, mlp_checkpoint_filename)\n",
        "mlp_rgb_model1.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten (Flatten)            (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 512)               2097664   \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 37)                9509      \n",
            "_________________________________________________________________\n",
            "layer (Layer)                (None, 37)                0         \n",
            "=================================================================\n",
            "Total params: 19,019,813\n",
            "Trainable params: 19,019,813\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pr15H-J7RKJ7",
        "outputId": "bbc65fb0-177e-4d0a-e575-c16e92e0d4c8"
      },
      "source": [
        "num_epochs  = 5\n",
        "bs = 32\n",
        "\n",
        "spe = math.ceil(len(train_img_dir*0.8)/bs)\n",
        "val_steps = math.ceil(len(train_img_dir*0.2)/bs)\n",
        "\n",
        "stop_callback = myCallback()\n",
        "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=mlp_checkpoint_filepath, save_weights_only=False, save_best_only=False)\n",
        "\n",
        "train_gen, val_gen, test_gen = get_generators(image_shape = (64,64), batch_size = bs, color_mode = 'gray', augmentation= False)\n",
        "\n",
        "history = mlp_rgb_model1.fit(train_gen, validation_data = val_gen, epochs= num_epochs, steps_per_epoch = spe, \\\n",
        "                            validation_steps = val_steps, verbose = 1, callbacks=[stop_callback, savemodel_callback])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/5\n",
            "1540/1540 [==============================] - 937s 608ms/step - loss: 0.0394 - val_loss: 0.0273\n",
            "INFO:tensorflow:Assets written to: /content/content/MyDrive/mlp_gray_model_without_aug/assets\n",
            "Epoch 2/5\n",
            "1540/1540 [==============================] - 922s 599ms/step - loss: 0.0271 - val_loss: 0.0272\n",
            "INFO:tensorflow:Assets written to: /content/content/MyDrive/mlp_gray_model_without_aug/assets\n",
            "Epoch 3/5\n",
            "1540/1540 [==============================] - 921s 598ms/step - loss: 0.0271 - val_loss: 0.0272\n",
            "INFO:tensorflow:Assets written to: /content/content/MyDrive/mlp_gray_model_without_aug/assets\n",
            "Epoch 4/5\n",
            "1540/1540 [==============================] - 933s 606ms/step - loss: 0.0271 - val_loss: 0.0272\n",
            "INFO:tensorflow:Assets written to: /content/content/MyDrive/mlp_gray_model_without_aug/assets\n",
            "Epoch 5/5\n",
            "1540/1540 [==============================] - 927s 602ms/step - loss: 0.0273 - val_loss: 0.0272\n",
            "INFO:tensorflow:Assets written to: /content/content/MyDrive/mlp_gray_model_without_aug/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "s8hGB15CRuJc",
        "outputId": "fe3b891e-da8c-4629-aeca-73d8b468e3dc"
      },
      "source": [
        "plt = plot_graphs(history, string = 'loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TjYGLFHaRj8Y",
        "outputId": "9231f8c7-dffd-40e1-d7b9-af1dc9416c75"
      },
      "source": [
        "steps = math.ceil(len(test_img_dir)/bs)\n",
        "print(\"Total Test Images\",len(test_img_dir))\n",
        "\n",
        "test_pred = mlp_rgb_model1.predict(test_gen, batch_size = bs, steps= steps, verbose=1)\n",
        "print(\"Test Prediction Shape\",test_pred.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total Test Images 79975\n",
            "2500/2500 [==============================] - 642s 257ms/step\n",
            "Test Prediction Shape (79975, 37)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_wmY1DHvFNn",
        "outputId": "f323c8ed-bd4b-498c-a0e6-e954a7e095aa"
      },
      "source": [
        "test_prediction_filename, test_pred_df = createDF(test_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(79975, 37)\n",
            "['633521', '619275']\n",
            "      GalaxyID  Class1.1  Class1.2  ...  Class11.4  Class11.5  Class11.6\n",
            "0       633521  0.424601  0.550695  ...        0.0        0.0   0.077123\n",
            "1       619275  0.424601  0.550695  ...        0.0        0.0   0.077123\n",
            "2       170145  0.424601  0.550695  ...        0.0        0.0   0.077123\n",
            "3       349543  0.424601  0.550695  ...        0.0        0.0   0.077123\n",
            "4       798880  0.424601  0.550695  ...        0.0        0.0   0.077123\n",
            "...        ...       ...       ...  ...        ...        ...        ...\n",
            "79970   467624  0.424601  0.550695  ...        0.0        0.0   0.077123\n",
            "79971   410061  0.424601  0.550695  ...        0.0        0.0   0.077123\n",
            "79972   671680  0.424601  0.550695  ...        0.0        0.0   0.077123\n",
            "79973   813752  0.424601  0.550695  ...        0.0        0.0   0.077123\n",
            "79974   370646  0.424601  0.550695  ...        0.0        0.0   0.077123\n",
            "\n",
            "[79975 rows x 38 columns]\n",
            "Input Filename with csv extensiontest_predictions_gray1.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cpXMrGoSS8XV"
      },
      "source": [
        "#Mulilayer Pecrceptron\n",
        "\n",
        "\n",
        "*   With Data Augmentation\n",
        "*   Grayscale Images\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ywo7IBOUTFcZ",
        "outputId": "ba29878c-fd17-4113-aae3-dd33850346a1"
      },
      "source": [
        "mlp_checkpoint_filepath = '/content/content/MyDrive/mlp_gray_model_with_aug2'\n",
        "mlp_checkpoint_filename = 'mlp_gray_model_with_aug2'\n",
        "\n",
        "mlp_rgb_model2 = get_ann_model((64,64) , mlp_checkpoint_filepath, mlp_checkpoint_filename)\n",
        "mlp_rgb_model2.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_1 (Flatten)          (None, 4096)              0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 4096)              16781312  \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 512)               2097664   \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 37)                9509      \n",
            "_________________________________________________________________\n",
            "layer_1 (Layer)              (None, 37)                0         \n",
            "=================================================================\n",
            "Total params: 19,019,813\n",
            "Trainable params: 19,019,813\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "mvEvyvOcTPzI",
        "outputId": "12b44746-6b41-4b65-8e5e-86fde5fc83d1"
      },
      "source": [
        "num_epochs  = 5\n",
        "bs = 32\n",
        "\n",
        "spe = math.ceil(round(0.8*len(train_img_dir))/bs)\n",
        "val_steps = math.ceil(round(0.2*len(train_img_dir))/bs)\n",
        "#print(round(0.8*len(train_img_dir)),round(0.2*len(train_img_dir)))\n",
        "\n",
        "stop_callback = myCallback()\n",
        "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=mlp_checkpoint_filepath, save_weights_only=False, save_best_only=False)\n",
        "\n",
        "train_gen, val_gen, test_gen = get_generators(image_shape = (64,64), batch_size = bs, color_mode = 'gray', augmentation= True)\n",
        "  \n",
        "history = mlp_rgb_model2.fit(train_gen, validation_data = val_gen, epochs= num_epochs, steps_per_epoch = spe, \\\n",
        "                            validation_steps = val_steps, verbose = 1, callbacks=[stop_callback, savemodel_callback])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training data size 49262\n",
            "Validation data size 12316\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "error",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31merror\u001b[0m                                     Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-32-15224682e623>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mtrain_gen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_gen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_gen\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_generators\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage_shape\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor_mode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'gray'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maugmentation\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mhistory\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmlp_rgb_model2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_gen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalidation_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval_gen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mnum_epochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps_per_epoch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspe\u001b[0m\u001b[0;34m,\u001b[0m                             \u001b[0mvalidation_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mval_steps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstop_callback\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msavemodel_callback\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1145\u001b[0m           \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1146\u001b[0m           \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1147\u001b[0;31m           steps_per_execution=self._steps_per_execution)\n\u001b[0m\u001b[1;32m   1148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1149\u001b[0m       \u001b[0;31m# Container that configures and calls `tf.keras.Callback`s.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/data_adapter.py\u001b[0m in \u001b[0;36mget_data_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1362\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"model\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"_cluster_coordinator\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1363\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_ClusterCoordinatorDataHandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1364\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mDataHandler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1365\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1366\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/data_adapter.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution, distribute)\u001b[0m\n\u001b[1;32m   1164\u001b[0m         \u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0muse_multiprocessing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1165\u001b[0m         \u001b[0mdistribution_strategy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mds_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_strategy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1166\u001b[0;31m         model=model)\n\u001b[0m\u001b[1;32m   1167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1168\u001b[0m     \u001b[0mstrategy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mds_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_strategy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/data_adapter.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, sample_weights, workers, use_multiprocessing, max_queue_size, model, **kwargs)\u001b[0m\n\u001b[1;32m    807\u001b[0m     \u001b[0;31m# Since we have to know the dtype of the python generator when we build the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    808\u001b[0m     \u001b[0;31m# dataset, we have to look at a batch to infer the structure.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 809\u001b[0;31m     \u001b[0mpeek\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_peek_and_restore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    810\u001b[0m     \u001b[0mpeek\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_standardize_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpeek\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    811\u001b[0m     \u001b[0mpeek\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_tensorlike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpeek\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/data_adapter.py\u001b[0m in \u001b[0;36m_peek_and_restore\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m    864\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    865\u001b[0m   \u001b[0;32mdef\u001b[0m \u001b[0m_peek_and_restore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 866\u001b[0;31m     \u001b[0mpeek\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    867\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mpeek\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitertools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mpeek\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-20-77474c7f9452>\u001b[0m in \u001b[0;36mdataGenerator\u001b[0;34m(directory, image_paths, labels, batch_size, size, color_mode, augmentation)\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0maugmentation\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m         \u001b[0maug_images\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImageAugmentation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresized_img\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhflip\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvflip\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrot_angle\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m180\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbright\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mz\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m         \u001b[0;34m[\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m255\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mimage\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maug_images\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0;34m[\u001b[0m\u001b[0mY\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maug_images\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-25-f51d1777c63e>\u001b[0m in \u001b[0;36mImageAugmentation\u001b[0;34m(image, hflip, vflip, rot_angle, bright, z)\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mbright\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0maug_images\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbrightness\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlow\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhigh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-23-5c30de75ca2d>\u001b[0m in \u001b[0;36mbrightness\u001b[0;34m(img, low, high)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mbrightness\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhigh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muniform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhigh\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mhsv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcvtColor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mimg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCOLOR_BGR2HSV\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mhsv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhsv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat64\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mhsv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhsv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31merror\u001b[0m: OpenCV(4.1.2) /io/opencv/modules/imgproc/src/color.simd_helpers.hpp:92: error: (-2:Unspecified error) in function 'cv::impl::{anonymous}::CvtHelper<VScn, VDcn, VDepth, sizePolicy>::CvtHelper(cv::InputArray, cv::OutputArray, int) [with VScn = cv::impl::{anonymous}::Set<3, 4>; VDcn = cv::impl::{anonymous}::Set<3>; VDepth = cv::impl::{anonymous}::Set<0, 5>; cv::impl::{anonymous}::SizePolicy sizePolicy = (cv::impl::<unnamed>::SizePolicy)2u; cv::InputArray = const cv::_InputArray&; cv::OutputArray = const cv::_OutputArray&]'\n> Invalid number of channels in input image:\n>     'VScn::contains(scn)'\n> where\n>     'scn' is 1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IcwvkqufTY7W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "55e0871f-f016-46cf-9e16-d1bc161df179"
      },
      "source": [
        "plot_graphs(history, string = 'loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWKFfMchTZl1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "90b470ce-548e-4264-bc65-15bda03035c3"
      },
      "source": [
        "steps = math.ceil(len(test_img_dir)/bs)\n",
        "print(\"Total Test Images\",len(test_img_dir))\n",
        "\n",
        "test_pred = mlp_rgb_model2.predict(test_gen, batch_size = bs, steps= steps, verbose=1)\n",
        "print(\"Test Prediction Shape\",test_pred.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total Test Images 79975\n",
            "2500/2500 [==============================] - 648s 259ms/step\n",
            "Test Prediction Shape (79975, 37)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tvWvMOMhtOv3",
        "outputId": "e1ccff4a-009e-4cc3-bfb3-01aec5b16e91"
      },
      "source": [
        "test_filename, test_pred_df = createDF(test_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(79975, 37)\n",
            "['633521', '619275']\n",
            "      GalaxyID  Class1.1  Class1.2  ...  Class11.4  Class11.5  Class11.6\n",
            "0       633521  0.424511  0.550484  ...        0.0        0.0   0.077141\n",
            "1       619275  0.424511  0.550484  ...        0.0        0.0   0.077141\n",
            "2       170145  0.424511  0.550484  ...        0.0        0.0   0.077141\n",
            "3       349543  0.424511  0.550484  ...        0.0        0.0   0.077141\n",
            "4       798880  0.424511  0.550484  ...        0.0        0.0   0.077141\n",
            "...        ...       ...       ...  ...        ...        ...        ...\n",
            "79970   467624  0.424511  0.550484  ...        0.0        0.0   0.077141\n",
            "79971   410061  0.424511  0.550484  ...        0.0        0.0   0.077141\n",
            "79972   671680  0.424511  0.550484  ...        0.0        0.0   0.077141\n",
            "79973   813752  0.424511  0.550484  ...        0.0        0.0   0.077141\n",
            "79974   370646  0.424511  0.550484  ...        0.0        0.0   0.077141\n",
            "\n",
            "[79975 rows x 38 columns]\n",
            "Input Filename with csv extensiontest_predictions_gray_with_aug.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ezXkmdqXThwa"
      },
      "source": [
        "#Multilayer Perceptron\n",
        "\n",
        "\n",
        "*   Without Data Augmentation\n",
        "*   RGB Images\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bj265HI3Tifj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93e651eb-1141-43b0-9fab-f71f7813eef3"
      },
      "source": [
        "mlp_checkpoint_filepath = '/content/content/MyDrive/mlp_rgb_model_without_aug'\n",
        "mlp_checkpoint_filename = 'mlp_rgb_model_without_aug'\n",
        "\n",
        "mlp_rgb_model3 = get_ann_model((128, 128, 3), mlp_checkpoint_filepath, mlp_checkpoint_filename)\n",
        "mlp_rgb_model3.summary() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_1 (Flatten)          (None, 49152)             0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 4096)              201330688 \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 512)               2097664   \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 37)                9509      \n",
            "_________________________________________________________________\n",
            "layer_2 (Layer)              (None, 37)                0         \n",
            "=================================================================\n",
            "Total params: 203,569,189\n",
            "Trainable params: 203,569,189\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YA-6gxYjT7Yv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa0e4521-9933-4552-d97b-f93278409f3a"
      },
      "source": [
        "num_epochs  = 4\n",
        "bs = 32\n",
        "\n",
        "spe = math.ceil(round(0.8*len(train_img_dir))/bs)\n",
        "val_steps = math.ceil(round(0.2*len(train_img_dir))/bs)\n",
        "\n",
        "stop_callback = myCallback()\n",
        "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=mlp_checkpoint_filepath, save_weights_only=False, save_best_only=False)\n",
        "\n",
        "train_gen, val_gen, test_gen = get_generators(image_shape = (128,128), batch_size = bs, color_mode = 'rgb', augmentation= False)\n",
        "\n",
        "history = mlp_rgb_model3.fit(train_gen, validation_data = val_gen, epochs= num_epochs, steps_per_epoch = spe, \\\n",
        "                            validation_steps = val_steps, verbose = 1, callbacks=[stop_callback, savemodel_callback])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training data size 49262\n",
            "Validation data size 12316\n",
            "Epoch 1/4\n",
            "1540/1540 [==============================] - 2265s 1s/step - loss: 0.0887 - val_loss: 0.0358\n",
            "INFO:tensorflow:Assets written to: /content/content/MyDrive/mlp_rgb_model_without_aug/assets\n",
            "Epoch 2/4\n",
            "1540/1540 [==============================] - 2210s 1s/step - loss: 0.0319 - val_loss: 0.0315\n",
            "INFO:tensorflow:Assets written to: /content/content/MyDrive/mlp_rgb_model_without_aug/assets\n",
            "Epoch 3/4\n",
            "1540/1540 [==============================] - 2242s 1s/step - loss: 0.0314 - val_loss: 0.0315\n",
            "INFO:tensorflow:Assets written to: /content/content/MyDrive/mlp_rgb_model_without_aug/assets\n",
            "Epoch 4/4\n",
            "1540/1540 [==============================] - 2274s 1s/step - loss: 0.0314 - val_loss: 0.0315\n",
            "INFO:tensorflow:Assets written to: /content/content/MyDrive/mlp_rgb_model_without_aug/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fWnIF_SgUC69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "afae6971-fa84-4179-ddd8-ab097bd6cd71"
      },
      "source": [
        "plot_graphs(history, string = 'loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "30KyvJCsUV--",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9495bd83-dcbc-455b-a668-47f70845e457"
      },
      "source": [
        "steps = math.ceil(len(test_img_dir)/bs)\n",
        "print(\"Total Test Images\",len(test_img_dir))\n",
        "\n",
        "test_pred = mlp_rgb_model3.predict(test_gen, batch_size = bs, steps= steps, verbose=1)\n",
        "print(\"Test Prediction Shape\",test_pred.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total Test Images 79975\n",
            "2500/2500 [==============================] - 1255s 502ms/step\n",
            "Test Prediction Shape (79975, 37)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X5LK2Nifb1wj"
      },
      "source": [
        "test_filename, test_pred_df = createDF(test_pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mxBcqkwaR2q4"
      },
      "source": [
        "#Multilayer Perceptron\n",
        "\n",
        "\n",
        "*   With Data Augmentation\n",
        "*   RGB images\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DkwpojagWc9w",
        "outputId": "86bce1a8-90b6-41b5-9167-8530abea8727"
      },
      "source": [
        "mlp_checkpoint_filepath = '/content/content/MyDrive/mlp_rgb_model_with_aug2'\n",
        "mlp_checkpoint_filename = 'mlp_rgb_model_with_aug2'\n",
        "\n",
        "mlp_rgb_model4 = get_ann_model((64, 64, 3), mlp_checkpoint_filepath, mlp_checkpoint_filename)\n",
        "mlp_rgb_model4.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "flatten_3 (Flatten)          (None, 12288)             0         \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 4096)              50335744  \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 512)               2097664   \n",
            "_________________________________________________________________\n",
            "dense_14 (Dense)             (None, 256)               131328    \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 37)                9509      \n",
            "_________________________________________________________________\n",
            "layer_3 (Layer)              (None, 37)                0         \n",
            "=================================================================\n",
            "Total params: 52,574,245\n",
            "Trainable params: 52,574,245\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dT_MF-jN3Tsv",
        "outputId": "68e5ad4d-fa36-4e1a-f099-ec8f822ad669"
      },
      "source": [
        "num_epochs  = 5\n",
        "bs = 32\n",
        "\n",
        "spe = math.ceil(round(0.8*len(train_img_dir))/bs)\n",
        "val_steps = math.ceil(round(0.2*len(train_img_dir))/bs)\n",
        "\n",
        "stop_callback = myCallback()\n",
        "savemodel_callback = tf.keras.callbacks.ModelCheckpoint(filepath=mlp_checkpoint_filepath, save_weights_only=False, save_best_only=False)\n",
        "\n",
        "train_gen, val_gen, test_gen = get_generators(image_shape = (64,64), batch_size = bs, color_mode = 'rgb', augmentation= True)\n",
        "\n",
        "history = mlp_rgb_model4.fit(train_gen, validation_data = val_gen, epochs= num_epochs, steps_per_epoch = spe, \\\n",
        "                            validation_steps = val_steps, verbose = 1, callbacks=[stop_callback, savemodel_callback])\n",
        "plot_graphs(history, string = 'loss')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training data size 49262\n",
            "Validation data size 12316\n",
            "Epoch 1/5\n",
            "1540/1540 [==============================] - 1607s 1s/step - loss: 0.0358 - val_loss: 0.0274\n",
            "INFO:tensorflow:Assets written to: /content/content/MyDrive/mlp_rgb_model_with_aug2/assets\n",
            "Epoch 2/5\n",
            "1540/1540 [==============================] - 1607s 1s/step - loss: 0.0271 - val_loss: 0.0271\n",
            "INFO:tensorflow:Assets written to: /content/content/MyDrive/mlp_rgb_model_with_aug2/assets\n",
            "Epoch 3/5\n",
            " 978/1540 [==================>...........] - ETA: 9:09 - loss: 0.0270"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "kh_taXq05k1o",
        "outputId": "64acffc0-5d5d-4ed8-f56b-01bd14d9c130"
      },
      "source": [
        "plot_graphs(history, string = 'loss')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-ef6da6367fe4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplot_graphs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhistory\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstring\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'loss'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'plot_graphs' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "id": "sqi4UgZemvgh",
        "outputId": "87274383-d97f-467d-d5be-900c524761de"
      },
      "source": [
        "steps = math.ceil(len(X_test)/bs)\n",
        "print(\"Total Test Images\",len(X_test))\n",
        "\n",
        "test_pred = mlp_rgb_model4.predict(test_gen, batch_size = bs, steps= steps, verbose=1)\n",
        "print(\"Test Prediction Shape\",test_pred.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total Test Images 79975\n",
            "2499/2500 [============================>.] - ETA: 0s"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "InvalidArgumentError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-32-73428f20ca81>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Total Test Images\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_img_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtest_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmlp_rgb_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_gen\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0msteps\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Test Prediction Shape\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtest_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1700\u001b[0m           \u001b[0;32mfor\u001b[0m \u001b[0mstep\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1701\u001b[0m             \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_predict_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1702\u001b[0;31m             \u001b[0mtmp_batch_outputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1703\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1704\u001b[0m               \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    887\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    922\u001b[0m       \u001b[0;31m# In this case we have not created variables on the first call. So we can\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    923\u001b[0m       \u001b[0;31m# run the first trace but we should fail if variables are created.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 924\u001b[0;31m       \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    925\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_created_variables\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    926\u001b[0m         raise ValueError(\"Creating variables on a non-first call to a function\"\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3022\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[1;32m   3023\u001b[0m     return graph_function._call_flat(\n\u001b[0;32m-> 3024\u001b[0;31m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[0m\u001b[1;32m   3025\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3026\u001b[0m   \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1959\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1960\u001b[0m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0;32m-> 1961\u001b[0;31m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[0m\u001b[1;32m   1962\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1963\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    594\u001b[0m               \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m               \u001b[0mattrs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattrs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 596\u001b[0;31m               ctx=ctx)\n\u001b[0m\u001b[1;32m    597\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    598\u001b[0m           outputs = execute.execute_with_cancellation(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0;32m---> 60\u001b[0;31m                                         inputs, attrs, num_outputs)\n\u001b[0m\u001b[1;32m     61\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mInvalidArgumentError\u001b[0m:  Input to reshape is a tensor with 86016 values, but the requested shape requires a multiple of 49152\n\t [[node sequential/flatten/Reshape (defined at /usr/local/lib/python3.7/dist-packages/keras/layers/core.py:672) ]] [Op:__inference_predict_function_1149]\n\nErrors may have originated from an input operation.\nInput Source operations connected to node sequential/flatten/Reshape:\n IteratorGetNext (defined at /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:1533)\t\n sequential/flatten/Const (defined at /usr/local/lib/python3.7/dist-packages/keras/layers/core.py:667)\n\nFunction call stack:\npredict_function\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UM9y2nKxmwEk",
        "outputId": "90436f44-c60a-4c44-986d-b0be60d80427"
      },
      "source": [
        "test_prediction_filename, test_pred_df = createDF(test_pred)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(79975, 37)\n",
            "['633521', '619275']\n",
            "      GalaxyID  Class1.1  Class1.2  ...  Class11.4  Class11.5  Class11.6\n",
            "0       633521  0.387842  0.485520  ...        0.0        0.0   0.066076\n",
            "1       619275  0.434483  0.534984  ...        0.0        0.0   0.063960\n",
            "2       170145  0.440331  0.538160  ...        0.0        0.0   0.065235\n",
            "3       349543  0.407064  0.511812  ...        0.0        0.0   0.061448\n",
            "4       798880  0.462234  0.541889  ...        0.0        0.0   0.072573\n",
            "...        ...       ...       ...  ...        ...        ...        ...\n",
            "79970   467624  0.464735  0.543693  ...        0.0        0.0   0.075170\n",
            "79971   410061  0.392918  0.496341  ...        0.0        0.0   0.062567\n",
            "79972   671680  0.433015  0.526072  ...        0.0        0.0   0.067791\n",
            "79973   813752  0.447817  0.538917  ...        0.0        0.0   0.070284\n",
            "79974   370646  0.433646  0.520262  ...        0.0        0.0   0.072107\n",
            "\n",
            "[79975 rows x 38 columns]\n",
            "Input Filename with csv extensiontest_predictions_7.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "OHaWCPZD-oci",
        "outputId": "9b102212-9ddc-4058-c486-6cc0a88b1e93"
      },
      "source": [
        "#test_pred_df\n",
        "test_prediction_filename\n",
        "download_file('/content/'+ test_prediction_filename)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "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",
              "  "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "application/javascript": [
              "download(\"download_e8d1b165-be34-4eff-9537-0c5078e479b8\", \"test_predictions_7.csv\", 25311802)"
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Yri6M-uCFNzQ"
      },
      "source": [
        "def show_image(directory,filenames,gray = True, subplot_cols = 2):\n",
        "  imgs = []\n",
        "  for filename in filenames:\n",
        "    img = cv2.imread(os.path.join(directory,filename))\n",
        "    if gray:\n",
        "      img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n",
        "    imgs.append(img)\n",
        "  \n",
        "  if subplot_cols==1:\n",
        "    plt.imshow(imgs[0])\n",
        "    return \n",
        "\n",
        "  rows = len(imgs)//subplot_cols\n",
        "  cols = subplot_cols\n",
        "  fig, ax = plt.subplots(rows, cols, figsize = (7,7))\n",
        "  j, k = 0,0\n",
        "  for i in range(len(imgs)):\n",
        "    if k==cols:\n",
        "      k=0\n",
        "      j+=1\n",
        "    if gray:\n",
        "      ax[j,k].imshow(imgs[i], cmap='gray')\n",
        "    else:\n",
        "      ax[j,k].imshow(imgs[i])\n",
        "    k+=1\n",
        "    #ax[j,k].axis('off')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GfwgU8f6FOZI"
      },
      "source": [
        "def show_augmented_images(directory,filename):\n",
        "  img = cv2.imread(os.path.join(directory,filename))\n",
        "  aug_imgs = ImageAugmentation(img[84:340,84:340,:],hflip=True,vflip=False,rot_angle=-45)\n",
        "  fig, axis = plt.subplots(2,2)\n",
        "  axis[0,0].imshow(aug_imgs[0])\n",
        "  axis[0,1].imshow(aug_imgs[1])\n",
        "  axis[1,0].imshow(aug_imgs[2])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "3roqchwsVtQQ",
        "outputId": "7538e41c-35f7-4e2d-a0a9-e3c31c709520"
      },
      "source": [
        "lst = [train_img_dir[80], train_img_dir[82], train_img_dir[92], train_img_dir[93]]\n",
        "show_image(train_imgs_path, lst,  gray = False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qOoJQXYPxp-P",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "outputId": "4c1402e5-51ad-49d6-f3e1-ddb5d23d8fc0"
      },
      "source": [
        "img = cv2.imread(os.path.join(train_imgs_path,train_img_dir[82]))\n",
        "plt.imshow(img)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f78c600ccd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "RaCjkZRWQc9r",
        "outputId": "f0b85500-468e-4048-e8bb-91c9d6be35e4"
      },
      "source": [
        "img_cropped = img[100:324,100:324,:]\n",
        "plt.imshow(img_cropped)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f78c5ed7cd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "pL-Ig1qOSnzu",
        "outputId": "ef7d27e9-1a32-4b91-d9db-5d1a4ec592a0"
      },
      "source": [
        "img_resized = cv2.resize(img_cropped, (128, 128), interpolation = cv2.INTER_AREA)\n",
        "plt.imshow(img_resized)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f78c5dcb050>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "ucrFxENQTVUc",
        "outputId": "4a908401-2c55-4232-e013-4b4ffc03ea36"
      },
      "source": [
        "img2 = cv2.imread(os.path.join(train_imgs_path,train_img_dir[982]))\n",
        "plt.imshow(img2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f78c8385990>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0giomPU3T5le"
      },
      "source": [
        "image = img2[100:324, 100:324, :]\n",
        "image = cv2.resize(image, (128, 128), interpolation = cv2.INTER_AREA)\n",
        "#augmentated_imgs = ImageAugmentation(image, hflip = True, vflip = True, rot_angle = 180, bright = True, z = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "o1fgAdcFYdkq",
        "outputId": "539c604a-2383-4986-9a82-9f12cd7e5db3"
      },
      "source": [
        "fig, ax = plt.subplots(nrows = 1, ncols = 2, figsize =(7,15))\n",
        "plt.subplots_adjust( wspace = 0.5)\n",
        "\n",
        "bright_image = brightness(image, low = 1, high = 2.0) \n",
        "ax[0].imshow(image)\n",
        "ax[1].imshow(bright_image)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f78c4741290>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "D3bfXHJeZ_1s",
        "outputId": "a5b48664-1731-4a1f-e531-ce2060f638db"
      },
      "source": [
        "plt.imshow(augmentated_imgs[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f78c6957890>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8gfZZZCbwYjb"
      },
      "source": [
        "#Transfer Learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KGKyo45hwWUF"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}