Flower_Classification_and_augmentation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyP4QQV/P8ruUbLDZ2U76Pge",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MUKESH-SK/Machine-Learning-Deep-Learning-Projects/blob/main/Flower_Classification_and_augmentation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4SJndBodyQ2T"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import cv2\n",
        "import os\n",
        "import PIL\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.models import Sequential"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_url = \"https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\"\n",
        "data_dir = tf.keras.utils.get_file('flower_photos',origin = dataset_url,cache_dir=\".\",untar=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TAYTF0CuzD5t",
        "outputId": "16018bc4-17e6-4985-b573-a149eddaea84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz\n",
            "228813984/228813984 [==============================] - 4s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_dir"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "F7D20Vkqy4bD",
        "outputId": "d0478ab3-79ce-474a-f61b-486205c4edf6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'./datasets/flower_photos'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pathlib\n",
        "data_dir = pathlib.Path(data_dir)\n",
        "data_dir"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SPUJDioh0CVm",
        "outputId": "9b07d9c7-4ec9-46fd-c9b9-181f62c20b8e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PosixPath('datasets/flower_photos')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "image_count=len(list(data_dir.glob(\"*/*.jpg\")))\n",
        "image_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yOBSNDIb0hoA",
        "outputId": "a783c937-805f-4b6f-9674-a7eb68da35f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3670"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "roses = list(data_dir.glob(\"roses/*\"))\n",
        "roses[:5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-16Nj5P03n-",
        "outputId": "5e32d2f9-8fa8-46b8-e577-3057dfcc870b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[PosixPath('datasets/flower_photos/roses/4675532860_890504a4a3_m.jpg'),\n",
              " PosixPath('datasets/flower_photos/roses/22982871191_ec61e36939_n.jpg'),\n",
              " PosixPath('datasets/flower_photos/roses/3208417632_19138d8e35_n.jpg'),\n",
              " PosixPath('datasets/flower_photos/roses/23891005905_17ce9e6936.jpg'),\n",
              " PosixPath('datasets/flower_photos/roses/4575042086_7674b76297_n.jpg')]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PIL.Image.open(str(roses[5]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "id": "R7oAXt6P1VY3",
        "outputId": "e1734e4c-4ba7-4722-eeca-625b8bf699e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=320x213 at 0x7FDDBB270CA0>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "flowers_images_dict ={\n",
        "    'roses': list(data_dir.glob('roses/*')),\n",
        "    'daisy': list(data_dir.glob('daisy/*')),\n",
        "    'dandelion': list(data_dir.glob('dandelion/*')),\n",
        "    'sunflowers': list(data_dir.glob('sunflowers/*')),\n",
        "    'tulips': list(data_dir.glob('tulips/*')),\n",
        "}"
      ],
      "metadata": {
        "id": "AkWPNCFA1kUs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "flowers_labels_dict={\n",
        "    'roses': 0,\n",
        "    'daisy': 1,\n",
        "    'dandelion':2,\n",
        "    'sunflowers':3,\n",
        "    'tulips':4,\n",
        "}"
      ],
      "metadata": {
        "id": "1B1H3T7C2mbz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "str(flowers_images_dict['roses'][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "F5Vm_4vO3TeS",
        "outputId": "23b64f8f-778c-4428-ffed-aa19a287e146"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'datasets/flower_photos/roses/4675532860_890504a4a3_m.jpg'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# converting an image into an array\n",
        "img = cv2.imread(str(flowers_images_dict['roses'][0]))\n",
        "img.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zfXK5lSf284J",
        "outputId": "ff839609-b80e-42cc-9bbf-e7c9ae1e015e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(240, 160, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# making the dimension same for all image\n",
        "X,y = [],[]\n",
        "for flower_name,images in flowers_images_dict.items():\n",
        "  for image in images:\n",
        "    img = cv2.imread(str(image))\n",
        "    resized_img = cv2.resize(img,(180,180))\n",
        "    X.append(resized_img)\n",
        "    y.append(flowers_labels_dict[flower_name])"
      ],
      "metadata": {
        "id": "MfGeA6xQ3dU9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X= np.array(X)\n",
        "y= np.array(y)"
      ],
      "metadata": {
        "id": "z0S6ln6u47ZQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 0)"
      ],
      "metadata": {
        "id": "GLd20t2o6MOD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9BrPbAi265XH",
        "outputId": "2971ab72-cd6f-4b3e-c8fc-baffa97cf0b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[102, 111, 100],\n",
              "        [ 83, 112,  76],\n",
              "        [ 83,  86,  77],\n",
              "        ...,\n",
              "        [ 94, 177, 183],\n",
              "        [105, 179, 183],\n",
              "        [103, 179, 182]],\n",
              "\n",
              "       [[ 97, 119,  71],\n",
              "        [ 81,  87,  62],\n",
              "        [ 76,  65,  71],\n",
              "        ...,\n",
              "        [ 86, 173, 180],\n",
              "        [ 96, 177, 184],\n",
              "        [102, 181, 188]],\n",
              "\n",
              "       [[ 99, 119,  82],\n",
              "        [ 84,  71,  67],\n",
              "        [ 86,  78,  81],\n",
              "        ...,\n",
              "        [ 80, 172, 177],\n",
              "        [ 86, 173, 181],\n",
              "        [ 92, 176, 186]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[129, 174, 232],\n",
              "        [128, 176, 230],\n",
              "        [129, 180, 231],\n",
              "        ...,\n",
              "        [ 42, 109, 124],\n",
              "        [ 42, 109, 124],\n",
              "        [ 43, 110, 125]],\n",
              "\n",
              "       [[121, 171, 230],\n",
              "        [124, 178, 230],\n",
              "        [127, 185, 231],\n",
              "        ...,\n",
              "        [ 43, 110, 125],\n",
              "        [ 44, 111, 126],\n",
              "        [ 45, 112, 127]],\n",
              "\n",
              "       [[116, 171, 232],\n",
              "        [122, 179, 232],\n",
              "        [123, 184, 231],\n",
              "        ...,\n",
              "        [ 43, 110, 125],\n",
              "        [ 44, 111, 126],\n",
              "        [ 45, 112, 127]]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_scaled=X_train/255\n",
        "X_test_scaled= X_test/255"
      ],
      "metadata": {
        "id": "GerXntLT-Sid"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cnn = Sequential([\n",
        "    layers.Conv2D(16,3,padding = \"same\",activation = \"relu\"),\n",
        "    layers.MaxPooling2D(),\n",
        "    layers.Conv2D(32,3,padding = \"same\",activation = \"relu\"),\n",
        "    layers.MaxPooling2D(),\n",
        "    layers.Conv2D(64,3,padding = \"same\",activation = \"relu\"),\n",
        "    layers.MaxPooling2D(),\n",
        "\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(128,activation ='relu'),\n",
        "    layers.Dense(5)\n",
        "])\n",
        "cnn.compile(\n",
        "    optimizer = tf.keras.optimizers.Adam(),\n",
        "    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = True),\n",
        "    metrics = ['accuracy']\n",
        ")\n",
        "cnn.fit(X_train_scaled,y_train,epochs =15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tjv-Eu02-aVk",
        "outputId": "722cbef7-b026-4116-a940-30fd6129ecbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "86/86 [==============================] - 66s 761ms/step - loss: 1.4019 - accuracy: 0.4110\n",
            "Epoch 2/15\n",
            "86/86 [==============================] - 79s 925ms/step - loss: 0.9875 - accuracy: 0.6166\n",
            "Epoch 3/15\n",
            "86/86 [==============================] - 75s 878ms/step - loss: 0.7945 - accuracy: 0.7057\n",
            "Epoch 4/15\n",
            "86/86 [==============================] - 69s 807ms/step - loss: 0.6486 - accuracy: 0.7613\n",
            "Epoch 5/15\n",
            "86/86 [==============================] - 74s 863ms/step - loss: 0.4607 - accuracy: 0.8307\n",
            "Epoch 6/15\n",
            "86/86 [==============================] - 67s 784ms/step - loss: 0.2832 - accuracy: 0.9092\n",
            "Epoch 7/15\n",
            "86/86 [==============================] - 69s 806ms/step - loss: 0.1624 - accuracy: 0.9524\n",
            "Epoch 8/15\n",
            "86/86 [==============================] - 90s 1s/step - loss: 0.0914 - accuracy: 0.9753\n",
            "Epoch 9/15\n",
            "86/86 [==============================] - 67s 785ms/step - loss: 0.0573 - accuracy: 0.9855\n",
            "Epoch 10/15\n",
            "86/86 [==============================] - 77s 889ms/step - loss: 0.0409 - accuracy: 0.9909\n",
            "Epoch 11/15\n",
            "86/86 [==============================] - 70s 812ms/step - loss: 0.0427 - accuracy: 0.9906\n",
            "Epoch 12/15\n",
            "86/86 [==============================] - 67s 775ms/step - loss: 0.0374 - accuracy: 0.9906\n",
            "Epoch 13/15\n",
            "86/86 [==============================] - 69s 804ms/step - loss: 0.0655 - accuracy: 0.9775\n",
            "Epoch 14/15\n",
            "86/86 [==============================] - 84s 981ms/step - loss: 0.0446 - accuracy: 0.9876\n",
            "Epoch 15/15\n",
            "86/86 [==============================] - 70s 815ms/step - loss: 0.0475 - accuracy: 0.9866\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fdd77921c70>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.evaluate(X_test_scaled,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KbRLiOVIAY_X",
        "outputId": "b8f03936-339b-4f02-bf99-9f7def8e669a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "29/29 [==============================] - 7s 242ms/step - loss: 2.1035 - accuracy: 0.6133\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.103543519973755, 0.6132897734642029]"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions= cnn.predict(X_test_scaled)\n",
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6UyKhha8FOPP",
        "outputId": "aa1879c1-90de-490a-8700-3671b2c76bc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "29/29 [==============================] - 7s 232ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "score = tf.nn.softmax(predictions[0])\n",
        "score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QYNdku9ZFXZY",
        "outputId": "90d77a15-6fd0-4e8d-a1c3-b036758c97b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(5,), dtype=float32, numpy=\n",
              "array([1.1971776e-09, 9.9999774e-01, 4.2278441e-07, 1.7920852e-14,\n",
              "       1.8342531e-06], dtype=float32)>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.argmax(score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "asF7H5PRFx8-",
        "outputId": "93b2c72c-ed23-47be-8c49-068ad8858384"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yaancULmGCO7",
        "outputId": "977bc606-b8ce-4268-941a-1f560b50003b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "img_height = 180\n",
        "img_width= 180"
      ],
      "metadata": {
        "id": "mXL3of39RN9q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_augmentation = keras.Sequential(\n",
        "  [\n",
        "    layers.experimental.preprocessing.RandomFlip(\"horizontal\",\n",
        "                                                 input_shape=(img_height,\n",
        "                                                              img_width,\n",
        "                                                              3)),\n",
        "    layers.experimental.preprocessing.RandomRotation(0.1),\n",
        "    layers.experimental.preprocessing.RandomZoom(0.1),\n",
        "  ]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qUklfPuBPw63",
        "outputId": "2e1a0826-a101-405f-dbbf-a4ecea059ddf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn = Sequential([\n",
        "    data_augmentation,\n",
        "    layers.Conv2D(16,3,padding = \"same\",activation = \"relu\"),\n",
        "    layers.MaxPooling2D(),\n",
        "    layers.Conv2D(32,3,padding = \"same\",activation = \"relu\"),\n",
        "    layers.MaxPooling2D(),\n",
        "    layers.Conv2D(64,3,padding = \"same\",activation = \"relu\"),\n",
        "    layers.MaxPooling2D(),\n",
        "    layers.Dropout(0.2),\n",
        "    layers.Flatten(),\n",
        "    layers.Dense(128,activation ='relu'),\n",
        "    layers.Dense(5)\n",
        "])\n",
        "cnn.compile(\n",
        "    optimizer = tf.keras.optimizers.Adam(),\n",
        "    loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = True),\n",
        "    metrics = ['accuracy']\n",
        ")\n",
        "cnn.fit(X_train_scaled,y_train,epochs =15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EEt0OgQfJAw7",
        "outputId": "5e403a9f-dc4b-4325-8620-5e558dce008b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting RngReadAndSkip cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting Bitcast cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting StatelessRandomUniformV2 cause there is no registered converter for this op.\n",
            "WARNING:tensorflow:Using a while_loop for converting ImageProjectiveTransformV3 cause there is no registered converter for this op.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "86/86 [==============================] - 79s 885ms/step - loss: 1.3428 - accuracy: 0.4073\n",
            "Epoch 2/15\n",
            "86/86 [==============================] - 76s 881ms/step - loss: 1.0610 - accuracy: 0.5745\n",
            "Epoch 3/15\n",
            "86/86 [==============================] - 75s 872ms/step - loss: 0.9442 - accuracy: 0.6283\n",
            "Epoch 4/15\n",
            "86/86 [==============================] - 75s 874ms/step - loss: 0.8676 - accuracy: 0.6664\n",
            "Epoch 5/15\n",
            "86/86 [==============================] - 76s 882ms/step - loss: 0.7995 - accuracy: 0.6919\n",
            "Epoch 6/15\n",
            "86/86 [==============================] - 76s 880ms/step - loss: 0.7601 - accuracy: 0.7140\n",
            "Epoch 7/15\n",
            "86/86 [==============================] - 75s 873ms/step - loss: 0.7082 - accuracy: 0.7322\n",
            "Epoch 8/15\n",
            "86/86 [==============================] - 78s 910ms/step - loss: 0.6697 - accuracy: 0.7456\n",
            "Epoch 9/15\n",
            "86/86 [==============================] - 76s 884ms/step - loss: 0.6335 - accuracy: 0.7718\n",
            "Epoch 10/15\n",
            "86/86 [==============================] - 76s 881ms/step - loss: 0.5983 - accuracy: 0.7733\n",
            "Epoch 11/15\n",
            "86/86 [==============================] - 75s 877ms/step - loss: 0.5438 - accuracy: 0.7958\n",
            "Epoch 12/15\n",
            "86/86 [==============================] - 76s 879ms/step - loss: 0.5382 - accuracy: 0.7932\n",
            "Epoch 13/15\n",
            "86/86 [==============================] - 75s 868ms/step - loss: 0.4799 - accuracy: 0.8180\n",
            "Epoch 14/15\n",
            "86/86 [==============================] - 75s 875ms/step - loss: 0.4426 - accuracy: 0.8325\n",
            "Epoch 15/15\n",
            "86/86 [==============================] - 75s 870ms/step - loss: 0.4434 - accuracy: 0.8408\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7fdd7888d970>"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.evaluate(X_test_scaled,y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gXH8uu9VKS8Q",
        "outputId": "fd469062-af84-47a7-c720-03f5129087ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "29/29 [==============================] - 7s 223ms/step - loss: 0.9834 - accuracy: 0.6852\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9833586812019348, 0.6851851940155029]"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "69jxI_X-XNzN"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}