Model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled1.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X3XwEu530ZpM",
        "outputId": "752ccdbe-279b-4968-e1ce-085b6271ed2c"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "me88iWM11Ody"
      },
      "source": [
        "import  os\n",
        "import cv2\n",
        "import keras\n",
        "from PIL import Image\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.models import load_model\n",
        "from matplotlib import pyplot\n",
        "from keras.optimizers import Adam\n",
        "from keras.models import model_from_json\n",
        "from keras.models import Sequential\n",
        "from sklearn.metrics import classification_report,confusion_matrix\n",
        "from keras.layers import Dense,Flatten,Conv2D,MaxPool2D,Dropout\n",
        "from tensorflow.keras.preprocessing import image\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
        "from tensorflow.python.keras import backend as K\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YrSt5Rq81Py6",
        "outputId": "ea6fd7aa-87ae-4026-be80-50f82dff463f"
      },
      "source": [
        "categories = os.listdir('/content/gdrive/My Drive/food20dataset/test_set')\n",
        "\n",
        "img_size = 256\n",
        "def get_data(data_dir):\n",
        "    data = []\n",
        "    for category in categories:\n",
        "        path = os.path.join(data_dir, category)\n",
        "        class_num = categories.index(category)\n",
        "        for img in os.listdir(path):\n",
        "            try:\n",
        "                img_arr = cv2.imread(os.path.join(path,img))\n",
        "                resized_arr = cv2.resize(img_arr,(img_size,img_size))\n",
        "                data.append([resized_arr, class_num])\n",
        "            except Exception as e:\n",
        "                print(e)\n",
        "    return np.array(data)        \n",
        "    \n",
        "    \n",
        "train = get_data('/content/gdrive/My Drive/food20dataset/train_set')  \n",
        "val = get_data('/content/gdrive/My Drive/food20dataset/test_set')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:16: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n",
            "  app.launch_new_instance()\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jpaIqq9t1SXU"
      },
      "source": [
        "x_train = []\n",
        "y_train = []\n",
        "x_val = []\n",
        "y_val = []\n",
        "for feature, label in train:\n",
        "    x_train.append(feature)\n",
        "    y_train.append(label)\n",
        "    \n",
        "for feature, label in val:\n",
        "    x_val.append(feature)\n",
        "    y_val.append(label)\n",
        "    \n",
        "x_train = np.array(x_train) / 255\n",
        "x_val = np.array(x_val) / 255\n",
        "\n",
        "x_train.reshape(-1, img_size,img_size,1)\n",
        "y_train = np.array(y_train)\n",
        "\n",
        "x_val.reshape(-1,img_size,img_size,1)\n",
        "y_val = np.array(y_val)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b8JS-cOH1ZXh",
        "outputId": "b645115d-4aa2-49e5-ad72-c77552590165"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(128,3,padding='same',activation='relu',input_shape=(256,256,3)))\n",
        "model.add(MaxPool2D())\n",
        "\n",
        "model.add(Conv2D(32,3,padding='same',activation='relu'))\n",
        "model.add(MaxPool2D())\n",
        "\n",
        "model.add(Conv2D(64,3,padding='same',activation='relu'))\n",
        "model.add(MaxPool2D())\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(256,activation='relu'))\n",
        "model.add(Dense(14,activation='softmax'))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 256, 256, 128)     3584      \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 128, 128, 128)     0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 128, 128, 32)      36896     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 64, 64, 32)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 64, 64, 64)        18496     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 32, 32, 64)        0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 65536)             0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 256)               16777472  \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 14)                3598      \n",
            "=================================================================\n",
            "Total params: 16,840,046\n",
            "Trainable params: 16,840,046\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-4fBAl9N1fMS"
      },
      "source": [
        "opt = Adam(lr =0.00012)\n",
        "model.compile(optimizer = opt, loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits = True), metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYC3kJGJ1hZq",
        "outputId": "5ed6f154-92f1-4d32-a638-d16cce7e667f"
      },
      "source": [
        "history = model.fit(x_train,y_train, epochs = 13, validation_data=(x_val,y_val))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/13\n",
            "31/31 [==============================] - 215s 7s/step - loss: 2.6592 - accuracy: 0.0804 - val_loss: 2.5186 - val_accuracy: 0.1595\n",
            "Epoch 2/13\n",
            "31/31 [==============================] - 218s 7s/step - loss: 2.4444 - accuracy: 0.1799 - val_loss: 2.1021 - val_accuracy: 0.3048\n",
            "Epoch 3/13\n",
            "31/31 [==============================] - 218s 7s/step - loss: 2.0263 - accuracy: 0.3613 - val_loss: 1.8009 - val_accuracy: 0.4548\n",
            "Epoch 4/13\n",
            "31/31 [==============================] - 214s 7s/step - loss: 1.7352 - accuracy: 0.4753 - val_loss: 1.6026 - val_accuracy: 0.4952\n",
            "Epoch 5/13\n",
            "31/31 [==============================] - 223s 7s/step - loss: 1.3989 - accuracy: 0.5858 - val_loss: 1.5031 - val_accuracy: 0.4976\n",
            "Epoch 6/13\n",
            "31/31 [==============================] - 217s 7s/step - loss: 1.1446 - accuracy: 0.6734 - val_loss: 1.2923 - val_accuracy: 0.5857\n",
            "Epoch 7/13\n",
            "31/31 [==============================] - 217s 7s/step - loss: 0.8441 - accuracy: 0.7673 - val_loss: 1.2097 - val_accuracy: 0.6595\n",
            "Epoch 8/13\n",
            "31/31 [==============================] - 232s 7s/step - loss: 0.6391 - accuracy: 0.8379 - val_loss: 1.2265 - val_accuracy: 0.6357\n",
            "Epoch 9/13\n",
            "31/31 [==============================] - 219s 7s/step - loss: 0.5081 - accuracy: 0.8797 - val_loss: 1.2404 - val_accuracy: 0.6310\n",
            "Epoch 10/13\n",
            "31/31 [==============================] - 229s 7s/step - loss: 0.3262 - accuracy: 0.9278 - val_loss: 1.1733 - val_accuracy: 0.6667\n",
            "Epoch 11/13\n",
            "31/31 [==============================] - 226s 7s/step - loss: 0.2191 - accuracy: 0.9674 - val_loss: 1.1031 - val_accuracy: 0.7119\n",
            "Epoch 12/13\n",
            "31/31 [==============================] - 220s 7s/step - loss: 0.1118 - accuracy: 0.9890 - val_loss: 1.1617 - val_accuracy: 0.7024\n",
            "Epoch 13/13\n",
            "31/31 [==============================] - 229s 7s/step - loss: 0.0937 - accuracy: 0.9919 - val_loss: 1.1311 - val_accuracy: 0.7143\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 403
        },
        "id": "I1ZouTM6B5Z_",
        "outputId": "17993104-d58e-4e4f-c691-3bd3c2167036"
      },
      "source": [
        "epochs_range = range(13)\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "fig,(ax1,ax2) = plt.subplots(1,2,figsize=(15,6))\n",
        "plt.rc('xtick',labelsize=10)\n",
        "plt.rc('ytick',labelsize=10)\n",
        "ax1.plot(epochs_range,acc,label='Training Accuracy',c='blue',linewidth = 4)\n",
        "ax1.plot(epochs_range,val_acc, label='vaalidation_accuracy',c='red',linewidth=4)\n",
        "ax1.legend()\n",
        "ax1.set_title('Training and Accuracy')\n",
        "ax1.set_xlabel('Accuracy', fontsize=10)\n",
        "ax1.set_ylabel('Val_accuracy', fontsize=10)\n",
        "\n",
        "\n",
        "ax2.plot(epochs_range,loss,label='Training Loss',c='green',linewidth = 4)\n",
        "ax2.plot(epochs_range,val_loss, label='vaalidation_loss',c='orange',linewidth=4)\n",
        "ax2.legend()\n",
        "ax2.set_title('Training and Validation')\n",
        "ax2.set_xlabel('Loss', fontsize=10)\n",
        "ax2.set_ylabel('Val_loss', fontsize=10)\n",
        "\n",
        "fig.tight_layout(pad=3.0)\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nUPUPnHMB-re",
        "outputId": "ecf9fdf2-ea4b-4f12-db1b-2a7d65f55dc1"
      },
      "source": [
        "predictions = model.predict_classes(x_val)\n",
        "predictions = predictions.reshape(1,-1)[0]\n",
        "print(classification_report(y_val,predictions,target_names=categories))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "                  precision    recall  f1-score   support\n",
            "\n",
            "           poori       0.87      0.67      0.75        30\n",
            "tandoori chicken       0.79      0.87      0.83        30\n",
            "      kathi roll       0.70      0.53      0.60        30\n",
            "      ven pongal       0.61      0.83      0.70        30\n",
            "            upma       0.74      0.57      0.64        30\n",
            "         noodles       0.87      0.67      0.75        30\n",
            "       paniyaram       0.69      0.83      0.76        30\n",
            "        vada pav       0.77      0.67      0.71        30\n",
            "          dhokla       0.84      0.87      0.85        30\n",
            "     gulab jamun       0.62      0.50      0.56        30\n",
            "           chaat       0.76      0.87      0.81        30\n",
            "            idly       0.72      0.70      0.71        30\n",
            "           halwa       0.49      0.70      0.58        30\n",
            "        chappati       0.71      0.73      0.72        30\n",
            "\n",
            "        accuracy                           0.71       420\n",
            "       macro avg       0.73      0.71      0.71       420\n",
            "    weighted avg       0.73      0.71      0.71       420\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 355
        },
        "id": "Sxvaf4o5kxsz",
        "outputId": "3c706419-dc01-4f65-951d-b5bec93ffd70"
      },
      "source": [
        "cm1 = confusion_matrix(y_val,predictions)\n",
        "df_cm  = pd.DataFrame(cm1,index=[i for i in categories],columns = [i for i in categories])\n",
        "plt.figure(figsize=(12,4))\n",
        "import seaborn as ns\n",
        "ns.heatmap(df_cm,annot=True,cmap='RdPu')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3726bf2390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yv_4_nihNCTK"
      },
      "source": [
        "model.save('/content/gdrive/My Drive/food20dataset/my_modelcv14.h5')\n",
        "model_json = model.to_json()\n",
        "with open(\"model14.json\", \"w\") as json_file:\n",
        "    json_file.write(model_json)\n",
        "# serialize weights to HDF5\n",
        "model.save_weights('/content/gdrive/My Drive/food20dataset/my_modeljsn14.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7NnFjwxvNnbx",
        "outputId": "305d6271-fe58-44cb-fa50-ea3557f40620"
      },
      "source": [
        "model.save('/content/gdrive/My Drive/food20dataset/my_modelcv14.pb')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /content/gdrive/My Drive/food20dataset/my_modelcv14.pb/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JLIQ4WiWOkUM",
        "outputId": "ad945cab-62b3-44fe-e3da-487f8d85a6a2"
      },
      "source": [
        "json_file = open('model14.json', 'r')\n",
        "loaded_model_json = json_file.read()\n",
        "json_file.close()\n",
        "loaded_model = model_from_json(loaded_model_json)\n",
        "# load weights into new model\n",
        "loaded_model.load_weights('/content/gdrive/My Drive/food20dataset/my_modeljsn14.h5')\n",
        "img = cv2.imread('/content/gdrive/My Drive/food20dataset/pred/dhokla.jpg')\n",
        "img = cv2.resize(img,(256,256))\n",
        "img = np.reshape(img,[1,256,256,3])\n",
        "\n",
        "classes = np.argmax(loaded_model.predict(img),axis=-1)\n",
        "print(categories)\n",
        "print(classes)\n",
        "\n",
        "names = [categories[i] for i in classes]\n",
        "\n",
        "print(names)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:8 out of the last 21 calls to <function Model.make_predict_function.<locals>.predict_function at 0x7f371b6deef0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
            "['poori', 'tandoori chicken', 'kathi roll', 'ven pongal', 'upma', 'noodles', 'paniyaram', 'vada pav', 'dhokla', 'gulab jamun', 'chaat', 'idly', 'halwa', 'chappati']\n",
            "[8]\n",
            "['dhokla']\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}