Food_101.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Food-101.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMLFMywnhSrxJQ8czd1vGXq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Shakilgithub20/Basic-Computer-Vision-DL/blob/main/Food_101.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PMqnHfSKTKjS",
        "outputId": "9e2d8ba0-9bb3-4e18-b18a-c2b854207720"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-05-24 18:11:25--  https://storage.googleapis.com/ztm_tf_course/food_vision/pizza_steak.zip\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.197.128, 74.125.135.128, 74.125.195.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.197.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 109540975 (104M) [application/zip]\n",
            "Saving to: ‘pizza_steak.zip.1’\n",
            "\n",
            "pizza_steak.zip.1   100%[===================>] 104.47M   308MB/s    in 0.3s    \n",
            "\n",
            "2022-05-24 18:11:25 (308 MB/s) - ‘pizza_steak.zip.1’ saved [109540975/109540975]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "import zipfile\n",
        "\n",
        "# Download zip file of pizza_steak images\n",
        "!wget https://storage.googleapis.com/ztm_tf_course/food_vision/pizza_steak.zip \n",
        "\n",
        "# Unzip the downloaded file\n",
        "zip_ref = zipfile.ZipFile(\"pizza_steak.zip\", \"r\")\n",
        "zip_ref.extractall()\n",
        "zip_ref.close()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls pizza_steak"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kQ1SxP1dTfTO",
        "outputId": "978cd7d1-a0df-4736-8aed-6469fe28547d"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test  train\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls pizza_steak/train/steak/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KLxddLIJTg5o",
        "outputId": "65222238-c2bc-443a-aa74-c902454fa746"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "# Another way to find out how many images are in a file\n",
        "num_steak_images_train = len(os.listdir(\"pizza_steak/train/steak\"))\n",
        "\n",
        "num_steak_images_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ssqAdAz6ToaF",
        "outputId": "3d6cf59e-3395-4e42-b4d3-a6c65164e003"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "750"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the class names (programmatically, this is much more helpful with a longer list of classes)\n",
        "import pathlib\n",
        "import numpy as np\n",
        "data_dir = pathlib.Path(\"pizza_steak/train/\") # turn our training path into a Python path\n",
        "class_names = np.array(sorted([item.name for item in data_dir.glob('*')])) # created a list of class_names from the subdirectories\n",
        "print(class_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x9EeU6tATt80",
        "outputId": "ad873bd6-35a1-4763-a48b-38664b06b45f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['pizza' 'steak']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# View an image\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import random\n",
        "\n",
        "def view_random_image(target_dir, target_class):\n",
        "  # Setup target directory (we'll view images from here)\n",
        "  target_folder = target_dir+target_class\n",
        "\n",
        "  # Get a random image path\n",
        "  random_image = random.sample(os.listdir(target_folder), 1)\n",
        "\n",
        "  # Read in the image and plot it using matplotlib\n",
        "  img = mpimg.imread(target_folder + \"/\" + random_image[0])\n",
        "  plt.imshow(img)\n",
        "  plt.title(target_class)\n",
        "  plt.axis(\"off\");\n",
        "\n",
        "  print(f\"Image shape: {img.shape}\") # show the shape of the image\n",
        "\n",
        "  return img"
      ],
      "metadata": {
        "id": "dab7XaEwTw48"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# View a random image from the training dataset\n",
        "img = view_random_image(target_dir=\"pizza_steak/train/\",\n",
        "                        target_class=\"steak\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "sBerNZyVT0ma",
        "outputId": "1886505a-45e5-4a1f-b4f8-9546f9ba30e8"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Image shape: (512, 512, 3)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# View the img (actually just a big array/tensor)\n",
        "img"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "slFdQMRFT27W",
        "outputId": "e41ada95-541b-4e69-891b-0e22c1e44802"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[186, 154, 116],\n",
              "        [189, 157, 119],\n",
              "        [188, 156, 118],\n",
              "        ...,\n",
              "        [ 22,  22,  10],\n",
              "        [ 16,  18,   7],\n",
              "        [ 10,  11,   3]],\n",
              "\n",
              "       [[185, 152, 117],\n",
              "        [187, 154, 119],\n",
              "        [186, 153, 118],\n",
              "        ...,\n",
              "        [ 21,  21,  11],\n",
              "        [ 16,  16,   8],\n",
              "        [ 12,  12,   4]],\n",
              "\n",
              "       [[185, 152, 117],\n",
              "        [185, 152, 117],\n",
              "        [182, 149, 114],\n",
              "        ...,\n",
              "        [ 19,  19,  11],\n",
              "        [ 15,  14,   9],\n",
              "        [ 12,  11,   6]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[182, 147, 115],\n",
              "        [179, 144, 112],\n",
              "        [175, 141, 106],\n",
              "        ...,\n",
              "        [170, 135, 103],\n",
              "        [166, 131,  99],\n",
              "        [164, 129,  97]],\n",
              "\n",
              "       [[177, 142, 110],\n",
              "        [176, 142, 107],\n",
              "        [175, 141, 106],\n",
              "        ...,\n",
              "        [168, 133, 101],\n",
              "        [166, 131,  99],\n",
              "        [164, 129,  97]],\n",
              "\n",
              "       [[170, 136, 101],\n",
              "        [172, 138, 103],\n",
              "        [174, 140, 105],\n",
              "        ...,\n",
              "        [167, 132, 100],\n",
              "        [167, 132, 100],\n",
              "        [167, 132, 100]]], dtype=uint8)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# View the image shape\n",
        "img.shape # returns (width, height, colour channels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dF8R-wZrT5Z0",
        "outputId": "e8b91435-241b-44a7-f3e4-e7bf750aa57f"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(512, 512, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get all the pixel values between 0 & 1\n",
        "img/255. "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hmvf5ZcJT7sl",
        "outputId": "bf112775-5773-4d38-96bb-e2253a9f7de1"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0.72941176, 0.60392157, 0.45490196],\n",
              "        [0.74117647, 0.61568627, 0.46666667],\n",
              "        [0.7372549 , 0.61176471, 0.4627451 ],\n",
              "        ...,\n",
              "        [0.08627451, 0.08627451, 0.03921569],\n",
              "        [0.0627451 , 0.07058824, 0.02745098],\n",
              "        [0.03921569, 0.04313725, 0.01176471]],\n",
              "\n",
              "       [[0.7254902 , 0.59607843, 0.45882353],\n",
              "        [0.73333333, 0.60392157, 0.46666667],\n",
              "        [0.72941176, 0.6       , 0.4627451 ],\n",
              "        ...,\n",
              "        [0.08235294, 0.08235294, 0.04313725],\n",
              "        [0.0627451 , 0.0627451 , 0.03137255],\n",
              "        [0.04705882, 0.04705882, 0.01568627]],\n",
              "\n",
              "       [[0.7254902 , 0.59607843, 0.45882353],\n",
              "        [0.7254902 , 0.59607843, 0.45882353],\n",
              "        [0.71372549, 0.58431373, 0.44705882],\n",
              "        ...,\n",
              "        [0.0745098 , 0.0745098 , 0.04313725],\n",
              "        [0.05882353, 0.05490196, 0.03529412],\n",
              "        [0.04705882, 0.04313725, 0.02352941]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[0.71372549, 0.57647059, 0.45098039],\n",
              "        [0.70196078, 0.56470588, 0.43921569],\n",
              "        [0.68627451, 0.55294118, 0.41568627],\n",
              "        ...,\n",
              "        [0.66666667, 0.52941176, 0.40392157],\n",
              "        [0.65098039, 0.51372549, 0.38823529],\n",
              "        [0.64313725, 0.50588235, 0.38039216]],\n",
              "\n",
              "       [[0.69411765, 0.55686275, 0.43137255],\n",
              "        [0.69019608, 0.55686275, 0.41960784],\n",
              "        [0.68627451, 0.55294118, 0.41568627],\n",
              "        ...,\n",
              "        [0.65882353, 0.52156863, 0.39607843],\n",
              "        [0.65098039, 0.51372549, 0.38823529],\n",
              "        [0.64313725, 0.50588235, 0.38039216]],\n",
              "\n",
              "       [[0.66666667, 0.53333333, 0.39607843],\n",
              "        [0.6745098 , 0.54117647, 0.40392157],\n",
              "        [0.68235294, 0.54901961, 0.41176471],\n",
              "        ...,\n",
              "        [0.65490196, 0.51764706, 0.39215686],\n",
              "        [0.65490196, 0.51764706, 0.39215686],\n",
              "        [0.65490196, 0.51764706, 0.39215686]]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "# Set the seed\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "# Preprocess data (get all of the pixel values between 1 and 0, also called scaling/normalization)\n",
        "train_datagen = ImageDataGenerator(rescale=1./255)\n",
        "valid_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "# Setup the train and test directories\n",
        "train_dir = \"pizza_steak/train/\"\n",
        "test_dir = \"pizza_steak/test/\"\n",
        "\n",
        "# Import data from directories and turn it into batches\n",
        "train_data = train_datagen.flow_from_directory(train_dir,\n",
        "                                               batch_size=32, # number of images to process at a time \n",
        "                                               target_size=(224, 224), # convert all images to be 224 x 224\n",
        "                                               class_mode=\"binary\", # type of problem we're working on\n",
        "                                               seed=42)\n",
        "\n",
        "valid_data = valid_datagen.flow_from_directory(test_dir,\n",
        "                                               batch_size=32,\n",
        "                                               target_size=(224, 224),\n",
        "                                               class_mode=\"binary\",\n",
        "                                               seed=42)\n",
        "\n",
        "# Create a CNN model (same as Tiny VGG - https://poloclub.github.io/cnn-explainer/)\n",
        "model_1 = tf.keras.models.Sequential([\n",
        "  tf.keras.layers.Conv2D(filters=10, \n",
        "                         kernel_size=3, # can also be (3, 3)\n",
        "                         activation=\"relu\", \n",
        "                         input_shape=(224, 224, 3)), # first layer specifies input shape (height, width, colour channels)\n",
        "  tf.keras.layers.Conv2D(10, 3, activation=\"relu\"),\n",
        "  tf.keras.layers.MaxPool2D(pool_size=2, # pool_size can also be (2, 2)\n",
        "                            padding=\"valid\"), # padding can also be 'same'\n",
        "  tf.keras.layers.Conv2D(10, 3, activation=\"relu\"),\n",
        "  tf.keras.layers.Conv2D(10, 3, activation=\"relu\"), # activation='relu' == tf.keras.layers.Activations(tf.nn.relu)\n",
        "  tf.keras.layers.MaxPool2D(2),\n",
        "  tf.keras.layers.Flatten(),\n",
        "  tf.keras.layers.Dense(1, activation=\"sigmoid\") # binary activation output\n",
        "])\n",
        "\n",
        "# Compile the model\n",
        "model_1.compile(loss=\"binary_crossentropy\",\n",
        "              optimizer=tf.keras.optimizers.Adam(),\n",
        "              metrics=[\"accuracy\"])\n",
        "\n",
        "# Fit the model\n",
        "history_1 = model_1.fit(train_data,\n",
        "                        epochs=5,\n",
        "                        steps_per_epoch=len(train_data),\n",
        "                        validation_data=valid_data,\n",
        "                        validation_steps=len(valid_data))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "llXVDtziUAOs",
        "outputId": "080cfc84-e2be-46b6-c69d-9893ec19064c"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1500 images belonging to 2 classes.\n",
            "Found 500 images belonging to 2 classes.\n",
            "Epoch 1/5\n",
            "47/47 [==============================] - 23s 206ms/step - loss: 0.5620 - accuracy: 0.7073 - val_loss: 0.4151 - val_accuracy: 0.8260\n",
            "Epoch 2/5\n",
            "47/47 [==============================] - 9s 193ms/step - loss: 0.4412 - accuracy: 0.8033 - val_loss: 0.4001 - val_accuracy: 0.8380\n",
            "Epoch 3/5\n",
            "47/47 [==============================] - 9s 191ms/step - loss: 0.4119 - accuracy: 0.8167 - val_loss: 0.3378 - val_accuracy: 0.8540\n",
            "Epoch 4/5\n",
            "47/47 [==============================] - 9s 191ms/step - loss: 0.3715 - accuracy: 0.8340 - val_loss: 0.3157 - val_accuracy: 0.8580\n",
            "Epoch 5/5\n",
            "47/47 [==============================] - 10s 207ms/step - loss: 0.3224 - accuracy: 0.8667 - val_loss: 0.3315 - val_accuracy: 0.8460\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check out the layers in our model\n",
        "model_1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1gaEBGTzUCpL",
        "outputId": "b83b6747-7b67-4bd6-bdd0-6b0275c1c6a8"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 222, 222, 10)      280       \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 220, 220, 10)      910       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 110, 110, 10)     0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 108, 108, 10)      910       \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 106, 106, 10)      910       \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 53, 53, 10)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 28090)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 28091     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 31,101\n",
            "Trainable params: 31,101\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    }
  ]
}