Deep_Learning_flowers.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "flowers_DNN.ipynb",
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    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
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    "language_info": {
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      "title_cell": "Table of Contents",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itW2L-jYo0ct",
        "colab_type": "text"
      },
      "source": [
        "# Deep Neural Networks (DNN)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "6aNIoBTZKSAq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f5d6443b-46b6-46fa-ac96-88685be6a5f9"
      },
      "source": [
        "# Original dataset\n",
        "# http://download.tensorflow.org/example_images/flower_photos.tgz \n",
        "\n",
        "# Download tiny version of the dataset from VisionCog website\n",
        "# After download and unzip, remember to comment the following two lines. \n",
        "\n",
        "! wget https://www.visioncog.com/rpk/tiny_FR.zip\n",
        "! unzip tiny_FR.zip"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-07-22 14:09:13--  https://www.visioncog.com/rpk/tiny_FR.zip\n",
            "Resolving www.visioncog.com (www.visioncog.com)... 103.21.58.10\n",
            "Connecting to www.visioncog.com (www.visioncog.com)|103.21.58.10|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 35283693 (34M) [application/zip]\n",
            "Saving to: ‘tiny_FR.zip’\n",
            "\n",
            "tiny_FR.zip         100%[===================>]  33.65M  6.21MB/s    in 5.7s    \n",
            "\n",
            "2020-07-22 14:09:20 (5.93 MB/s) - ‘tiny_FR.zip’ saved [35283693/35283693]\n",
            "\n",
            "Archive:  tiny_FR.zip\n",
            "   creating: tiny_FR/\n",
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            " extracting: tiny_FR/tulip/155097272_70feb13184.jpg  \n",
            "  inflating: tiny_FR/tulip/164578909_51f245d3fa_n.jpg  \n",
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            "  inflating: tiny_FR/tulip/176458518_f81d4bff8e.jpg  \n",
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            " extracting: tiny_FR/tulip/2256230386_08b54ca760.jpg  \n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "d3z1rwLSRyh3"
      },
      "source": [
        "# Flower Classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "znLO1DCzK7Kw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cf80080a-e254-4074-8fec-8e5c23eb4cfb"
      },
      "source": [
        "# Install TensorFlow\n",
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "\n",
        "from tensorflow import keras\n",
        "tf.random.set_seed(42)\n",
        "\n",
        "import numpy as np\n",
        "np.random.seed(42)\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "import glob\n",
        "import PIL\n",
        "from PIL import Image"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "xq21xG28Lb7u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "318432d4-b3ef-447a-81d8-773dd7cd5177"
      },
      "source": [
        "imgFiles = glob.glob(\"tiny_FR/*/*.jpg\")# Use glob to access the file paths of all images\n",
        "\n",
        "for items in imgFiles[:5]:\n",
        "  print(items)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tiny_FR/daisy/721595842_bacd80a6ac.jpg\n",
            "tiny_FR/daisy/1355787476_32e9f2a30b.jpg\n",
            "tiny_FR/daisy/488202750_c420cbce61.jpg\n",
            "tiny_FR/daisy/11642632_1e7627a2cc.jpg\n",
            "tiny_FR/daisy/512477177_d9004cbcf1_n.jpg\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "FMjvRsyJLfLm",
        "colab": {}
      },
      "source": [
        "X = []\n",
        "y = []\n",
        "\n",
        "for fName in imgFiles:\n",
        "  X_i = Image.open(fName)\n",
        "  X_i = X_i.resize((299,299))\n",
        "  X_i = np.array(X_i) / 255.0\n",
        "  X.append(X_i)\n",
        "  label = fName.split(\"/\")\n",
        "  y_i = label[1]\n",
        "  y.append(y_i)\n",
        "    \n",
        "    # Prepare the dataset and populate X and y\n",
        " "
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "HDx51_C6Ljvm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "da55e98e-0bde-41e1-cd05-01b5e8a28577"
      },
      "source": [
        "print(set(y))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'daisy', 'sunflower', 'dandelion', 'tulip', 'rose'}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "cAtReEUBLmbV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "88921b22-a252-4c9d-dc16-53b2124d9c1b"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "lEncoder = LabelEncoder()\n",
        "y = lEncoder.fit_transform(y)# Transform the string labels to numeric labels\n",
        "\n",
        "print(set(y))\n",
        "print(lEncoder.classes_)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{0, 1, 2, 3, 4}\n",
            "['daisy' 'dandelion' 'rose' 'sunflower' 'tulip']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "t5t9d-m8Loy9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "955b63f1-6d98-492d-debf-79bd34fc81e9"
      },
      "source": [
        "X = np.array(X)\n",
        "y = np.array(y)\n",
        "\n",
        "print(X.shape)\n",
        "print(y.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(500, 299, 299, 3)\n",
            "(500,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a1yyzLnbo0dK",
        "colab_type": "text"
      },
      "source": [
        "## Splitting data to 80% training and 20% testing "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jC2wqF-qLqcN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "5bb363ae-c329-41d2-c7ea-a66b880adbfa"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Split the data into 80% training and 20% testing.\n",
        "# Make stratified split of the data.\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,stratify=y,random_state=42 )# Pass appropriate arguments)\n",
        "\n",
        "print(\"X_train_shape: {}\".format(X_train.shape))\n",
        "print(\"X_test_shape: {}\".format(X_test.shape))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "X_train_shape: (400, 299, 299, 3)\n",
            "X_test_shape: (100, 299, 299, 3)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "CTfYQwEzLsCd",
        "colab": {}
      },
      "source": [
        "# Manually Standard-Scale the data\n",
        "\n",
        "mu = X_train.mean()\n",
        "std = X_train.std()\n",
        "\n",
        "X_train_std = (X_train-mu)/std # Apply feature scaling to X_train\n",
        "X_test_std = (X_test-mu)/std# Apply feature scaling to X_test"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eRpQyT8po0dR",
        "colab_type": "text"
      },
      "source": [
        "## Construct the network"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "zrHWTARpLtmW",
        "colab": {}
      },
      "source": [
        "# Create the network using Functional API method\n",
        "# The first hidden layer should contain 1000 neurons,\n",
        "# Second hidden layer should contain 100 neurons.\n",
        "\n",
        "input_ = keras.layers.Input(shape = X_train.shape[1:])\n",
        "\n",
        "x = keras.layers.Flatten()(input_)# Add the flatten layer\n",
        "x = keras.layers.Dense(units=1000, activation='relu')(x)# Add first hidden layer with 1000 units\n",
        "x = keras.layers.Dense(units=100, activation='relu')(x)# Add second hidden layer with 100 units\n",
        "\n",
        "# Put appropriate units and activation to the output layer\n",
        "output_ = keras.layers.Dense(units=5, activation='softmax' )(x)\n",
        "\n",
        "# How the inputs and outputs arguments should be passed?\n",
        "model_DNN = keras.models.Model(inputs=[input_], outputs=[output_])"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "J-45uZzJL1Ta",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "outputId": "05675bfe-eb1b-4565-e1d8-2c07b8513a7c"
      },
      "source": [
        "model_DNN.summary()"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "input_2 (InputLayer)         [(None, 299, 299, 3)]     0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 268203)            0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1000)              268204000 \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 100)               100100    \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 5)                 505       \n",
            "=================================================================\n",
            "Total params: 268,304,605\n",
            "Trainable params: 268,304,605\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_gu7HHY_o0dY",
        "colab_type": "text"
      },
      "source": [
        "## Compile and train the network\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "gRxEixbCL3Pu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "outputId": "d4e52081-92c1-4833-bfaa-3485de64f4d2"
      },
      "source": [
        "# Set the appropriate options to compile the network\n",
        "model_DNN.compile(loss='sparse_categorical_crossentropy', \n",
        "                  optimizer='adam', \n",
        "                  metrics=['accuracy'])\n",
        "\n",
        "# Set x, y, number of epochs, validation percentage and batch size \n",
        "history_DNN = model_DNN.fit(x=X_train, \n",
        "                            y=y_train, \n",
        "                            epochs=25, \n",
        "                            validation_split=0.1, \n",
        "                            batch_size=16 )"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/25\n",
            "23/23 [==============================] - 3s 133ms/step - loss: 123.7806 - accuracy: 0.1917 - val_loss: 29.3957 - val_accuracy: 0.2500\n",
            "Epoch 2/25\n",
            "23/23 [==============================] - 3s 119ms/step - loss: 15.8239 - accuracy: 0.3861 - val_loss: 11.9878 - val_accuracy: 0.3250\n",
            "Epoch 3/25\n",
            "23/23 [==============================] - 3s 118ms/step - loss: 6.5022 - accuracy: 0.5361 - val_loss: 8.0171 - val_accuracy: 0.2750\n",
            "Epoch 4/25\n",
            "23/23 [==============================] - 3s 119ms/step - loss: 6.2956 - accuracy: 0.5833 - val_loss: 9.8122 - val_accuracy: 0.3250\n",
            "Epoch 5/25\n",
            "23/23 [==============================] - 3s 119ms/step - loss: 7.2035 - accuracy: 0.5583 - val_loss: 16.4231 - val_accuracy: 0.2750\n",
            "Epoch 6/25\n",
            "23/23 [==============================] - 3s 120ms/step - loss: 14.4919 - accuracy: 0.4556 - val_loss: 26.9790 - val_accuracy: 0.2000\n",
            "Epoch 7/25\n",
            "23/23 [==============================] - 3s 119ms/step - loss: 11.5277 - accuracy: 0.5167 - val_loss: 17.5417 - val_accuracy: 0.1500\n",
            "Epoch 8/25\n",
            "23/23 [==============================] - 3s 119ms/step - loss: 6.4508 - accuracy: 0.4667 - val_loss: 4.1855 - val_accuracy: 0.4000\n",
            "Epoch 9/25\n",
            "23/23 [==============================] - 3s 118ms/step - loss: 2.1922 - accuracy: 0.6389 - val_loss: 4.9281 - val_accuracy: 0.3250\n",
            "Epoch 10/25\n",
            "23/23 [==============================] - 3s 119ms/step - loss: 1.5462 - accuracy: 0.6472 - val_loss: 5.2410 - val_accuracy: 0.3750\n",
            "Epoch 11/25\n",
            "23/23 [==============================] - 3s 119ms/step - loss: 0.9693 - accuracy: 0.7750 - val_loss: 4.4891 - val_accuracy: 0.4000\n",
            "Epoch 12/25\n",
            "23/23 [==============================] - 3s 118ms/step - loss: 1.9910 - accuracy: 0.6611 - val_loss: 9.7360 - val_accuracy: 0.3250\n",
            "Epoch 13/25\n",
            "23/23 [==============================] - 3s 119ms/step - loss: 3.1998 - accuracy: 0.5417 - val_loss: 6.4329 - val_accuracy: 0.2500\n",
            "Epoch 14/25\n",
            "23/23 [==============================] - 3s 119ms/step - loss: 1.7157 - accuracy: 0.6361 - val_loss: 6.5601 - val_accuracy: 0.3250\n",
            "Epoch 15/25\n",
            "23/23 [==============================] - 3s 118ms/step - loss: 1.8195 - accuracy: 0.4417 - val_loss: 1.9355 - val_accuracy: 0.2250\n",
            "Epoch 16/25\n",
            "23/23 [==============================] - 3s 120ms/step - loss: 1.2158 - accuracy: 0.3806 - val_loss: 1.7989 - val_accuracy: 0.1500\n",
            "Epoch 17/25\n",
            "23/23 [==============================] - 3s 119ms/step - loss: 1.1749 - accuracy: 0.4417 - val_loss: 2.0512 - val_accuracy: 0.2500\n",
            "Epoch 18/25\n",
            "23/23 [==============================] - 3s 118ms/step - loss: 1.3244 - accuracy: 0.4083 - val_loss: 2.0014 - val_accuracy: 0.2000\n",
            "Epoch 19/25\n",
            "23/23 [==============================] - 3s 118ms/step - loss: 1.3952 - accuracy: 0.4389 - val_loss: 1.9686 - val_accuracy: 0.1750\n",
            "Epoch 20/25\n",
            "23/23 [==============================] - 3s 118ms/step - loss: 1.4127 - accuracy: 0.4000 - val_loss: 2.0359 - val_accuracy: 0.2000\n",
            "Epoch 21/25\n",
            "23/23 [==============================] - 3s 120ms/step - loss: 1.4548 - accuracy: 0.4250 - val_loss: 1.6934 - val_accuracy: 0.1250\n",
            "Epoch 22/25\n",
            "23/23 [==============================] - 3s 119ms/step - loss: 1.5361 - accuracy: 0.2667 - val_loss: 1.6072 - val_accuracy: 0.2000\n",
            "Epoch 23/25\n",
            "23/23 [==============================] - 3s 120ms/step - loss: 1.4501 - accuracy: 0.3694 - val_loss: 2.6884 - val_accuracy: 0.2000\n",
            "Epoch 24/25\n",
            "23/23 [==============================] - 3s 123ms/step - loss: 1.2112 - accuracy: 0.4944 - val_loss: 1.6342 - val_accuracy: 0.1750\n",
            "Epoch 25/25\n",
            "23/23 [==============================] - 3s 121ms/step - loss: 1.5353 - accuracy: 0.2611 - val_loss: 1.7961 - val_accuracy: 0.1750\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "uMABG9QXMB9Y",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "outputId": "ae36d771-af6b-4220-b5ce-05a8b00f2d69"
      },
      "source": [
        "keys = ['accuracy', 'val_accuracy']\n",
        "progress = {k:v for k,v in history_DNN.history.items() if k in keys}\n",
        "\n",
        "import pandas as pd\n",
        "pd.DataFrame(progress).plot()\n",
        "\n",
        "plt.xlabel(\"epochs\")\n",
        "plt.ylabel(\"accuracy\")\n",
        "\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "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": "markdown",
      "metadata": {
        "id": "IVH3TcoBo0dh",
        "colab_type": "text"
      },
      "source": [
        "## Evaluate the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "X2DGy2h-MUb7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "202481a2-ac76-49a2-b57d-55bcbdde4dac"
      },
      "source": [
        "# Pass the appropriate arguments for evaluate function.\n",
        "test_loss, test_accuracy = model_DNN.evaluate(X_test,y_test)\n",
        "\n",
        "print(\"Test-loss: %f, Test-accuracy: %f\" % (test_loss, test_accuracy))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4/4 [==============================] - 0s 23ms/step - loss: 1.6759 - accuracy: 0.2100\n",
            "Test-loss: 1.675871, Test-accuracy: 0.210000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v3FAtiFK7SYJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}