mainCode(tensorflow).ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RyDg5GPcwxYM"
      },
      "source": [
        "# Main code(used tensorflow)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AJpFrc0sT3YW"
      },
      "source": [
        "# Import some libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "zCmjnsZK4gyN"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.utils import image_dataset_from_directory #Load data\n",
        "from tensorflow.keras import layers #Data augmantation\n",
        "from sklearn.model_selection import train_test_split #Split data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lWYU2s92T9xx"
      },
      "source": [
        "# Convert data to train and test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "38sMGPHoSGgp",
        "outputId": "45bf6805-358d-4990-f732-de0124a1484b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 500 files belonging to 5 classes.\n",
            "Using 400 files for training.\n",
            "Found 500 files belonging to 5 classes.\n",
            "Using 100 files for validation.\n"
          ]
        }
      ],
      "source": [
        "image_size = (500, 500)   #CHANGED\n",
        "batch_size = 20           #CHANGED\n",
        "\n",
        "train_ds = image_dataset_from_directory('/content/drive/MyDrive/Grapevine_Leaves_Image_Dataset', validation_split=0.2, subset='training', seed=420, image_size=image_size, batch_size=batch_size)\n",
        "# 20 batch of size 20\n",
        "test_ds = image_dataset_from_directory('/content/drive/MyDrive/Grapevine_Leaves_Image_Dataset', validation_split=0.2, subset='validation', seed=420, image_size=image_size, batch_size=batch_size)\n",
        "# 5 batch of size 20\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "juykFkzyU3eI"
      },
      "source": [
        "# Check some data "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 591
        },
        "id": "nEMuVaaQTyRS",
        "outputId": "c68cf961-10d0-453e-8e46-abf0665ff0f0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x720 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "class_names = train_ds.class_names\n",
        "\n",
        "plt.figure(figsize=(10, 10))\n",
        "for images, labels in train_ds.take(1):\n",
        "  for i in range(9):\n",
        "    ax = plt.subplot(3, 3, i + 1)\n",
        "    plt.imshow(images[i].numpy().astype(\"uint8\"))\n",
        "    plt.title(class_names[labels[i]])\n",
        "    plt.axis(\"off\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DPIzwZEDWu_b"
      },
      "source": [
        "# Data augmentation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "am1y15-wU8b0"
      },
      "outputs": [],
      "source": [
        "#Create augm func\n",
        "data_augmentation = tf.keras.Sequential([\n",
        "  layers.RandomFlip(\"horizontal_and_vertical\"),\n",
        "  layers.RandomTranslation(0.06, 0.06),\n",
        "  layers.RandomRotation(0.25),\n",
        "  layers.RandomZoom(0.2, 0.2)\n",
        "])\n",
        "\n",
        "#Lists for train and label\n",
        "Xs = []\n",
        "Ys = []\n",
        "for i in train_ds:\n",
        "  for j in range(len(i[0])):\n",
        "    Xs.append(i[0][j])\n",
        "    Ys.append(i[1][j])\n",
        "  \n",
        "#Augment\n",
        "for i in range(len(Xs)):\n",
        "  augmented_image = data_augmentation(Xs[i])\n",
        "  Xs.append(augmented_image)\n",
        "  Ys.append(Ys[i])\n",
        "\n",
        "  augmented_image1 = data_augmentation(Xs[i])\n",
        "  Xs.append(augmented_image1)\n",
        "  Ys.append(Ys[i])\n",
        "\n",
        "  augmented_image2 = data_augmentation(Xs[i])\n",
        "  Xs.append(augmented_image2)\n",
        "  Ys.append(Ys[i])\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_F2hGVOcsIE2"
      },
      "source": [
        "# Split data to val and train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "7DDWN3UBawfS"
      },
      "outputs": [],
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(Xs, Ys, test_size=0.2, random_state=420)\n",
        "\n",
        "#Back to dataSet\n",
        "train_ds = tf.data.Dataset.from_tensor_slices((X_train, y_train))\n",
        "train_ds = train_ds.shuffle(buffer_size=1024).batch(20)         #CHANGED\n",
        "val_ds = tf.data.Dataset.from_tensor_slices((X_val, y_val))\n",
        "val_ds = val_ds.shuffle(buffer_size=1024).batch(20)             #CHANGED"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "2lLjZQijdeAI"
      },
      "outputs": [],
      "source": [
        "#resize data\n",
        "train_data = train_ds.map(lambda image, label: (tf.image.resize(image, [256, 256])/255,label))  #ADDED\n",
        "val_data = val_ds.map(lambda image, label: (tf.image.resize(image, [256, 256])/255, label))     #ADDED\n",
        "test_data = test_ds.map(lambda image, label: (tf.image.resize(image, [256, 256])/255, label))   #ADDED"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ur1ZkXY0ujx0"
      },
      "source": [
        "# First test with simple CNN"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "aUo8gCro4mAy"
      },
      "outputs": [],
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(16, (3,3), 1, activation='relu', input_shape=(256,256,3)))\n",
        "model.add(MaxPooling2D())\n",
        "\n",
        "model.add(Conv2D(32, (3,3), 1, activation='relu'))\n",
        "model.add(MaxPooling2D())\n",
        "\n",
        "model.add(Conv2D(16, (3,3), 1, activation='relu'))\n",
        "model.add(MaxPooling2D())\n",
        "\n",
        "model.add(Flatten())\n",
        "\n",
        "model.add(Dense(units=256,activation='relu'))\n",
        "model.add(Dense(units=5,activation='softmax'))\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F_Nd1QPB42Lj",
        "outputId": "64a48767-023f-4d43-981d-ada6379f5e6b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 254, 254, 16)      448       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 127, 127, 16)     0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 125, 125, 32)      4640      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 62, 62, 32)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 60, 60, 16)        4624      \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 30, 30, 16)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 14400)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 256)               3686656   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 5)                 1285      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,697,653\n",
            "Trainable params: 3,697,653\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "verXQ0Zq5bnn"
      },
      "outputs": [],
      "source": [
        "def m2tex(model):\n",
        "    stringlist = []\n",
        "    model.summary(line_length=70, print_fn=lambda x: stringlist.append(x))\n",
        "    del stringlist[1:-4:2]\n",
        "    del stringlist[-1]\n",
        "    for ix in range(1,len(stringlist)-3):\n",
        "        tmp = stringlist[ix]\n",
        "        stringlist[ix] = tmp[0:31]+\"& \"+tmp[31:59]+\"& \"+tmp[59:]+\"\\\\\\\\ \\hline\"\n",
        "    stringlist[0] = \"Model: test \\\\\\\\ \\hline\"\n",
        "    stringlist[1] = stringlist[1]+\" \\hline\"\n",
        "    stringlist[-4] = stringlist[-4]+\" \\hline\"\n",
        "    stringlist[-3] = stringlist[-3]+\" \\\\\\\\\"\n",
        "    stringlist[-2] = stringlist[-2]+\" \\\\\\\\\"\n",
        "    stringlist[-1] = stringlist[-1]+\" \\\\\\\\ \\hline\"\n",
        "    prefix = [\"\\\\begin{table}[]\", \"\\\\begin{tabular}{lll}\"]\n",
        "    suffix = [\"\\end{tabular}\", \"\\caption{Model summary for test.}\", \"\\label{tab:model-summary}\" , \"\\end{table}\"]\n",
        "    stringlist = prefix + stringlist + suffix \n",
        "    out_str = \" \\n\".join(stringlist)\n",
        "    out_str = out_str.replace(\"_\", \"\\_\")\n",
        "    out_str = out_str.replace(\"#\", \"\\#\")\n",
        "    print(out_str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A6TeAX_K5c7O",
        "outputId": "e85a3145-45ac-4a1f-f1a7-72326c3814d7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\\begin{table}[] \n",
            "\\begin{tabular}{lll} \n",
            "Model: test \\\\ \\hline \n",
            " Layer (type)                  & Output Shape                & Param \\#    \\\\ \\hline \\hline \n",
            " conv2d (Conv2D)               & (None, 254, 254, 16)        & 448        \\\\ \\hline \n",
            " max\\_pooling2d (MaxPooling2D)  & (None, 127, 127, 16)        & 0          \\\\ \\hline \n",
            " conv2d\\_1 (Conv2D)             & (None, 125, 125, 32)        & 4640       \\\\ \\hline \n",
            " max\\_pooling2d\\_1 (MaxPooling2D &  (None, 62, 62, 32)         & 0          \\\\ \\hline \n",
            "                               &                             &            \\\\ \\hline \n",
            "                               &                             &            \\\\ \\hline \n",
            " )                             &                             &            \\\\ \\hline \n",
            " flatten (Flatten)             & (None, 14400)               & 0          \\\\ \\hline \n",
            " dense (Dense)                 & (None, 256)                 & 3686656    \\\\ \\hline \n",
            " dense\\_1 (Dense)               & (None, 5)                   & 1285       \\\\ \\hline \n",
            "===============================& ============================& ===========\\\\ \\hline \\hline \n",
            "Total params: 3,697,653 \\\\ \n",
            "Trainable params: 3,697,653 \\\\ \n",
            "Non-trainable params: 0 \\\\ \\hline \n",
            "\\end{tabular} \n",
            "\\caption{Model summary for test.} \n",
            "\\label{tab:model-summary} \n",
            "\\end{table}\n"
          ]
        }
      ],
      "source": [
        "m2tex(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kdb44rb84l-o",
        "outputId": "76b4154f-abe5-494e-98f8-e81b305ddf4e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "~~~~~~~~~~Fit_model:~~~~~~~~~~\n",
            "Epoch 1/10\n",
            "64/64 [==============================] - 6s 78ms/step - loss: 1.6151 - accuracy: 0.2477 - val_loss: 1.5833 - val_accuracy: 0.2812\n",
            "Epoch 2/10\n",
            "64/64 [==============================] - 5s 75ms/step - loss: 1.5014 - accuracy: 0.3836 - val_loss: 1.5161 - val_accuracy: 0.3375\n",
            "Epoch 3/10\n",
            "64/64 [==============================] - 4s 69ms/step - loss: 1.2477 - accuracy: 0.4992 - val_loss: 1.5889 - val_accuracy: 0.3219\n",
            "Epoch 4/10\n",
            "64/64 [==============================] - 4s 69ms/step - loss: 0.9148 - accuracy: 0.6523 - val_loss: 2.0223 - val_accuracy: 0.3250\n",
            "Epoch 5/10\n",
            "64/64 [==============================] - 5s 76ms/step - loss: 0.5431 - accuracy: 0.8125 - val_loss: 2.2630 - val_accuracy: 0.3375\n",
            "Epoch 6/10\n",
            "64/64 [==============================] - 5s 77ms/step - loss: 0.2322 - accuracy: 0.9289 - val_loss: 3.4347 - val_accuracy: 0.3594\n",
            "Epoch 7/10\n",
            "64/64 [==============================] - 4s 69ms/step - loss: 0.0879 - accuracy: 0.9750 - val_loss: 4.2399 - val_accuracy: 0.3156\n",
            "Epoch 8/10\n",
            "64/64 [==============================] - 5s 75ms/step - loss: 0.0246 - accuracy: 0.9969 - val_loss: 5.2338 - val_accuracy: 0.3531\n",
            "Epoch 9/10\n",
            "64/64 [==============================] - 4s 70ms/step - loss: 0.0076 - accuracy: 1.0000 - val_loss: 5.7093 - val_accuracy: 0.3719\n",
            "Epoch 10/10\n",
            "64/64 [==============================] - 5s 76ms/step - loss: 0.0016 - accuracy: 1.0000 - val_loss: 5.9540 - val_accuracy: 0.3656\n",
            "~~~~~~~~~~Evaluate_on_test_data~~~~~~~~~~~\n",
            "5/5 [==============================] - 1s 31ms/step - loss: 4.7126 - accuracy: 0.3500\n",
            "test loss, test acc: [4.712641716003418, 0.3499999940395355]\n",
            "~~~~~~~~~~Confusion_Matrix~~~~~~~~~~\n",
            "tf.Tensor(\n",
            "[[ 8  4  3  2  2]\n",
            " [ 5  6  4  3  4]\n",
            " [ 0  2  8  5  4]\n",
            " [ 2  0  6  5 13]\n",
            " [ 0  1  3  2  8]], shape=(5, 5), dtype=int32)\n"
          ]
        }
      ],
      "source": [
        "print(\"~~~~~~~~~~Fit_model:~~~~~~~~~~\")\n",
        "model.fit(train_data , epochs=10, validation_data=val_data)       #CHANGED\n",
        "# Evaluate the model on the test data \n",
        "print(\"~~~~~~~~~~Evaluate_on_test_data~~~~~~~~~~~\")\n",
        "results = model.evaluate(test_data)                              #CHANGED\n",
        "print(\"test loss, test acc:\", results)\n",
        "# Create confusion matrix\n",
        "print(\"~~~~~~~~~~Confusion_Matrix~~~~~~~~~~\")\n",
        "real_label = []\n",
        "pred_label = []\n",
        "for i in test_data:                                              #CHANGED\n",
        "  temp = model.predict(i[0])\n",
        "  for j in temp:\n",
        "    pred_label.append(max(range(len(j)), key=j.__getitem__))\n",
        "  for j in i[1]:\n",
        "    real_label.append(int(j))\n",
        "confusion = tf.math.confusion_matrix(labels=real_label, predictions=pred_label, num_classes=5)\n",
        "print(confusion)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SnTtoZpj2cnj"
      },
      "source": [
        "# Ok, let's try some pre_trained models(in this case MobileNet V2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d7CNpjL52eUP",
        "outputId": "5a366c9f-5118-4e16-f528-1481e600daa5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v2/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
            "9412608/9406464 [==============================] - 0s 0us/step\n",
            "9420800/9406464 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "# Create the base model from the pre-trained model MobileNet V2\n",
        "IMG_SIZE = (256, 256)\n",
        "IMG_SHAPE = IMG_SIZE + (3,)\n",
        "base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,\n",
        "                                               include_top=False,\n",
        "                                               weights='imagenet')\n",
        "base_model.trainable = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "s4CD4ez259Fw"
      },
      "outputs": [],
      "source": [
        "#Create clasification layer\n",
        "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
        "zero_layer = Dense(units=512,activation='relu')\n",
        "first_layer = Dense(units=256,activation='relu')\n",
        "second_layer = Dense(units=128,activation='relu')\n",
        "third_layer = Dense(units=5,activation='softmax')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "DgsvSKFy2eM8"
      },
      "outputs": [],
      "source": [
        "inputs = tf.keras.Input(shape=(256, 256, 3))\n",
        "x = base_model(inputs, training=False)\n",
        "x = global_average_layer(x)\n",
        "x = zero_layer(x)\n",
        "x = first_layer(x)\n",
        "# x = second_layer(x)\n",
        "outputs = third_layer(x)\n",
        "model = tf.keras.Model(inputs, outputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "4zxfaCFw2eK8"
      },
      "outputs": [],
      "source": [
        "base_learning_rate = 0.0001\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VqF6hVMTHxT4",
        "outputId": "04390efa-0615-49cc-8015-e7899c900f43"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 256, 256, 3)]     0         \n",
            "                                                                 \n",
            " mobilenetv2_1.00_224 (Funct  (None, 8, 8, 1280)       2257984   \n",
            " ional)                                                          \n",
            "                                                                 \n",
            " global_average_pooling2d (G  (None, 1280)             0         \n",
            " lobalAveragePooling2D)                                          \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 512)               655872    \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 256)               131328    \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 5)                 1285      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,046,469\n",
            "Trainable params: 788,485\n",
            "Non-trainable params: 2,257,984\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xJoV9d03H4Rb",
        "outputId": "953c641f-45f8-424e-ee36-4624521c4b9f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\\begin{table}[] \n",
            "\\begin{tabular}{lll} \n",
            "Model: test \\\\ \\hline \n",
            " Layer (type)                  & Output Shape                & Param \\#    \\\\ \\hline \\hline \n",
            " input\\_2 (InputLayer)          & [(None, 256, 256, 3)]       & 0          \\\\ \\hline \n",
            " mobilenetv2\\_1.00\\_224 (Functio &  (None, 8, 8, 1280)         & 2257984    \\\\ \\hline \n",
            "                               &                             &            \\\\ \\hline \n",
            " balAveragePooling2D)          &                             &            \\\\ \\hline \n",
            " dense\\_6 (Dense)               & (None, 512)                 & 655872     \\\\ \\hline \n",
            " dense\\_7 (Dense)               & (None, 256)                 & 131328     \\\\ \\hline \n",
            " dense\\_9 (Dense)               & (None, 5)                   & 1285       \\\\ \\hline \n",
            "===============================& ============================& ===========\\\\ \\hline \\hline \n",
            "Total params: 3,046,469 \\\\ \n",
            "Trainable params: 788,485 \\\\ \n",
            "Non-trainable params: 2,257,984 \\\\ \\hline \n",
            "\\end{tabular} \n",
            "\\caption{Model summary for test.} \n",
            "\\label{tab:model-summary} \n",
            "\\end{table}\n"
          ]
        }
      ],
      "source": [
        "m2tex(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "diCYZrTB2eI0",
        "outputId": "d3e3ee87-f111-438e-cc9c-68ef0f8d76fc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "~~~~~~~~~~Fit_model:~~~~~~~~~~\n",
            "Epoch 1/10\n",
            "64/64 [==============================] - 10s 101ms/step - loss: 1.1887 - accuracy: 0.5602 - val_loss: 0.8111 - val_accuracy: 0.7875\n",
            "Epoch 2/10\n",
            "64/64 [==============================] - 6s 92ms/step - loss: 0.7330 - accuracy: 0.7508 - val_loss: 0.5733 - val_accuracy: 0.8500\n",
            "Epoch 3/10\n",
            "64/64 [==============================] - 6s 87ms/step - loss: 0.5528 - accuracy: 0.8328 - val_loss: 0.4523 - val_accuracy: 0.8625\n",
            "Epoch 4/10\n",
            "64/64 [==============================] - 6s 86ms/step - loss: 0.4500 - accuracy: 0.8641 - val_loss: 0.4093 - val_accuracy: 0.8594\n",
            "Epoch 5/10\n",
            "64/64 [==============================] - 6s 95ms/step - loss: 0.3825 - accuracy: 0.8781 - val_loss: 0.3858 - val_accuracy: 0.8625\n",
            "Epoch 6/10\n",
            "64/64 [==============================] - 6s 88ms/step - loss: 0.3301 - accuracy: 0.9031 - val_loss: 0.3764 - val_accuracy: 0.8562\n",
            "Epoch 7/10\n",
            "64/64 [==============================] - 6s 87ms/step - loss: 0.2882 - accuracy: 0.9180 - val_loss: 0.3874 - val_accuracy: 0.8500\n",
            "Epoch 8/10\n",
            "64/64 [==============================] - 6s 87ms/step - loss: 0.2544 - accuracy: 0.9234 - val_loss: 0.3485 - val_accuracy: 0.8625\n",
            "Epoch 9/10\n",
            "64/64 [==============================] - 6s 85ms/step - loss: 0.2161 - accuracy: 0.9312 - val_loss: 0.2990 - val_accuracy: 0.8875\n",
            "Epoch 10/10\n",
            "64/64 [==============================] - 6s 86ms/step - loss: 0.1781 - accuracy: 0.9578 - val_loss: 0.3124 - val_accuracy: 0.8813\n",
            "~~~~~~~~~~Evaluate_on_test_data~~~~~~~~~~~\n",
            "5/5 [==============================] - 1s 38ms/step - loss: 0.3496 - accuracy: 0.8800\n",
            "test loss, test acc: [0.34957990050315857, 0.8799999952316284]\n",
            "~~~~~~~~~~Confusion_Matrix~~~~~~~~~~\n",
            "tf.Tensor(\n",
            "[[12  3  0  3  1]\n",
            " [ 0 21  1  0  0]\n",
            " [ 0  0 19  0  0]\n",
            " [ 1  1  0 23  1]\n",
            " [ 1  0  0  0 13]], shape=(5, 5), dtype=int32)\n"
          ]
        }
      ],
      "source": [
        "print(\"~~~~~~~~~~Fit_model:~~~~~~~~~~\")\n",
        "model.fit(train_data, epochs=10, validation_data=val_data)            #CHANGED\n",
        "# Evaluate the model on the test data \n",
        "print(\"~~~~~~~~~~Evaluate_on_test_data~~~~~~~~~~~\")\n",
        "results = model.evaluate(test_data)                                  #CHANGED\n",
        "print(\"test loss, test acc:\", results)\n",
        "# Create confusion matrix\n",
        "print(\"~~~~~~~~~~Confusion_Matrix~~~~~~~~~~\")\n",
        "real_label = []\n",
        "pred_label = [] \n",
        "for i in test_data:                                                  #CHANGED\n",
        "  temp = model.predict(i[0])\n",
        "  for j in temp:\n",
        "    pred_label.append(max(range(len(j)), key=j.__getitem__))\n",
        "  for j in i[1]:\n",
        "    real_label.append(int(j))\n",
        "confusion = tf.math.confusion_matrix(labels=real_label, predictions=pred_label, num_classes=5)\n",
        "print(confusion)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ksG3dq9zKbdi"
      },
      "source": [
        "# Same model, different librarie"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ELGhXmGxKcLu"
      },
      "outputs": [],
      "source": [
        "#CODE IS IN ANOTHER FILE"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nTcTMVEBPdeL"
      },
      "source": [
        "# Use auto-encoder to see the result"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "xz4TephvV9Z6"
      },
      "outputs": [],
      "source": [
        "import keras\n",
        "from keras import layers\n",
        "\n",
        "input_img = keras.Input(shape=(500, 500, 3))\n",
        "\n",
        "x = layers.Conv2D(3, (3, 3), activation='relu', padding='same')(input_img)\n",
        "x = layers.MaxPooling2D((2, 2), padding='same')(x)\n",
        "#middle of auto encoder\n",
        "x = layers.UpSampling2D((2, 2))(x)\n",
        "decoded = layers.Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)\n",
        "\n",
        "autoencoder = keras.Model(input_img, decoded)\n",
        "autoencoder.compile(optimizer='adam', loss='binary_crossentropy')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "shW3fun_U_IM"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QT2GeOKYbmub",
        "outputId": "eac073af-c1e6-45b4-d0fc-7f7efe7531cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_6 (InputLayer)        [(None, 500, 500, 3)]     0         \n",
            "                                                                 \n",
            " conv2d_22 (Conv2D)          (None, 500, 500, 3)       84        \n",
            "                                                                 \n",
            " max_pooling2d_13 (MaxPoolin  (None, 250, 250, 3)      0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " up_sampling2d_4 (UpSampling  (None, 500, 500, 3)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_23 (Conv2D)          (None, 500, 500, 3)       84        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 168\n",
            "Trainable params: 168\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "autoencoder.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cd__xToKPBQi",
        "outputId": "4ea3c7e8-5f36-4330-9ad0-16eafaf0a961"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\\begin{table}[] \n",
            "\\begin{tabular}{lll} \n",
            "Model: test \\\\ \\hline \n",
            " Layer (type)                  & Output Shape                & Param \\#    \\\\ \\hline \\hline \n",
            " input\\_6 (InputLayer)          & [(None, 500, 500, 3)]       & 0          \\\\ \\hline \n",
            " conv2d\\_22 (Conv2D)            & (None, 500, 500, 3)         & 84         \\\\ \\hline \n",
            " max\\_pooling2d\\_13 (MaxPooling2 &  (None, 250, 250, 3)        & 0          \\\\ \\hline \n",
            "                               &                             &            \\\\ \\hline \n",
            " )                             &                             &            \\\\ \\hline \n",
            " conv2d\\_23 (Conv2D)            & (None, 500, 500, 3)         & 84         \\\\ \\hline \n",
            "===============================& ============================& ===========\\\\ \\hline \\hline \n",
            "Total params: 168 \\\\ \n",
            "Trainable params: 168 \\\\ \n",
            "Non-trainable params: 0 \\\\ \\hline \n",
            "\\end{tabular} \n",
            "\\caption{Model summary for test.} \n",
            "\\label{tab:model-summary} \n",
            "\\end{table}\n"
          ]
        }
      ],
      "source": [
        "m2tex(autoencoder)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2jFb0dbQu2w7",
        "outputId": "73d4844c-a03b-4f68-e734-174329400389"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 500 files belonging to 5 classes.\n",
            "Using 250 files for validation.\n"
          ]
        }
      ],
      "source": [
        "image_size = (500, 500)\n",
        "batch_size = 1\n",
        "all_data = image_dataset_from_directory('/content/drive/MyDrive/Grapevine_Leaves_Image_Dataset', seed=420, image_size=image_size, batch_size=batch_size)\n",
        "\n",
        "all_images = []\n",
        "for i in all_data:\n",
        "  image, label = i\n",
        "  all_images.append(image/255)\n",
        "\n",
        "  \n",
        "dataset = tf.data.Dataset.from_tensor_slices((all_images, all_images))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e57FRXnO2d-3",
        "outputId": "3440c814-1cef-4c77-9cb8-b1f94d3cb675"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "250/250 [==============================] - 16s 10ms/step - loss: 0.3734\n",
            "Epoch 2/30\n",
            "250/250 [==============================] - 3s 11ms/step - loss: 0.2404\n",
            "Epoch 3/30\n",
            "250/250 [==============================] - 3s 10ms/step - loss: 0.2242\n",
            "Epoch 4/30\n",
            "250/250 [==============================] - 2s 10ms/step - loss: 0.2231\n",
            "Epoch 5/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2225\n",
            "Epoch 6/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2216\n",
            "Epoch 7/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2208\n",
            "Epoch 8/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2200\n",
            "Epoch 9/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2194\n",
            "Epoch 10/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2189\n",
            "Epoch 11/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2185\n",
            "Epoch 12/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2182\n",
            "Epoch 13/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2179\n",
            "Epoch 14/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2176\n",
            "Epoch 15/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2173\n",
            "Epoch 16/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2170\n",
            "Epoch 17/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2167\n",
            "Epoch 18/30\n",
            "250/250 [==============================] - 2s 9ms/step - loss: 0.2165\n",
            "Epoch 19/30\n",
            "250/250 [==============================] - 2s 9ms/step - loss: 0.2162\n",
            "Epoch 20/30\n",
            "250/250 [==============================] - 3s 10ms/step - loss: 0.2160\n",
            "Epoch 21/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2158\n",
            "Epoch 22/30\n",
            "250/250 [==============================] - 3s 10ms/step - loss: 0.2156\n",
            "Epoch 23/30\n",
            "250/250 [==============================] - 2s 9ms/step - loss: 0.2154\n",
            "Epoch 24/30\n",
            "250/250 [==============================] - 2s 9ms/step - loss: 0.2152\n",
            "Epoch 25/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2150\n",
            "Epoch 26/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2149\n",
            "Epoch 27/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2148\n",
            "Epoch 28/30\n",
            "250/250 [==============================] - 2s 9ms/step - loss: 0.2147\n",
            "Epoch 29/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2146\n",
            "Epoch 30/30\n",
            "250/250 [==============================] - 2s 8ms/step - loss: 0.2145\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7ff88a4c05d0>"
            ]
          },
          "execution_count": 4,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "autoencoder.fit(dataset, epochs=30)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "lb2Q5t80K3sE"
      },
      "outputs": [],
      "source": [
        "# check the encoder \n",
        "for i in dataset:\n",
        "  first_batch = i[0]\n",
        "  break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "WR3ghrzY2d6O"
      },
      "outputs": [],
      "source": [
        "decoded_imgs = autoencoder.predict(first_batch)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "gmVdD-vU2d4C",
        "outputId": "92d03fa2-58cf-45d0-f07d-56758d72b4e0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#real image\n",
        "imgplot = plt.imshow(first_batch[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "81vyYm3G2d1c",
        "outputId": "7cab35e2-e59a-4feb-f452-807139e868f8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#image created\n",
        "imgplot = plt.imshow(decoded_imgs[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3rjzsVU9Ojws",
        "outputId": "15b74e66-61b1-4bad-d6dc-ebd4a026d1f2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 500, 500, 3)]     0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 500, 500, 3)       84        \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 250, 250, 3)      0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " up_sampling2d (UpSampling2D  (None, 500, 500, 3)      0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 500, 500, 3)       84        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 168\n",
            "Trainable params: 168\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "autoencoder.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "mFR8mCkBSr6c"
      },
      "outputs": [],
      "source": [
        "#create new model\n",
        "from tensorflow.keras.models import Model\n",
        "model2 = Model(autoencoder.input, autoencoder.layers[-3].output)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IFngRBjVSr4a",
        "outputId": "8b7d5c29-94ff-49f8-8092-91d6ae8b3a0e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_1 (InputLayer)        [(None, 500, 500, 3)]     0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 500, 500, 3)       84        \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 250, 250, 3)      0         \n",
            " )                                                               \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 84\n",
            "Trainable params: 84\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model2.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "GOT-wE4ToygV",
        "outputId": "b041f6b2-2a9f-43e8-a941-4ce7b23e9640"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#check the model 2\n",
        "decoded_imgss = model2.predict(first_batch)\n",
        "imgplot = plt.imshow(decoded_imgss[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "9v4Q_mBtSr2o"
      },
      "outputs": [],
      "source": [
        "#dim-reduction \n",
        "#for train dataset\n",
        "X_train = []\n",
        "Y_train = []\n",
        "for i in train_ds:\n",
        "  result = model2.predict(i[0])\n",
        "  X_train.append(result)\n",
        "  Y_train.append(i[1])\n",
        "train_ds_reduc = tf.data.Dataset.from_tensor_slices((X_train, Y_train))\n",
        "\n",
        "#for val dataset\n",
        "X_val = []\n",
        "Y_val = []\n",
        "for i in val_ds:\n",
        "  result = model2.predict(i[0])\n",
        "  X_val.append(result)\n",
        "  Y_val.append(i[1])\n",
        "val_ds_reduc = tf.data.Dataset.from_tensor_slices((X_val, Y_val))\n",
        "\n",
        "#for test dataset\n",
        "X_test = []\n",
        "Y_test = []\n",
        "for i in test_ds:\n",
        "  result = model2.predict(i[0])\n",
        "  X_test.append(result)\n",
        "  Y_test.append(i[1])\n",
        "test_ds_reduc = tf.data.Dataset.from_tensor_slices((X_test, Y_test))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kCASDJ2qSr0n",
        "outputId": "4aeeb5ef-5eae-48e7-a5c1-93d2d36c963d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
            "~~~~~~~~~~Fit_model:~~~~~~~~~~\n",
            "Epoch 1/10\n",
            "48/48 [==============================] - 7s 64ms/step - loss: 1.5762 - accuracy: 0.2865 - val_loss: 1.4883 - val_accuracy: 0.3917\n",
            "Epoch 2/10\n",
            "48/48 [==============================] - 2s 46ms/step - loss: 1.4229 - accuracy: 0.4583 - val_loss: 1.4013 - val_accuracy: 0.4417\n",
            "Epoch 3/10\n",
            "48/48 [==============================] - 2s 46ms/step - loss: 1.3288 - accuracy: 0.5156 - val_loss: 1.3305 - val_accuracy: 0.4667\n",
            "Epoch 4/10\n",
            "48/48 [==============================] - 2s 46ms/step - loss: 1.2512 - accuracy: 0.5469 - val_loss: 1.2799 - val_accuracy: 0.4792\n",
            "Epoch 5/10\n",
            "48/48 [==============================] - 2s 46ms/step - loss: 1.1867 - accuracy: 0.5802 - val_loss: 1.2421 - val_accuracy: 0.5125\n",
            "Epoch 6/10\n",
            "48/48 [==============================] - 2s 46ms/step - loss: 1.1326 - accuracy: 0.6021 - val_loss: 1.2119 - val_accuracy: 0.5167\n",
            "Epoch 7/10\n",
            "48/48 [==============================] - 2s 46ms/step - loss: 1.0840 - accuracy: 0.6135 - val_loss: 1.1833 - val_accuracy: 0.5208\n",
            "Epoch 8/10\n",
            "48/48 [==============================] - 2s 46ms/step - loss: 1.0414 - accuracy: 0.6344 - val_loss: 1.1585 - val_accuracy: 0.5208\n",
            "Epoch 9/10\n",
            "48/48 [==============================] - 2s 52ms/step - loss: 1.0000 - accuracy: 0.6458 - val_loss: 1.1356 - val_accuracy: 0.5375\n",
            "Epoch 10/10\n",
            "48/48 [==============================] - 2s 47ms/step - loss: 0.9626 - accuracy: 0.6594 - val_loss: 1.1184 - val_accuracy: 0.5500\n",
            "~~~~~~~~~~Evaluate_on_test_data~~~~~~~~~~~\n",
            "5/5 [==============================] - 0s 34ms/step - loss: 1.0008 - accuracy: 0.6000\n",
            "test loss, test acc: [1.0007885694503784, 0.6000000238418579]\n",
            "~~~~~~~~~~Confusion_Matrix~~~~~~~~~~\n",
            "tf.Tensor(\n",
            "[[10  3  2  3  1]\n",
            " [ 1 12  3  4  2]\n",
            " [ 1  1 12  2  3]\n",
            " [ 0  0  2 16  8]\n",
            " [ 2  1  0  1 10]], shape=(5, 5), dtype=int32)\n"
          ]
        }
      ],
      "source": [
        "# Create the base model from the pre-trained model MobileNet V2\n",
        "IMG_SIZE = (250, 250)\n",
        "IMG_SHAPE = IMG_SIZE + (3,)\n",
        "base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,\n",
        "                                               include_top=False,\n",
        "                                               weights='imagenet')\n",
        "base_model.trainable = False\n",
        "#Create clasification layer\n",
        "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
        "zero_layer = Dense(units=512,activation='relu')\n",
        "first_layer = Dense(units=256,activation='relu')\n",
        "second_layer = Dense(units=128,activation='relu')\n",
        "third_layer = Dense(units=5,activation='softmax')\n",
        "#add clasification\n",
        "inputs = tf.keras.Input(shape=(250, 250, 3))\n",
        "x = base_model(inputs, training=False)\n",
        "x = global_average_layer(x)\n",
        "x = zero_layer(x)\n",
        "x = first_layer(x)\n",
        "# x = second_layer(x)\n",
        "outputs = third_layer(x)\n",
        "model = tf.keras.Model(inputs, outputs)\n",
        "base_learning_rate = 0.0001\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),\n",
        "              metrics=['accuracy'])\n",
        "print(\"~~~~~~~~~~Fit_model:~~~~~~~~~~\")\n",
        "model.fit(train_ds_reduc, epochs=10, validation_data=val_ds_reduc)\n",
        "# Evaluate the model on the test data \n",
        "print(\"~~~~~~~~~~Evaluate_on_test_data~~~~~~~~~~~\")\n",
        "results = model.evaluate(test_ds_reduc)\n",
        "print(\"test loss, test acc:\", results)\n",
        "# Create confusion matrix\n",
        "print(\"~~~~~~~~~~Confusion_Matrix~~~~~~~~~~\")\n",
        "real_label = []\n",
        "pred_label = []\n",
        "for i in test_ds_reduc:\n",
        "  temp = model.predict(i[0])\n",
        "  for j in temp:\n",
        "    pred_label.append(max(range(len(j)), key=j.__getitem__))\n",
        "  for j in i[1]:\n",
        "    real_label.append(int(j))\n",
        "confusion = tf.math.confusion_matrix(labels=real_label, predictions=pred_label, num_classes=5)\n",
        "print(confusion)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ryJeoiIwogTx"
      },
      "source": [
        "#Use SVM for the part of clasification after the cnn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cKtkQUeRyaR1",
        "outputId": "e43093f4-5186-4305-8610-efb6aafc48dc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        }
      ],
      "source": [
        "# Create the base model from the pre-trained model MobileNet V2\n",
        "IMG_SIZE = (256, 256)\n",
        "IMG_SHAPE = IMG_SIZE + (3,)\n",
        "base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,\n",
        "                                               include_top=False,\n",
        "                                               weights='imagenet')\n",
        "base_model.trainable = False\n",
        "global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
        "inputs = tf.keras.Input(shape=(256, 256, 3))\n",
        "x = base_model(inputs, training=False)\n",
        "outputs = global_average_layer(x)\n",
        "model = tf.keras.Model(inputs, outputs)\n",
        "base_learning_rate = 0.0001\n",
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),\n",
        "              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),\n",
        "              metrics=['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "iofJVkXirdX_"
      },
      "outputs": [],
      "source": [
        "#get the data ready for the svm\n",
        "\n",
        "#train data\n",
        "X_train = []\n",
        "Y_train = []\n",
        "for i in train_data:\n",
        "  result = model.predict(i[0])\n",
        "  for j in range(len(i[0])):\n",
        "    X_train.append(result[j])\n",
        "    Y_train.append(i[1][j])\n",
        "\n",
        "#val data\n",
        "X_val = []\n",
        "Y_val = []\n",
        "for i in val_data:\n",
        "  result = model.predict(i[0])\n",
        "  for j in range(len(i[0])):\n",
        "    X_val.append(result[j])\n",
        "    Y_val.append(i[1][j])\n",
        "\n",
        "#test data\n",
        "X_test = []\n",
        "Y_test = []\n",
        "for i in test_data:\n",
        "  result = model.predict(i[0])\n",
        "  for j in range(len(i[0])):\n",
        "    X_test.append(result[j])\n",
        "    Y_test.append(i[1][j])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "AUyuXqKeqWNA"
      },
      "outputs": [],
      "source": [
        "from sklearn import svm"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n3OqoB1NrZRy",
        "outputId": "0e1f246e-d92c-41aa-aadc-59b891e89dae"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "SVC(decision_function_shape='ovo')"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "#add train and val to gether\n",
        "X_train_new = X_train + X_val\n",
        "Y_train_new = Y_train + Y_val\n",
        "\n",
        "clf = svm.SVC(decision_function_shape='ovo')\n",
        "clf.fit(X_train_new, Y_train_new)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "PD6EqIkCumm7"
      },
      "outputs": [],
      "source": [
        "#get the result of the test dataset\n",
        "predicted_list = []\n",
        "for i in range(len(X_test)):\n",
        "  result = clf.predict([X_test[i]])\n",
        "  predicted_list.append(result)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KfX3jaQQtsBK",
        "outputId": "69a01fd1-6998-45a1-d7c2-86c1b75cd33a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "accuracy for svm is: 0.94\n",
            "Confusion_Matrix is as below: \n",
            "tf.Tensor(\n",
            "[[17  1  0  1  0]\n",
            " [ 1 19  2  0  0]\n",
            " [ 1  0 16  2  0]\n",
            " [ 3  0  0 23  0]\n",
            " [ 1  0  0  0 13]], shape=(5, 5), dtype=int32)\n"
          ]
        }
      ],
      "source": [
        "#calculate accuracy\n",
        "right = 0\n",
        "wrong = 0\n",
        "for i in range(len(predicted_list)):\n",
        "  if predicted_list[i] == Y_test[i]:\n",
        "    right += 1\n",
        "  else:\n",
        "    wrong += 1\n",
        "\n",
        "acc = right / (right+wrong)\n",
        "print(f\"accuracy for svm is: {acc}\")\n",
        "print(\"Confusion_Matrix is as below: \")\n",
        "confusion = tf.math.confusion_matrix(labels=Y_test, predictions=predicted_list, num_classes=5)\n",
        "print(confusion)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6UpvSfKru0MD"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N0puV5q4hY9Q"
      },
      "source": [
        "# 10 validation cross fold "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XDVMsutDi1Ub"
      },
      "source": [
        "## split the train to ten parts and import mobile net"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2lcecuj0jAaf",
        "outputId": "8e5ad40d-cd7c-47ff-f46c-2d8f64fcb4a2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "60"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(train_data) + len(val_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bfX_YWURi0k4",
        "outputId": "1eb94721-1694-4193-9086-89b1582682a9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--------------------------the bacth 0 is for validation-------------------------\n",
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "~~~~~~~~~~Fit_model:~~~~~~~~~~\n",
            "Epoch 1/10\n",
            "54/54 [==============================] - 5s 57ms/step - loss: 1.2726 - accuracy: 0.5417 - val_loss: 0.9919 - val_accuracy: 0.6750\n",
            "Epoch 2/10\n",
            "54/54 [==============================] - 2s 39ms/step - loss: 0.7767 - accuracy: 0.7556 - val_loss: 0.7408 - val_accuracy: 0.7500\n",
            "Epoch 3/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.5837 - accuracy: 0.8222 - val_loss: 0.5995 - val_accuracy: 0.7917\n",
            "Epoch 4/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.4720 - accuracy: 0.8574 - val_loss: 0.5167 - val_accuracy: 0.8500\n",
            "Epoch 5/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.3968 - accuracy: 0.8806 - val_loss: 0.4658 - val_accuracy: 0.8583\n",
            "Epoch 6/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.3404 - accuracy: 0.9037 - val_loss: 0.4302 - val_accuracy: 0.8583\n",
            "Epoch 7/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2949 - accuracy: 0.9222 - val_loss: 0.4033 - val_accuracy: 0.8583\n",
            "Epoch 8/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2567 - accuracy: 0.9407 - val_loss: 0.3790 - val_accuracy: 0.8833\n",
            "Epoch 9/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2231 - accuracy: 0.9491 - val_loss: 0.3655 - val_accuracy: 0.8750\n",
            "Epoch 10/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.1951 - accuracy: 0.9593 - val_loss: 0.3551 - val_accuracy: 0.8750\n",
            "~~~~~~~~~~Evaluate_on_test_data~~~~~~~~~~~\n",
            "5/5 [==============================] - 2s 51ms/step - loss: 0.3516 - accuracy: 0.8800\n",
            "test loss, test acc: [0.3515980839729309, 0.8799999952316284]\n",
            "~~~~~~~~~~Confusion_Matrix~~~~~~~~~~\n",
            "tf.Tensor(\n",
            "[[18  1  0  0  0]\n",
            " [ 1 17  4  0  0]\n",
            " [ 1  0 18  0  0]\n",
            " [ 2  0  1 22  1]\n",
            " [ 1  0  0  0 13]], shape=(5, 5), dtype=int32)\n",
            "--------------------------the bacth 1 is for validation-------------------------\n",
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "~~~~~~~~~~Fit_model:~~~~~~~~~~\n",
            "Epoch 1/10\n",
            "54/54 [==============================] - 5s 55ms/step - loss: 1.2093 - accuracy: 0.5333 - val_loss: 1.0621 - val_accuracy: 0.5750\n",
            "Epoch 2/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.7358 - accuracy: 0.7611 - val_loss: 0.8221 - val_accuracy: 0.6833\n",
            "Epoch 3/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.5447 - accuracy: 0.8287 - val_loss: 0.7146 - val_accuracy: 0.7500\n",
            "Epoch 4/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.4368 - accuracy: 0.8657 - val_loss: 0.6614 - val_accuracy: 0.7750\n",
            "Epoch 5/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.3611 - accuracy: 0.8981 - val_loss: 0.6388 - val_accuracy: 0.7917\n",
            "Epoch 6/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.3050 - accuracy: 0.9222 - val_loss: 0.6286 - val_accuracy: 0.8000\n",
            "Epoch 7/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2606 - accuracy: 0.9380 - val_loss: 0.6225 - val_accuracy: 0.8000\n",
            "Epoch 8/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2237 - accuracy: 0.9463 - val_loss: 0.6180 - val_accuracy: 0.8083\n",
            "Epoch 9/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.1920 - accuracy: 0.9583 - val_loss: 0.6142 - val_accuracy: 0.8083\n",
            "Epoch 10/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.1651 - accuracy: 0.9685 - val_loss: 0.6177 - val_accuracy: 0.8167\n",
            "~~~~~~~~~~Evaluate_on_test_data~~~~~~~~~~~\n",
            "5/5 [==============================] - 2s 50ms/step - loss: 0.3340 - accuracy: 0.8600\n",
            "test loss, test acc: [0.33401811122894287, 0.8600000143051147]\n",
            "~~~~~~~~~~Confusion_Matrix~~~~~~~~~~\n",
            "tf.Tensor(\n",
            "[[17  0  0  2  0]\n",
            " [ 2 16  4  0  0]\n",
            " [ 1  0 17  1  0]\n",
            " [ 2  0  0 23  1]\n",
            " [ 1  0  0  0 13]], shape=(5, 5), dtype=int32)\n",
            "--------------------------the bacth 2 is for validation-------------------------\n",
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "~~~~~~~~~~Fit_model:~~~~~~~~~~\n",
            "Epoch 1/10\n",
            "54/54 [==============================] - 6s 55ms/step - loss: 1.2157 - accuracy: 0.5528 - val_loss: 0.8816 - val_accuracy: 0.6833\n",
            "Epoch 2/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.7510 - accuracy: 0.7528 - val_loss: 0.6457 - val_accuracy: 0.7750\n",
            "Epoch 3/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.5677 - accuracy: 0.8176 - val_loss: 0.5228 - val_accuracy: 0.8083\n",
            "Epoch 4/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.4606 - accuracy: 0.8546 - val_loss: 0.4527 - val_accuracy: 0.8417\n",
            "Epoch 5/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.3868 - accuracy: 0.8870 - val_loss: 0.4048 - val_accuracy: 0.8750\n",
            "Epoch 6/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.3323 - accuracy: 0.9028 - val_loss: 0.3741 - val_accuracy: 0.8750\n",
            "Epoch 7/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2881 - accuracy: 0.9259 - val_loss: 0.3511 - val_accuracy: 0.8667\n",
            "Epoch 8/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2502 - accuracy: 0.9417 - val_loss: 0.3367 - val_accuracy: 0.8583\n",
            "Epoch 9/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.2179 - accuracy: 0.9481 - val_loss: 0.3215 - val_accuracy: 0.8583\n",
            "Epoch 10/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.1905 - accuracy: 0.9574 - val_loss: 0.3087 - val_accuracy: 0.8667\n",
            "~~~~~~~~~~Evaluate_on_test_data~~~~~~~~~~~\n",
            "5/5 [==============================] - 2s 42ms/step - loss: 0.3700 - accuracy: 0.8700\n",
            "test loss, test acc: [0.3700306713581085, 0.8700000047683716]\n",
            "~~~~~~~~~~Confusion_Matrix~~~~~~~~~~\n",
            "tf.Tensor(\n",
            "[[17  0  1  1  0]\n",
            " [ 2 17  3  0  0]\n",
            " [ 1  0 17  1  0]\n",
            " [ 2  0  1 23  0]\n",
            " [ 1  0  0  0 13]], shape=(5, 5), dtype=int32)\n",
            "--------------------------the bacth 3 is for validation-------------------------\n",
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "~~~~~~~~~~Fit_model:~~~~~~~~~~\n",
            "Epoch 1/10\n",
            "54/54 [==============================] - 5s 55ms/step - loss: 1.2346 - accuracy: 0.5463 - val_loss: 0.9097 - val_accuracy: 0.7083\n",
            "Epoch 2/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.7464 - accuracy: 0.7537 - val_loss: 0.6745 - val_accuracy: 0.7667\n",
            "Epoch 3/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.5599 - accuracy: 0.8157 - val_loss: 0.5673 - val_accuracy: 0.8250\n",
            "Epoch 4/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.4559 - accuracy: 0.8602 - val_loss: 0.5004 - val_accuracy: 0.8417\n",
            "Epoch 5/10\n",
            "54/54 [==============================] - 2s 42ms/step - loss: 0.3841 - accuracy: 0.8824 - val_loss: 0.4557 - val_accuracy: 0.8667\n",
            "Epoch 6/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.3304 - accuracy: 0.9019 - val_loss: 0.4250 - val_accuracy: 0.8833\n",
            "Epoch 7/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2877 - accuracy: 0.9213 - val_loss: 0.4021 - val_accuracy: 0.8833\n",
            "Epoch 8/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2515 - accuracy: 0.9352 - val_loss: 0.3808 - val_accuracy: 0.8833\n",
            "Epoch 9/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2207 - accuracy: 0.9463 - val_loss: 0.3654 - val_accuracy: 0.8917\n",
            "Epoch 10/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.1941 - accuracy: 0.9583 - val_loss: 0.3510 - val_accuracy: 0.8833\n",
            "~~~~~~~~~~Evaluate_on_test_data~~~~~~~~~~~\n",
            "5/5 [==============================] - 2s 42ms/step - loss: 0.3726 - accuracy: 0.8700\n",
            "test loss, test acc: [0.37262770533561707, 0.8700000047683716]\n",
            "~~~~~~~~~~Confusion_Matrix~~~~~~~~~~\n",
            "tf.Tensor(\n",
            "[[16  1  1  1  0]\n",
            " [ 1 18  3  0  0]\n",
            " [ 1  0 17  1  0]\n",
            " [ 2  0  1 23  0]\n",
            " [ 1  0  0  0 13]], shape=(5, 5), dtype=int32)\n",
            "--------------------------the bacth 4 is for validation-------------------------\n",
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "~~~~~~~~~~Fit_model:~~~~~~~~~~\n",
            "Epoch 1/10\n",
            "54/54 [==============================] - 6s 67ms/step - loss: 1.1774 - accuracy: 0.5759 - val_loss: 0.8412 - val_accuracy: 0.7333\n",
            "Epoch 2/10\n",
            "54/54 [==============================] - 2s 42ms/step - loss: 0.7382 - accuracy: 0.7556 - val_loss: 0.6222 - val_accuracy: 0.8000\n",
            "Epoch 3/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.5625 - accuracy: 0.8130 - val_loss: 0.5118 - val_accuracy: 0.8583\n",
            "Epoch 4/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.4596 - accuracy: 0.8565 - val_loss: 0.4468 - val_accuracy: 0.8750\n",
            "Epoch 5/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.3879 - accuracy: 0.8824 - val_loss: 0.4051 - val_accuracy: 0.8833\n",
            "Epoch 6/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.3334 - accuracy: 0.9056 - val_loss: 0.3753 - val_accuracy: 0.8917\n",
            "Epoch 7/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2894 - accuracy: 0.9204 - val_loss: 0.3498 - val_accuracy: 0.9167\n",
            "Epoch 8/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2528 - accuracy: 0.9352 - val_loss: 0.3336 - val_accuracy: 0.9083\n",
            "Epoch 9/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2212 - accuracy: 0.9426 - val_loss: 0.3212 - val_accuracy: 0.9000\n",
            "Epoch 10/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.1947 - accuracy: 0.9500 - val_loss: 0.3116 - val_accuracy: 0.8917\n",
            "~~~~~~~~~~Evaluate_on_test_data~~~~~~~~~~~\n",
            "5/5 [==============================] - 2s 42ms/step - loss: 0.3694 - accuracy: 0.8700\n",
            "test loss, test acc: [0.36938154697418213, 0.8700000047683716]\n",
            "~~~~~~~~~~Confusion_Matrix~~~~~~~~~~\n",
            "tf.Tensor(\n",
            "[[17  0  1  1  0]\n",
            " [ 1 18  2  1  0]\n",
            " [ 1  1 16  1  0]\n",
            " [ 2  0  0 23  1]\n",
            " [ 1  0  0  0 13]], shape=(5, 5), dtype=int32)\n",
            "--------------------------the bacth 5 is for validation-------------------------\n",
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "~~~~~~~~~~Fit_model:~~~~~~~~~~\n",
            "Epoch 1/10\n",
            "54/54 [==============================] - 6s 55ms/step - loss: 1.2918 - accuracy: 0.5009 - val_loss: 0.8955 - val_accuracy: 0.7583\n",
            "Epoch 2/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.8033 - accuracy: 0.7370 - val_loss: 0.6524 - val_accuracy: 0.7833\n",
            "Epoch 3/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.6021 - accuracy: 0.8028 - val_loss: 0.5296 - val_accuracy: 0.8583\n",
            "Epoch 4/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.4869 - accuracy: 0.8352 - val_loss: 0.4645 - val_accuracy: 0.8583\n",
            "Epoch 5/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.4090 - accuracy: 0.8602 - val_loss: 0.4197 - val_accuracy: 0.8750\n",
            "Epoch 6/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.3498 - accuracy: 0.8944 - val_loss: 0.3881 - val_accuracy: 0.9083\n",
            "Epoch 7/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.3036 - accuracy: 0.9167 - val_loss: 0.3635 - val_accuracy: 0.9167\n",
            "Epoch 8/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.2651 - accuracy: 0.9296 - val_loss: 0.3433 - val_accuracy: 0.9250\n",
            "Epoch 9/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2331 - accuracy: 0.9444 - val_loss: 0.3232 - val_accuracy: 0.9250\n",
            "Epoch 10/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.2043 - accuracy: 0.9500 - val_loss: 0.3088 - val_accuracy: 0.9250\n",
            "~~~~~~~~~~Evaluate_on_test_data~~~~~~~~~~~\n",
            "5/5 [==============================] - 2s 48ms/step - loss: 0.3717 - accuracy: 0.8800\n",
            "test loss, test acc: [0.37166255712509155, 0.8799999952316284]\n",
            "~~~~~~~~~~Confusion_Matrix~~~~~~~~~~\n",
            "tf.Tensor(\n",
            "[[15  1  1  2  0]\n",
            " [ 2 19  1  0  0]\n",
            " [ 0  0 18  1  0]\n",
            " [ 2  0  1 23  0]\n",
            " [ 1  0  0  0 13]], shape=(5, 5), dtype=int32)\n",
            "--------------------------the bacth 6 is for validation-------------------------\n",
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "~~~~~~~~~~Fit_model:~~~~~~~~~~\n",
            "Epoch 1/10\n",
            "54/54 [==============================] - 6s 55ms/step - loss: 1.2231 - accuracy: 0.5509 - val_loss: 0.9212 - val_accuracy: 0.7083\n",
            "Epoch 2/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.7497 - accuracy: 0.7769 - val_loss: 0.6738 - val_accuracy: 0.7500\n",
            "Epoch 3/10\n",
            "54/54 [==============================] - 2s 44ms/step - loss: 0.5604 - accuracy: 0.8296 - val_loss: 0.5520 - val_accuracy: 0.8000\n",
            "Epoch 4/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.4556 - accuracy: 0.8639 - val_loss: 0.4859 - val_accuracy: 0.8250\n",
            "Epoch 5/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.3828 - accuracy: 0.8796 - val_loss: 0.4447 - val_accuracy: 0.8583\n",
            "Epoch 6/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.3267 - accuracy: 0.9000 - val_loss: 0.4154 - val_accuracy: 0.8583\n",
            "Epoch 7/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2819 - accuracy: 0.9139 - val_loss: 0.3934 - val_accuracy: 0.8583\n",
            "Epoch 8/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.2437 - accuracy: 0.9343 - val_loss: 0.3815 - val_accuracy: 0.8583\n",
            "Epoch 9/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.2114 - accuracy: 0.9444 - val_loss: 0.3691 - val_accuracy: 0.8583\n",
            "Epoch 10/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.1829 - accuracy: 0.9556 - val_loss: 0.3578 - val_accuracy: 0.8583\n",
            "~~~~~~~~~~Evaluate_on_test_data~~~~~~~~~~~\n",
            "5/5 [==============================] - 2s 42ms/step - loss: 0.3588 - accuracy: 0.8900\n",
            "test loss, test acc: [0.35880565643310547, 0.8899999856948853]\n",
            "~~~~~~~~~~Confusion_Matrix~~~~~~~~~~\n",
            "tf.Tensor(\n",
            "[[17  1  1  0  0]\n",
            " [ 1 18  3  0  0]\n",
            " [ 0  0 19  0  0]\n",
            " [ 2  0  1 22  1]\n",
            " [ 1  0  0  0 13]], shape=(5, 5), dtype=int32)\n",
            "--------------------------the bacth 7 is for validation-------------------------\n",
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "~~~~~~~~~~Fit_model:~~~~~~~~~~\n",
            "Epoch 1/10\n",
            "54/54 [==============================] - 5s 54ms/step - loss: 1.2215 - accuracy: 0.5509 - val_loss: 0.8267 - val_accuracy: 0.7833\n",
            "Epoch 2/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.7426 - accuracy: 0.7731 - val_loss: 0.5868 - val_accuracy: 0.8250\n",
            "Epoch 3/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.5596 - accuracy: 0.8352 - val_loss: 0.4813 - val_accuracy: 0.8500\n",
            "Epoch 4/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.4555 - accuracy: 0.8611 - val_loss: 0.4230 - val_accuracy: 0.8833\n",
            "Epoch 5/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.3837 - accuracy: 0.8880 - val_loss: 0.3875 - val_accuracy: 0.8833\n",
            "Epoch 6/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.3280 - accuracy: 0.9083 - val_loss: 0.3609 - val_accuracy: 0.8750\n",
            "Epoch 7/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2827 - accuracy: 0.9204 - val_loss: 0.3411 - val_accuracy: 0.9000\n",
            "Epoch 8/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2445 - accuracy: 0.9380 - val_loss: 0.3261 - val_accuracy: 0.9000\n",
            "Epoch 9/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.2120 - accuracy: 0.9491 - val_loss: 0.3127 - val_accuracy: 0.9000\n",
            "Epoch 10/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.1834 - accuracy: 0.9593 - val_loss: 0.3024 - val_accuracy: 0.9000\n",
            "~~~~~~~~~~Evaluate_on_test_data~~~~~~~~~~~\n",
            "5/5 [==============================] - 2s 53ms/step - loss: 0.3782 - accuracy: 0.8600\n",
            "test loss, test acc: [0.3782474398612976, 0.8600000143051147]\n",
            "~~~~~~~~~~Confusion_Matrix~~~~~~~~~~\n",
            "tf.Tensor(\n",
            "[[16  1  1  1  0]\n",
            " [ 1 16  5  0  0]\n",
            " [ 1  0 18  0  0]\n",
            " [ 2  0  1 23  0]\n",
            " [ 1  0  0  0 13]], shape=(5, 5), dtype=int32)\n",
            "--------------------------the bacth 8 is for validation-------------------------\n",
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "~~~~~~~~~~Fit_model:~~~~~~~~~~\n",
            "Epoch 1/10\n",
            "54/54 [==============================] - 5s 56ms/step - loss: 1.1600 - accuracy: 0.5722 - val_loss: 0.8919 - val_accuracy: 0.7083\n",
            "Epoch 2/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.7135 - accuracy: 0.7759 - val_loss: 0.7076 - val_accuracy: 0.7333\n",
            "Epoch 3/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.5406 - accuracy: 0.8296 - val_loss: 0.6200 - val_accuracy: 0.7417\n",
            "Epoch 4/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.4396 - accuracy: 0.8694 - val_loss: 0.5735 - val_accuracy: 0.7583\n",
            "Epoch 5/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.3702 - accuracy: 0.8917 - val_loss: 0.5407 - val_accuracy: 0.7833\n",
            "Epoch 6/10\n",
            "54/54 [==============================] - 2s 45ms/step - loss: 0.3180 - accuracy: 0.9111 - val_loss: 0.5212 - val_accuracy: 0.8083\n",
            "Epoch 7/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.2756 - accuracy: 0.9231 - val_loss: 0.5042 - val_accuracy: 0.8000\n",
            "Epoch 8/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2407 - accuracy: 0.9389 - val_loss: 0.4890 - val_accuracy: 0.8000\n",
            "Epoch 9/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2098 - accuracy: 0.9528 - val_loss: 0.4752 - val_accuracy: 0.8000\n",
            "Epoch 10/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.1831 - accuracy: 0.9602 - val_loss: 0.4629 - val_accuracy: 0.8083\n",
            "~~~~~~~~~~Evaluate_on_test_data~~~~~~~~~~~\n",
            "5/5 [==============================] - 2s 45ms/step - loss: 0.3549 - accuracy: 0.8600\n",
            "test loss, test acc: [0.35491475462913513, 0.8600000143051147]\n",
            "~~~~~~~~~~Confusion_Matrix~~~~~~~~~~\n",
            "tf.Tensor(\n",
            "[[16  1  1  1  0]\n",
            " [ 1 17  4  0  0]\n",
            " [ 1  0 18  0  0]\n",
            " [ 2  0  1 22  1]\n",
            " [ 1  0  0  0 13]], shape=(5, 5), dtype=int32)\n",
            "--------------------------the bacth 9 is for validation-------------------------\n",
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "~~~~~~~~~~Fit_model:~~~~~~~~~~\n",
            "Epoch 1/10\n",
            "54/54 [==============================] - 5s 56ms/step - loss: 1.2468 - accuracy: 0.5426 - val_loss: 0.8848 - val_accuracy: 0.7417\n",
            "Epoch 2/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.7536 - accuracy: 0.7556 - val_loss: 0.6621 - val_accuracy: 0.8083\n",
            "Epoch 3/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.5623 - accuracy: 0.8278 - val_loss: 0.5779 - val_accuracy: 0.8083\n",
            "Epoch 4/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.4552 - accuracy: 0.8648 - val_loss: 0.5288 - val_accuracy: 0.8000\n",
            "Epoch 5/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.3814 - accuracy: 0.8907 - val_loss: 0.4928 - val_accuracy: 0.8000\n",
            "Epoch 6/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.3254 - accuracy: 0.9037 - val_loss: 0.4701 - val_accuracy: 0.8000\n",
            "Epoch 7/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2802 - accuracy: 0.9176 - val_loss: 0.4492 - val_accuracy: 0.8083\n",
            "Epoch 8/10\n",
            "54/54 [==============================] - 2s 41ms/step - loss: 0.2429 - accuracy: 0.9417 - val_loss: 0.4307 - val_accuracy: 0.8083\n",
            "Epoch 9/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.2115 - accuracy: 0.9519 - val_loss: 0.4171 - val_accuracy: 0.8250\n",
            "Epoch 10/10\n",
            "54/54 [==============================] - 2s 40ms/step - loss: 0.1843 - accuracy: 0.9565 - val_loss: 0.4023 - val_accuracy: 0.8250\n",
            "~~~~~~~~~~Evaluate_on_test_data~~~~~~~~~~~\n",
            "5/5 [==============================] - 2s 46ms/step - loss: 0.3381 - accuracy: 0.8900\n",
            "test loss, test acc: [0.3380942642688751, 0.8899999856948853]\n",
            "~~~~~~~~~~Confusion_Matrix~~~~~~~~~~\n",
            "tf.Tensor(\n",
            "[[17  1  1  0  0]\n",
            " [ 1 19  2  0  0]\n",
            " [ 1  0 17  1  0]\n",
            " [ 2  0  1 23  0]\n",
            " [ 1  0  0  0 13]], shape=(5, 5), dtype=int32)\n"
          ]
        }
      ],
      "source": [
        "Xlist_data = []\n",
        "Ylist_data = []\n",
        "\n",
        "for i in train_data:\n",
        "  Xlist_data.append(i[0])\n",
        "  Ylist_data.append(i[1])\n",
        "\n",
        "for i in val_data:\n",
        "  Xlist_data.append(i[0])\n",
        "  Ylist_data.append(i[1])\n",
        "\n",
        "tempE = 0\n",
        "for i in range(10):\n",
        "  train_kfold = []\n",
        "  val_kfold = []\n",
        "\n",
        "  tempE += 6\n",
        "  \n",
        "  val_kfold = tf.data.Dataset.from_tensor_slices((Xlist_data[tempE - 6: tempE], Ylist_data[tempE - 6: tempE]))  \n",
        "  train_kfold = tf.data.Dataset.from_tensor_slices((Xlist_data[:tempE - 6] + Xlist_data[tempE:], Ylist_data[:tempE - 6] + Ylist_data[tempE:]))  \n",
        "\n",
        "\n",
        "  # Create the base model from the pre-trained model MobileNet V2\n",
        "  print(f\"--------------------------the bacth {i} is for validation-------------------------\")\n",
        "  IMG_SIZE = (256, 256)\n",
        "  IMG_SHAPE = IMG_SIZE + (3,)\n",
        "  base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE,\n",
        "                                                include_top=False,\n",
        "                                                weights='imagenet')\n",
        "  base_model.trainable = False\n",
        "  #Create clasification layer\n",
        "  global_average_layer = tf.keras.layers.GlobalAveragePooling2D()\n",
        "  zero_layer = Dense(units=512,activation='relu')\n",
        "  first_layer = Dense(units=256,activation='relu')\n",
        "  second_layer = Dense(units=128,activation='relu')\n",
        "  third_layer = Dense(units=5,activation='softmax')\n",
        "  inputs = tf.keras.Input(shape=(256, 256, 3))\n",
        "  x = base_model(inputs, training=False)\n",
        "  x = global_average_layer(x)\n",
        "  x = zero_layer(x)\n",
        "  x = first_layer(x)\n",
        "  # x = second_layer(x)\n",
        "  outputs = third_layer(x)\n",
        "  model = tf.keras.Model(inputs, outputs)\n",
        "  base_learning_rate = 0.0001\n",
        "  model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=base_learning_rate),\n",
        "                loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),\n",
        "                metrics=['accuracy'])\n",
        "  \n",
        "  print(\"~~~~~~~~~~Fit_model:~~~~~~~~~~\")\n",
        "  model.fit(train_kfold, epochs=10, validation_data=val_kfold)            #CHANGED\n",
        "  # Evaluate the model on the test data \n",
        "  print(\"~~~~~~~~~~Evaluate_on_test_data~~~~~~~~~~~\")\n",
        "  results = model.evaluate(test_data)                                  #CHANGED\n",
        "  print(\"test loss, test acc:\", results)\n",
        "  # Create confusion matrix\n",
        "  print(\"~~~~~~~~~~Confusion_Matrix~~~~~~~~~~\")\n",
        "  real_label = []\n",
        "  pred_label = [] \n",
        "  for i in test_data:                                                  #CHANGED\n",
        "    temp = model.predict(i[0])\n",
        "    for j in temp:\n",
        "      pred_label.append(max(range(len(j)), key=j.__getitem__))\n",
        "    for j in i[1]:\n",
        "      real_label.append(int(j))\n",
        "  confusion = tf.math.confusion_matrix(labels=real_label, predictions=pred_label, num_classes=5)\n",
        "  print(confusion)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KFvjDqSEkslI"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "ProjectDMTensorFlow.ipynb",
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}