diff --git a/6- Deployment/Rice Type Classifier.ipynb b/6- Deployment/Rice Type Classifier.ipynb deleted file mode 100644 index 725360a..0000000 --- a/6- Deployment/Rice Type Classifier.ipynb +++ /dev/null @@ -1,5873 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [], - "collapsed_sections": [ - "ra_CeKBUQtHR", - "5e0K4XjiQ2Cg", - "JWgPKWfef1WP", - "5wtypxYHmPgz", - "qBvaqxVyUEGh" - ], - "authorship_tag": "ABX9TyNYesIopMUCu/Bxgp8vOT/B", - "include_colab_link": true - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - }, - "accelerator": "GPU", - "gpuClass": "standard" - }, - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "markdown", - "source": [ - "> Nama : Muhammad Ammar Nabil
\n", - "Kelas : M03
\n", - "Email : mammarnabil1@gmail.com\n" - ], - "metadata": { - "id": "IEO8X6kLOWdh" - } - }, - { - "cell_type": "markdown", - "source": [ - "# **Rice Classification**\n", - "---\n", - "\n", - "This model build for classify rice. This model can classify 5 type of rice:\n", - " * Arborio Rice\n", - " * Basmati Rice\n", - " * Ipsala Rice\n", - " * Jasmine Rice\n", - " * Karacadag Rice\n", - "\n", - "> Dataset [Rice Image Dataset](https://www.kaggle.com/datasets/muratkokludataset/rice-image-dataset?select=Rice_Image_Dataset) by Murat Koklu. Total of image 75000 image, consist of:\n", - " * Arborio Rice : 15000 image\n", - " * Basmati Rice : 15000 image\n", - " * Ipsala Rice : 15000 image\n", - " * Jasmine Rice : 15000 image\n", - " * Karacadag Rice : 15000 image" - ], - "metadata": { - "id": "AsXrSdtBOXxR" - } - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "cGTFMdZXHzMk" - }, - "outputs": [], - "source": [ - "import os\n", - "import cv2\n", - "import numpy as np\n", - "import tensorflow as tf\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.image as mpimg\n", - "from google.colab import files, drive\n", - "from keras import layers, callbacks\n", - "from keras.models import Sequential\n", - "from keras.preprocessing.image import ImageDataGenerator\n", - "from keras.preprocessing import image" - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Preparing Data" - ], - "metadata": { - "id": "ra_CeKBUQtHR" - } - }, - { - "cell_type": "code", - "source": [ - "# Download kaggle.json API\n", - "!mkdir ~/.kaggle\n", - "files.upload()\n", - "!mv kaggle.json ~/.kaggle/" - ], - "metadata": { - "colab": { - "resources": { - "http://localhost:8080/nbextensions/google.colab/files.js": { - "data": "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", - "ok": true, - "headers": [ - [ - "content-type", - "application/javascript" - ] - ], - "status": 200, - "status_text": "OK" - } - }, - "base_uri": "https://localhost:8080/", - "height": 74 - }, - "id": "WQkWUURwJDv6", - "outputId": "dd35f115-3214-4deb-e9d4-487265459c33" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " \n", - " Upload widget is only available when the cell has been executed in the\n", - " current browser session. Please rerun this cell to enable.\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saving kaggle.json to kaggle.json\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Change permission\n", - "!chmod 600 ~/.kaggle/kaggle.json\n", - "!ls ~/.kaggle/ -la" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0Wa0VGWxJRj3", - "outputId": "cc428c28-a7dd-4227-f4d9-92e67c85eadf" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "total 16\n", - "drwxr-xr-x 2 root root 4096 Sep 6 06:49 .\n", - "drwx------ 1 root root 4096 Sep 6 06:49 ..\n", - "-rw------- 1 root root 63 Sep 6 06:49 kaggle.json\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Download and extract kaggle dataset\n", - "!kaggle datasets download -d muratkokludataset/rice-image-dataset\n", - "!unzip rice-image-dataset.zip" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "QN_62ntdKq11", - "outputId": "f36d5ca6-68be-43cc-e0da-58699e23e2fe" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", - " inflating: Rice_Image_Dataset/Karacadag/Karacadag (55).jpg \n", - " inflating: Rice_Image_Dataset/Karacadag/Karacadag (550).jpg \n", - " inflating: Rice_Image_Dataset/Karacadag/Karacadag (5500).jpg \n", - " inflating: Rice_Image_Dataset/Karacadag/Karacadag (5501).jpg \n", - " inflating: Rice_Image_Dataset/Karacadag/Karacadag (5502).jpg \n", - " inflating: Rice_Image_Dataset/Karacadag/Karacadag (5503).jpg \n", - " inflating: Rice_Image_Dataset/Karacadag/Karacadag (5504).jpg \n", - " inflating: Rice_Image_Dataset/Karacadag/Karacadag (5505).jpg \n", - " inflating: Rice_Image_Dataset/Karacadag/Karacadag (5506).jpg \n", - " inflating: Rice_Image_Dataset/Karacadag/Karacadag (5507).jpg \n", - 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"sizes = [len(os.listdir(base_path + name)) for name in class_names]\n", - "for name, data in zip(class_names, sizes):\n", - " print(name , ' : ' , data, 'images')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "mMdAuzcWL5gz", - "outputId": "96e35cac-d203-4715-c5ec-258285fb28bc" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Arborio : 15000 images\n", - "Basmati : 15000 images\n", - "Ipsala : 15000 images\n", - "Jasmine : 15000 images\n", - "Karacadag : 15000 images\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Comparision Rice Grain\n", - "img1 = mpimg.imread(f'{base_path}Arborio/Arborio (1).jpg')\n", - "img2 = mpimg.imread(f'{base_path}Basmati/Basmati (1).jpg')\n", - "img3 = mpimg.imread(f'{base_path}Ipsala/Ipsala (1).jpg')\n", - "img4 = mpimg.imread(f'{base_path}Jasmine/Jasmine (1).jpg')\n", - "img5 = mpimg.imread(f'{base_path}Karacadag/Karacadag (1).jpg')\n", - "\n", - "plt.style.use('dark_background')\n", - "\n", - "fig, axs = plt.subplots(2, 3, figsize=(10,6))\n", - "# Axis 0,0\n", - "axs[0, 0].imshow(img1)\n", - "axs[0, 0].set_title('Arborio', size=20)\n", - "# Axis 0,1\n", - "axs[0, 1].imshow(img2)\n", - "axs[0, 1].set_title('Basmati', size=20)\n", - "# Axis 0,2\n", - "axs[0, 2].imshow(img3)\n", - "axs[0, 2].set_title('Ipsala', size=20)\n", - "# Axis 1,0\n", - "axs[1, 0].imshow(img4)\n", - "axs[1, 0].set_title('Jasmine', size=20)\n", - "# Axis 1,1\n", - "axs[1, 1].imshow(img5)\n", - "axs[1, 1].set_title('Karacadag', size=20)\n", - "# Hide x labels and tick labels\n", - "for ax in axs.flat:\n", - " ax.get_yaxis().set_visible(False)\n", - " ax.get_xaxis().set_visible(False)\n", - " ax.axis('off')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 378 - }, - "id": "j9HszsDgFUc-", - "outputId": "b906e96c-b737-4ddf-f60e-ffce5259616a" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Create Generator\n", - "generator = ImageDataGenerator(rescale=1./255,\n", - " rotation_range=20,\n", - " validation_split=0.2)" - ], - "metadata": { - "id": "3h-ZOEB8UJHe" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Apply Generator\n", - "train_set = generator.flow_from_directory(base_path, \n", - " (150, 150), \n", - " seed=2)\n", - "\n", - "val_set = generator.flow_from_directory(base_path,\n", - " (150, 150),\n", - " seed=2,\n", - " subset='validation')" - ], - "metadata": { - "id": "pMHSRfAIVw1u", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "d1121823-6db4-4ce6-8b31-7db16b29def1" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Found 75000 images belonging to 5 classes.\n", - "Found 15000 images belonging to 5 classes.\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Create Model" - ], - "metadata": { - "id": "JWgPKWfef1WP" - } - }, - { - "cell_type": "code", - "source": [ - "# Create Model\n", - "model = Sequential([\n", - " layers.Conv2D(32, (3,3), activation='relu', input_shape=(150, 150, 3)),\n", - " layers.MaxPooling2D(2, 2),\n", - " layers.Conv2D(64, (3,3), activation='relu'),\n", - " layers.MaxPooling2D(2,2),\n", - " layers.Conv2D(128, (3,3), activation='relu'),\n", - " layers.MaxPooling2D(2,2),\n", - " layers.Conv2D(512, (3,3), activation='relu'),\n", - " layers.MaxPooling2D(2,2),\n", - " layers.Conv2D(1024, (3,3), activation='relu'),\n", - " layers.MaxPooling2D(2,2),\n", - " layers.Flatten(),\n", - " layers.Dense(1024, activation='relu'),\n", - " layers.Dense(5, activation='softmax')\n", - " ])\n", - "model.summary()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "pZTl-MOUfR9B", - "outputId": "edec1488-81da-4725-e6b6-926495f6d869" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Model: \"sequential\"\n", - "_________________________________________________________________\n", - " Layer (type) Output Shape Param # \n", - "=================================================================\n", - " conv2d (Conv2D) (None, 148, 148, 32) 896 \n", - " \n", - " max_pooling2d (MaxPooling2D (None, 74, 74, 32) 0 \n", - " ) \n", - " \n", - " conv2d_1 (Conv2D) (None, 72, 72, 64) 18496 \n", - " \n", - " max_pooling2d_1 (MaxPooling (None, 36, 36, 64) 0 \n", - " 2D) \n", - " \n", - " conv2d_2 (Conv2D) (None, 34, 34, 128) 73856 \n", - " \n", - " max_pooling2d_2 (MaxPooling (None, 17, 17, 128) 0 \n", - " 2D) \n", - " \n", - " conv2d_3 (Conv2D) (None, 15, 15, 512) 590336 \n", - " \n", - " max_pooling2d_3 (MaxPooling (None, 7, 7, 512) 0 \n", - " 2D) \n", - " \n", - " conv2d_4 (Conv2D) (None, 5, 5, 1024) 4719616 \n", - " \n", - " max_pooling2d_4 (MaxPooling (None, 2, 2, 1024) 0 \n", - " 2D) \n", - " \n", - " flatten (Flatten) (None, 4096) 0 \n", - " \n", - " dense (Dense) (None, 1024) 4195328 \n", - " \n", - " dense_1 (Dense) (None, 5) 5125 \n", - " \n", - "=================================================================\n", - "Total params: 9,603,653\n", - "Trainable params: 9,603,653\n", - "Non-trainable params: 0\n", - "_________________________________________________________________\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Create Callback\n", - "class AccCallback(tf.keras.callbacks.Callback):\n", - " def on_epoch_end(self, epoch, logs={}):\n", - " if(logs.get('accuracy')>0.92 and logs.get('val_accuracy')>0.92):\n", - " print(\"\\nAccuracy and Val Accuracy above 92%\")\n", - " self.model.stop_training = True\n", - "\n", - "Acc_callbacks = AccCallback()\n", - "\n", - "callback = [\n", - " callbacks.ModelCheckpoint('BestModel.h5', save_best_only=True, \n", - " monitor='val_accuracy', \n", - " mode='max'),\n", - " Acc_callbacks\n", - " ]" - ], - "metadata": { - "id": "hqoj3FWUhCgx" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Compile Model\n", - "model.compile(loss='categorical_crossentropy',\n", - " optimizer='adam',\n", - " metrics=['accuracy'])" - ], - "metadata": { - "id": "QvCfuiANhWhy" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "## Train Model" - ], - "metadata": { - "id": "5wtypxYHmPgz" - } - }, - { - "cell_type": "code", - "source": [ - "# Test GPU Avaibility\n", - "tf.test.gpu_device_name()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 35 - }, - "id": "C3iSVqOYmygX", - "outputId": "6305a4dc-7dce-4680-ccaf-204e82d3f66b" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "'/device:GPU:0'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 12 - } - ] - }, - { - "cell_type": "code", - "source": [ - "%%time\n", - "# Train Model\n", - "with tf.device(\"/device:GPU:0\"):\n", - " history = model.fit(\n", - " train_set,\n", - " epochs=15,\n", - " validation_data=val_set,\n", - " callbacks=callback,\n", - " steps_per_epoch=30,\n", - " )" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "HdwWplsAlNSd", - "outputId": "b05197c5-5cbd-4602-ef13-957c59fa5725" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Epoch 1/15\n", - "30/30 [==============================] - 118s 4s/step - loss: 1.3038 - accuracy: 0.4094 - val_loss: 0.7706 - val_accuracy: 0.6031\n", - "Epoch 2/15\n", - "30/30 [==============================] - 89s 3s/step - loss: 0.4861 - accuracy: 0.7948 - val_loss: 0.2295 - val_accuracy: 0.9263\n", - "Epoch 3/15\n", - "30/30 [==============================] - 91s 3s/step - loss: 0.3840 - accuracy: 0.8354 - val_loss: 0.4693 - val_accuracy: 0.7889\n", - "Epoch 4/15\n", - "30/30 [==============================] - 88s 3s/step - loss: 0.2757 - accuracy: 0.8958 - val_loss: 0.2689 - val_accuracy: 0.9057\n", - "Epoch 5/15\n", - "30/30 [==============================] - 89s 3s/step - loss: 0.2583 - accuracy: 0.9094 - val_loss: 0.1639 - val_accuracy: 0.9391\n", - "Epoch 6/15\n", - "30/30 [==============================] - ETA: 0s - loss: 0.2014 - accuracy: 0.9281\n", - "Accuracy and Val Accuracy above 92%\n", - "30/30 [==============================] - 88s 3s/step - loss: 0.2014 - accuracy: 0.9281 - val_loss: 0.1921 - val_accuracy: 0.9303\n", - "CPU times: user 9min 30s, sys: 9.7 s, total: 9min 40s\n", - "Wall time: 12min 25s\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Visualize Model Training" - ], - "metadata": { - "id": "qBvaqxVyUEGh" - } - }, - { - "cell_type": "code", - "source": [ - "# Make plot function\n", - "def printPlot(label: list, title: str, legend: list, *line: str):\n", - " '''\n", - " Print plot of history\n", - " * label[0] = x_label\n", - " * label[1] = y_label\n", - " * label[n] = n_label\n", - " * line[n] = line_n\n", - " * legend[n] = [location, line_n, ...]\n", - " '''\n", - " plt.style.use('dark_background')\n", - "\n", - " plt.title(title)\n", - " plt.plot(history.history[line[0]], '#1f77b4')\n", - " plt.plot(history.history[line[1]], '#ff7f0e')\n", - " plt.xlabel(label[0])\n", - " plt.ylabel(label[1])\n", - " plt.legend(legend[1:], loc=legend[0])\n", - " plt.show()" - ], - "metadata": { - "id": "G3Eare9ATzlf" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Print Plot\n", - "printPlot(['Epoch', 'Accuracy'], 'Accuracy', \n", - " ['lower right', 'Acc', 'Val Acc'],\n", - " 'accuracy', 'val_accuracy')\n", - "printPlot(['Epoch', 'Loss'], 'Loss', \n", - " ['upper right', 'Loss', 'Val Loss'],\n", - " 'loss', 'val_loss')" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 573 - }, - "id": "WABaXVy7qvu0", - "outputId": "ed8e3ff7-9354-45b3-81f7-9a2dcc4ca10c" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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JLKboQA5rJ6oQ2Y7D/50NwhJuKvgpveFjs6LmYgbK9q5Cbf5JathJswmWCHQ6HSIjIxEeHo6CggLEx8fj2WefrbNNu3btUFpaCsYY3n//fSxfvlyocNzWnFg52t9nb+B2u85Z8fB/9Pj+WS/8Ms0LL3xnxIZT1MA4mwTA2wMV+OswBUqsajxbMweHQvvAWnIRlSkroD/9i2APISOehwlVRo8ezc6cOcNycnLYBx98wACwBQsWsHHjxjEAbNKkSezs2bPszJkzLDExkcnl8gb3qdPpBIvX1YpWDlbyroZ9n6ASZP9+XhK27wUvxj7yZh8+1vB/GyotV8LCQ1nK650Z+8ib/fin4azX618z3xEvM3lAZ9Fjo+KapYG2U/wAW/BgPKp8MEjO2EferH9HTrA65DzYyglKxj7yZmsmqpiCF/+43bVwXj5M2388e3H2y6z0zx2Z/s9+bO70J5kq8iEGTip6fFRcu9yr7aQ7i12UtwJ4+2EFtp0x4+hl4e6cNFmBF76rQWaJDX8foUREGwmeXG9EUSt+HIFL4aXw6jwA6p7D4PtAL/xZkYTnpOuQVq7Bs5tMyCqgS6qJ8CgRuKjXB8jRViXB/L3OuQFn4QETzl6z4b8TVTgyQ42x6ww4SWsbNJs8sAs0vYbDq/tj4FVadDWm4zO8ha7SKiw8UIsP91S69fP8SetCicAF+SiAtx5S4NssM1ILnddabM2yYNAK++Osf3tJjfjNRvxIaxs0Gq/1g7rnUGh6DYOsXQhs5lrUZP+GF61b8El0CYr1DMNXGes8NZYQZ6BE4ILmPqSAr0qC+b84/3b8OmsbxKvwzk+1WEJrG9yVRKaAV5eBUPcaDmXYg5BIONTknUTlka3Q5v2K1U8AcZ2l2JppwYxtNSg10pAbcT5KBC6mjRJ48yE5tmSakXFVnLGDW9c2WHx9bYM5tLbBLSRQhPaCptcweHV5BJzCC+byK6g4kAT9qT2wlF/B2C5SLJ+uhFouwcvfG/H1Mbpjl4iHEoGLefMhBXyU4vQGbnX72gadfTk8vdHQwDNv3JvUtyPUvYZB09N+t6+t1gB91n7oT+5Bbf5pAAxKKbDkCSVejZEjrdCKZ7cYkFVCGZSIixKBC/FVAnMfkmPTaXOrWISe4ba1DaarMXadETketLaBRKGGutsgqHsPhzKoO9gtd/sasw/VeZpmb38O6yap0NOfx/8drMUHu2thoukA0gpQInAhbz2sgEYO0XsDt7t1bYPDM9SYuN5w14Vx3IKEgzIi2j70E/kQJFI5TCUXUXaPu31fHyDH/45QoNTI8PhqPX4678Z/H+JyKBG4iHYqCd4YIMfGUxacKm59Z9wH8qwYsMz+WIqfnvdyy7UNZO3Doek1HOoeQ8BrfGE1VKAqYyf0J3fDdCWn3u/4qyVYOUGF0ZFSJJ8xY3pyDUoMNCFMWhdKBC7i7YFyqOXAAifdN9Ac7ri2AeflA3WPwdD0Gg55h05gVguM53SoPrkHxnM6wHb3y2dHd5Zi5ZNKaOUS/P4HI/511L0SI3EflAhcgJ+XBK/FyrH+pAWZrXxi0S3WNrjlbl/VA/0g4aWoLcxG6U9fQZ+5DzZj5T2/ruCB/x2pwOsDFMi4YsXQLQacboW9OEJuoETgAt4ZKIdK2rp7A7dy1bUNbr/b11J1DZW6b6E/tQfmkkuN2kfP9vYJ4d4deCw5VIs//FyLWpoOIK0cJYJWrr2XBHNi5Fh30owz11zrrNIV1jao725fY/ZBVJ/cg5oL6QBr/N/81RgZ/vm4EhU1DKP+q8fOc63rWAm5G0oErdx7j8ihlAIf73XNu3db49oG97rbV5/1K5jJ0KT9tfeSYPkEJcZ2keGHs2a8lFxDD+UjLoUSQSvWQS3B72PkWHPCjGwXvjY/s8SGAcv02PKMCuuf9kLXdjX4ZJ+zE1vDd/s2R1wnHiufVKGNUoLXthvxxRFXmgwhxI4SQSv23iNyyHmI0Gi2vBIDw4jVBnw9VomPhyrRzY/HS98ZBR8/b8zdvs2h4IG/jVDgzYcUOHHVipGr6WmsxHVRImilAjQSzO4vx+oMs9vcqeustQ2acrdvc3T3s08I9wng8fkRE977qQY19BBW4sIoEbRSf3hUDhkP/GW/a1wp1BQLD5iQXWrD6qdaaG0DCQe5fwQUoQ9CGdobqvC+jbrbtzle6S/DoseVqDIxjFlroMdwE7dAiaAV6qiV4OV+cnyTYcb5MvecdNySacGF8maubSDhIO/wAJShvaEI6Q1lSE9wCjUAwHwtH1XpO6A/teeud/s2h5+XBMvGKTGhmwzbsy148TsjrtKEMHETlAhaoT88qgAvAf6yz/16A7dq9NoGEg7yDp3sDX9obyiDe9zS8OdBn7kPNZdOoDbvJKzVpS0e54gHeKx6UoW2Kgnm7qjBZ4dNzZxZIKR1okTQygRpJZgVLcOKdDMulLt/c3NjbYPVT92ytsF2Ezj/zlCG9LY3/sE9wCm8AFxv+E/vQ03eCdReOgGrvkyw2OQ88NdhCrwzUIFTRVaMWmPAcZHWgCBESJQIWpkPBikgkQB/dcO5gbsxWHk8uz8Yf5Wr8Ha/C+jV50HMtryFSmhgKrkE/elfUHPpBGryTsCmL3dKTF3b2SeEowJ5fKkz4Z1dNTDSdABxU5QIWpEQbwlmRMuwPM2MSxVu3BvgeMgDbjnjD+oOTuGFzwFcKt2Cf7TZgk2mORi33oyLlyucHt6sfjIsjlPCYGYYv86AbWcpAxD3RomgFflgkAIA8D/u1hvgeMgDIqEMvaXhl6sAAKbii9Cf2nP9jP8UPjOUIzWUx9YpKhz8nQQT1/NOW9ugrco+IfxUdxl2nbNg2rdGXKl244RMyHWUCFqJUB8JXoqSYVmqudU/nK1BnBSKwM72K3pCe0MR1AOcXAkAMBVfQPWJn1GbdxI1eSdhM9x5xv/rJfvaBtsS7GsbvPJDDZYLvLbBsAj7hHB7tQRv7azBkkM0IUw8ByWCVuKPgxRgzEV7A5wUisBI+xU9ob2h6Nj9ZsNflIvq47tuNvwNPML5hvNlN9c2+M/1tQ3+IMDaBjIO+MswBd4ZKMeZEhvGrjMg/QpNCBPPQomgFQhvI8GLfWX46pgZBVUucB7KS6EI6OK4nFMR1A2crG7DX5N3ArV5pxrd8NfnxtoGS0Yp8e5ABbq05fDclpZb2yCyLYe1k1To35HHV0dNeGsnTQgTz0SJoBX402MKWBnwt9baG+ClUAR2ddzApQjqerPhv3oe1Rk7UXPpJGrzTsJWU9WiVVsZ8NptaxuMW2dA/n0On02PkuHTUUrUWBieWm/At1mUAYjnEjQRxMXF4dNPPwXP81i2bBkWLlxY5/OQkBB88803aNOmDXiexx/+8Ads375dyJBanQd8JZjWR4alOhMKW8vE5C0NvzK0N+Qdu4GTKcCYDeaiXFSn70BN3kn7GX8LN/x3s/T62gbrn/bCkRlqTEgyQHe56UM4vkrg63EqPN1Dht3nLZj6rRGXXaEXRojAmBCF4ziWk5PDIiIimEwmY+np6ax79+51tvn3v//NXnnlFQaAde/eneXm5ja4X51OJ0i8YpXl45XM8IGWBWgk4sXBy5gipBfzeSSBdYj/Hxby1mYWNu97FvpeMguYtoT5DpvBVJ0HME6pEf3v1d2PY+de1zDDB1o2uYe0Sd8dHMazvDc1zPQnLXt3oJxJWsF/fypUnFXu1XYK1iOIjY1FTk4OcnNzAQBJSUmYMGECMjMzHdswxuDt7Q0A8PHxweXLl4UKp1Xq3JbD831k+OywybmXKfIyKDp2u3k5Z8eukEjlYMxmH+pJ+/HmGH+t3nlxNcKNtQ22TlFhw2QvfJhSg7808JhuKQd8PFSBeY/IkX3NhoeSDEgtpAlhQm4QLBEEBQUhLy/P8To/Px8DBgyos838+fOxa9cuvPbaa1Cr1RgxYkS9+5o5cyZmzZoFAPDz8xMqZKf78DE5TFb70ziFJJHKIe94fagn5JaG32aF6ep5VKZ+j9pLJ1GTfwqslTX89SkxMAxfZV/b4JOhSnRrx2N6cv1rG3TylWDtJC/EBvFITDVh7o4aGGjtGELqEHWyOCEhAStXrsSiRYvw0EMPYfXq1ejVqxcYq3t2nJiYiMTERACATqcTI9QW16Udh+d6y7D4kEmwZQ0VQd3Q5rFpUHTsBolUdr3hP4fKY9vsl3Pmn3aJhr8+N9Y2yLpmw9+GKxHhK8FTt61t8EJfGT4frYTJCkzaYMCWTJoQJqQ+giWCgoIChISEOF4HBwejoKCgzjbTp0/HqFGjAACHDh2CUqmEn58fiouLhQqr1fjwMTlqLMD/CtgbaPv4q+C8fFB5LBm1l07YG/4mrsfb2v39VxPOXqu7tkF+pQ3/HqvCMz1lSMm1Twjf71VGhLgzTqgd63Q6REZGIjw8HDKZDPHx8UhOTq6zzaVLlzB8+HAAQLdu3aBUKj0iCXTz45DQS4YvdCYUGwTqDQT3hNw/AhX7V6P8lxUwnj/qdknghi2ZFgxaoYeUA357SY3jr2jwVDcp3t9dgxGr7/9SU0I8gWCz1KNHj2ZnzpxhOTk57IMPPmAA2IIFC9i4ceMYYL9S6Ndff2Xp6eksLS2NjRw58r5mvl2lrJ2oYlXva1k7lXBXCvlNmMeCX1/HJFKF6MfrrNJRK2FHZqjZmTlq1r8jJ3o8VKi0ptJA2yl+gC14MK2+9GjPMeuftex/hgvXQPOadiz03e9YmyEvin68zi6S60XsOKhQaW1FlMtHSf3+/JgCehPwz9+EmxvQRI0GJBJUp/0oWB2tFRM7AEJckGBzBOROPdtzmNxTis+OmFBqFKjJ4qXQ9omDMUcHS8VVYeoghLgVSgRO9NFgBapNwP/9JtwzhdRdHwWv9kVV6jbB6iCEuBdKBE7yYAcOk3vKsOSQCWU1wtWj7TcW5mt5qLmQIVwlhBC3QonAST4arEB5DcPiQ8L1BuQBkVB07Iaq1B9Ao+WEkMaiROAEfTpwmNjd3hsoF7I3ED0GtloDqk/uFq4SQojboUTgBPOH2HsDSwTsDXAqb6i7P4bqk3vATEbB6iGEuB9KBAKLDuTwZDcZ/u9gLSoEXHdG0ycOEqkcVWnfC1cJIcQtUSIQ2PzBCpQaGT49JOATRiUctFGjYbyQBsu1fOHqIYS4JUoEAurfkcO4rvbeQJWAeUAVOQBSb39UHaPeACGk6SgRCGj+YAWuGWz4/LCw6w14R4+FpeIqjOfc4xHdhBDnokQgkNggHmO6yPCP30yC9gZkfmFQhvVBVeqPAKNVtwghTUeJQCALhihQrLfhiyPC9ga00WNgM9ei+vguQeshhLgvSgQCeCiYx6jOUvzjNxP0Ai6LKFGooe45DIbMvbDVVAlXESHErVEiEMCCIQoU6W1YqhO2N6DpPRycXHn9TmJCCGkeSgQt7JEQHo93kuJ/D5gEXiRdAm3UWNTkn4bp6jkhKyKEuDlKBC1swRAFrlTb8KXAvQFlRBRkbTuiKpUuGSWE3B9KBC1oUCiP4Q9IsfCACUaLsHVp+42DpboUhjO/CVsRIcTtUSJoQQuGKFBYZcNXR4XtDUjbBEL1QD9Up+8AbAJnHEKI26NE0EIGh/EYGiHF3w+YUCN0byB6DGCzoTp9u7AVEUI8AiWCFrJgiAIFlTZ8fUzY3oBEpoCm9wgYzhyAVV8maF2EEM/QYCIYO3YsJBKJM2JxWcMieAwOl+Jvv9YK3htQ9xgCTqmhSWJCSItpMBFMmTIF2dnZWLhwIbp27eqMmFzOgiEK5FfasCxV0OtFAdgniWuv5KC2IFPwugghnqHBRPD8888jKioK586dw8qVK/Hbb79h5syZ0Gg0zoiv1RvxAI9HQ6X4n/21qLUKW5cipBfk7cPpBjJCSItq1BxBVVUVNm3ahKSkJAQGBuKpp55Camoq5syZI3R8rd6CIQpcqrDhP2lO6A1Ej4XVWAlD5l7B6yKEeI4GE8G4ceOwZcsW/PLLL5DJZIiNjcUTTzyBPn364O2333ZGjK1WXCceA0Ok+Ov+WpgE7g3wWj94dXkY1Rm7wCzCTkgTQjyLtKENJk2ahMWLF2P//v113jcajZg+fbpggbmCBUOUuFBuwwpn9Ab6jgIAVKX9KPaZDPEAAB2dSURBVHhdhBDP0mAimD9/PgoLCx2vlUolOnTogIsXL2LPnj2CBteaje4sxYBgHjO3GWEWehkAXgpNn1Ew5hyBtbJI4MoIIZ6mwaGhjRs3wma72dJZrVZs3LixUTuPi4tDVlYWsrOzMW/evDs+X7RoEdLS0pCWloYzZ86grMx1rotfMESB3DIbVqYL3xtQdxsEXt2GLhklhAiiwR6BVCqF2XyzsTObzZDL5Q3umOM4LF26FCNHjkR+fj50Oh2Sk5ORmXnzsse33nrL8fucOXMQFRXV1PhFMSZSipggHtOTjbA4YVEwbfRYmK/loeZihvCVEUI8ToM9guLiYowbN87xevz48SgpKWlwx7GxscjJyUFubi7MZjOSkpIwYcKEu26fkJCAdevWNTJscS0YosC5UhtWZQjfG5AHdoGiY1dU0sL0hBCBNNgjeOWVV7BmzRp88cUXkEgkyMvLw9SpUxvccVBQEPLy8hyv8/PzMWDAgHq3DQ0NRURExF3nHGbOnIlZs2YBAPz8/BqsW0jju0rRryOPF751Xm/AVmuA/pTnzscQQoTVYCI4f/48Hn74YajVagCAXq9v8SDi4+OxadOmOnMRt0pMTERiYiIAQKfTtXj9TTF/sALZ16z473HhewOclw/U3QahKmMHmMkoeH2EEM/UYCIAgCeeeAI9e/aEUql0vPfJJ5/c8zsFBQUICQlxvA4ODkZBQUG928bHx+PVV19tTCiierKbFFGBPKZuNcLKhK9P0ycOEqmM7iQmhAiqwTmCf/3rX5gyZQpee+01SCQSTJ48GWFhYQ3uWKfTITIyEuHh4ZDJZIiPj0dycvId23Xt2hW+vr44ePBg847ASSSwzw2cKbFi7QnhewOQcNBGPQFjbiospfnC10cI8VgNJoKBAwdi2rRpKCsrw8cff4yHH34YXbp0aXDHVqsVc+bMwc6dO5GZmYkNGzbg9OnTWLBgQZ3J5/j4eCQlJd3fUTjBxO5SPNiBx8f7ap3SG/CKfAhSrR9dMkoIcQp2r3L48GEGgB08eJAFBgYyuVzOsrOz7/kdIYtOp3N6nRKAHX9FzU7/Xs04iXPq7JDwNxb08jIGCSfa35oKFSruU+7VdjY4R7Bt2zb4+PjgH//4B1JTU8EYc0zceoqne0jRuwOPhM0G2Jjw9cn8wqAM7Y2ylOUAc8KlSYQQj3bPRCCRSLB7925UVFRgy5Yt+P7776FUKlFZWems+ETHSYCPBitwutiKDaecsz6wNnosbOZaVB/f5ZT6CCGe7Z5zBIwxLF261PHaZDJ5VBIAgGd6StHTn8f8X2qd0hvgFGqoew6F/vQvsNVUC18hIcTjNThZvHv3bkycONEZsbQ6nAT482MKnLhqxabTzukNqHuPACdX0iQxIcRpGkwEL7/8MjZu3Ija2lpUVFSgsrISFRUVzohNdPG9pOjenseCvbVwQmcAgATa6DGoyTsFc1GuU2okhJAGJ4u9vb2dEUerw1/vDWRcsWJLpnN6A8oH+kHm2xHl+1Y7pT5CCAEakQgGDRpU7/u3L1Tjbp7tLUNXPx5PrTc4qTcAeEePhaXqGgxnf3NSjYQQ0ohE8O677zp+VyqViI2NxbFjxzB8+HBBAxMTLwE+fEyOtEIrvs1yTm9A6tsRqk79Ub7/v4BN4HUvCSHkFg0mgvHjx9d5HRwcjCVLlggWUGvwuwdliGzHY0KSwWl1aqOeALOaUZWxw2l1EkII0IjJ4tvl5+eje/fuQsTSKkg54MPHFDh22YrkM87pDUhkSmh6j4DhzAHY9OVOqZMQQm5osEfw2WefgTH7KDnHcejbty9SU1MFD0wszz8oQ6e2HMatc15vQN1zKDilhhafIYSIosFEcPToUcfvFosF69atw2+/uedkpux6b0BXYMX3Z53TGwDsdxLXXsmB6XKW0+okhJAbGkwEmzZtQk1NjWPRGI7joFKpYDS630Ip0/rKEOHL4dUfndcbUIT0hrx9GEp+WOy0Ogkh5FaNurNYpVI5XqtUKvz888+CBiUGGQf8aZACh/It2J7jxN5Av7GwGipgyHLvy3EJIa1Xg4lAqVTWWZ5Sr9fDy8tL0KDE8GKUDGFtOMz/pdZpdfLa9vCKfAjVx3eBWUxOq5cQQm7VYCLQ6/WIiopyvI6Ojna7YSE5b+8NHMyzYOc5513Dr40aDQCoSvvRaXUSQsjtGpwjmDt3LjZu3IjLly9DIpEgICAAU6ZMcUZsTjM9SoYQHw4vJTsxwfEyaPrEwZhzBNbKYufVSwght2nUVUPdunVD165dAQBnzpyBxeK8MXShKXjgg0EK/HrJgp/PO683oO42CLyXDz1llBAiugaHhn7/+99DrVbj1KlTOHXqFDQaDWbPnu2M2JxiRrQMwd4cPnLi3ABgnyQ2lVxCzcUMp9ZLCCG3azARzJw5s85jp8vLyzFz5kxBg3IWpRR4/1EF9l20YE+u83oD8sAuUAR2od4AIaRVaHBoiOf5Oq85joNcLhcsIGea1U+OIG8Oz21x7uS3NnosbLUG6E+lOLVeQgipT4OJYMeOHVi/fj3+/e9/A7AvVLN9+3bBAxOaUgr84RE5UnIt2HvReb0BzqsN1N0HoSptO5jJva6+IoS4pgYTwbx58zBr1iy88sorAIDjx48jICBA8MCE9kp/OQK1HKZscnJvoE8cJLwMVWk/OLVeQgi5mwbnCBhjOHz4MC5cuIDY2FgMGzYMmZmZzohNMCopMO8ROXaft2D/JSc++5/joYkaDWNuKiylBc6rlxBC7uGuPYLIyEgkJCQgISEBJSUlWL9+PQBg2LBhTgtOKLNj5AjQcHh6g3N7A16RD0Gq9UPpzqVOrZcQQu7lrokgKysL+/fvx9ixY3Hu3DkAwJtvvum0wITiJbP3Bnads+BAnnNXAtNGj4W5/AqM5485tV5CCLmXuw4NTZw4EYWFhUhJScHXX3+NYcOGQSKRODM2QbwaI4e/2vn3Dcjah0MZ2htVqT8AzObUugkh5F7umgi+++47JCQkoFu3bkhJScHcuXPh7++PL7/8EiNHjnRmjC1GLQPeHSjHjhwLDuU7vzdgM9dAf+Inp9ZLCCENaXCy2GAwYN26dRg/fjyCg4ORlpaGefPmOSO2FjcnVo72IvQGOIUa6p5DoD+9F7aaaqfWTQghDWnSmsXl5eVITEzEiBEjGrV9XFwcsrKykJ2dfdfkMXnyZJw6dQonT57EmjVrmhJOk2jl9t7Aj9lmHClwbm9A/eDj4GRKVNFSlISQVooJUTiOYzk5OSwiIoLJZDKWnp7OunfvXmebzp07s9TUVNamTRsGgLVv377B/ep0umbF88EgOWMfebP+HTlBjveuRcKxjrMSWYdn/+7ceqlQoULllnKvtrPBG8qaKzY2Fjk5OcjNzQUAJCUlYcKECXXuQZg5cyaWLl2K8vJyAEBxsXCPY16VYUaZkeHoZedO1KoioiHzDUT5vm+cWi8hhDRWk4aGmiIoKAh5eXmO1/n5+QgKCqqzTZcuXdClSxf8+uuvOHjwIOLi4urd18yZM6HT6aDT6eDn59esePIrGf511Nys794Pbb9xsFRdg+HsQafXTQghjSFYj6BRlUuliIyMxJAhQxAcHIx9+/ahd+/edZ52CgCJiYlITEwEAOh0OjFCbRapb0eoHuiH8v3/BWzOnZcghJDGEqxHUFBQgJCQEMfr4OBgFBTUfaxCfn4+kpOTYbFYcOHCBZw9exaRkZFCheR02ugxYFYzqjJ2iB0KIYTclWCJQKfTITIyEuHh4ZDJZIiPj0dycnKdbb799lsMGTIEANCuXTt06dIF58+fFyokp5LIlND0HgF91q+w6cvFDocQQu5KsERgtVoxZ84c7Ny5E5mZmdiwYQNOnz6NBQsWYNy4cQCAnTt34tq1azh16hRSUlLw7rvvorS0VKiQnErdcyg4hZoWnyGEtHoS2C8fchk6nQ4xMTFih9GgwOlLwcwmXFnl+s9nIoS4vnu1nYL1CDyZMvRByP3CUJW6TexQCCGkQZQIBKCNHguroQL6zP1ih0IIIQ2iRNDCeO/2UEUOQHXGTsDq/PsWCCGkqSgRtDBt3ycAAFVprr+uMyHEM1AiaEm8DJo+j8OYfRjWKuEel0EIIS2JEkELUnd/DLyXDyppkpgQ4kIoEbQgbb9xMJVcRO2lE2KHQgghjUaJoIXIO3aFIqAzrTlACHE5lAhaiHf0ONhq9dCfShE7FEIIaRJKBC2AU7eBV7dHUH3iZzBzjdjhEEJIk1AiaAHaPqMg4WWoSv1B7FAIIaTJKBHcL46Hpu9oGM8fhaXsstjREEJIk1EiuE9eXR6GVNuOJokJIS6LEsF90kaPg7msEMbcVLFDIYSQZqFEcB9k/hFQhvREVdoPALOJHQ4hhDQLJYL7oI0eC5upBvrjP4kdCiGENBslgmbilBqoewyG/nQKbLV6scMhhJBmo0TQTJoHR4KTKemSUUKIy6NE0BwSDpqoMai5dALm4gtiR0MIIfeFEkEzqB7oD1mbAFqYnhDiFigRNIO231hYqkpgyD4kdiiEEHLfKBE0kbRtEFQR0ahO2w7YrGKHQwgh940SQRNpo8aAWcyoytghdiiEENIiKBE0gUSugqb3COiz9sNmqBA7HEIIaRGUCJpA3XMYOIUXTRITQtwKJYIm8O43FrWXz8JUeFbsUAghpMVQImgkZVgfyNqFUG+AEOJ2KBE0kjZ6LKz6cuiz9osdCiGEtChBE0FcXByysrKQnZ2NefPm3fH5tGnTUFRUhLS0NKSlpWH69OlChtNsvLc/VJ1jUZ2xE7CaxQ6HEEJalFSoHXMch6VLl2LkyJHIz8+HTqdDcnIyMjMz62y3fv16vPbaa0KF0SK0UaMBAFXp20WOhBBCWp5gPYLY2Fjk5OQgNzcXZrMZSUlJmDBhglDVCUYilUPTJw6G7EOwVpWIHQ4hhLQ4wRJBUFAQ8vLyHK/z8/MRFBR0x3aTJk1CRkYGNm7ciODg4Hr3NXPmTOh0Ouh0Ovj5+QkVcr28uj8GXuVNS1ESQtyWqJPF27ZtQ3h4OPr06YOffvoJ33zzTb3bJSYmIiYmBjExMSgpce5ZuTZ6LEzFF1Cbd8Kp9RJCiLMIlggKCgoQEhLieB0cHIyCgoI625SWlsJkMgEAli1bhn79+gkVTrMogrpBEdCZegOEELcmWCLQ6XSIjIxEeHg4ZDIZ4uPjkZycXGebgIAAx+/jx4+/YyJZbNrosbDVVEN/+hexQyGEEMEIdtWQ1WrFnDlzsHPnTvA8j+XLl+P06dNYsGABjh49im3btuH111/H+PHjYbFYUFpaihdeeEGocJqMV/vCq+ujqEr9HsxcI3Y4hBAiKOZKRafTOaUen0cSWNi875m0TaDox0yFChUq91vu1XYK1iNwaZwUmr6jYTx3FJbyQrGjIYTcxtfXF3PnzkV4eDgkEonY4bQajDFcuHABS5YsQVlZWaO/R4mgHl5dB0KqaYtr2z8TOxRCSD3mzp2Lo0eP4uOPP4bVSgtE3cDzPMaMGYO5c+fio48+avT36FlD9dBGj4G57DJqzh8TOxRCSD3Cw8Px448/UhK4jdVqxQ8//IDw8PAmfY8SwW1k/g9AGdwTVak/wD58RghpbSQSCSWBu7BarU0eLqNEcBvvfmNhM9Wg+sTPYodCCCFOQYngFpxSC6/ug6E/lQJWqxc7HEJIKzdhwgQwxtC1a1exQ7kvlAhuoXnwcXAyBS0+QwhplISEBOzfvx8JCQlih3Jf6KqhGyQctNFPoObicZhLLoodDSGkkXyHz4Tc/4EW3aep6DzKdifecxu1Wo1HH30UQ4cOxbZt2zB//nxwHIeFCxdi1KhRsNlsSExMxBdffIH+/fvj008/hVqtRm1tLYYPH47q6uoWjfl+UCK4TtWpP6Q+HVC25z9ih0IIcQETJkzAjh07kJ2djWvXriE6OhqxsbEIDw9H3759YbVa4evrC5lMhvXr12PKlCk4evQotFotjEaj2OHXQYngOm2/cbBUFsOQfUjsUAghTdDQmbtQEhIS8OmnnwIAkpKSkJCQgIiICHz11VeOK5rKysrQq1cvFBYW4ujRowCAqqoqUeK9F0oEAKTtgqEKj0LZ3m8AZhM7HEJIK+fr64thw4ahd+/eYIyB53kwxqDT6cQOrVloshiANmoMmMWM6uO7xA6FEOICnn76aaxevRrh4eGIiIhAaGgocnNzkZGRgZdffhk8zwOwJ4wzZ84gMDAQ/fv3BwBoNBrH562FxycCiVwFTa/h0Gfth81QIXY4hBAXkJCQgK1bt9Z5b/PmzQgMDMSlS5dw/PhxpKen49lnn4XZbMaUKVPw+eefIz09HT/99BOUSqVIkdfP44eGNL2Gg1N4oerYNrFDIYS4iGHDht3x3ueff+74/e23367z2dGjR/Hwww8LHldzeXyPQBs9BrWXz8B0JVvsUAghRBQenQiU4X0haxdCN5ARQjyaRycCbfRYWPXl0GftFzsUQggRjccmAqlPB6g6x6I6YwdgtYgdDiGEiMZjE4Em6gmAMVSlbxc7FEIIEZVHJgKJVAHNg4/DcPYgrFXXxA6HEEJE5ZGJQN3jMfAqLU0SE0KaZc+ePXj88cfrvPfGG2/gyy+/vOt3UlJS0K9fv3o/a9euHUwmE15++eUWjbOxPDIRaKPHwlSUi9q8k2KHQghxQevWrUN8fHyd9+Lj47Fu3bpm7W/y5Mk4dOiQaI+z9rgbyhRB3SHv0AnXdnze8MaEkFZvcZwCfQNa9pEN6VeseHNn7V0/37RpE/7yl79AJpPBbDYjLCwMHTt2xP79+/Hll18iJiYGKpUKmzZtwvz58xusLyEhAW+//TbWrl2LoKAgFBQUAACef/55vPPOO2CM4fjx45g6dSr8/f3x1Vdf4YEH7I/enj17Ng4ePHhfx+txiUDbbxysNdXQn/5F7FAIIS6qrKwMR44cwejRo5GcnIz4+Hhs2LABAPDHP/4RZWVl4DgOu3fvRu/evXHixIm77is4OBiBgYHQ6XTYsGEDpkyZgkWLFqFHjx7405/+hIEDB+LatWvw9fUFAHz22WfYu3cvJk6cCI7joNFo7vt4PCoR8Jq28OoyEFXHtoGZ757tCSGu415n7kK6MTx0IxFMnz4dAPDMM89g1qxZkEqlCAwMRI8ePe6ZCKZMmeJIIklJSVi+fDkWLVqEYcOGYePGjbh2zX5BS1lZGQD74y2mTp0KALDZbKisrLzvY/GoRKDpMwrgOFSl/Sh2KIQQF/fdd99h8eLFiIqKgpeXF1JTUxEeHo533nkHMTExKC8vx4oVKxp8wFxCQgICAgLw3HPPAQA6duyIzp07O+MQHDxnspiTQtN3FIznj8FSXih2NIQQF6fX65GSkoLly5c7Jom9vb2h1+tRUVEBf39/jB49+p77iIyMhEajQXBwMCIiIhAREYG//e1vSEhIwJ49ezB58mS0bdsWABxDQ7t378bs2bMBABzHwdvb+76PxWMSgVfXgZBq2tJTRgkhLWbdunXo27evIxEcP34caWlpyMrKwtq1a3HgwIF7fv9uj7NOSEjA6dOn8de//hV79+5Feno6Fi1aBMB+merQoUNx/PhxHDt2DD169GiRY2FClbi4OJaVlcWys7PZvHnz7rrdxIkTGWOM9evXr8F96nS6ZsWi6hTL2j/1RwZIBDteKlSoOKesWrVK9Bhac6nv73OvtlOwOQKO47B06VKMHDkS+fn50Ol0SE5ORmZmZp3tNBoN3njjDRw6JOxawcZzR2A8d0TQOgghxBUJNjQUGxuLnJwc5Obmwmw2IykpCRMmTLhju08++QQLFy5ETU2NUKEQQgi5B8ESQVBQEPLy8hyv8/PzERQUVGebqKgohISE4Mcf730Vz8yZM6HT6aDT6eDn5ydIvIQQ13FjwXhyJ57nwRhr0ndEmyyWSCRYtGjRHUu61ScxMRExMTGIiYlBSUmJE6IjhLRmFy5cwJgxYygZ3IbneYwZMwYXLlxo0vcEmyMoKChASEiI43VwcLDjtmkA0Gq16NWrF3755RcAQEBAAJKTkzF+/HgcO3ZMqLAIIW5gyZIlmDt3LiZNmgSJRCJ2OK0GYwwXLlzAkiVLmv5dIQrP8+zcuXMsPDycyWQylp6eznr06HHX7VNSUgS9aogKFSpUPLncq+0UbGjIarVizpw52LlzJzIzM7FhwwacPn0aCxYswLhx44SqlhBCSBNJYM8ILkOn0yEmJkbsMAghxKXcq+30mDuLCSGE1M/legRFRUW4ePFis77r5+fncVcd0TF7Bjpmz3A/xxwWFgZ/f/+7fi76JIaziidONNMxe0ahY/aMItQx09AQIYR4OEoEhBDi4XgA88UOwplSU1PFDsHp6Jg9Ax2zZxDimF1uspgQQkjLoqEhQgjxcJQICCHEw3lMIoiLi0NWVhays7Mxb948scMR3H/+8x9cvXoVJ06cEDsUpwkODsaePXtw6tQpnDx5Eq+//rrYIQlOoVDg8OHDSE9Px8mTJzF//nyxQ3IKjuOQmpqKbds8Y+nZ3NxcxzKYOp1OkDpEvzZW6MJxHMvJyWERERGOB+B1795d9LiELIMGDWJRUVHsxIkTosfirBIQEMCioqIYAKbRaNiZM2fc/r8zAKZWqxkAJpVK2aFDh9iAAQNEj0no8uabb7I1a9awbdu2iR6LM0pubi5r166dYPv3iB5BY1dLcyf79+9HaWmp2GE41ZUrV5CWlgYAqK6uRmZm5h2LIbkjvV4PAJDJZJDJZE1elMTVBAUFYcyYMVi2bJnYobgNj0gEjVktjbiXsLAwREVF4fDhw2KHIjiO45CWloaioiL89NNPOHLEvdfmXrJkCd577z3YbDaxQ3Eaxhh27dqFo0ePYubMmS2+f49IBMSzqNVqbN68GXPnzkVVVZXY4QjOZrMhKioKwcHBiI2NRc+ePcUOSTBjxoxBUVGRx90/8Oijj6Jfv34YPXo0Xn31VQwaNKhF9+8RiaCh1dKI+5BKpdi8eTPWrFmDrVu3ih2OU1VUVCAlJQWjRo0SOxTBPPLIIxg/fjxyc3ORlJSEYcOGYfXq1WKHJbjLly8DAIqLi7F161bExsa2eB2iT4QIXZq6Wpq7lLCwMI+aLAbAvvnmG7Z48WLR43BW8fPzYz4+PgwAUyqVbN++fWzMmDGix+WMMnjwYI+YLPby8mIajcbx+4EDB1hcXFxL1yP+gTqjjB49mp05c4bl5OSwDz74QPR4hC5r165lly9fZiaTieXl5bGXXnpJ9JiELo888ghjjLGMjAyWlpbG0tLS2OjRo0WPS8jSu3dvlpqayjIyMtiJEyfYhx9+KHpMziqekggiIiJYeno6S09PZydPnhSk/aJHTBBCiIfziDkCQgghd0eJgBBCPBwlAkII8XCUCAghxMNRIiCEEA9HiYCQ21gsFqSlpTlKSz6tNiwszKOeCEtcg1TsAAhpbYxGI6KiosQOgxCnoR4BIY2Um5uLhQsX4vjx4zh8+DA6deoEwH6Wv3v3bmRkZODnn392PM7E398fW7ZsQXp6OtLT0/Hwww8DAHiex9dff42TJ09i586dUCqVoh0TITeIfuccFSqtqVgsFsedyWlpaeyZZ55hgP2Z8Dfu6nz++ecdd7UmJyezqVOnMgDsxRdfZFu3bmUAWFJSEnvjjTcYYF8Tw9vbm4WFhTGz2cz69OnDALD169ez5557TvRjpuLxRfQAqFBpVaWqqqre93Nzc1lERAQD7IvAlJSUMACsuLiYSaVSx/vFxcUMACsqKmJyubzOPsLCwtjZs2cdr9977z32xz/+UfRjpuLZhYaGCGmCWxd9ae4CMLW1tY7frVYrpFKaqiPiokRASBNMmTLF8fPgwYMAgN9++w3x8fEAgOeeew779+8HAOzevRuzZ88GYF88xtvbW4SICWkYnYoQchuVSuVY8hIAduzYgffffx8A4Ovri4yMDNTW1iIhIQEA8Nprr2HFihV49913UVxcjBdffBEA8MYbb+Drr7/G9OnTYbVaMXv2bBQWFjr/gAhpAD19lJBGys3NRf/+/XHt2jWxQyGkRdHQECGEeDjqERBCiIejHgEhhHg4SgSEEOLhKBEQQoiHo0RACCEejhIBIYR4uP8H1cAje5IbzhwAAAAASUVORK5CYII=\n" 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "## 5. Testing Model" - ], - "metadata": { - "id": "UbcyxE5fUP2b" - } - }, - { - "cell_type": "code", - "source": [ - "# Make Predict Function\n", - "def predictRice():\n", - " uploaded = files.upload()\n", - "\n", - " for fn in uploaded.keys():\n", - " path = fn\n", - " img = image.load_img(path, target_size=(150,150))\n", - "\n", - " imgplot = plt.imshow(img)\n", - " x = image.img_to_array(img)\n", - " x = np.expand_dims(x, axis=0)\n", - " images = np.vstack([x])\n", - "\n", - " classes = model.predict(images, batch_size=10) \n", - " print(fn)\n", - " if classes[0][0]==1:\n", - " print('\\nThis is Arborio Rice')\n", - " elif classes[0][1]==1:\n", - " print('\\nThis is Basmati Rice')\n", - " elif classes[0][2]==1:\n", - " print('\\nThis is Ipsala Rice')\n", - " elif classes[0][3]==1:\n", - " print('\\nThis is Jasmine Rice')\n", - " elif classes[0][4]==1:\n", - " print('\\nThis is Karacadag Rice')" - ], - "metadata": { - "id": "fogFZUJUUVWU" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Predict an Image\n", - "predictRice()" - ], - "metadata": { - "colab": { - "resources": { - "http://localhost:8080/nbextensions/google.colab/files.js": { - "data": 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- "ok": true, - "headers": [ - [ - "content-type", - "application/javascript" - ] - ], - "status": 200, - "status_text": "OK" - } - }, - "base_uri": "https://localhost:8080/", - "height": 380 - }, - "id": "NZjGbevAdZMj", - "outputId": "706fb9c1-88be-4041-f52e-876578add6b4" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - " \n", - " \n", - " Upload widget is only available when the cell has been executed in the\n", - " current browser session. Please rerun this cell to enable.\n", - " \n", - " " - ] - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Saving test9.png to test9 (1).png\n", - "test9.png\n", - "\n", - "This is Basmati Rice\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "We will use this image for test the model. Before we test the model, we must choose a grain and crop it for the model.\n", - "\n", - "
\n", - "\n", - "![dasd](https://media.istockphoto.com/photos/basmati-rice-picture-id1324287396?b=1&k=6&m=1324287396&s=170667a&w=0&h=IB0al7NntpQVsmcJWIviurlPq0IEOFMlAjqAQxk2mcI=)\n", - "\n", - "**Basmati Rice**\n", - "\n", - "Source : [freejpg.com](https://en.freejpg.com.ar/stockphotos/premium/1324287396/basmati-rice)" - ], - "metadata": { - "id": "SQaARYSgtSsK" - } - }, - { - "cell_type": "markdown", - "source": [ - "## Saving Model" - ], - "metadata": { - "id": "kpbvp0L5e4rN" - } - }, - { - "cell_type": "code", - "source": [ - "# Mount Drive\n", - "drive.mount('/content/drive')" - ], - "metadata": { - "id": "rjAAmLMle5Xp", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "5f9587eb-6389-41e2-efda-a4ce58e4d1ff" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# Save Model in HDF5 Type\n", - "!cp BestModel.h5 drive/MyDrive/Saved_Model/\n", - "\n", - "\n", - "# Save Model in SavedMode Type\n", - "drive_path = 'drive/MyDrive/Saved_Model/SavedMode/'\n", - "tf.saved_model.save(model, drive_path)" - ], - "metadata": { - "id": "xAIEg8ZWu6IN" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Convert Model in TFLite\n", - "converter = tf.lite.TFLiteConverter.from_saved_model('drive/MyDrive/Saved_Model/SavedMode/')\n", - "tflite_model = converter.convert()\n", - " \n", - "with tf.io.gfile.GFile('rice_class.tflite', 'wb') as f:\n", - " f.write(tflite_model)" - ], - "metadata": { - "id": "M2wL6Si0xyoU" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# Copy TFLite model in drive\n", - "!cp rice_class.tflite drive/MyDrive/Saved_Model/" - ], - "metadata": { - "id": "6Wfr8m1qySXL" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "PHAmWzbYyfje" - }, - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file