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"Guiding Future STEM Leaders through Innovative Research Training" ~ thinkingbeyond.education
Project: stephanie's main branch
Path: ThinkingBeyond Activities / BeyondAI-2024-Mentee-Projects / nafiul / Diabetic_Retinopathy_Detection_using_ViT.ipynb~1
Views: 1129Image: ubuntu2204
{
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"colab": {
"provenance": []
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"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},
"cells": [
{
"cell_type": "code",
"source": [
"pip install tensorflow tensorflow-addons\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "79nNfjEhx4qi",
"outputId": "10ac37d4-60d6-44e9-8f1f-e9103ab5eac8"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
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"Installing collected packages: typeguard, tensorflow-addons\n",
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" Found existing installation: typeguard 4.4.1\n",
" Uninstalling typeguard-4.4.1:\n",
" Successfully uninstalled typeguard-4.4.1\n",
"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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"\u001b[0mSuccessfully installed tensorflow-addons-0.23.0 typeguard-2.13.3\n"
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]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KMl1T7L-nffe"
},
"outputs": [],
"source": [
"from tensorflow import lite\n",
"import tensorflow as tf\n",
"from tensorflow import keras\n",
"from tensorflow.keras import layers\n",
"import numpy as np\n",
"import pandas as pd\n",
"import random, os\n",
"import shutil\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.image import imread\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.keras.metrics import categorical_accuracy\n",
"from sklearn.model_selection import train_test_split\n"
]
},
{
"cell_type": "code",
"source": [
"import kagglehub\n",
"\n",
"# Download latest version\n",
"path = kagglehub.dataset_download(\"sovitrath/diabetic-retinopathy-224x224-2019-data\")\n",
"\n",
"print(\"Path to dataset files:\", path)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bjEyW9HSuQmh",
"outputId": "00137744-655b-4d10-cfbc-63c5fccb1eda"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading from https://www.kaggle.com/api/v1/datasets/download/sovitrath/diabetic-retinopathy-224x224-2019-data?dataset_version_number=4...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 238M/238M [00:02<00:00, 85.7MB/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Extracting files...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Path to dataset files: /root/.cache/kagglehub/datasets/sovitrath/diabetic-retinopathy-224x224-2019-data/versions/4\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# Add an additional column, mapping to the type\n",
"df = pd.read_csv(r'../root/.cache/kagglehub/datasets/sovitrath/diabetic-retinopathy-224x224-2019-data/versions/4/train.csv')\n",
"\n",
"diagnosis_dict_binary = {\n",
" 0: 'No_DR',\n",
" 1: 'DR',\n",
" 2: 'DR',\n",
" 3: 'DR',\n",
" 4: 'DR'\n",
"}\n",
"\n",
"diagnosis_dict = {\n",
" 0: 'No_DR',\n",
" 1: 'Mild',\n",
" 2: 'Moderate',\n",
" 3: 'Severe',\n",
" 4: 'Proliferate_DR',\n",
"}\n",
"\n",
"\n",
"df['binary_type'] = df['diagnosis'].map(diagnosis_dict_binary.get)\n",
"df['type'] = df['diagnosis'].map(diagnosis_dict.get)\n",
"df.head()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
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"id": "uS-RyGjsuZc1",
"outputId": "399f3850-d987-4bd3-dd94-7a4dc5d0e876"
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"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" id_code diagnosis binary_type type\n",
"0 000c1434d8d7 2 DR Moderate\n",
"1 001639a390f0 4 DR Proliferate_DR\n",
"2 0024cdab0c1e 1 DR Mild\n",
"3 002c21358ce6 0 No_DR No_DR\n",
"4 005b95c28852 0 No_DR No_DR"
],
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" document.querySelector('#df-638777bd-a3e2-49ff-944a-ef71d0cc54a0 button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
"\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df",
"summary": "{\n \"name\": \"df\",\n \"rows\": 3662,\n \"fields\": [\n {\n \"column\": \"id_code\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3662,\n \"samples\": [\n \"90960ddf4d14\",\n \"4e0656629d02\",\n \"3b018e8b7303\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"diagnosis\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1,\n \"min\": 0,\n \"max\": 4,\n \"num_unique_values\": 5,\n \"samples\": [\n 4,\n 3,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"binary_type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"No_DR\",\n \"DR\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"Proliferate_DR\",\n \"Severe\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"source": [
"train_intermediate, val = train_test_split(df, test_size = 0.15, stratify = df['type'])\n",
"train, test = train_test_split(train_intermediate, test_size = 0.15 / (1 - 0.15), stratify = train_intermediate['type'])"
],
"metadata": {
"id": "722Qy1YQud6v"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#create directories for train, val and test. update it everytime the code runs with shutil.rmtree\n",
"base_dir = '.\\dataset'\n",
"\n",
"train_dir = os.path.join(base_dir, 'train')\n",
"val_dir = os.path.join(base_dir, 'val')\n",
"test_dir = os.path.join(base_dir, 'test')\n",
"\n",
"if os.path.exists(base_dir):\n",
" shutil.rmtree(base_dir)\n",
"\n",
"#make directories for train, val and test\n",
"os.makedirs(train_dir, exist_ok=True)\n",
"os.makedirs(val_dir, exist_ok=True)\n",
"os.makedirs(test_dir, exist_ok=True)\n",
"\n",
"valid_types = ['No_DR', 'Mild', 'Moderate', 'Severe', 'Proliferate_DR']\n",
"df = df[df['type'].isin(valid_types)]\n",
"assert all(train['type'].isin(valid_types))\n",
"assert all(val['type'].isin(valid_types))\n",
"assert all(test['type'].isin(valid_types))"
],
"metadata": {
"id": "q0UiwFUaui0w"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"src_dir = r'../root/.cache/kagglehub/datasets/sovitrath/diabetic-retinopathy-224x224-2019-data/versions/4/colored_images'\n",
"for index, row in train.iterrows():\n",
" diagnosis = row['type']\n",
" binary_diagnosis = row['binary_type']\n",
" id_code = row['id_code'] + \".png\"\n",
" srcfile = os.path.join(src_dir, diagnosis, id_code)\n",
" dstfile = os.path.join(train_dir, diagnosis)\n",
" os.makedirs(dstfile, exist_ok = True)\n",
" if os.path.exists(srcfile):\n",
" shutil.copy(srcfile, dstfile)\n",
"for index, row in val.iterrows():\n",
" diagnosis = row['type']\n",
" binary_diagnosis = row['binary_type']\n",
" id_code = row['id_code'] + \".png\"\n",
" srcfile = os.path.join(src_dir, diagnosis, id_code)\n",
" dstfile = os.path.join(val_dir, diagnosis)\n",
" os.makedirs(dstfile, exist_ok=True)\n",
" if os.path.exists(srcfile):\n",
" shutil.copy(srcfile, dstfile)\n",
"\n",
"for index, row in test.iterrows():\n",
" diagnosis = row['type']\n",
" binary_diagnosis = row['binary_type']\n",
" id_code = row['id_code'] + \".png\"\n",
" srcfile = os.path.join(src_dir, diagnosis, id_code)\n",
" dstfile = os.path.join(test_dir, diagnosis)\n",
" os.makedirs(dstfile, exist_ok=True)\n",
" if os.path.exists(srcfile):\n",
" shutil.copy(srcfile, dstfile)\n",
"for subdir in [train_dir, val_dir, test_dir]:\n",
" print(f\"\\nContents of {subdir}:\")\n",
" for root, dirs, files in os.walk(subdir):\n",
" print(f\"{root}: {len(files)} files\")"
],
"metadata": {
"id": "tLeNkAOZup18",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "62a01131-d667-4722-bef8-c22163b921b0"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Contents of .\\dataset/train:\n",
".\\dataset/train: 0 files\n",
".\\dataset/train/Moderate: 699 files\n",
".\\dataset/train/Mild: 258 files\n",
".\\dataset/train/Severe: 135 files\n",
".\\dataset/train/Proliferate_DR: 207 files\n",
".\\dataset/train/No_DR: 1263 files\n",
"\n",
"Contents of .\\dataset/val:\n",
".\\dataset/val: 0 files\n",
".\\dataset/val/Moderate: 150 files\n",
".\\dataset/val/Mild: 56 files\n",
".\\dataset/val/Severe: 29 files\n",
".\\dataset/val/Proliferate_DR: 44 files\n",
".\\dataset/val/No_DR: 271 files\n",
"\n",
"Contents of .\\dataset/test:\n",
".\\dataset/test: 0 files\n",
".\\dataset/test/Moderate: 150 files\n",
".\\dataset/test/Mild: 56 files\n",
".\\dataset/test/Severe: 29 files\n",
".\\dataset/test/Proliferate_DR: 44 files\n",
".\\dataset/test/No_DR: 271 files\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"\n",
"# Define image size for ViT\n",
"IMG_SIZE = 224\n",
"\n",
"# ImageDataGenerator for preprocessing and augmentation\n",
"train_datagen = ImageDataGenerator(\n",
" rescale=1./255,\n",
" rotation_range=15,\n",
" width_shift_range=0.1,\n",
" height_shift_range=0.1,\n",
" shear_range=0.1,\n",
" zoom_range=0.2,\n",
" horizontal_flip=True,\n",
" fill_mode='nearest'\n",
")\n",
"\n",
"val_datagen = ImageDataGenerator(rescale=1./255)\n",
"\n",
"train_batches = train_datagen.flow_from_directory(\n",
" train_dir,\n",
" target_size=(IMG_SIZE, IMG_SIZE),\n",
" batch_size=32,\n",
" class_mode='categorical'\n",
")\n",
"\n",
"val_batches = val_datagen.flow_from_directory(\n",
" val_dir,\n",
" target_size=(IMG_SIZE, IMG_SIZE),\n",
" batch_size=32,\n",
" class_mode='categorical'\n",
")\n",
"\n",
"test_batches = val_datagen.flow_from_directory(\n",
" test_dir,\n",
" target_size=(IMG_SIZE, IMG_SIZE),\n",
" batch_size=32,\n",
" class_mode='categorical'\n",
")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7a-kamJ5yQVr",
"outputId": "fffbf009-0b05-4e81-8f97-3a49af6c9933"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Found 2562 images belonging to 5 classes.\n",
"Found 550 images belonging to 5 classes.\n",
"Found 550 images belonging to 5 classes.\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"\n",
"# Initialize a dictionary to store one image per category\n",
"category_images = {}\n",
"\n",
"# Iterate through batches until we have one image for each category\n",
"for images, labels in train_batches:\n",
" for img, label in zip(images, labels):\n",
" # Get the category index\n",
" category = np.argmax(label)\n",
" # Check if this category is already collected\n",
" if category not in category_images:\n",
" category_images[category] = img\n",
" # Stop once we have one image for each category\n",
" if len(category_images) == len(diagnosis_dict):\n",
" break\n",
" if len(category_images) == len(diagnosis_dict):\n",
" break\n",
"\n",
"# Plot the images\n",
"plt.figure(figsize=(15, 5))\n",
"for i, (category, img) in enumerate(category_images.items()):\n",
" plt.subplot(1, len(diagnosis_dict), i + 1)\n",
" plt.imshow(img)\n",
" plt.axis('off')\n",
" plt.title(diagnosis_dict[category])\n",
"plt.suptitle(\"One Image per Category\")\n",
"plt.savefig('DR images.png')\n",
"plt.show()\n",
"\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 659
},
"id": "rUL0DQDUukiU",
"outputId": "8f8b7f1c-46e4-4a61-a546-2a09146b5c71"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 1500x500 with 5 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "error",
"ename": "FileNotFoundError",
"evalue": "Cannot find file: DR image.png",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-11-ed300baed475>\u001b[0m in \u001b[0;36m<cell line: 33>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mfiles\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mfiles\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'DR image.png'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/files.py\u001b[0m in \u001b[0;36mdownload\u001b[0;34m(filename)\u001b[0m\n\u001b[1;32m 231\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0m_os\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 232\u001b[0m \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Cannot find file: {}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 233\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mFileNotFoundError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# pylint: disable=undefined-variable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 234\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 235\u001b[0m \u001b[0mcomm_manager\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_IPython\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkernel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomm_manager\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mFileNotFoundError\u001b[0m: Cannot find file: DR image.png"
]
}
]
},
{
"cell_type": "code",
"source": [
"from google.colab import files\n",
"files.download('DR images.png')"
],
"metadata": {
"id": "PeqxoxGFvuUS",
"outputId": "0cdd759c-789d-48ff-fd6d-610cb697a1a3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
}
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"download(\"download_bd5101df-97b8-4f88-8cdc-20d6e746c19f\", \"DR images.png\", 274302)"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"class TransformerBlock(tf.keras.layers.Layer):\n",
" def __init__(self, embed_dim, num_heads, ff_dim, rate=0.1):\n",
" super(TransformerBlock, self).__init__()\n",
" self.att = tf.keras.layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim)\n",
" self.ffn = tf.keras.Sequential([\n",
" tf.keras.layers.Dense(ff_dim, activation=\"relu\"),\n",
" tf.keras.layers.Dense(embed_dim),\n",
" ])\n",
" self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
" self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n",
" self.dropout1 = tf.keras.layers.Dropout(rate)\n",
" self.dropout2 = tf.keras.layers.Dropout(rate)\n",
"\n",
" def call(self, inputs, training=None):\n",
" attn_output = self.att(inputs, inputs)\n",
" attn_output = self.dropout1(attn_output, training=training) # Use training here\n",
" out1 = self.layernorm1(inputs + attn_output)\n",
" ffn_output = self.ffn(out1)\n",
" ffn_output = self.dropout2(ffn_output, training=training) # Use training here\n",
" return self.layernorm2(out1 + ffn_output)\n",
"\n"
],
"metadata": {
"id": "BL5-apXWySys"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from tensorflow.keras.layers import Embedding, Dense, Conv2D, Reshape, Dropout, GlobalAveragePooling1D\n",
"\n",
"\n",
"\n",
"class PatchEncoder(tf.keras.layers.Layer):\n",
" def __init__(self, num_patches, embed_dim):\n",
" super(PatchEncoder, self).__init__()\n",
" self.num_patches = num_patches\n",
" self.projection = Dense(embed_dim)\n",
" self.position_embedding = Embedding(input_dim=num_patches, output_dim=embed_dim)\n",
"\n",
" def call(self, patch, training=None):\n",
" positions = tf.range(start=0, limit=self.num_patches, delta=1)\n",
" return self.projection(patch) + self.position_embedding(positions)\n",
"\n"
],
"metadata": {
"id": "3SIffTMfyckG"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"\n",
"def build_vit(input_shape=(224, 224, 3), num_classes=5, patch_size=16, num_patches=196):\n",
" inputs = layers.Input(shape=input_shape)\n",
"\n",
" # Create patches\n",
" patches = layers.Conv2D(filters=64, kernel_size=patch_size, strides=patch_size, padding=\"valid\")(inputs)\n",
" patches = layers.Reshape((num_patches, -1))(patches)\n",
"\n",
" # Encode patches\n",
" encoded_patches = PatchEncoder(num_patches=num_patches, embed_dim=64)(patches)\n",
"\n",
" # Add Transformer blocks\n",
" for _ in range(4):\n",
" encoded_patches = TransformerBlock(embed_dim=64, num_heads=4, ff_dim=128)(encoded_patches)\n",
"\n",
" # Classification head\n",
" representation = layers.GlobalAveragePooling1D()(encoded_patches)\n",
" representation = layers.Dropout(0.1)(representation)\n",
" representation = layers.Dense(64, activation=\"relu\")(representation)\n",
" representation = layers.Dropout(0.1)(representation)\n",
" outputs = layers.Dense(num_classes, activation=\"softmax\")(representation)\n",
"\n",
" return tf.keras.Model(inputs=inputs, outputs=outputs)\n",
"\n"
],
"metadata": {
"id": "sKSci_LQydAH"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"model = build_vit(input_shape=(224, 224, 3), num_classes=5)\n",
"\n",
"\n",
"model.compile(\n",
" optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy']\n",
")\n"
],
"metadata": {
"id": "84cNL6RMypuT"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"history = model.fit(\n",
" train_batches,\n",
" epochs=10,\n",
" validation_data=val_batches\n",
")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CUF5Ed470277",
"outputId": "c53f0fb6-36b3-47bf-8afb-476e7eab9fe2"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/10\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/keras/src/trainers/data_adapters/py_dataset_adapter.py:122: UserWarning: Your `PyDataset` class should call `super().__init__(**kwargs)` in its constructor. `**kwargs` can include `workers`, `use_multiprocessing`, `max_queue_size`. Do not pass these arguments to `fit()`, as they will be ignored.\n",
" self._warn_if_super_not_called()\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m196s\u001b[0m 2s/step - accuracy: 0.4676 - loss: 1.3611 - val_accuracy: 0.4927 - val_loss: 1.2127\n",
"Epoch 2/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 2s/step - accuracy: 0.4858 - loss: 1.2580 - val_accuracy: 0.5418 - val_loss: 1.2067\n",
"Epoch 3/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m182s\u001b[0m 2s/step - accuracy: 0.4666 - loss: 1.2626 - val_accuracy: 0.4982 - val_loss: 1.1438\n",
"Epoch 4/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m172s\u001b[0m 2s/step - accuracy: 0.5407 - loss: 1.1442 - val_accuracy: 0.6636 - val_loss: 0.8960\n",
"Epoch 5/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m179s\u001b[0m 2s/step - accuracy: 0.6381 - loss: 0.9434 - val_accuracy: 0.6782 - val_loss: 0.8757\n",
"Epoch 6/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m193s\u001b[0m 2s/step - accuracy: 0.6884 - loss: 0.8699 - val_accuracy: 0.7255 - val_loss: 0.7761\n",
"Epoch 7/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 2s/step - accuracy: 0.6989 - loss: 0.8506 - val_accuracy: 0.7109 - val_loss: 0.8191\n",
"Epoch 8/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m172s\u001b[0m 2s/step - accuracy: 0.6962 - loss: 0.8391 - val_accuracy: 0.7036 - val_loss: 0.8090\n",
"Epoch 9/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 2s/step - accuracy: 0.7111 - loss: 0.8211 - val_accuracy: 0.7236 - val_loss: 0.7857\n",
"Epoch 10/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m171s\u001b[0m 2s/step - accuracy: 0.7205 - loss: 0.7829 - val_accuracy: 0.7182 - val_loss: 0.7877\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score\n",
"\n",
"# Custom Callback to compute metrics after each epoch\n",
"class MetricsCallback(tf.keras.callbacks.Callback):\n",
" def __init__(self, validation_batches):\n",
" self.val_batches = validation_batches\n",
" self.sensitivity = []\n",
" self.specificity = []\n",
" self.f1_scores = []\n",
"\n",
" def on_epoch_end(self, epoch, logs=None):\n",
" # Get predictions and ground truths\n",
" y_pred_probs = self.model.predict(self.val_batches) # Probabilities\n",
" y_pred = np.argmax(y_pred_probs, axis=1) # Predicted classes\n",
" y_true = self.val_batches.labels # True labels\n",
"\n",
" # Compute confusion matrix\n",
" cm = confusion_matrix(y_true, y_pred)\n",
" tp = np.diag(cm) # True positives for each class\n",
" fp = np.sum(cm, axis=0) - tp # False positives for each class\n",
" fn = np.sum(cm, axis=1) - tp # False negatives for each class\n",
" tn = np.sum(cm) - (tp + fp + fn) # True negatives for each class\n",
"\n",
" # Avoid division by zero\n",
" recall = tp / (tp + fn + np.finfo(float).eps) # Sensitivity (Recall)\n",
" specificity = tn / (tn + fp + np.finfo(float).eps)\n",
" precision = tp / (tp + fp + np.finfo(float).eps)\n",
" f1 = 2 * (precision * recall) / (precision + recall + np.finfo(float).eps)\n",
"\n",
" # Store averages across classes\n",
" self.sensitivity.append(np.mean(recall))\n",
" self.specificity.append(np.mean(specificity))\n",
" self.f1_scores.append(np.mean(f1))\n",
"\n",
"# Initialize callback\n",
"metrics_callback = MetricsCallback(validation_batches=val_batches)\n",
"\n",
"# Train the model with the callback\n",
"history = model.fit(train_batches,\n",
" epochs=50,\n",
" validation_data=val_batches,\n",
" callbacks=[metrics_callback])\n",
"\n",
"# Plot Accuracy\n",
"plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
"plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Accuracy')\n",
"plt.title('Accuracy vs Epochs')\n",
"plt.legend()\n",
"plt.savefig('vit acc vs epoc.png')\n",
"plt.show()\n",
"\n",
"# Plot Sensitivity, Specificity, and F1-score\n",
"epochs = range(1, len(metrics_callback.sensitivity) + 1)\n",
"plt.plot(epochs, metrics_callback.sensitivity, label='Sensitivity')\n",
"plt.plot(epochs, metrics_callback.specificity, label='Specificity')\n",
"plt.plot(epochs, metrics_callback.f1_scores, label='F1 Score')\n",
"plt.xlabel('Epochs')\n",
"plt.ylabel('Metrics')\n",
"plt.title('Sensitivity, Specificity, and F1-Score vs Epochs')\n",
"plt.legend()\n",
"plt.savefig('vit sens spec f1.png')\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "fRhc3mOrOWwB",
"outputId": "d5c20697-3578-4c80-e76b-5fae08908f3c"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Epoch 1/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 665ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m194s\u001b[0m 2s/step - accuracy: 0.7140 - loss: 0.8105 - val_accuracy: 0.7291 - val_loss: 0.7718\n",
"Epoch 2/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 677ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m188s\u001b[0m 2s/step - accuracy: 0.7022 - loss: 0.7968 - val_accuracy: 0.7200 - val_loss: 0.7665\n",
"Epoch 3/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 678ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 2s/step - accuracy: 0.7098 - loss: 0.8147 - val_accuracy: 0.7236 - val_loss: 0.7663\n",
"Epoch 4/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 619ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m195s\u001b[0m 2s/step - accuracy: 0.7223 - loss: 0.7740 - val_accuracy: 0.7218 - val_loss: 0.7744\n",
"Epoch 5/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 654ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m203s\u001b[0m 2s/step - accuracy: 0.7039 - loss: 0.8199 - val_accuracy: 0.7182 - val_loss: 0.7652\n",
"Epoch 6/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 677ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m194s\u001b[0m 2s/step - accuracy: 0.7263 - loss: 0.7648 - val_accuracy: 0.7255 - val_loss: 0.7548\n",
"Epoch 7/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 668ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m211s\u001b[0m 2s/step - accuracy: 0.7172 - loss: 0.7829 - val_accuracy: 0.7145 - val_loss: 0.7767\n",
"Epoch 8/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 701ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m189s\u001b[0m 2s/step - accuracy: 0.7144 - loss: 0.7865 - val_accuracy: 0.7291 - val_loss: 0.7584\n",
"Epoch 9/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 593ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m207s\u001b[0m 2s/step - accuracy: 0.7049 - loss: 0.7905 - val_accuracy: 0.7236 - val_loss: 0.7850\n",
"Epoch 10/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 683ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m194s\u001b[0m 2s/step - accuracy: 0.7224 - loss: 0.7744 - val_accuracy: 0.7127 - val_loss: 0.7776\n",
"Epoch 11/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 664ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m191s\u001b[0m 2s/step - accuracy: 0.7165 - loss: 0.7671 - val_accuracy: 0.7218 - val_loss: 0.7615\n",
"Epoch 12/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 682ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m188s\u001b[0m 2s/step - accuracy: 0.7242 - loss: 0.7770 - val_accuracy: 0.7200 - val_loss: 0.7594\n",
"Epoch 13/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 619ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m194s\u001b[0m 2s/step - accuracy: 0.7148 - loss: 0.7687 - val_accuracy: 0.7182 - val_loss: 0.7556\n",
"Epoch 14/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 650ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m195s\u001b[0m 2s/step - accuracy: 0.7178 - loss: 0.7792 - val_accuracy: 0.7218 - val_loss: 0.7938\n",
"Epoch 15/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 585ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m192s\u001b[0m 2s/step - accuracy: 0.7236 - loss: 0.7661 - val_accuracy: 0.7255 - val_loss: 0.7663\n",
"Epoch 16/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 663ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m221s\u001b[0m 2s/step - accuracy: 0.7135 - loss: 0.7750 - val_accuracy: 0.7236 - val_loss: 0.7554\n",
"Epoch 17/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 666ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m185s\u001b[0m 2s/step - accuracy: 0.7132 - loss: 0.7639 - val_accuracy: 0.7200 - val_loss: 0.7426\n",
"Epoch 18/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 658ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m208s\u001b[0m 2s/step - accuracy: 0.7361 - loss: 0.7594 - val_accuracy: 0.7236 - val_loss: 0.7432\n",
"Epoch 19/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 695ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m205s\u001b[0m 2s/step - accuracy: 0.7122 - loss: 0.7574 - val_accuracy: 0.7291 - val_loss: 0.7432\n",
"Epoch 20/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 682ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 2s/step - accuracy: 0.7239 - loss: 0.7659 - val_accuracy: 0.7182 - val_loss: 0.7469\n",
"Epoch 21/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 600ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m184s\u001b[0m 2s/step - accuracy: 0.7279 - loss: 0.7597 - val_accuracy: 0.7218 - val_loss: 0.7550\n",
"Epoch 22/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 686ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 2s/step - accuracy: 0.7263 - loss: 0.7691 - val_accuracy: 0.7164 - val_loss: 0.7775\n",
"Epoch 23/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 661ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m188s\u001b[0m 2s/step - accuracy: 0.7246 - loss: 0.7599 - val_accuracy: 0.7091 - val_loss: 0.7805\n",
"Epoch 24/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 685ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m208s\u001b[0m 2s/step - accuracy: 0.7432 - loss: 0.7244 - val_accuracy: 0.7145 - val_loss: 0.7663\n",
"Epoch 25/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 722ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m193s\u001b[0m 2s/step - accuracy: 0.7302 - loss: 0.7393 - val_accuracy: 0.7255 - val_loss: 0.7446\n",
"Epoch 26/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 664ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m193s\u001b[0m 2s/step - accuracy: 0.7358 - loss: 0.7415 - val_accuracy: 0.7182 - val_loss: 0.7575\n",
"Epoch 27/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 662ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m186s\u001b[0m 2s/step - accuracy: 0.7230 - loss: 0.7591 - val_accuracy: 0.7345 - val_loss: 0.7559\n",
"Epoch 28/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 677ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m212s\u001b[0m 2s/step - accuracy: 0.7268 - loss: 0.7534 - val_accuracy: 0.7236 - val_loss: 0.7434\n",
"Epoch 29/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 675ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m193s\u001b[0m 2s/step - accuracy: 0.7388 - loss: 0.7412 - val_accuracy: 0.7364 - val_loss: 0.7440\n",
"Epoch 30/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 593ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m199s\u001b[0m 2s/step - accuracy: 0.7341 - loss: 0.7604 - val_accuracy: 0.7218 - val_loss: 0.7647\n",
"Epoch 31/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 617ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m181s\u001b[0m 2s/step - accuracy: 0.7088 - loss: 0.7762 - val_accuracy: 0.7236 - val_loss: 0.7540\n",
"Epoch 32/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 670ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 2s/step - accuracy: 0.7230 - loss: 0.7639 - val_accuracy: 0.7236 - val_loss: 0.7607\n",
"Epoch 33/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 610ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m186s\u001b[0m 2s/step - accuracy: 0.7263 - loss: 0.7461 - val_accuracy: 0.7273 - val_loss: 0.7450\n",
"Epoch 34/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 561ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m209s\u001b[0m 2s/step - accuracy: 0.7340 - loss: 0.7312 - val_accuracy: 0.7164 - val_loss: 0.7551\n",
"Epoch 35/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 629ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m184s\u001b[0m 2s/step - accuracy: 0.7171 - loss: 0.7639 - val_accuracy: 0.7327 - val_loss: 0.7420\n",
"Epoch 36/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 647ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m184s\u001b[0m 2s/step - accuracy: 0.7242 - loss: 0.7509 - val_accuracy: 0.7218 - val_loss: 0.7457\n",
"Epoch 37/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 631ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m185s\u001b[0m 2s/step - accuracy: 0.7296 - loss: 0.7391 - val_accuracy: 0.7291 - val_loss: 0.7468\n",
"Epoch 38/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 600ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m192s\u001b[0m 2s/step - accuracy: 0.7196 - loss: 0.7288 - val_accuracy: 0.7127 - val_loss: 0.7529\n",
"Epoch 39/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 648ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m193s\u001b[0m 2s/step - accuracy: 0.7337 - loss: 0.7338 - val_accuracy: 0.7236 - val_loss: 0.7467\n",
"Epoch 40/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 781ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m212s\u001b[0m 2s/step - accuracy: 0.7319 - loss: 0.7487 - val_accuracy: 0.7291 - val_loss: 0.7541\n",
"Epoch 41/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 652ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 2s/step - accuracy: 0.7447 - loss: 0.7210 - val_accuracy: 0.7309 - val_loss: 0.7364\n",
"Epoch 42/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 673ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m194s\u001b[0m 2s/step - accuracy: 0.7272 - loss: 0.7500 - val_accuracy: 0.7255 - val_loss: 0.7387\n",
"Epoch 43/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 676ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m212s\u001b[0m 2s/step - accuracy: 0.7337 - loss: 0.7118 - val_accuracy: 0.7073 - val_loss: 0.7616\n",
"Epoch 44/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 684ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m185s\u001b[0m 2s/step - accuracy: 0.7432 - loss: 0.7334 - val_accuracy: 0.7200 - val_loss: 0.7516\n",
"Epoch 45/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 681ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m206s\u001b[0m 2s/step - accuracy: 0.7394 - loss: 0.7194 - val_accuracy: 0.7182 - val_loss: 0.7673\n",
"Epoch 46/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 686ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m213s\u001b[0m 2s/step - accuracy: 0.7307 - loss: 0.7264 - val_accuracy: 0.7127 - val_loss: 0.7571\n",
"Epoch 47/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 641ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m195s\u001b[0m 2s/step - accuracy: 0.7257 - loss: 0.7418 - val_accuracy: 0.7182 - val_loss: 0.7528\n",
"Epoch 48/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 663ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m195s\u001b[0m 2s/step - accuracy: 0.7428 - loss: 0.7366 - val_accuracy: 0.7309 - val_loss: 0.7573\n",
"Epoch 49/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 675ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m214s\u001b[0m 3s/step - accuracy: 0.7194 - loss: 0.7354 - val_accuracy: 0.7273 - val_loss: 0.7377\n",
"Epoch 50/50\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 682ms/step\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m205s\u001b[0m 2s/step - accuracy: 0.7140 - loss: 0.7621 - val_accuracy: 0.7127 - val_loss: 0.7558\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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/o4mKiuLff/+la9euuV5m4cKFmTlzJiEhIVy6dIlff/0VJycnhg0bxp9//pmubJEiRbC2tk43rXTp0gD6sUlXrlzh/PnzGT6rtHJpn9e1a9cwMjLCx8cnV/V+nrQAmJvBrlnx9vamWrVq6Xb0ixYtonr16pQsWTJXy9y/fz+NGzfG2toae3t7nJyc9GMwng43aTvJJz393b5x44b+jKYnlSlTJkf1qlu3Lo0bN9Y/atWqlaP5Qe0yDQ0NTfdIG2sG0LRpUzZv3kxkZCR79uyhX79+3Lhxg1atWqX7nhQpUoSCBQtmuZ4bN25gZGSU4TNwdXXF3t6eGzdupJvu5eWVYRlXrlxh06ZNGb63jRs3Bp4/yLlz587cunVLP+7q2rVrHD9+PF1XbOfOnalVqxa9e/fGxcWFLl26sHTp0mwHHUdHx3SfSdrDw8MjXTkjI6MMY4Ce/p3euHGDIkWKUKBAgXTlypYtq38+7XVk93eane/nDz/8QGRkJKVLl6ZChQp8/fXXnDlz5rnLNnRySoOBMjMzo1q1alSrVo3SpUvTq1cvli1bxvDhw9HpdGg0GjZu3JjpOJWnT1fN6gynJ4+oypYty6VLl/j333/ZtGkTK1asYMaMGQwbNoyRI0fmyWuqXr06np6eLF26lPfee49169aRkJCQYdxJTjg4ONCqVSsWLVrEsGHDWL58OUlJSXlyBoVGo6F06dKULl2ali1bUqpUKRYtWkTv3r1ztBydTkeFChWYOHFips+7u7u/cF3zU/fu3fn888+5ffs2SUlJHDp0iGnTpuVqWdeuXaNRo0Z4e3szceJE3N3dMTMzY8OGDUyaNCnDTi873+3XSdrlHp6U2anoVlZW1KlThzp16uDo6MjIkSPZuHEjPXr0yNH6shvwMzszSqfT0aRJE7755ptM50kLB1lp3bo1VlZWLF26lJo1a7J06VKMjIzo1KlTuvXu2bOHnTt3sn79ejZt2sSSJUto2LAhW7ZsyfbZma+r7Hw/69aty7Vr11izZg1btmzhjz/+YNKkScyaNSvH2xpDIuHmPyDtzIKQkBAASpQogaIoeHl5PXcDkxPW1tZ07tyZzp07k5ycTPv27Rk9ejRDhgzJcmBuTltH3nnnHaZMmUJ0dDRLlizB09Mz0zOTcrKO7t2707ZtW44ePcqiRYuoXLky5cqVy1G9nqd48eI4ODjoP4M0d+/eJS4uLl3rzeXLlwH0O6wSJUpw+vRpGjVq9MzXUqJECXQ6HRcuXMDX1zdP65+2fIBz587pj76z61n17tKlCwMHDuTvv/8mISEBU1PTXAfWdevWkZSUxNq1a9Md9b7INV48PDwybfK/dOlSrpeZW5UqVcpwFoyrq+sz58ns979582YePHiQZeuNh4cHOp2OK1eu6FseQL2SdWRkZIaWjcyUKFGC2NjYHH9X0lhbW9OqVSuWLVvGxIkTWbJkCXXq1KFIkSLpyhkZGdGoUSMaNWrExIkT+emnnxg6dCg7d+7M9bqfptPpuH79errt5dO/Uw8PD7Zt20ZMTEy61pvAwED98/ByfqcFCxakV69e9OrVi9jYWOrWrcuIESP+0+FGuqUMyM6dOzM94kzrP09rRm/fvj3GxsaMHDkyQ3lFUbh//36O1/30PGZmZvj4+KAoCikpKVnOZ21tneHsrGfp3LkzSUlJ/PXXX2zatIl33nnnufOkBYes1tO8eXMcHR0ZO3Ysu3fvfqFWm8OHDxMXF5dh+pEjR7h//36GrozU1FT9abOgdjv89ttvODk54efnB6iB7s6dO8yePTvDchMSEvTra9euHUZGRvzwww8ZWijyoiWiSpUqeHl5MXny5Azv5fOW/6zP2dHRkebNm7Nw4UIWLVpEs2bNnnk9mGdJO9J9sj5RUVHMnTs3V8sDaNGiBYcOHeLIkSP6aREREXk+ZiY7HBwcMnShpB04bN++PdN5nv79d+jQAUVRMm1RTXvfWrRoAahnND4prfWwZcuWz63rO++8w8GDB9m8eXOG5yIjI0lNTX3uMjp37szdu3f5448/OH36dIbQ++DBgwzzpAWGzC6T8CKebE1UFIVp06ZhampKo0aNAPU902q1GVodJ02ahEajoXnz5kDe/06f3vba2NhQsmTJPH/9bxppuTEgAwYMID4+nrfffhtvb2+Sk5M5cOCAvoWjV69egHrkMGrUKIYMGUJwcDDt2rWjQIECBAUFsWrVKj766CMGDRqUo3W/9dZbuLq6UqtWLVxcXLh48SLTpk2jZcuWGfqgn+Tn58fMmTMZNWoUJUuWxNnZ+ZlXBq1SpQolS5Zk6NChJCUlZesIv0SJEtjb2zNr1iwKFCiAtbU1AQEB+nECpqamdOnShWnTpmFsbJzpYMK0q9E+7yrHCxYsYNGiRbz99tv4+flhZmbGxYsXmTNnDhYWFhmuv1GkSBHGjh1LcHAwpUuXZsmSJZw6dYrff/8dU1NTALp168bSpUv55JNP2LlzJ7Vq1UKr1RIYGMjSpUv11xdJe1/SBpK2b98ec3Nzjh49SpEiRRgzZsxz36tnMTIyYubMmbRu3RpfX1969epF4cKFCQwM5Pz585nuxNL4+fmxbds2Jk6cSJEiRfDy8iIgIED/fPfu3enYsSMAP/74Y4b5g4OD8fLyokePHs+8yvFbb72FmZkZrVu35uOPPyY2NpbZs2fj7OycodUsu7755hv9LSk+//xzrK2t+f333/Hw8MjTsQ179uzRD7KNiIggLi6OUaNGAWrXQ926dZ85f9u2bfHy8qJ169aUKFGCuLg4tm3bxrp166hWrRqtW7cGoEGDBnTr1o1ff/2VK1eu0KxZM3Q6HXv37qVBgwb079+fSpUq0aNHD37//XciIyOpV68eR44c4a+//qJdu3YZusYy8/XXX7N27VpatWpFz5498fPzIy4ujrNnz7J8+XKCg4OfG2JbtGhBgQIFGDRoEMbGxnTo0CHd8z/88AN79uyhZcuWeHh4EB4ezowZMyhatGi2TjK4c+dOuqtyp7GxsaFdu3b6vy0sLNi0aRM9evQgICCAjRs3sn79er777jucnJwAtRutQYMGDB06lODgYCpVqsSWLVtYs2YNX3zxhb7lM69/pz4+PtSvXx8/Pz8KFizIsWPHWL58eboB0P9Jr/jsLPESbdy4Ufnggw8Ub29vxcbGRjEzM1NKliypDBgwQAkLC8tQfsWKFUrt2rUVa2trxdraWvH29lb69eunXLp0SV+mXr16mZ7i3aNHj3Snsv72229K3bp1lUKFCinm5uZKiRIllK+//lqJiorSl8nsVPDQ0FClZcuWSoECBRRAf/rr06chP2no0KEKoJQsWTLT9yGzU53XrFmj+Pj4KCYmJpmeFn7kyBEFUN56661Mlzl16tRMT3d92pkzZ5Svv/5aqVKlilKwYEHFxMREKVy4sNKpUyflxIkTGepZrlw55dixY0qNGjUUCwsLxcPDQ5k2bVqG5SYnJytjx45VypUrp5ibmysODg6Kn5+fMnLkyHTvsaIoypw5c5TKlSvry9WrV0/ZunXrM98fsnEqeJp9+/YpTZo0UQoUKKBYW1srFStWVKZOnap/PrNTwQMDA5W6desqlpaWCpDhtPCkpCTFwcFBsbOzUxISEjK8/rNnzyqAMnjw4AzPPW3t2rVKxYoVFQsLC8XT01MZO3asMmfOnAzfPQ8Pj0xPoc3s/Tlz5oxSr149xcLCQnFzc1N+/PFH/anX2T0V/HmnK2d1yjiQrdPi//77b6VLly5KiRIlFEtLS8XCwkLx8fFRhg4dqkRHR6crm5qaqvzyyy+Kt7e3YmZmpjg5OSnNmzdXjh8/ri+TkpKijBw5UvHy8lJMTU0Vd3d3ZciQIUpiYmK6ZWX1PiqKosTExChDhgxRSpYsqZiZmSmOjo5KzZo1lfHjx2e4REVWunbtqgBK48aNMzy3fft2pW3btkqRIkUUMzMzpUiRIsq7776b4fTzzDzrVPAnt209evRQrK2tlWvXrilvvfWWYmVlpbi4uCjDhw/PcCp3TEyM8uWXXypFihRRTE1NlVKlSim//PJLulO80zzvd5rd7+eoUaMUf39/xd7eXrG0tFS8vb2V0aNHZ/v9NVRybykhgNOnT+Pr68v8+fPp1q1bhuffeecdgoOD03VNvKj69etz7969bF100dClpqZSpEgRWrduneGMMlBvsPrNN99w7do1XFxc8qGG4r+qZ8+eLF++/LW96a3InHRLCYF6hWUbGxvat2+f4TlFUdi1a1emzdcib6xevZqIiAi6d++e6fM7d+7ks88+k2AjhMgWCTfiP23dunVcuHCB33//nf79+2e45gyoZ/q8DjceNUSHDx/mzJkz/Pjjj1SuXDnLCycuW7bsFddMCPEmk3Aj/tMGDBhAWFgYLVq0yLPr8YjsmzlzJgsXLsTX1/eZA4WFECInZMyNEEIIIQyKXOdGCCGEEAZFwo0QQgghDMp/bsyNTqfj7t27FChQIE/ulCyEEEKIl09RFGJiYihSpEiGO6o/7T8Xbu7evfvG32hQCCGE+K+6desWRYsWfWaZ/1y4SbsVwK1bt7C1tc3n2gghhBAiO6Kjo3F3d3/mLX3S/OfCTVpXlK2trYQbIYQQ4g2TnSElMqBYCCGEEAZFwo0QQgghDIqEGyGEEEIYFAk3QgghhDAoEm6EEEIIYVAk3AghhBDCoEi4EUIIIYRBkXAjhBBCCIMi4UYIIYQQBkXCjRBCCCEMioQbIYQQQhgUCTdCCCGEMCgSboQQkBwHipLftRBCiDwh4UaI/zJFgb0T4edisOBtiLuf3zUSQogXJuFGiP+qlERY+RFsHwm6VLi+E36vD3dP5XfNXr0r2+D0EtDp8rsmwtAlRsO5FbB5KJxbqbaaijxnkt8VMCjXdkCxmmBqkd81yT5tKgT+C4oWPGpBAdeXty5FgTNL4OI6cCoDXnXBPQBMLV/eOkXmYsLgn/fgzjHQGEPdr9XP5mEQzGkKradApS45X+6DIEhNAmfvvK/zy5CapO5kjs5W/z6zBN7+DWyc8rde4uXRaSH+PsRFqI/YR//G3wNnHyjXHozy+Lg/+i5c2gCBGyBoD+hSHj9nagWlm0H59lCySfb2H0kxELxfXdaNfWBspm6/veqAe3Uwt8nb+r+BNIry3+poj46Oxs7OjqioKGxtbfNuweGBMLMm2BWFpqPBuxVoNHm3/JchPBDW9IU7xx9PK1hc/ZF41ALPWmBfLG/WFXYe1n8FNw+mn25sBkX91aDjVRfc/MDELG/WKTJ395QabKLvgIU9vPMXFK8PCQ/VlpwrW9RyAZ/AW6PA2PT5yww5A3vGqcEV1M+y3rfgWfslvYg8EHkLlvV4/P03NgdtEti4Qoc/1B2FePPdvwZbh8GD6xAbrgYbnrHbK1IZmo2FYgG5X6eiQESgeuAYuAHunkj/fKFS6vKD9kDkzcfTzQpAmeZq0CnREEzM1empSXDrCATthuu71e+sos183RpjcKui/vY8axtU2MnJ/jvfw8306dP55ZdfCA0NpVKlSkydOhV/f/8sy0+ePJmZM2dy8+ZNHB0d6dixI2PGjMHCInutJS8t3FzfBas+gZgQ9W+vutDsZ3Apl3fryCvaVDjwK+waA9pkMLcDh2IQeo4MP3o7d/CoqYad4vXAwTNn60qMhl0/w+FZ6o/R1Aqq9VY3MkG7H79faUytoFh19f0r1RScy778kHhsDlzaBPW/VcPVy5Ycr260jIxf/rqedn61+j1NTVA3sO8tgUIlHj+v06nfiz3j1L89akGneWDjnPny7pyAPb+oR6UAaNTXpUtV/yxWE+p9o4an1ynsX90GK/pAwgM14LX/Xf2uL++l7pQ0Rmo4q/t1/nxOIm8EboBVH0NS9FNPaMCqIFg7g7UjWDupAeDcKkiOUYuU7whNRqoHrNl1/xqcWgznV6ph6sn1Fa0G3i2gTEtwKq1OVhQ1+Jxbqf42o28/nsXcDko3VVuVbh5Sf7NPcvBSt8ledSE1GYL3QfBeiLyRvpyRCRSpom5LUdTfuKJVW7DS/atTW9FLNlJbkqwKZv91Pykt2KUmQRHf3C0jC29MuFmyZAndu3dn1qxZBAQEMHnyZJYtW8alS5dwds64MV28eDEffPABc+bMoWbNmly+fJmePXvSpUsXJk6cmK11vrRwA5AUC/smwYGp6hGgxgiqfgD1vwPrQtlcRoyazOPvqTt328J5W8fwi7C67+MjiVJvQavJYOcGCZFw6zDc2K82ed49mfHooHAl8GkLPu3S7xSfpiiP+5VjQ9VpZdtA05/A3v1xmfvX1JATvFc9iol/akBrweJqK1jZ1uBWNe+biy9tgr87q//XGEPtL9SdWtoRU16KvKWGyhPz1bDQeITaBJ6bnX5yvBrKQk6pn4lHTXCtBMZZ9DQrCuweB7t+Uv8u0Qg6zgFL+8zLX/xXDUHJMVCgCHReCEWfCH63jqjLu7r10QQNlO8AdQeBmQ3sn6y+Tm2y+nRRf/V9Ldkof0OOTqvWe/dYQIHCvvDOfHDwUJ9PjoON38DJherfnnWg/ezs/w6T4+HBNXAs/XK+Q/kp7h5c3qR+vs5l1d9mdlr18oNOB7t/fvQ5A8VqqEHVxkX97VkWzPy3EhsOO36EEwsABUwsofaXUHMAmFllvq6kWLiwBk4tUredaYzN1FDv3RJKN4cCLs+v8+2jcH4VXFid8cDPxuVRC3c9NdRk1ar+8MajbXha2LmZebln0RirrT5lW6v1ty3y7PJRt9X91vVd6vY8NgxKNob3V+R83c/wxoSbgIAAqlWrxrRp0wDQ6XS4u7szYMAABg8enKF8//79uXjxItu3b9dP++qrrzh8+DD79u3L1jpfarhJ8zBYbQa9sEb928JODTjVPsy4MVAUuH9V7Qq4vBluHHiiP1ajfokrdla/ZOYFcl8nbSocmKK2oqS11jT/GSq9m/XOJilW/bGlhZ1bh9OHHZfyasjxafv4SATU7q4Ng9QfFqgbwea/QKnGz66jTgcRFyForzp+6fouNSSmsXFVj3y8W6k7nRftvnpwXR1AmxgFhUqqnwOo/e7tZqjN03nh/jXYNxFO//O4RSNNUX818LlXy96yUpPhxF+wZ/zj0JjGzAbc/R+3tBWpovbfJ8er3Y/nV6nlqveFJj9mHYTSRFxWu6/uX1E31C0nqO/T7nHq4GNQA3yFd6DOV+m/A6COM9g/BY7Pg9REdVqRKmrIKd301YecuPuwsrf63QLw66W2rmY2xuH0Evj3S0iJAytHdRxOZt9fRVG7XK9tV5d746D6nTWzUbsVyrRQDyCye3CT17Qp6vc8MQpcK+Z8PKBOq37WJ+arrSBPjhUxMgXHUuDkrYadtH8dvJ7/3XqZXqR7Nc3dU7Bp8ONudNui8NYPjw9GFEXdHp5coLa4JMc+mlGjfu6+76nf8dxus3U6dd1Xt6njIL3qqu9vbn4zaWEn8pZ6cKgxVlsjNcbq79foiX9jQiFwPYSdS78Mt6rqPqhsa/WgNuGhup1OCzNp2840JhZquOm8ME9/529EuElOTsbKyorly5fTrl07/fQePXoQGRnJmjVrMsyzePFi+vbty5YtW/D39+f69eu0bNmSbt268d1332W6nqSkJJKSHu8go6OjcXd3f7nhJk3QXvUHkvZFcSwDzcaoO5/g/XBls/oDfBicfj4HL7AqpA72TGNiqSboip2hRIOc/VDDLqg7t7sn1b9LNYXWk5+fxp8Wd0/94l9Yo36hn9xRO5VVQ05KPByaoT5nYgF1BqlHPbkZZJ0Uo/64L/6rvk9PNi2b20GZZlCtT/aDwZOS4+HPtyDsrNpc3HODelT675dqq5nGGOoMhLrf5D5EhZ2HvRPUUKE8OgvHsw7U+kLtM98/WX2/QG31aDT8cQvC07SpcOYf2DUWoh4didkXU4NF2Hm4eUDdgT3J2ByKVlVb5MLPqzujlhPAr0f2X0NiNKz+VB078CQjE3XAce2Bz27BA3Xw8oFf1ZamtNfrWlE9kvZulfetcZm5fQyW9lCb/U0sodUk8H332fPcuwrLe0LoWfXvWl9Aw/+p7/O1nWqYubYjY8g0sUzfhaAxUlsOyjRXw87z3q/cSAsx4Rch4pJ6kBAeqO500gKJiYU6gL94PfCqr3YZZNXlFnkTTi5SWyOibj2e7lpR/ewjLqnBLzPG5mp3fLEa4FFDHfOR0wHa2hT1oCApWm1dy+5vMPQcLOmqblNNLB8NjO+cs3WnURT1t7vl+8fdRcVqQPEGcHZp+h26gxdU7qoeLOakG+t19eC6ut29uA5uH0n/nG1Rdbzek0MYNEbqgUvx+ur3q6j/Szmx5o0IN3fv3sXNzY0DBw5Qo0YN/fRvvvmG3bt3c/jw4Uzn+/XXXxk0aBCKopCamsonn3zCzJkzs1zPiBEjGDlyZIbpryTcgHrkc+Iv2DHqcZeLsdnj5vq0vz1qqUd4pZs+3vg9CIKzy9QzOJ78IVk5qjtDn7ZgZq0GCW2KuhHTpjz6O1n9f/hFdSeqTVZbkJqNVXdKL5qm4x+o4ywurFE39E8e0YG6EW/2c9Y765xKTVLDYuA69QgyLlydbmSqjpco3z77y1IUtcvlzD9qX/tHu9VuOVAD3IZBj1s5XMqrrTiFK2V/+bePw97xT4xDQQ2UdQepLStpokNg5yh1J4Ki7hRq9FUDg8Wj76ZOBxdWwc4xagsKqC1Y9b6Gyt0fb/R1Ogi/oLb83div/pv2HoEalt9ZoA4SzymdTg1pO0erO7bKXdWm+pyOv4qNgIPT4MjsxztGZx/1ffFpl/djW5Lj1B389R1qKNSlQMES0HlB9sfCpSTClv89PpvKxkXtunhyw25iqTbhl2ykdvcVKql2F17aqH4Hnj4Kdiytjmmwc894JK3/99F0bYraKpASr76e5Fg1mCfHqe9hcpzaQnbvSsbfYBozG3UsRVxE+ukWdmrYTuvmcPBU63xi/qPWrUev0cJePaiq0g1cK6jTdDo19EQEPgpUj/69d/lxgH1SoZLqWLpiNdV/CxZXt0HaVHVHmhbGMgtlZgXU97ZMCyjVJOuxIGeWwdoBarC0LwadF0HhipmXzYnkeHWowb5J6UOrqTWUawe+XdUD1tdpTFleig6BS+vVsBO89/FBrWMZ9XtTvL66/8qqizsvq2Ko4WbXrl106dKFUaNGERAQwNWrV/n888/p06cP33//fabrydeWmyclRKrN+Ud+U78ctm7qD7XUW+rG5Vmj2dMGnZ1ZCmeXqy0LOVW6mTq2Jq/H8ID62i5vUoNOYrTaUlOmWd6vJ41Oq3aXHZj6qEVBAy1+Af8+2Zv/6B/qmVsaY+i+JvOzYs6vUsvE31d36HUGqV0vaWFCm6oesUfdUY/qou6oRzOhZ5/od9eoAbTOV8/eyIacgS1D1TFHoIbXBt9BgcKw8ye1dQnUcQJ1BqoDsp93+nzaeKYb+9Wm5kpdXjxo3rui7ihf9DsU/0Bt3Tv82+PWuEKl1JBTvmPOuzR0WvVIPey8+gh/9O+DINKFkLKtoe2Mx8ExJy6sgTUDIOlR65hLebX7oWQjtWXiWUepD2+ov49LG9RxEE93TeYVU2v1EgtpXURO3uop+XaPxrjdu/K4GyFo7+PXksbINH1A8qoLVXqorWvZPQrX6SAyWA34Nw+oA2HDL2QslzaQ9/7V9Ad6TzKzUcctPTkOT2OshqMnW8G0KWrryuFHB7klGqlnu+V2QGxWom6rY3iiQ9Tfdbl2LzZU4E2U8FDtAXDyznnLfx54I8JNbrql6tSpQ/Xq1fnll1/00xYuXMhHH31EbGwsRtlo3n4lY26eWYEQdYPuWDp3SV+bom6gzixRN1AajbpRMjZ59O+jR9r/TSzUHVuFToZ3ZKHTqoM/j/6h/l1/iDqe41mv8/YxmNNM3Yg3+RFqfZZ12dgIWD8QLq5V/y5USj06ibqjBpu0rqanGZmoR7q1v1THJGSHoqg7wC3/y9h/bW6rBsbqnxrWxjQhUg04h2ZAYqQ6zcFLDXAVu2TsitBp1TNBIi498QhUH5m1FoDaMudSTt0Z+fV6sd9ATKg6FqOIb+6vB5UYBVe2quNYkmIfn6WS4ewVnfqvsam6kzezUltpzWzUMwrNrB8/rBzVUJPWEpQd2lQIOQ1Bu9SBoDcPqWOFChRWWyIqd1VbV/JC/AN1APrNg+p67p5IH2hMrdT6O5VVw1jav3bujw7sTqrB8PKmjK1ghUqp703IafXvOoPUAwM5w80gvRHhBtQBxf7+/kydOhVQBxQXK1aM/v37Zzqg2M/Pj8aNGzN27Fj9tL///psPP/yQmJgYjI2f/4XO93Aj8paiqEdTu8aof/t/pHa9ZbaRj42A3+upLSxl26hnyTxvZ6co6mmd6weppw0/ychEPXqxLar+a+embpBLvZX7VhJtijo2ZdcYtSsu4GOo+VneH4W+ThKj1YB6cNrjo3Q7d7UlLjXpcZC5f+XxwOSnmVioR5Mu5dSHs4/6b1ansIv0UhLUFqZCJV/+YOCURDXgJMU8CmXFsh/K9K1gGx+1gj3RdfX2LCjb6uXVW+S7NybcLFmyhB49evDbb7/h7+/P5MmTWbp0KYGBgbi4uNC9e3fc3NwYM0bdcY0YMYKJEyfy+++/67ulPv30U/z8/FiyZEm21inhxkAdmQ0bvgYUdTxSu1npj/y1qbCgndpn7Fga+uzIWStIbIQ6ANy8gBpm7NzUpvWXNRg2JUHtvjCklprnSY6DY3PVwcexYZmXMTZXPz+nMurDsbQaYgoWl6P1/5rEKLi6XW3ZqdI9+62k4o2Vk/13vt5+oXPnzkRERDBs2DBCQ0Px9fVl06ZNuLio1wO4efNmuq6m//3vf2g0Gv73v/9x584dnJycaN26NaNHj86vlyBeF/59wNJBvWDXuRVql0fnBWqzPajXrgjeq45L6Lww56HBxgkqv5/n1c7Sf/GWFGbWULO/esmEEwvUrogChR8HGacyYO8hIUaoLOzUEwlycjKB+M/I9ysUv2rScmPgrm6DJd3UMRhFq8F7S9Xm66Xd1Oc7zpWNoRBCvIFysv+Wu4ILw1KyMXRfq7bi3D6q3gRydV/1uRr9JdgIIcR/gIQbYXjcq0GvTeptA+5dVm8h4FFLvd2BEEIIgyfhRhgmZ2/4cLN6RVXHMmp31Ot6HxwhhBB5Kl8HFAvxUtkXg4/3qKdzv4pL/AshhHgtSLgRhk2jMbyLFwohhHgmOZwVQgghhEGRcCOEEEIIgyLhRgghhBAGRcKNEEIIIQyKhBshhBBCGBQJN0IIIYQwKBJuhBBCCGFQJNwIIYQQwqBIuBFCCCGEQZFwI4QQQgiDIuFGCCGEEAZFwo0QQgghDIqEGyGEEEIYFAk3QgghhDAoEm6EEEIIYVAk3AghhBDCoEi4EUIIIYRBkXAjhBBCCIMi4UYIIYQQBkXCjRBCCCEMioQbIYQQQhgUCTdCCCGEMCgSboQQQghhUCTcCCGEEMKgSLgRQgghhEGRcCOEEEIIgyLhRgghhBAGRcKNEEIIIQyKhBshhBBCGBQJN0IIIYQwKBJuhBBCCGFQJNwIIYQQwqBIuBFCCCGEQZFwI4QQQgiDIuFGCCGEEAbltQg306dPx9PTEwsLCwICAjhy5EiWZevXr49Go8nwaNmy5SussRBCCCFeV/kebpYsWcLAgQMZPnw4J06coFKlSjRt2pTw8PBMy69cuZKQkBD949y5cxgbG9OpU6dXXHMhhBBCvI7yPdxMnDiRPn360KtXL3x8fJg1axZWVlbMmTMn0/IFCxbE1dVV/9i6dStWVlYSboQQQggB5HO4SU5O5vjx4zRu3Fg/zcjIiMaNG3Pw4MFsLePPP/+kS5cuWFtbZ/p8UlIS0dHR6R5CCCGEMFz5Gm7u3buHVqvFxcUl3XQXFxdCQ0OfO/+RI0c4d+4cvXv3zrLMmDFjsLOz0z/c3d1fuN5CCCGEeH3le7fUi/jzzz+pUKEC/v7+WZYZMmQIUVFR+setW7deYQ2FEEII8aqZ5OfKHR0dMTY2JiwsLN30sLAwXF1dnzlvXFwc//zzDz/88MMzy5mbm2Nubv7CdRVCCCHEmyFfW27MzMzw8/Nj+/bt+mk6nY7t27dTo0aNZ867bNkykpKSeP/99192NYUQQgjxBsnXlhuAgQMH0qNHD6pWrYq/vz+TJ08mLi6OXr16AdC9e3fc3NwYM2ZMuvn+/PNP2rVrR6FChfKj2kIIIYR4TeV7uOncuTMREREMGzaM0NBQfH192bRpk36Q8c2bNzEySt/AdOnSJfbt28eWLVvyo8pCCCGEeI1pFEVR8rsSr1J0dDR2dnZERUVha2ub39URQgghRDbkZP/9Rp8tJYQQQgjxNAk3QgghhDAoEm6EEEIIYVAk3AghhBDCoEi4EUIIIYRBkXAjhBBCCIMi4UYIIYQQBkXCjRBCCCEMioQbIYQQQhgUCTdCCCGEMCgSboQQQghhUCTcCCGEEMKgSLgRQgghhEGRcCOEEEIIgyLhRgghhBAGRcKNEEIIIQyKhBshhBBCGBQJN0IIIYQwKBJuhBBCCGFQJNwIIYQQwqBIuBFCCCGEQZFwI4QQQgiDIuFGCCGEEAZFwo0QQgghDIqEGyGEEEIYFAk3QgghhDAoEm6EEEIIYVAk3AghhBDCoEi4EUIIIYRBkXAjhBBCCIMi4UYIIYQQBkXCjRBCCCEMioQbIYQQQhgUCTdCCCGEMCgSboQQQghhUCTcCCGEEMKgSLgRQgghhEGRcCOEEEIIgyLhRgghhBAGRcKNEEIIIQxKvoeb6dOn4+npiYWFBQEBARw5cuSZ5SMjI+nXrx+FCxfG3Nyc0qVLs2HDhldUWyGEEEK87kzyc+VLlixh4MCBzJo1i4CAACZPnkzTpk25dOkSzs7OGconJyfTpEkTnJ2dWb58OW5ubty4cQN7e/tXX3khhBBCvJY0iqIo+bXygIAAqlWrxrRp0wDQ6XS4u7szYMAABg8enKH8rFmz+OWXXwgMDMTU1DRX64yOjsbOzo6oqChsbW1fqP5CCCGEeDVysv/Ot26p5ORkjh8/TuPGjR9XxsiIxo0bc/DgwUznWbt2LTVq1KBfv364uLhQvnx5fvrpJ7RabZbrSUpKIjo6Ot1DCCGEEIYr38LNvXv30Gq1uLi4pJvu4uJCaGhopvNcv36d5cuXo9Vq2bBhA99//z0TJkxg1KhRWa5nzJgx2NnZ6R/u7u55+jqEEEII8XrJ9wHFOaHT6XB2dub333/Hz8+Pzp07M3ToUGbNmpXlPEOGDCEqKkr/uHXr1iussRBCCCFetXwbUOzo6IixsTFhYWHppoeFheHq6prpPIULF8bU1BRjY2P9tLJlyxIaGkpycjJmZmYZ5jE3N8fc3DxvKy+EEEKI11a+tdyYmZnh5+fH9u3b9dN0Oh3bt2+nRo0amc5Tq1Ytrl69ik6n00+7fPkyhQsXzjTYCCGEEOK/J1+7pQYOHMjs2bP566+/uHjxIp9++ilxcXH06tULgO7duzNkyBB9+U8//ZQHDx7w+eefc/nyZdavX89PP/1Ev3798uslCCGEEOI1k6/XuencuTMREREMGzaM0NBQfH192bRpk36Q8c2bNzEyepy/3N3d2bx5M19++SUVK1bEzc2Nzz//nG+//Ta/XoIQQgghXjP5ep2b/CDXuRFCCCHePG/EdW6EEEIIIV4GCTdCCCGEMCgSboQQQghhUCTcCCGEEMKgSLgRQgghhEGRcCOEEEIIgyLhRgghhBAGRcKNEEIIIQyKhBshhBBCGBQJN0IIIYQwKBJuhBBCCGFQJNwIIYQQwqBIuBFCCCGEQZFwI4QQQgiDIuFGCCGEEAZFwo0QQgghDIqEGyGEEEIYFJP8roAQQgiRE1qtlpSUlPyuhngJzMzMMDJ68XYXCTdCCCHeCIqiEBoaSmRkZH5XRbwkRkZGeHl5YWZm9kLLkXAjhBDijZAWbJydnbGyskKj0eR3lUQe0ul03L17l5CQEIoVK/ZCn6+EGyGEEK89rVarDzaFChXK7+qIl8TJyYm7d++SmpqKqalprpcjA4qFEEK89tLG2FhZWeVzTcTLlNYdpdVqX2g5Em6EEEK8MaQryrDl1ecr4UYIIYQQBkXCjRBCCGHAevbsSbt27Z5ZZteuXWg0mmyfiRYcHIxGo+HUqVMvXL+XQcKNEEII8RJFRETw6aefUqxYMczNzXF1daVp06bs37//lax/ypQpzJs3T/93/fr1+eKLL9KVqVmzJiEhIdjZ2WVrme7u7oSEhFC+fHkg5+HoZZOzpYQQQoiXqEOHDiQnJ/PXX39RvHhxwsLC2L59O/fv338l689OYDEzM8PV1TXbyzQ2Ns5R+VdNWm6EEEKIlyQyMpK9e/cyduxYGjRogIeHB/7+/gwZMoQ2bdroy/Tu3RsnJydsbW1p2LAhp0+f1i9jxIgR+Pr6smDBAjw9PbGzs6NLly7ExMToyyxfvpwKFSpgaWlJoUKFaNy4MXFxcUD6bqmePXuye/dupkyZgkajQaPREBwcnK7lJTo6GktLSzZu3JjutaxatYoCBQoQHx+frlsqODiYBg0aAODg4IBGo6Fnz57Mnz+fQoUKkZSUlG457dq1o1u3bnn+Xj9Jwo0QQog3kqIoxCenvvKHoijZrqONjQ02NjasXr06w04+TadOnQgPD2fjxo0cP36cKlWq0KhRIx48eKAvc+3aNVavXs2///7Lv//+y+7du/n5558BCAkJ4d133+WDDz7g4sWL7Nq1i/bt22dazylTplCjRg369OlDSEgIISEhuLu7pytja2tLq1atWLx4cbrpixYtol27dhlOx3d3d2fFihUAXLp0iZCQEKZMmUKnTp3QarWsXbtWXzY8PJz169fzwQcfZPs9zA3plhJCCPFGSkjR4jNs8ytf74UfmmJllr3dp4mJCfPmzaNPnz7MmjWLKlWqUK9ePbp06ULFihXZt28fR44cITw8HHNzcwDGjx/P6tWrWb58OR999BGgXr133rx5FChQAIBu3bqxfft2Ro8eTUhICKmpqbRv3x4PDw8AKlSokGl97OzsMDMzw8rK6pndSl27dqVbt27Ex8djZWVFdHQ069evZ9WqVRnKGhsbU7BgQQCcnZ2xt7fXP/fee+8xd+5cOnXqBMDChQspVqwY9evXz9b7l1u5ark5ceIEZ8+e1f+9Zs0a2rVrx3fffUdycnKeVU4IIYR403Xo0IG7d++ydu1amjVrxq5du6hSpQrz5s3j9OnTxMbGUqhQIX0rj42NDUFBQVy7dk2/DE9PT32wAShcuDDh4eEAVKpUiUaNGlGhQgU6derE7Nmzefjw4QvVuUWLFpiamupbXVasWIGtrS2NGzfO0XL69OnDli1buHPnDgDz5s2jZ8+eL/16Rblqufn4448ZPHgwFSpU4Pr163Tp0oW3336bZcuWER8fz+TJk/O4mkIIIUR6lqbGXPihab6sN6csLCxo0qQJTZo04fvvv6d3794MHz6cvn37UrhwYXbt2pVhnidbQJ6+FYFGo0Gn0wFqy8nWrVs5cOAAW7ZsYerUqQwdOpTDhw/j5eWV47qCOsC4Y8eOLF68mC5durB48WI6d+6MiUnOYkPlypWpVKkS8+fP56233uL8+fOsX78+V3XKiVyFm8uXL+Pr6wvAsmXLqFu3LosXL2b//v106dJFwo0QQoiXTqPRZLt76HXj4+PD6tWrqVKlCqGhoZiYmODp6Znr5Wk0GmrVqkWtWrUYNmwYHh4erFq1ioEDB2Yoa2Zmlq3bG3Tt2pUmTZpw/vx5duzYwahRo7Is+6zbJvTu3ZvJkydz584dGjdunGGMz8uQq24pRVH0iXHbtm20aNECUAcV3bt3L+9qJ4QQQrzB7t+/T8OGDVm4cCFnzpwhKCiIZcuWMW7cONq2bUvjxo2pUaMG7dq1Y8uWLQQHB3PgwAGGDh3KsWPHsrWOw4cP89NPP3Hs2DFu3rzJypUriYiIoGzZspmW9/T05PDhwwQHB3Pv3j39/vxpdevWxdXVla5du+Ll5UVAQECWdfDw8ECj0fDvv/8SERFBbGys/rn33nuP27dvM3v27Jc+kDhNrsJN1apVGTVqFAsWLGD37t20bNkSgKCgIFxcXPK0gkIIIcSbysbGhoCAACZNmkTdunUpX74833//PX369GHatGloNBo2bNhA3bp16dWrF6VLl6ZLly7cuHEj2/tTW1tb9uzZQ4sWLShdujT/+9//mDBhAs2bN8+0/KBBgzA2NsbHxwcnJydu3ryZaTmNRsO7777L6dOn6dq16zPr4ObmxsiRIxk8eDAuLi70799f/5ydnR0dOnTAxsbmuVdKzisaJSfntD1y5swZunbtys2bNxk4cCDDhw8HYMCAAdy/fz/D6WOvk+joaOzs7IiKisLW1ja/qyOEECIbEhMTCQoKwsvLCwsLi/yujsihRo0aUa5cOX799ddnlnvW55yT/XeuOisrVqyY7mypNL/88gvGxjkfaCWEEEIIw/Pw4UN27drFrl27mDFjxitbb67CzdGjR9HpdBn6306fPo2xsTFVq1bNk8oJIYQQ4s1VuXJlHj58yNixYylTpswrW2+uxtz069ePW7duZZh+584d+vXr98KVEkIIIcSbLzg4mKioKAYNGvRK15urcHPhwgWqVKmSYXrlypW5cOHCC1dKCCGEECK3chVuzM3NCQsLyzA9JCQkxxf4AZg+fTqenp5YWFgQEBDAkSNHsiw7b948/c2+0h4yuEwIIYQQaXIVbt566y2GDBlCVFSUflpkZCTfffcdTZo0ydGylixZoj/j6sSJE1SqVImmTZvqLyudGVtbW/0Nv0JCQrhx40ZuXoYQQgghDFCuws348eO5desWHh4eNGjQgAYNGuDl5UVoaCgTJkzI0bImTpxInz596NWrFz4+PsyaNQsrKyvmzJmT5TwajQZXV1f9Q66tI4QQQog0uQo3bm5unDlzhnHjxuHj44Ofnx9Tpkzh7NmzObqscnJyMsePH093Iy4jIyMaN27MwYMHs5wvNjYWDw8P3N3dadu2LefPn8+ybFJSEtHR0ekeQgghhDBcub4ph7W1tf5W7Ll17949tFpthpYXFxcXAgMDM52nTJkyzJkzh4oVKxIVFcX48eOpWbMm58+fp2jRohnKjxkzhpEjR75QPYUQQgjx5sh2uFm7di3NmzdPdwv0rLRp0+aFK5aVGjVqUKNGDf3fNWvWpGzZsvz222/8+OOPGcoPGTIk3Y3DoqOjX8lNu4QQQojXhUajYdWqVfrbHwQGBtKzZ09OnTqFt7c3q1evxsvLi5MnT+pvjP0sPXv2JDIyktWrV7/UeudWtsNNu3btCA0NxdnZ+Zn3htBoNNm62yiAo6MjxsbGGc68CgsLw9XVNVvLMDU1pXLlyly9ejXT583NzTE3N8/WsoQQQoi8FhERwbBhw1i/fj1hYWE4ODhQqVIlhg0bRq1atV5JHUJCQnBwcND/PXz4cKytrbl06RI2NjbY29sTEhKCo6NjtpY3ZcoUnrx7U/369fH19WXy5Ml5XfVcyfaYG51Oh7Ozs/7/WT2yG2xAvUW6n58f27dvT7ee7du3p2udeRatVsvZs2cpXLhwttcrhBBCvCodOnTg5MmT/PXXX1y+fJm1a9dSv3597t+//8rq4Orqmu5A/9q1a9SuXRsPDw8KFSqEsbExrq6u2b6ci52dHfb29i+ptnlAyaHk5GSlYcOGyuXLl3M6a6b++ecfxdzcXJk3b55y4cIF5aOPPlLs7e2V0NBQRVEUpVu3bsrgwYP15UeOHKls3rxZuXbtmnL8+HGlS5cuioWFhXL+/PlsrS8qKkoBlKioqDypvxBCiJcvISFBuXDhgpKQkJDfVcmRhw8fKoCya9euLMsAyowZM5RmzZopFhYWipeXl7Js2bJ0ZW7evKl06tRJsbOzUxwcHJQ2bdooQUFB6cr8+eefio+Pj2JmZqa4uroq/fr1S7eOVatW6f//5GP48OFKUFCQAignT57Uz3Pu3DmlZcuWSoECBRQbGxuldu3aytWrVxVFUZQePXoobdu21f//6WVev35dKVGihPLLL7+kq+PJkycVQLly5Uqm78WzPuec7L9zfLaUqakpZ86cyZNgBdC5c2fGjx/PsGHD8PX15dSpU2zatEk/yPjmzZuEhIToyz98+JA+ffpQtmxZWrRoQXR0NAcOHMDHxyfP6iSEEOINoCiQHPfqH090xzyPjY0NNjY2rF69mqSkpCzLff/993To0IHTp0/TtWtXunTpwsWLFwFISUmhadOmFChQgL1797J//35sbGxo1qwZycnJAMycOZN+/frx0UcfcfbsWdauXUvJkiUzXVdISAjlypXjq6++IiQkJNNbI9y5c4e6detibm7Ojh07OH78OB988AGpqakZyk6ZMoUaNWrQp08f/fXnihUrxgcffMDcuXPTlZ07dy5169bNsm55RfMoxeXIl19+ibm5OT///PPLqNNLlZNbpgshhHg9JCYmEhQUhJeX1+Or0ifHwU9FXn1lvrsLZtbZLr5ixQr69OlDQkICVapUoV69enTp0oWKFSsC6ljVTz75hJkzZ+rnqV69OlWqVGHGjBksXLiQUaNGcfHiRTQaDaBeSsXe3p7Vq1fz1ltv4ebmRq9evRg1alSmdXh6QLGvry/t2rVjxIgRgHoPqCcHFH/33Xf8888/XLp0CVNT0wzLe3pAcWZjbu7evUuxYsU4cOAA/v7+pKSkUKRIEcaPH0+PHj0yrWemn/MjOdl/5+pU8NTUVObMmcO2bdvw8/PD2jr9hzxx4sTcLFYIIYQwOB06dKBly5bs3buXQ4cOsXHjRsaNG8cff/xBz549ATKMM61RowanTp0C4PTp01y9epUCBQqkK5OYmMi1a9cIDw/n7t27NGrUKM/qfOrUKerUqZNpsMmuIkWK0LJlS+bMmYO/vz/r1q0jKSmJTp065Vk9s5KrcHPu3Dn9jTMvX76cpxUSQgghssXUSm1FyY/15pCFhQVNmjShSZMmfP/99/Tu3Zvhw4frw82zxMbG4ufnx6JFizI85+TkhJFRrq7H+0yWlpZ5spzevXvTrVs3Jk2axNy5c+ncuTNWVjl//3IqV+Fm586deV0PIYQQImc0mhx1D71OfHx80l0j5tChQ3Tv3j3d35UrVwagSpUqLFmyBGdn5yy7Yzw9Pdm+fTsNGjTIk/pVrFiRv/76i5SUlGy13piZmWV6tnSLFi2wtrZm5syZbNq0iT179uRJ/Z4nV3Hvgw8+ICYmJsP0uLg4PvjggxeulBBCCGEI7t+/T8OGDVm4cCFnzpwhKCiIZcuWMW7cONq2basvt2zZMubMmcPly5cZPnw4R44coX///gB07doVR0dH2rZty969ewkKCmLXrl189tln3L59G4ARI0YwYcIEfv31V65cucKJEyeYOnVqruvdv39/oqOj6dKlC8eOHePKlSssWLCAS5cuZVre09OTw4cPExwczL1799DpdAAYGxvTs2dPhgwZQqlSpbJ9mZcXlatw89dff5GQkJBhekJCAvPnz3/hSgkhhBCGwMbGhoCAACZNmkTdunUpX74833//PX369GHatGn6ciNHjuSff/6hYsWKzJ8/n7///lt/FrCVlRV79uyhWLFitG/fnrJly/Lhhx+SmJiob8np0aMHkydPZsaMGZQrV45WrVpx5cqVXNe7UKFC7Nixg9jYWOrVq4efnx+zZ8/OshVn0KBBGBsb4+Pjg5OTEzdv3tQ/9+GHH5KcnEyvXr1yXZ+cytHZUtHR0SiKgoODA1euXMHJyUn/nFarZd26dQwePJi7d/OhDzSb5GwpIYR48zzrLJo33dNnMhmavXv30qhRI27dupXhXpJPy5ezpezt7dFoNGg0GkqXLp3heY1GIzepFEIIIQRJSUlEREQwYsQIOnXq9Nxgk5dyFG527tyJoig0bNiQFStWULBgQf1zZmZmeHh4UKRIPlxzQAghhBCvlb///psPP/wQX1/fVz5kJUfhpl69egAEBQVRrFgx/cWEhBBCCJE7ubiW7huhZ8+e2TrV/WXI1YBiDw8P9u3bx/vvv0/NmjW5c+cOAAsWLGDfvn15WkEhhBBCiJzIVbhZsWIFTZs2xdLSkhMnTujvlxEVFcVPP/2UpxUUQgghhMiJXIWbUaNGMWvWrAynhdWqVYsTJ07kWeWEEEIIIXIqV+Hm0qVL1K1bN8N0Ozs7IiMjX7ROQgghhBC5lqtw4+rqytWrVzNM37dvH8WLF3/hSgkhhBBC5Fauwk2fPn34/PPPOXz4MBqNhrt377Jo0SIGDRrEp59+mtd1FEIIIYTItlzdOHPw4MHodDoaNWpEfHw8devWxdzcnEGDBjFgwIC8rqMQQgghRLblquVGo9EwdOhQHjx4wLlz5zh06BARERH8+OOPeV0/IYQQ4o3Ws2dP/dX9n3ykDe/Ys2cPrVu3pkiRImg0mnR3C8+KVqvl559/xtvbG0tLSwoWLEhAQAB//PHHS341b4Yctdxk947fc+bMyVVlhBBCCEPUrFkz5s6dm25a2v0Z4+LiqFSpEh988AHt27fP1vJGjhzJb7/9xrRp06hatSrR0dEcO3aMhw8f5nnd0yQnJ2NmZvbSlp+XchRu5s2bh4eHB5UrVzbYKyoKIYQQec3c3BxXV9dMn2vevDnNmzfP0fLWrl1L37596dSpk35apUqV0pXR6XSMHz+e33//XX/Tyo8//pihQ4cCcPbsWT7//HMOHjyIlZUVHTp0YOLEidjY2ABqi1NkZCTVqlVj+vTpmJubExQUxK1bt/jqq6/YsmULRkZG1KlThylTpuDp6Zmj1/Ay5SjcfPrpp/z9998EBQXRq1cv3n///XT3lxJCCCFeFUVRSEhNeOXrtTSxzPfbD7m6urJjxw769u2rbwF62pAhQ5g9ezaTJk2idu3ahISEEBgYCKitRU2bNqVGjRocPXqU8PBwevfuTf/+/Zk3b55+Gdu3b8fW1patW7cCkJKSop9v7969mJiYMGrUKJo1a8aZM2dem5adHIWb6dOnM3HiRFauXMmcOXMYMmQILVu25MMPP+Stt97K9w9bCCHEf0dCagIBiwNe+XoPv3cYK1OrHM3z77//6ltEQG2tWbZsWa7rMHHiRDp27IirqyvlypWjZs2atG3bVt8CFBMTw5QpU5g2bRo9evQAoESJEtSuXRuAxYsXk5iYyPz587G2tgZg2rRptG7dmrFjx+rv4G1tbc0ff/yhDy0LFy5Ep9Pxxx9/6Pf5c+fOxd7enl27dvHWW2/l+jXlpRwPKDY3N+fdd99l69atXLhwgXLlytG3b188PT2JjY19GXUUQggh3mgNGjTg1KlT+sevv/76Qsvz8fHRn9DzwQcfEB4eTuvWrenduzcAFy9eJCkpiUaNGmU6/8WLF6lUqZI+2IB6lwGdTselS5f00ypUqJCuNeb06dNcvXqVAgUKYGNjg42NDQULFiQxMZFr16690GvKS7k6FTyNkZERGo0GRVHQarV5VSchhBDiuSxNLDn83uF8WW9OWVtbU7JkyTyth5GREdWqVaNatWp88cUXLFy4kG7dujF06FAsLXNex8w8GX4AYmNj8fPzY9GiRRnKZtU9lh9yHG6SkpL03VL79u2jVatWTJs2jWbNmmFklKszy4UQQogc02g0Oe4eMmQ+Pj6AOp6mVKlSWFpasn37dn1rzpPKli3LvHnziIuL0weY/fv3Y2RkRJkyZbJcR5UqVViyZAnOzs7Y2tq+nBeSB3KURvr27UvhwoX5+eefadWqFbdu3WLZsmW0aNFCgo0QQgiRC7GxsfruKoCgoCBOnTrFzZs3s5ynY8eOTJo0icOHD3Pjxg127dpFv379KF26NN7e3lhYWPDtt9/yzTffMH/+fK5du8ahQ4f4888/AejatSsWFhb06NGDc+fOsXPnTgYMGEC3bt30420y07VrVxwdHWnbti179+4lKCiIXbt28dlnn3H79u08fV9eRI5abmbNmkWxYsUoXrw4u3fvZvfu3ZmWW7lyZZ5UTgghhDB0x44do0GDBvq/Bw4cCECPHj3Snbn0pKZNm/L3338zZswYoqKicHV1pWHDhowYMQITE3XX/v3332NiYsKwYcO4e/cuhQsX5pNPPgHAysqKzZs38/nnn1OtWrV0p4I/i5WVFXv27OHbb7+lffv2xMTE4ObmRqNGjV6rlhyNkoML1qRdZfF5nr5Q0eskOjoaOzs7oqKiXqsPQgghRNYSExMJCgrCy8sLCwuL/K6OeEme9TnnZP+d44v4CSGEEEK8zmSgjBBCCCEMioQbIYQQQhgUCTdCCCGEMCgSboQQQghhUCTcCCGEeGPodLr8roJ4iXJwAvczvdDtF4QQQohXwczMDCMjI+7evYuTkxNmZmZys2YDoygKERERaDQaTE1NX2hZEm6EEEK89oyMjPDy8iIkJIS7d+/md3XES6LRaChatCjGxsYvtBwJN0IIId4IZmZmFCtWjNTUVLlZs4EyNTV94WADEm6EEEK8QdK6LF6020IYNhlQLIQQQgiDIuFGCCGEEAZFwo0QQgghDMprEW6mT5+Op6cnFhYWBAQEcOTIkWzN988//6DRaGjXrt3LraAQQggh3hj5Hm6WLFnCwIEDGT58OCdOnKBSpUo0bdqU8PDwZ84XHBzMoEGDqFOnziuqqRBCCCHeBPkebiZOnEifPn3o1asXPj4+zJo1CysrK+bMmZPlPFqtlq5duzJy5EiKFy/+CmsrhBBCiNddvoab5ORkjh8/TuPGjfXTjIyMaNy4MQcPHsxyvh9++AFnZ2c+/PDDV1FNIYQQQrxB8vU6N/fu3UOr1eLi4pJuuouLC4GBgZnOs2/fPv78809OnTqVrXUkJSWRlJSk/zs6OjrX9RVCCCHE6y/fu6VyIiYmhm7dujF79mwcHR2zNc+YMWOws7PTP9zd3V9yLYUQQgiRn/K15cbR0RFjY2PCwsLSTQ8LC8PV1TVD+WvXrhEcHEzr1q3109LuEGtiYsKlS5coUaJEunmGDBnCwIED9X9HR0dLwBFCCCEMWL6GGzMzM/z8/Ni+fbv+dG6dTsf27dvp379/hvLe3t6cPXs23bT//e9/xMTEMGXKlExDi7m5Oebm5i+l/kIIIYR4/eT7vaUGDhxIjx49qFq1Kv7+/kyePJm4uDh69eoFQPfu3XFzc2PMmDFYWFhQvnz5dPPb29sDZJguhBBCiP+mfA83nTt3JiIigmHDhhEaGoqvry+bNm3SDzK+efMmRkZv1NAgIYQQQuQjjaIoSn5X4lWKjo7Gzs6OqKgobG1t87s6QgghhMiGnOy/pUlECCGEEAZFwo0QQgghDIqEGyGEEEIYFAk3QgghhDAoEm6EEEIIYVAk3AghhBDCoEi4EUIIIYRBkXAjhBBCCIMi4UYIIYQQBkXCjRBCCCEMioQbIYQQQhgUCTdCCCGEMCgSboQQQghhUCTcCCGEEMKgSLgRQgghhEGRcCOEEEIIgyLhRgghhBAGRcKNEEIIIQyKhBshhBBCGBQJN0IIIYQwKBJuhBBCCGFQJNwIIYQQwqBIuBFCCCGEQZFwI4QQQgiDIuFGCCGEEAZFwo0QQgghDIqEGyGEEEIYFAk3QgghhDAoEm6EEEIIYVAk3AghhBDCoEi4EUIIIYRBkXAjhBBCCIMi4UYIIYQQBkXCjRBCCCEMioQbIYQQQhgUCTdCCCGEMCgSboQQQghhUCTcCCGEEMKgSLgRQgghhEGRcCOEEEIIg/JahJvp06fj6emJhYUFAQEBHDlyJMuyK1eupGrVqtjb22NtbY2vry8LFix4hbUVQgghxOss38PNkiVLGDhwIMOHD+fEiRNUqlSJpk2bEh4enmn5ggULMnToUA4ePMiZM2fo1asXvXr1YvPmza+45kIIIYR4HWkURVHyswIBAQFUq1aNadOmAaDT6XB3d2fAgAEMHjw4W8uoUqUKLVu25Mcff3xu2ejoaOzs7IiKisLW1vaF6i6EEEKIVyMn++98bblJTk7m+PHjNG7cWD/NyMiIxo0bc/DgwefOrygK27dv59KlS9StW/dlVlUIIYQQbwiT/Fz5vXv30Gq1uLi4pJvu4uJCYGBglvNFRUXh5uZGUlISxsbGzJgxgyZNmmRaNikpiaSkJP3f0dHReVN5IYQQQryW8jXc5FaBAgU4deoUsbGxbN++nYEDB1K8eHHq16+foeyYMWMYOXLkq6+kEEIIIfJFvoYbR0dHjI2NCQsLSzc9LCwMV1fXLOczMjKiZMmSAPj6+nLx4kXGjBmTabgZMmQIAwcO1P8dHR2Nu7t73rwAIYQQQrx28nXMjZmZGX5+fmzfvl0/TafTsX37dmrUqJHt5eh0unRdT08yNzfH1tY23UMIIYQQhivfu6UGDhxIjx49qFq1Kv7+/kyePJm4uDh69eoFQPfu3XFzc2PMmDGA2s1UtWpVSpQoQVJSEhs2bGDBggXMnDkzP1+GEEIIIV4T+R5uOnfuTEREBMOGDSM0NBRfX182bdqkH2R88+ZNjIweNzDFxcXRt29fbt++jaWlJd7e3ixcuJDOnTvn10sQQgghxGsk369z86rJdW6EEEKIN88bc50bIYQQQoi8JuFGCCGEEAZFwo0QQgghDIqEGyGEEEIYFAk3QgghhDAoEm6EEEIIYVAk3AghhBDCoEi4EUIIIYRBkXAjhBBCCIMi4UYIIYQQBkXCjRBCCCEMioQbIYQQQhgUCTdCCCGEMCgSboQQQghhUCTcCCGEEMKgSLgRQgghhEGRcCOEEEIIgyLhRgghhBAGRcKNEEIIIQyKhBshhBBCGBQJN0IIIYQwKBJuhBBCCGFQJNwIIYQQwqBIuBFCCCGEQZFwI4QQQgiDIuFGiP+4yPhkvll+moPX7ud3VYQQIk9IuBHiP27m7mssPXabgUtPkZSqze/qCCHEC5NwI8R/WFKqlmXHbgMQEpXI0kf/F0KIN5mEGyH+wzadC+VBXDIajfr3zJ1XSU7V5W+lhBDZoigKa07dYd+Ve/ldldeOhBsh/sMWHboJwKf1SuBia87dqESWH5fWGyHeBPMP3uDzf07Rfc5hjgU/yO/qvFYk3AjxH3UpNIYjwQ8wNtLQo6Ynn9QrAcB0ab0R4rV3LPgBP/57AQCdAl8sOUV0Yko+1+r1IeFGiP+oRYdvANCkrAsutha8618MpwLm3IlMYNVJab0R4nUVHpNI30UnSNUpNC3ngntBS24/TGD4mvP5XbXXhoQbIf6D4pJSWXniDgDvV/cAwMLUmI/rFgdg2s6rpGil9Ub8d8QkppCY8vqfLZii1dF/0UnCY5Io7WLDxHd8mdy5MsZGGladvMOaU3fyu4qvBQk3QvwHrT19l9ikVDwLWVGzRCH99K4BHjjamHPrQQKrTspGUhi+q+ExfLP8NH4/bqPxxN1cj4jN7yo9008bLnIk+AEFzE2Y9b4f1uYm+Hk4MKBhSQD+t+octx7E53Mt85+EGyH+YxRFYeEhtUuqa4AHRkYa/XOWZo9bb6bvvEpqDltvlh+/zR97r6PTKXlX4TdIVHwKS47eJDYpNb+rIp5BURQOX7/Ph/OO0njiHpYeu02yVsfthwl0mnWQc3ei8ruKmVpz6g5z9wcDMOGdShR3stE/179BSaoUsycmKZWBS0+h/Y/+BtNIuBHiP+b07SjO343GzMSIjn5FuRl9k07rOrHyykoAulYvRiFrM27cj2fNqbvZXu60HVcYtOw0o9Zf5Jctl15W9V+KA1fvsfrkHRQl9zuEmMQUuv55iG9XnOW7lWfzsHYir2h1ChvOhtBuxgE6/36I7YHhaDTQtJwL83pVo7ybLffjkuny+6HX7ordgaHRDF6hfq/6NSjBW+VcURSFRRcXsSloEybGRkzuXBkbcxOOBj9k5q6r+Vzj/GWS3xUQ+e9KWAy3HsaTnKojKVWX7t9k7aN/U3X4eTjQwNv5hda14WwIk7ddpqpnQVpWKEyAV0FMjCVjv0qLHrXatKpQGAdrM6Yfmk/gg0CmnJhC6+KtsTIzpU/d4vy8MZBpO6/S1rfIcz+j2XuuM37LZf3fM3ddw6uQNe9Uc3+pryUvXA2PofucI6TqFE7efMiINuXQaDTPn/EJiSlaPpp/nHN3ogG1269bDQ+qeRZ8GVUWOZSYomXZo1bFG/fVLpu0cN+7tpe+BcTPw4E+849x6PoDesw9wrR3K/NWOdc8r09UQgo37sdR2qUAFqbG2Sr/8YLjJKRoqVPKkYFNygDwd+Df/HzkZ4w0RnjZeVGmUBl+aFuOgUtPM2nbFWqXcsLX3T7P6/8m0CgvcqjyBoqOjsbOzo6oqChsbW3zuzr57sC1e7z/x2Gy24I5pYsvbX3dcrWuwNBo2k3fT2LK466OQtZmNCvvqgad4oUwNsrZTuVNczU8lsnbLlOpqD19HnX/vEpR8SkEjNlGYoqOFZ/WpGJRGxosa0BUktoMP6HeBN7yfIu4pFRqj93Bw/gUJnWuxNuVi2a5zPkHgxn26CyNr5qUJkWr49cdVzEx0vDXB/7UKun4Sl5bbiiKQvc5R9j7xEXQutfwYGQOAo5Wp9B/8Qk2ngvFxtyEKh4O7LkcQbkitqztX9vgv9OvuythMbz3x2EiYpIAsLcypXt1D7rX9MTRxjxD+cQULZ/9fZItF8Iw0sDYDhXpVPXFQnpIVAJHgx9yLPgBR4MfEhgajaKArYUJrSoVoUOVolQpZp/pd06nU+gz/xjbA8Nxs7fk3wG1cbA249KDS7y3/j2SdckAVHWpypymcwD47J9TrDt9F49CVmz4rA7W5obRjpGT/bdhvGIDcz0ilmsRcTQu65zjI8iciE5MYdDS0+gU8ChkhZONOWYmRurDWP3X3MQYMxMjwqMT2R4YzrcrzlDapQBlC+csGMYkpvDpwhMkpuioUbwQno5WbDoXyv24ZBYdvsmiwzdxtFGDTosKhQnwMqygk6rV8ce+ICZuvUxyqo5/z4RQo0QhyrvZvdJ6rDhxm8QUHd6uBahSzJ4dt3bogw3AyisrecvzLazNTehdpzi/bL7E1B1XaVPJLdPP458jN/XBpl+DEgxoVApFUQi+H8/a03f5ZOFxVvWtSUnnAq/sNebEtovh7L1yDzNjI/o1KMnk7ZeZf/AGigI/tH1+wFEUhf+tPsfGc6GYGRvxezc/yrgWoP74XZy/G82So7d4L6DYK3o14mmpWh0Dl54mIiYJN3tLPqpbnE5Vi2JllvWuz8LUmBldqzBk5VmWHb/N18vPEBmfku2DEZ1O4WpELEeDH3As+CFHgh5wJzIhQzkbcxOiE1NZfPgmiw/fpLijNe2ruPF2laK42Vvqy03feZXtgeGYmRjxWzc/HKzNiE+JZ9DuQSTrkqnmWo0zEWc4FnaMLTe20NSzKaPalefEjYfcuB/PyHXnGdexUs7fvDectNy8RlK1On7bc53J2y6TolXo5FeUMe0rvLRum4FLT7HyxJ1spXutTqHXvKPsuRyBRyEr1vavjZ2labbWoygK/RefZP3ZEIrYWfDvZ3UoaG1GilbHwWv32XA2hE3nQ4mMf3wBKkcbcz5vVJL3q3u81ID3KlwOi+HrZac5fVsNEfZWpkTGp1CnlCMLPgx4ZfVQFIXGE3dzLSKOUe3K8351D77c+SXbbm6jcbHGbLu5DQ0aNnbYiJuNG7GPWm8i41MybbFbdfI2A5eeRlGgd20vhrYsq/+sElO0vP/HYY7deIh7QUtW9a2V6VFyfkpM0fLWpD3cfBBPvwYl+LqpN8uO3eKbFWdQFHi/ejF+aFM+3YDrp03YooY/jQZmvFeF5hUKAzBnXxA//HuBgtZm7PyqPnZW2futvO4u3I0mLCaRpBQtiSk6ElO0JKWq/yam6EhM1ZKUosPfy4Fm5Qvnd3WZvvMqv2y+hK2FCdsG1sPZ1iLb8yqKwk8bLjJ7bxAAn9YvwTdNy2S6Pbr9MJ79V++x7+p9Dly9x/245HTPG2nAp4gt1TwLUs2zIFU9HHC0Mefg9fusOH6bjedCSXh0GrpGAzWKF6JDlaJYmxvz6aITKAqM61iRdx61IA3bP4xVV1fhbOnM8jbL+Tvwb2aenklh68KsabcGSxNLDl+/T5fZh1AUmNG1Ci0q5P/n8aJysv9+LcLN9OnT+eWXXwgNDaVSpUpMnToVf3//TMvOnj2b+fPnc+7cOQD8/Pz46aefsiz/tJcZbn7fc42m5VzxKGSd43mvhsfw1dLHO8A0b/m48Ou7lbPVL5sTm86F8MnCExhpYNknNfDzeP7YgIdxybSeto/bDxNo5O3M7O5Vn7nhTzNvfxAj1l3A1FjDko9rUKWYQ4YyKVodB67dZ8MZNehEJahBp51vEX5qX+GZR1qvq7SwOmXbFZK1OgpYmPB9Kx9qFC9Eowm7SdbqWPChP3VKOb2S+hy8dp93Zx/C2syYw0MboyWO+kvrk6pLZXnr5fxy7BcOhxzmk0qf0M+3H6AOEh6/5TIlnW3Y/EVdfevN+jMhDPj7BDoFulX3yLSV435sEm/POMDNB/FUKWbP4j7V8/x7/CLSdnyuthZs/6qePtwvP36br5eroe29gGKMapt5wJm7P4iR69QrxI5+uzxdAzz0z6VodbSYspcr4bH0rOnJiDblXs2Lekki45P53+pz/HsmJNvzvFO1KD+0LZ9vn/ml0BhaTd1LilZh4juVaF8l667VrCiKwqzd1xm7KRCAd/3dGdWuAtEJKRy8fp99V++x/+o9/TieNBamRlR2d6CaV0GqeTpQuZgDNs84eIxNSmXj2RBWnLjNoesZb6PwXkAxfnq7AgAbgzbyzZ5v0KDhz6Z/Us21GgmpCbRZ3YbQuFD6VurLp76fAvDL5kCm77yGnaUpm76oQ2G7xy1CiqIQm5TKw7gUHsQn8yAuiYRkHfZWphSyMaOgtRkFrcxeqzGRb1S4WbJkCd27d2fWrFkEBAQwefJkli1bxqVLl3B2zjh4tWvXrtSqVYuaNWtiYWHB2LFjWbVqFefPn8fN7fljQV5WuDl+4yEdZh5Ao1EDSe86xanq4fDcVgetTuGPvdeZ8Ki7wtbChBFtymFtbsKAxSdJ1uqoWaIQv3ev+swfR06ExyTSdNIeHsan0Ld+Cb5p5p3tec/diaLDzAMkper4snFpPm9c6pnlT958yDu/HSRFqzCslQ8f1PZ67jpStDr+OhDMmI2BaHUKZVwKMKubH16OOQ+N2bEjMIxtF8P5qE5xPPNoHYGh0Xy97AxnH51S2qCMEz+1r6DfuPyw7gJz9gdRrogt6/rXzlZIfFH9Fp9g/ZkQugYUY/TbFVh6aSk/HvqR0g6lWdFmhX6j6WLlwuYOmzE2MiY6MYXaP+8gOjGVae9VplXFImy9EManC4+TqlN4p2pRfm5fMcv6Xw2Ppf2M/UQnptKqYmF+7VL5lbzW5wmJSqDh+N0kpGgzbZV6MuC861+M0e3SB5w1p+7w+T+nAHWc0YBGGX8He69E0O3PIxgbadj4eR1Ku7yeXXPPs+tSON8sP0N4TBLGRhq8XdVBsBamRliYGGOu/1edlpCsZemxW+gU8Clsy6z3/ShWyOqV1jlVq6P9zAOcuR1F47LqgdjztsU3o29iZWqFo2XGMWJ/H7nJ0FVn0SlQxM6CkOhEntxzGhtp8HW3p1aJQtQq6UjlYg6YmeQuFNx6EM+qk3dYceI2N+7HU7mYPf98VB1zE2Nuxdyi07pOxKXE8XHFj+lfub9+vk3Bm/h699dYGFuwtt1aCtsUJkWro8Oj96G4kzWF7Sy4H5vMw/hkHsalkJyNSz3YWaphp5D1o8BjbYaxkQYNGjQa0ID+vTXSPJ5W1MGSnrWev73PiTcq3AQEBFCtWjWmTZsGgE6nw93dnQEDBjB48ODnzq/VanFwcGDatGl07979ueVfVrg5fzeKXzZfYtelCP20SkXt+LBOcZqXd8U0k/R7PSKWQctOc+JmJAD1yzjxc/uKuNqpTacHrt6jz/xjxCVrqVTUjnm9/HGwNnuheiqKQu+/1MFpPoVtWd2vVo5/hMuP32bQstNoNDCnR7Usz6B6GJdMq6n7uBOZQPPyrszoWiVHXUxHgh7Qb/EJImKSKGBuwoR3KuX5mQtR8SnUHruDmKRULEyNGPRWGXrV8sr1eJ8UrY6Zu64xdccVUrQKthYmDG9djvZV3NK99gdxydQbt5OYpFQmd/alXeWcD9JeeuwWW86H8Um94lR9zlk54TGJ1Byzg1SdwobP6uBTxJZuG7pxKuIUg6oOoke5HiRrk2m4rCFRSVFMbzSdukXrAjBl2xUmbbtMaRcbhjQvy8cLjpOs1dHOtwgT3vF97nt14No9uv+pno3Uv0FJBjUtk+PXmtc+/+cka07dpaqHA8s+qZHp93LF8dsM0gccd0a3q4CRkYZdl8Lp/dcxUnUKPWt6Mry1T5bf64/mH2PLhTBqlSzEwg8D3qgu1rikVEZvuMjiw+rNVYs7WTPpHV8qZePsm31X7vHZPyd5EJeMrYUJE9/xpbGPS7bXfeFuNNN3XuViaDRj3q5AQPFCz5/pCU92R20dWA+X53RHrb66muEHhmNubM6Y2mNo5NEoQ5kNZ0P44p9T+kBQ2sWGWiUdqVXCkYDiBSlgkbddj4qicDU8FveCVliYGpOiS6HHxh6cvXeWys6VmdN0DiZGJunK99rci+Nhx2nm2Yxf6v0CQNC9OFpM2avv9nqapamxPrRYmhrzMD6ZB3HJPIhP5kXSQZVi9qzsWyv3C8jEGxNukpOTsbKyYvny5bRr104/vUePHkRGRrJmzZrnLiMmJgZnZ2eWLVtGq1atnlv+ZY+5uRIWw5z9Qaw4cUd/88Eidhb0rOVJF/9i2FqYotMpzD0QzLhNgSSl6rAxN2FYKx86VS2aYeN3+lYkPece4WF8CiWdbVjwoX+6psWc+ufITQavPIuZsRHrBtSmjGvujib/t/osCw/dxNbChHUDamfoitPpFD786yg7L0XgWciKtQNqY2thiqIo3Im9g5uNW7Y29OHRifRbfIKjwQ8B6Fu/BF+9VSbPBhunjZkwNdaQolV/CpWL2fNLx4o5GgSr1SnsCAxn0tbLXAhRTwduXNaZ0W9XyHLDmrYBLupgyfav6mFukv3m+31X7tFtzmH9xuedqkUZ3LwsBbMIv2nrStvg3Iq+RYtVLTDSGLGt4zacrNSusbFHxrLw4kIaFWvE5AaTAfU01NpjdxCTmIqRRr1JX4sKrvzapXKGJuvz984TmRRJLbf0G7Vlx27x9fIzAPzS8cXPPnkRR4Mf0GnWQTQaWNe/9jMHda88cZuvlqkBp0s1dzpVdef9Pw6TkKKlTaUiTO7sm65FZ9uNbcw5N4cv/b6kmms1bt6Pp/Gk3SSn6pj1vh/Nyuf9acUvw7HgBwxcepqbj65026uWJ982885RF1NIVAJ9F53g5KODt771SzCwSelndnOcuhXJtB1X2HYxXD/NwtSI37tVpW7p7HXfXg6LodWv+0jW6pjQqRId/J7dHbXwwkLGHh2bblrfSn35uNLHGGnS1/XcnSiC7sUR4FUwR+N38sKk45OYc24OBcwKsLz1corYFMlQJvBBIJ3/7YxO0TG36VyqulYF4MztSI7feEhBazMcrNQg4/Co28nSLPPPVKtTiHwUdO7HJXM/Vu26ehifglanoAAo6r+KAgoKiqJuHxQUitpb0q2GZ56+B29MuLl79y5ubm4cOHCAGjVq6Kd/88037N69m8OHDz93GX379mXz5s2cP38eC4uMX7akpCSSkpL0f0dHR+Pu7v7SBxTfi01i0aGbLDgUzL1YdXCZtZkx71Rz5/ydaI48uj19nVKO/NyhYrrR8U+7Gh7D+38cITQ6ETd7Sxb2DshVF83N+/E0n7KHuGQt37Xw5qO6JdDqtCy8uJCQuBCMNEYYa4wz/9fImLIFy+p3WsmpOjr/fpCTNyMpW9iWlZ/WTPcjSduZmpsYsapvLXyKqO/1mMNjWBy4mOaezRlVexRmxs9viUrR6hizIZA5+9WBfbVLOjKliy+FXnCAamR8MrXH7iQ2KZWZXasQlZDC6PUXiUlKxczYiM8bl+KjusUzbXVLE5uUyrJjt5h3IFjf725vZcqI1uVo61vkmQEuIVlL/fE7CYtO4vtWPnyYjS47UHcaLX/dx4O4ZEo523AlPFa/3sHNvHmnqnu6Ha5Wp1B33E7uRCboxx7MODWDmadnUrNITX5r8pu+7NWHV3l77duYaEzY2mmrvol+4tbL/Lr9CqCGthld/TK0+AVHBdN+bXtSdCn0rtCbzyp/lu71p/X/mxprmP9BADVK5OxoPC9odQqtp+7jQkg07/oXY0z7Cs+dZ9XJ23z16KxCYyON+n6WduKP7lXTvQeHQw7zybZPSNWl4mLlwpp2a7A2tWb85ktM23mVog6WbBtY77Uad/S0pFQtk7dd4bfd1/RdMOM7VaJmLk/nT07V8dOGi8w7EAxAzRKF+PXdyhkGlx8NfsCv26/oT8k30kCrikWITEhhz+UIzIyNmN61Ck2e0/rzZHdUI29n/uiRdXeUoijMOj2LGadnAPB+2fcBWHhxIQCNijVidO3RWJu+nO7wnDhw9wAfb/0YgEn1J9HYo3GWZX84+APLLi+jjEMZlrRagrHR6/t9y6n/TLj5+eefGTduHLt27aJixYqZlhkxYgQjR47MMP1VnS2VmKJl7am7/LHvOpfDHt+zxMrMmKEty/Kef7FstWDcfhhPtz+PEHQvDkcbM/76wJ9yRbJ/GrFWp9D5t4Mcu/EQf6+C/N2nOsZGGuaem8vE4xOzvZy/mv1FFZcqAIRGJdJq6l7uxSbTzrcIkzr7otFo0l07Z1yHivoLuZ2OOM37G97XL8vPxY8pDaZgZ56917Hu9F2+XXGG+GQtRewsmPG+3wtdoCptp1O2sC3rB6jjXkKiEvhu5Vl2PupeLO9my7gOlfThLM2tB/H8dSCYJUdvEfPoUvu2Fia8G1CM3rWL41Qge8ErrSXNwcqU3d80wPY5TdvJqTq6/H6QEzcj8Slsy8q+NTl/N4qhq84RGBoDqC1Po9qV138/dgSG8cG8Y9hbmXJoSCPMTYxouaolt2Ju8VPtn2hdonW6dby/4X1OR5zmiypf8GGFDwG19abbn4dxd7BiwjuVMuygFUXho60fcSjkkH5alzJdGBIwRH/0q9MpfPbPSf49E4KthQlT3q1MgzIvdlHInFp0+AZDV53D1sKEnYPqU8jGnFRdKsP2DyM8Ppwfa/1IYZuMZ5WsPnmHgUtPoVPA192exX0C0g1yv/zwMj029iA2JRYNGhQUuvt05+tqXxOfnErD8bsJjU7McnzOy5Sq1fEwPgVjIw3GRhpMjTWYGBlhaqxJt+25GBLNl0tO6b9HHaoUZXgbn+d+J7Nj7em7DH7023WxNWdG1ypUKebAgWv3+XX7FQ4HqQd7xkYa3q7sRt/6JSjuZENyqo7P/j7JpvOhmBhpmNTZl9aVMrZYpJmx6yrjNj2/O0qn6Pjl6C/6INPPtx8fV/wYjUbDqiur+PHQj6ToUihpX5JfG/6Ke4G8aWlM0aVw+eFlToefJigqCO+C3tRyq4WrddYtevcS7tFxbUfuJ97nndLv8H2N75+5joeJD2m5qiUxyTEMqzGMTqU75UndXwdvTLh5kW6p8ePHM2rUKLZt20bVqlWzLJdfLTdPUxSFvVfu8deBYIyMNAxr5YN7wcwH2d2Mvsnlh5dpWKxhumbRe7FJdP/zCBdCoilgYcKcntWyfQXUmbuuMXZTIDbmJmz8vA7uBa249OASXdZ3IVWXStsSbSlkWQitTotW0aJTdPp/dYqOq5FXOR1xGi87L5a1Xoa5sbrzPnT9Pl3/OIxWpzCitQ8tKhSmxa/7uBebREe/oozvpF5fIVWXyrvr3yXwQSDVXKtx4f4F4lLiKG5XnJmNZ2baxJqZy2ExfLLgONfvxWFmbMRP7SvQ8TnNzpl5GJdM7bE7iEvWZuguUBSFVSfvMHLdBaISUjAx0tC3QUn6NyjJ6duRzNkXxObzofoLHxZ3tKZXLU86+D37+hmZSdXqaDZlL1fDY7M1uHvkuvPM3R9MAQsT1g+oox+omarVMe9AMJO2XiYuWYuRBnrU9GRgk9J8/s8pdgSG06eOF0Nb+nAq/BTdNnbD0sSSXe/swso0/fdw1ZVVDDswjGIFivHv2/9mK3ynDUY2MzKjV/le/H7mdxQU2pRow8iaI/VjAxJTtHT94zDHb6jdjJ38ivK/Vj7ZvqzAi4iKT6H++J08jE9hRGsf/WDHtFYsABcrF35r8hsl7EtkmH9noHpNnAENS6Yb+xYaF0rXDV0Jjw+ninMVupfrzhc7v8BYY8w/rf7Bu6C3fgCypakx27+qR5FntNTmVkxiCtcj4rgWEas+wuO4fi+W4HvxWQ4cNdKAibERpkYaElK06BT1wpo/ta9A01yMb3uY+JBz987haeeZIRBcCYvhk4XHuRYRh4mRhtIuBfRduKbGGjr6udO3fokM28VUrY6vlp1mzam7GGlgXMdKmf7ms9sdlapLZcSBEay5pu5fBvsPpmvZrunKnI44zRc7v+Bewj3szO0YX2881QtXz9X7cSbiDKciTnE64jTn7p0jITXjNW9K2pekjlsdarvVprJzZUyN1d+DTtHRd1tf9t/dT0n7kvzd8m8sTJ7fHbbo4iJ+PvIzDuYOrHt7XbYPIF93b0y4AXVAsb+/P1OnTgXUAcXFihWjf//+WQ4oHjduHKNHj2bz5s1Ur56zL9zrfJ0bgEsPLtFzU09iU2Lp59uPTyp9ku756MQUes87xpHgB1iYGtGhSlGalnOlevFCWQ4MvhgSTZtp+0jRKvprJSRpk+jybxeuRl6lvnt9fm3w6zN3YlFJUbRd3Zb7iff5qOJHDKg8QP/cH3uvM2r9RUyMNJR0tiEwNAZv1wKs6ltL31WV9mOzNbNl3dvriIiPoO/2voTHh+No6cj0RtPxKeSTrfcoJjGFQctOs/l8GCZGGlb1rUWFojn78Y7bFMiMXdfwKWzL+s9qZ/raw2MS+X71OTafDwPUlpnoxMc3RKxd0pEPa3tRr7TTc88AikqK4vLDywQ+CORa5DX8Xf1pUbwFAFsvhNFn/jEsTI3YNaiBfkD509afCaHf4hMAzO5eNdMm+tCoRH5cf4H1j07ZdS5gTkRsEooCOwfVx8vRmh8P/sjSy0tpU6INo2uPzrCM+JR4Gi5rSFxKHHOazqGaa7VnvraY5BjarG7DvYR79PXty6eVPmX99fUM3TcUraKlcbHGjK07Vt8FmZCs5ZfNl5h7IAhFAVdbC8a0r/DCt/Z4nhFrzzPvQDClXWzY8FkdTIyNOBp6lN5beqNTdDhZOhGREIGduR3TG02nktPzL3wWnRxNj409uBp5leJ2xZnffD525nZ8tesrttzYQkXHiixosQANGt757SBHgx/SulIRpr5bOctlpmh1HLp+n20Xwrj31PVSMvMwLplrEbGERSc9t+zzNPFxYUz7Ctm6JlHa+LmT4Sc5Hnack+EnuR51HQATIxO+rfYtnct0Tvfbik1KZfCKM/pTys1NjHjXvxgf1yv+zLGEWp3C0FVn+efoLQB+bFeebtUfn3qf+uisoNO3o2jo7cyfWXRHJWuT+WbPN2y/uR1jjTE/1PqBNiXaZLrOsLgwvtz1JWfvncVYY8ygqoPoWrZrltvJuJQ4/W/8/L3znI44TXB0cIZyBcwKUNGpIl62XpyJOMPZe2d5NIIFACsTK6oXrk4tt1qEx4fz25nfsDC24O+Wf1PSoWSW79GTUnQpdFrbiWtR13i/7Pt86/9ttuZ73b1R4WbJkiX06NGD3377DX9/fyZPnszSpUsJDAzExcWF7t274+bmxpgxYwAYO3Ysw4YNY/HixdSq9XjQoo2NDTY2NlmtRu9lhputN7ZS2blypqcSZsetmFt039idewlqv7MGDbMaz6KmW8105RKStfRffILtgY8H3dlamNCorAtNy7lSr7STPlQkpWppO20/gaExNPFx4fdufmg0Gn45+gvzL8ynoEVBVrZZSSHL549/2BK8ha92f4WJxoR/Wv1DmYLqWS+KojDg75P6DZa1mTFrB9SmxKP7tUTER9B6dWviUuL4vvr3vFPmHUA94u27vS9XHl7B0sSSCfUmUKdonWy9V4qi0G/xCTacDaWEkzX/DqiT5cC4pz2IS6bOo1ab37v5PfMMLEVR2HA2lGFrznE/LhkzEyPe9nXjg9pemQ7GTtvgX3pwicCHgQQ+COTyg8vcjUt/A0oTjQmr263Gw9YDRVHoNEvtMuxc1Z2xHTN2sV6LiKXN1H3EJWv5pF4JBjd/dgvPnssRDFtzjuBH44Bql3RkYe8AkrXJNFjagOjkaH5v8js1itTIdP6RB0ey/PJyWni1YGzdsZmWSZM2jsrD1oOVbVbqQ8yOmzsYtHsQKboUahapyaT6k9K1Eh0NfsA3y88QdC8OgI5+Rfn+JbXiBIZG0/LXfWh1Cot7B1CzpCORiZF0WNeB8Phw2pRow9dVv6bf9n6cuXcGSxNLJtWflGFg9JOStcl8su0TjoYexcnSiYUtFupbIMPiwmi7pm267/y5O1G0nrYPRYGlH9fA3+txq2tiipa9V+6x6Vwo2y6G6a/zlFNOBcwp4WRNCScb9eFsQwkna4o8Cg4pOh2pWoVUnUKqVkeqTiFFq0OrUzA1Nnpui9LVh1c5EnqEk+EnORF2gvCE8AxlnK2cCY9Xp7fwasHwGsPTfe6KorDs+G3uRibwXkAxnAtkb2CuTqfww78X9ON3hrYoq79qcFrL9LO6o+JT4vl85+ccCjmEqZEp4+uNp2Gxhs9cZ5I2iR8O/sDaa2sBaFuiLd/X+J7IxEguPbxE4AP1N37pwSVuxtzMdBnF7YpTyakSvs6+VHKqhJedV7oW+cjESA7cPcC+O/vYf3c/DxIzXucmN91LaeN0jDXGrGizItPWSFAPTnbc3MHGoI3ciL5BLbdatCreikpOlV67s/veqHADMG3aNP1F/Hx9ffn1118JCFCv3Fq/fn08PT2ZN28eAJ6enty4cSPDMoYPH86IESOeu66XFW7239lPv+39cLFyYXqj6dlO2GnuJ9yn+8bu3Iy5SSmHUng7eLPu+jrsze1Z2mpphnEAOp3C3qvqxnDrhVD9oGVQzy6oV9qJpuVcOXsnirn7gylkbcbmL+viaGPO4ZDD9N7SG4BpDadRz71etuqoKApf7PyCHbd2UL5QeRa2WKgfrBaXlEqHmQe4HBbDr++q10NJ883ub9gYvJEKjhVY0HxBugFuMckxfLnrSw6HHMZYY8z/qv+PjqU7Zqs+D+OSaTp5D+ExSTm6UNrPGwOZtfsa5d3Ua8xk5wf8IC6ZvVciqF3SMcuBzOuurWP8sfGZbpwA3Gzc8C7ozd3Yu1x8cJEmHk2YWF8d73T8xgM6zDyIkQY2f1GXUk9cEyU+OZV20/dzOSyW6sULsvDDgGxdWCsxRcvve66z/WIYP7QtTyV3e7bd2MaXu77E2dKZLR23ZDnY8Py983RZ3wUzIzN2vLMjy2btC/cv8O76d9EpukzD0sG7B/l85+ckpCZQ2bky0xtNp4DZ49eWkKxlwpZL/LlfbcVxsTVnTPsKNPTO/mnDz6MoCu/NPszB6/dpXt6Vme/7oSgKn+38jF23duFp68mSVkuwMrUiPiWeL3d9yYG7BzDRmDC69mh9C9uTdIqOwXsHszFoI9am1sxrNg/vgukDZ1prZQHTAqx9ey2Olo58t+osiw/fpGxhW/75qDq7L0ew+VwoOy+FE5/8+FRdRxszmvi4UsbF5rnfT2tzE0o4WVPcyealde+dCDvBzNMz042pAjWk+zj6UMW5ClWcq+Dr7Iu9uT3zL8xn0vFJaBUtJexKMLH+RIrb5+xeajpFR2JqYoZgNG7zJWbuugbAwCalaV7elZaPuqPGd8q8yyoqKYq+2/tyJkINrlMbTiWgcPauDq4oCgsvLmT8sfHoFB3mxuYkaTNvJXOxcsG7oDfeBb2p6FSRSk6VctQlpFN0XHxwkX2397Hvzj7O3DtDS6+WjK49OldB47Mdn7Hz1k5qFqnJrMaz9MuIT4lnz+09bAzayN47e0nRZQzTRW2K0qpEK1p6tcTTzjPH634Z3rhw8yq9rHATHBVMv+39uBlzExtTGybUm5ChxSUrscmxfLD5Ay4+uIibjZu+abv7xu5cuH+BCo4VmNdsXpZnFml1CiduPmTzuVA2nQ/l9sOMfbppLRTRydF0WNuB0LhQOpbuyPAaw3P0OsPjw2m3uh0xKTF8XfVrupd7fG2h5FQd4TGJFHV4vDE6ePcgH239CCONEX+3/DvTrqcUbQojDo7QHx31qdCHAZUHZOvHvPtyBD3mHAFg/gf+zz1d9H5sEnXG7SQ+Wcsf3avS2MeFtJ9Abo9S4lPiGX14tL7+pkamlLQvSZmCZfAu6E0ZhzKULlgaWzP1+3bl4RU6ruuITtGlG6D98YJjbD4fRuOyLvzRQx1HpigKA5eeZtXJOzgXMOffz2pn+0g3M5/v+Jwdt3bQq3wvBvoNzLKcoih0WteJSw8vZTomAUCr0/L+hvc5d/8czT2bM67euEyXdSr8FH239yUmOYayBcsyq8ksClqkHyt2/MYDvl52huuPWnE6VCnKsFY+eXLbgo1nQ/h00QnMTYzYNrAe7gWtWHxxMWOOjMHUyJRFLRZRtlBZffkUbQpD9w1lY/BGNGj41v/bDK9/4vGJzD03FxONCdMbT6dmkYy/da1Oy3sb3uPC/Qu0LN6Sn+v8zIO4ZOr/spPoJ06rT1PEzoKm5V1pVs6Vqp4FX4t7qx0NPcqs07M4Eqr+xkw0JvgX9lfDjEsVyjuWx9Ik89aeE2EnGLR7EBEJEViaWPJDzR9o5tXsueuMSopi9dXVLLm0hJDYEL6u9jXvlX1P/7yiKEzbcZUJW9W70DtYmfIwPiXL7qgnW4jtzO2Y2WgmFZyef5bc0w7ePcig3YOITo7GWGOs3oW7YBm8HbzxLqT+zh0sMl59/UWk6FIw0Zjkett0K/oWbde0JUWXwvh64zE1MmVT0CZ23d6VbuxPcbviNPdqTmmH0my/uZ2tN7ame76CYwVaFm9JM89m2Wrlf1kk3DzDy+yWikyM5POdn3Mi/ATGGmO+C/hO3wWTlWRtMn239eVw6GEczB2Y33y+PiXfib3DO+veITo5ms5lOvO/6v97bh0UReFCSDSbz4Wy+XwYl8Ji9FekBRi8dzDrr6/HvYA7y1svzzCYNDuWX17OyIMjsTSxZGWblRQtkPnAvWRtMh3WdiA4Opj3vN9jSMCQZ9Z7xukZzDo9C4DWxVszsuZI/cC6Zxm+5hx/HbyBcwFzNn9R95kXOhyz8SK/7b5OxaJ2rOlXC41Gw/ADw9kUtInO3p35sPyHOTrSCnwQyNe7vyY4OhgjjRGfVPqE3uV7P7feIw6MYMWVFVRwrMCiFovQaDRci4jlrUl70OoUln1Sg2qeBVl46Ab/W30OYyMNf/epnq4rI6ciEyNpsKwBqbpUVrZZSSmHZ5+1kxYASjmUYkXrFRk2sEsClzDq8ChsTG1Y226t/lo5mQl8EMjHWz/mQeIDvOy8mN1kNi7W6VtnElPUVpw/9qmtOM4FzHnXvxhNfFwoV8Q2xxv4Ww/i2Xw+lN/3XCc8JonPGpViYJPS6e6m/G21b3nf5/0M8+oUHT8f+Zm/A/8G4OOKH9PPtx8ajUb/vgCMrj06yzEboLaAvbv+XRQUZr81m+qFq/PXgWCGr1VvNurlaE2zR4GmYlG716IbQFEUjoQeYebpmRwPOw6oY2jeLvk2vSv0zvbgf1DP9Pl2z7f6cNS1bFe+8vsq09/H+Xvn+TvwbzYFb8rQMtK1bFe+rvp1upbGtLF+AAUsTNj6Zb0M49VOhJ3gy11f8iDxAU6WTvzW5Lfnfu+fJTY5ltuxt/G09czWwN7XweTjk/nz3J8Zphe1KUpzr+Y082pGKftS6b578Snx7Lq1i3+v/8uBuwfQKmqrorHGGH9Xf2zMbEjWJpOoTSQpNYkk7VOP1CTKOZZj9luz8/S1SLh5hpc9oDhZm8zIgyP1R/Hdfboz0G9gps3/Wp2Wb/Z8w5YbW7AysWJO0zmUc0zftbL39l76be+HgpLpabvPExmfjJ2lKRqNhk1Bm/h6z9cYaYz4q9lf+Dr75uo16hQdH27+kGNhx6heuDq/N/k9043y72d+Z+rJqRSyKMS6t9el647IysorK/nh4A9oFS3eBb35vvr3VHTK/DT/NAnJWlpN3cu1iDhaVijMtPcqZ1qfe7FJ1Bm7k4QULXN6VqWhtwu7b+2m/47HlzAvYFqAXuV70bVs12cGP0VR+OfSP4w/Op5kXTLOVs6MrTNWf9Gs57mXcI8WK1uQkJrAL3V/0R/RpnVbVC5mz4jW5eg06yDJWp3+ukQv4p/Afxh9eDTeBb1Z1nrZc8tHJUXRaFkjkrRJLG6xON3R7r2Ee7RZ1YaYlJgsW3aeFhQVRJ8tfQiLD8PNxo0J9SZk+L5DxlYcADd7SxqXdeatcq74exXM9NpDiqIQGBrD5vNqsL/46EwcUO96v+nzuiiaJDr/25ng6GDqFa3H1IZTn30dlDOzmHFKvQ5Kp9KdqF64OoN2D0JB4bPKn9GnYp/nvu6fDv/E34F/42HrwYo2KzAzMmPPlXu42JpTxqXAaxFoQH29B0MOMuv0LE6GnwTUVsj2pdrTu0LvZ56u/CypulSmn5rOH2f/AKCiU0Um1JuAq7UriamJbArexJLAJZy7f04/j3dBb7qU6cKDxAf8evJXAOoWrcu4uuPSXXfm7yM3+X3Pdb5t5p3hAolLLy1lzJExpOpSKeNQhikNp+Bmk/Mrgb/p4lPiabumLaFxobhYudDMsxnNvZrjUyjrK2s/6X7CfTYFb2L99fWcvXc22+stX6g8f7f6+0WqnoGEm2d4FWdLKYrC7LOzmXpSPQOsvnt9xtYZm6HvePTh0Sy5tAQTIxNmNJqR5eDO6aemM+v0LCyMLVjUchGlHUrnuE5hcWG8vfZtYpJjMtyTJDduRN+gw9oOJGmTGFVrFG1Ltk33/O2Y27Rb044kbRI/1/mZlsVbZnvZ++7s49s93xKdHI0GDZ1Kd+KzKp89s0XlzO1I2s84QKpOYVLnSrxdOWNr0k8bLvL7nutUKmrH6n61SNQm0m51O+7G3aWJRxNuRN/g8kO1qbuQRSE+qvgRnUp3ynCUGZUUxbD9w9hxawcA9YvW58daP2JvYZ/t1wgw6/Qspp+ajpuNG2vbrcXM2Izw6ETq/bKLhBQtBcxNiElK5S0fF357NBD8RXTd0JUzEf9v787joq7zP4C/BoYZBgQBuVU8ElFxwRUVkcwUAqkQz2zjUWTuKoqulu4+UjO11cUuz1jcVtP8VVpaeFAehIqJByoiKGgeqCiXeDHcw8zn9wcxOaE4IDA4vp6Pxzwcvt853vNmZN7zOdPrdCfWZ84vcxB/OR5j3Mdg4aCFdY73tOuJTS9t0nuhsNySXPx171+Ro8yBqcQUE/80EZFekXVyXKFSIz49DwmZ+Tj4a5HO0vHW5lIM7eGIF3o5YbC7A34tUGLPmXzszSzQrqgL1KyZMqCzHYI9nRDWpz1sLWV1dlPWpxvh23PfYsmxJTozWsZ1H4f5A+fr9TtRVikRti0MN8tv6mxq2FrkFOcgOTcZOy/vRPrNmlWkZSYyjO0+FhN6T2h0UfNHB3IOYO6huVBWKWErt0VQ5yDsvrIb9ypr9l8zMzFDcOdgjPcYrzOYdc+VPZh3aB4q1ZXwsPXAZwGf1RuTSq1CdEo0tvxaU8AHdw7GB4M+aFQrtbEoKi9CQVkBetr1rLPqckNcuXcFh3MPAwDMpeaQm8q1F3OpOWSmMpib1hy3NLOstzW3MVjc1KMlp4Lvzt6NeYfmoUpThZ52PbF62GptU3zs6Vj8J+0/kECCj4Z8hOGdH94XrdaoMTVxKg7nHkYn607Y/NJmtJE9emZYLY3QYHLCZBzNOwrPdp74vxf/D2Ymjz+WYV3GOqxIXQFrmTW2j9yunSUmhMC0fdNw8PpB+Dr74n9B/2vwB/Ot8ltYdnKZtgXMztwOs/vNxstdX37oY61OvIBPE36FlVyK3W8/p7Pq801lJQZ/tA8VKg3Wv1mzH1Ztc62LpQu2hW2DudQcu7J34bNTn+F6yXUANYOAo/pE4cUuL8LUxBSnCk/hnwf/ifzSfJiZmGFWv1l4rcdrjSo8ylRlCI0LRWF5oXZ/J+D3LSGAmhaHHdOefeyBoleLr+LluJdhIjFB4rhEvWf0ncg/gQl7JkAhVWD/K/thaWaJlLwUTNw7ERJI8M1L36C3fe8GxXK34i7+fezf2HVlFwCgu213LHl2SZ0BubUqVGoculCEhMwCJJ4r0Bk8/0dyqQkGuzsg2NMJgT2ddLoo799NeW3QWgxwGaB3zLuv7MacX+agWlON5zs8j+VDl+vs66PP/f+R9A+YmZghLiwOnaw7PfpOzaRMVYaU/BQk30hGcm4ycpQ52nNyUznGdR+HCb0nwNGi6afm5yhzMOvALGTdztIec7V0xTiPcRjtPrrOWKxa6TfTMX3fdG330uqA1fBsV7fVr6i8CLMOzEJqYSokkODvff+Oib0ntprWMXo8LG7q0dLr3KQVpmHG/hm4XXEbjhaOiAmIQfrNdPzr6L8AAHN95+IvPf7yyMe5U3EHr8S/gvzSfAS6BWLZ88v0/g9bO2vD3NQc34V+hy5tm2anVpVGhdd+fA3nbp9DcOdgfDLkEwBA4rVEzNw/E1ITKb4f8T26tm3YLIn7Hc8/jsVHF2vXzxjgPADzBs574GNWqzUY99+aLSEGdrXDN38dqF1/ZnF8JtYeykafjjaImzoIl+5ewrid41AtqrFq6CoMdRv6++tSq/DDhR+wJn2Ndlp+N5tuGOgyEJvObYJaqOFm5YaPh3ys99o8D1O7YJ6VzAo/jfoJNuY2UFao8MKyg7hXrsL3UwbVWR25VuLVRBzOPYy/9PjLI2fn1bb++bf3x5rANXrHJ4RA6LZQXC2+ioV+CzHimREYs3MMsu9l6z0O7GH2XNmDJUeX4E7lHUglUkz2noyJf5pYb+Gt1gicunYHCZkFSMgswOWiUliZSxHQw7FmGQQPhwcupFjfbsr6Sr+ZjtSCVIzvMf6hg2gfRgiBKT9PQXJucr1duQ1xr/Ie9l7di+vK67CQWsDSzBKWZpawMLvv+m/HS1QlOJx7GMk3kpFamIpqze/rNUklUvRx7AP/9v4Y2W1ko5ey0FeluhIrU1cityQXI7uNxOD2g/Vq+btRcgPTEqfh4t2LUEgVWDp4qc5U7rNFZzFj/wwUlBXAyswKS59bqt38lYwDi5t6GGIRv+vK64hKjMLle5ehkCpQUV0BAdHgP7IZNzPwxu43UK2p1vmmX59Ldy9hfPx4VKor9S6kGiLzViZe+/E1qIUaq4augq+Lr7Z/929/+hv+3vfvj/0cKrUKX2Z+iTWn16BSXQmpiRQTPCdgktekOoP6rhSV4sVVv6CsSq1dB6NQWYHnPtqPCpUGGyb0x5DuDnhz95tILUzF0I5DsWrYqgc+b3l1Ob7J+gbrzqyDskqpPf5y15fx3sD3mmTPGbVGjVfiX8Gvd37VWWzrdmkVVGrNQ5ePry1YAcBEYoJR3UZhap+pD/y2LYRAyA8huFFyo8FdhADwxZkvsPzkcnjZe2Go21CsTF0JO3M77By1UzsDrLGKyouw+OhiJF5LBAD0atcLS/yX6L2UQmFxBWwsZPXubH//bsp9HftiXfC6BrW6NJWc4hyM2jGqUV21tVQaFZJvJGPHpR04kHPggVN49dG+TXs82/5ZDHIdpB0g+iRQVikxO2k2DucehgQSzOo3C2/0egPxl+Ox6MgiVKor0aVtF6wcurLJvsRR68Hiph6GWqG4uKoYsw/MxpG8IwCAsd3H4v2B7zf421vtDBVTiSnWBq2tM4BVpVbhdsVt7WVl6kpk3c6Cv6s/YgNjm6V5tnZarKOFI4Z2HIpvz38LV0tXbBu5rcHfcOtzXXkd0SnROHj9IICaP9ALBy2ssyz6ppRrmPPbzufbp/ljy4nr+CI5G392s8EPUwZh+6XtmJ88HwqpAtvDtj9wL6H73au8hw1nNyDpehIiekXUGV/0uGqny9+/sN/D/HGQq4etB87fOQ8AMDc1x+u9Xsdbvd/S+bBKLUhFxO4IWEgtcGD8gQb/TorKi/DClhdQLaphZmIGlUbVqMHt9b2mn7J/wr+P/RvFVcUwMzFDVJ8oRHhGNEkRUvv+tJZZY2vo1kf+vptT7SB7O3M77Bi5Q6+ZeUIIZN7OxM5LO7Ere5fOOkrutu7o79QflepKlKpKtZey6rKaf1VlKKsugwQS9HPuB39Xf/i394eblX572rVGKo0K0cd+H1Pj5eClHSs0pMMQRA+O1mvyAj15WNzUw5DbL6g0KqzLWAe1UCPSK7JRu7UKITD30FzEX46HnbkdfJx8cKv8lraYKa4qrnOftvK2+GHED83Shw4AFdUVGL1jtE7f/ephq/F8x+eb/LmEENh3bR+iU6JRUFYAU4kpFg1apFNwCCHw1y9PIPFcIbo5tkHO7TJUVmuw8a0B8HIzw4htI3Cn8g7e8XkHE3pPaPIYG2PKz1Nw6MYhnYX9/uiPm/1N7TMVkV6RSLuZhmUnliHtZhoAwFZui0jvSO1g6NrVhsOeCcPiZxc3Kr6397+Nn6/9DKCma3Bt0Nom/3AsLCvEoiOLtMWrl70X5g6c+8CxFfq4rryOD49/iAM5BwAAK55fgYBOAU0UbeNUqaswdudYZN/LhkKqgIPCAe0U7WCvsEc783ba6/YKe9jIbZBamIqdl3bi4t2L2sewM7fDS11fwohnRjx0nJKxE0JgY+ZGfHriU+1A77/96W+Y9udpjzVgllo3Fjf1aO17S+mjTFWG8J/Cdf7g3c9UYgpbc1u0M28HBwsHTPaa3Ohp3/qqHWQK1MwOWz1sdbM+X5mqDP86+i/EX44HgDqFyk1lJYavOIhbv+3N49PJFlsj/bDoyCJ8f+F7dLPphu9Cv2uSgdVN4WEL+9V61GZ/tUXfitQV2v1s3KzcENUnCouPLYaySom1QWv1XpX1jw7dOIQpP09pknFU9RFCYPul7fgw5UOUqEoAAAFuAYjqE6X3+iQV1RVYf2Y91p1ZV9ONKZFiap+pek3bbgmnCk9h+r7p2llC+pCZyDDMbRhCnwnFINdBBulWa432X9uPr7K+wqs9XsULnV4wdDjUzFjc1MMYihugZtXN+MvxsDSzhJ25HezM7dDOvB3szO1gLbc2yLeXmLQY/HL9Fyx/fnmLNP1rhAbLTy7HhrMbAABver6Jt33e1r722g0pAeCrib6wbFuzdxcAbAzZiD87PnzzQkN40MJ+QM0AzH8m/RP7cvY9crM/lUaFuAtx+E/af3Cr4pb2uJOFE/aO3dvo94UQAl9nfY0OVh2apUXuj/JL87EydSV+vPwjBAQkkCCkSwim9plab7ddUk4SlqYs1c52G+A8AHN95z50Xx1DKa8uR2FZIYrKi1BUXoRb5bdq/q24pXPd1dIVoc+EIqhz0GOPbyJ60rG4qYexFDf0u/Vn1mPZyZqunBHPjMDCQQu1LTKbUq6hrEqN1/3aY3z8eFy8exGj3Udj0aBFhgz5gR60sF+pqhQz9s/AsbxjkJnI8PGQjx+52R9Q07L15dkvsf7sepRXl9fZyf1JcenuJcSkxSDhagKAmlbJsG5hmOw1WWel3BxlDj5M+RBJ15MA1Gze+I9+/0Bw5+AndmwJEelicVMPFjfGafvF7VhweAHUQo3nOjyHT4Z8ojNwdsOZDfj05Kewkdtg58idDV5wr6Xcv7DfxpCNmLl/JjKKMmAhtcDqYasbtDYLUFMwpRWmYUjHIa2mC64xMm9lIiYtRjseR2oixVj3sXjD8w3suLQDX2R8gSpNFaQSKV73fB2RXpFP9aJtRMaIxU09WNwYr6ScJMxOmo0KdQW8HbwRExCDtvK2yCvJQ9j2MJRXl+ODQR9glPsoQ4f6UPcv7NfGrA1KVCWwkdsgNjC2wYvlGaO0wjR8lvYZjuUdq3NuoMtAzPGd02zjgYjIsBry+c1h5WQ0hnQcgv8F/Q9WMiucvnkaEbsikF+aj+iUaJRXl6OvY98mn8bd1CzMLLRrH5WoSuCocMSG4RtY2Pymj2MfrA1ai3VB6+Dt4A2gZjzRp0M+xecvfM7ChogAsOXG0OFQM7hw5wIiEyJRWF4IG7kN7lbehVQixZbQLXovDmdIao0as5Nm41bFLUQPjn4qN/vThxACF+5eQEerjk26nhIRtU7slqoHi5unQ25JLiYnTNZOi57YeyJm+sw0aExERNR47Jaip55rG1dsDNmI5zo8B18XX0zymmTokIiIqIVwJSgyWrbmtogJiDF0GERE1MLYckNERERGhcUNERERGRUWN0RERGRUWNwQERGRUWFxQ0REREaFxQ0REREZFRY3REREZFRY3BAREZFRYXFDRERERoXFDRERERkVFjdERERkVFjcEBERkVFhcUNERERGhcUNERERGRWpoQNoaUIIAEBxcbGBIyEiIiJ91X5u136O1+epK26USiUAoGPHjgaOhIiIiBpKqVSibdu29d5GIvQpgYyIRqNBbm4urKysIJFI9L5fcXExOnbsiJycHFhbWzdjhAQw3y2N+W5ZzHfLYr5bVnPlWwgBpVIJV1dXmJjUP6rmqWu5MTExQYcOHRp9f2tra/7naEHMd8tivlsW892ymO+W1Rz5flSLTS0OKCYiIiKjwuKGiIiIjAqLGz3J5XIsWLAAcrnc0KE8FZjvlsV8tyzmu2Ux3y2rNeT7qRtQTERERMaNLTdERERkVFjcEBERkVFhcUNERERGhcUNERERGRUWN3qIiYlB586dYW5uDl9fX6SkpBg6JKNw8OBBhIaGwtXVFRKJBNu2bdM5L4TA+++/DxcXFygUCgQGBuLChQuGCdYIREdHo3///rCysoKjoyNGjhyJ8+fP69ymoqICUVFRaNeuHdq0aYMxY8agoKDAQBE/2WJjY+Hl5aVdyMzPzw+7du3Snmeum9fSpUshkUgwc+ZM7THmvOksXLgQEolE59KjRw/teUPnmsXNI3z77bd45513sGDBAqSmpsLb2xvBwcEoLCw0dGhPvNLSUnh7eyMmJuaB5z/66COsWrUKa9aswbFjx2BpaYng4GBUVFS0cKTGISkpCVFRUTh69CgSEhKgUqkQFBSE0tJS7W3efvtt7Ny5E1u2bEFSUhJyc3MxevRoA0b95OrQoQOWLl2KkydP4sSJExg2bBjCwsJw9uxZAMx1czp+/Dj++9//wsvLS+c4c960PD09kZeXp70cOnRIe87guRZUrwEDBoioqCjtz2q1Wri6uoro6GgDRmV8AIi4uDjtzxqNRjg7O4uPP/5Ye+zu3btCLpeLTZs2GSBC41NYWCgAiKSkJCFETX7NzMzEli1btLfJysoSAMSRI0cMFaZRsbW1FWvXrmWum5FSqRTu7u4iISFBDBkyRMyYMUMIwfd3U1uwYIHw9vZ+4LnWkGu23NSjqqoKJ0+eRGBgoPaYiYkJAgMDceTIEQNGZvyys7ORn5+vk/u2bdvC19eXuW8i9+7dAwDY2dkBAE6ePAmVSqWT8x49esDNzY05f0xqtRqbN29GaWkp/Pz8mOtmFBUVhZdeekkntwDf383hwoULcHV1RdeuXREeHo5r164BaB25fuo2zmyIoqIiqNVqODk56Rx3cnLCuXPnDBTV0yE/Px8AHpj72nPUeBqNBjNnzoS/vz969+4NoCbnMpkMNjY2OrdlzhsvIyMDfn5+qKioQJs2bRAXF4devXohLS2NuW4GmzdvRmpqKo4fP17nHN/fTcvX1xcbNmyAh4cH8vLysGjRIgwePBhnzpxpFblmcUP0FIqKisKZM2d0+sip6Xl4eCAtLQ337t3D1q1bERERgaSkJEOHZZRycnIwY8YMJCQkwNzc3NDhGL2QkBDtdS8vL/j6+qJTp0747rvvoFAoDBhZDXZL1cPe3h6mpqZ1RngXFBTA2dnZQFE9HWrzy9w3vWnTpiE+Ph779+9Hhw4dtMednZ1RVVWFu3fv6tyeOW88mUyGbt26wcfHB9HR0fD29sbKlSuZ62Zw8uRJFBYWom/fvpBKpZBKpUhKSsKqVasglUrh5OTEnDcjGxsbdO/eHRcvXmwV728WN/WQyWTw8fFBYmKi9phGo0FiYiL8/PwMGJnx69KlC5ydnXVyX1xcjGPHjjH3jSSEwLRp0xAXF4d9+/ahS5cuOud9fHxgZmamk/Pz58/j2rVrzHkT0Wg0qKysZK6bQUBAADIyMpCWlqa99OvXD+Hh4drrzHnzKSkpwaVLl+Di4tI63t8tMmz5CbZ582Yhl8vFhg0bRGZmppg0aZKwsbER+fn5hg7tiadUKsWpU6fEqVOnBACxbNkycerUKXH16lUhhBBLly4VNjY2Yvv27SI9PV2EhYWJLl26iPLycgNH/mSaMmWKaNu2rThw4IDIy8vTXsrKyrS3iYyMFG5ubmLfvn3ixIkTws/PT/j5+Rkw6ifXu+++K5KSkkR2drZIT08X7777rpBIJGLv3r1CCOa6Jdw/W0oI5rwpzZo1Sxw4cEBkZ2eL5ORkERgYKOzt7UVhYaEQwvC5ZnGjh9WrVws3Nzchk8nEgAEDxNGjRw0dklHYv3+/AFDnEhERIYSomQ4+f/584eTkJORyuQgICBDnz583bNBPsAflGoBYv3699jbl5eVi6tSpwtbWVlhYWIhRo0aJvLw8wwX9BHvrrbdEp06dhEwmEw4ODiIgIEBb2AjBXLeEPxY3zHnTGT9+vHBxcREymUy0b99ejB8/Xly8eFF73tC5lgghRMu0ERERERE1P465ISIiIqPC4oaIiIiMCosbIiIiMiosboiIiMiosLghIiIio8LihoiIiIwKixsiIiIyKixuiOipJJFIsG3bNkOHQUTNgMUNEbW4N998ExKJpM5l+PDhhg6NiIyA1NABENHTafjw4Vi/fr3OMblcbqBoiMiYsOWGiAxCLpfD2dlZ52JrawugpssoNjYWISEhUCgU6Nq1K7Zu3apz/4yMDAwbNgwKhQLt2rXDpEmTUFJSonObL774Ap6enpDL5XBxccG0adN0zhcVFWHUqFGwsLCAu7s7duzYoT13584dhIeHw8HBAQqFAu7u7nWKMSJqnVjcEFGrNH/+fIwZMwanT59GeHg4Xn31VWRlZQEASktLERwcDFtbWxw/fhxbtmzBzz//rFO8xMbGIioqCpMmTUJGRgZ27NiBbt266TzHokWL8MorryA9PR0vvvgiwsPDcfv2be3zZ2ZmYteuXcjKykJsbCzs7e1bLgFE1HgttkUnEdFvIiIihKmpqbC0tNS5LFmyRAhRs4N5ZGSkzn18fX3FlClThBBCfP7558LW1laUlJRoz//444/CxMRE5OfnCyGEcHV1FfPmzXtoDADEe++9p/25pKREABC7du0SQggRGhoqJkyY0DQvmIhaFMfcEJFBDB06FLGxsTrH7OzstNf9/Px0zvn5+SEtLQ0AkJWVBW9vb1haWmrP+/v7Q6PR4Pz585BIJMjNzUVAQEC9MXh5eWmvW1pawtraGoWFhQCAKVOmYMyYMUhNTUVQUBBGjhyJQYMGNeq1ElHLYnFDRAZhaWlZp5uoqSgUCr1uZ2ZmpvOzRCKBRqMBAISEhODq1av46aefkJCQgICAAERFReGTTz5p8niJqGlxzA0RtUpHjx6t83PPnj0BAD179sTp06dRWlqqPZ+cnAwTExN4eHjAysoKnTt3RmJi4mPF4ODggIiICHz11VdYsWIFPv/888d6PCJqGWy5ISKDqKysRH5+vs4xqVSqHbS7ZcsW9OvXD88++yy+/vprpKSkYN26dQCA8PBwLFiwABEREVi4cCFu3ryJ6dOn4/XXX4eTkxMAYOHChYiMjISjoyNCQkKgVCqRnJyM6dOn6xXf+++/Dx8fH3h6eqKyshLx8fHa4oqIWjcWN0RkELt374aLi4vOMQ8PD5w7dw5AzUymzZs3Y+rUqXBxccGmTZvQq1cvAICFhQX27NmDGTNmoH///rCwsMCYMWOwbNky7WNFRESgoqICy5cvx+zZs2Fvb4+xY8fqHZ9MJsOcOXNw5coVKBQKDB48GJs3b26CV05EzU0ihBCGDoKI6H4SiQRxcXEYOXKkoUMhoicQx9wQERGRUWFxQ0REREaFY26IqNVhbzkRPQ623BAREZFRYXFDRERERoXFDRERERkVFjdERERkVFjcEBERkVFhcUNERERGhcUNERERGRUWN0RERGRUWNwQERGRUfl/xGf5gkSqJ2EAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"files.download('vit acc vs epoc.png')\n",
"files.download('vit sens spec f1.png')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"id": "LdWR-BSP5MFA",
"outputId": "6c76e5dc-a88a-41c7-c5bd-43854a46524a"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"download(\"download_ccd729e9-e023-4e8d-88e2-6a3f55e3aeea\", \"vit acc vs epoc.png\", 60816)"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"download(\"download_e3e34f16-faec-4713-bc3d-0b9d3dd427ca\", \"vit sens spec f1.png\", 42126)"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import roc_curve, auc\n",
"from sklearn.preprocessing import label_binarize\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"test_loss, test_accuracy = model.evaluate(test_batches)\n",
"print(f\"Test Accuracy: {test_accuracy * 100:.2f}%\")\n",
"\n",
"y_test = test_batches.labels # Get the true labels\n",
"# Binarize the output (e.g., for 5 classes)\n",
"y_test_bin = label_binarize(y_test, classes=[0, 1, 2, 3, 4])\n",
"n_classes = y_test_bin.shape[1] # Number of classes\n",
"\n",
"# Ensure predictions are also in the right shape\n",
"y_pred_probs = model.predict(test_batches) # Probabilities for all classes\n",
"\n",
"# Compute ROC curve and AUC for each class\n",
"fpr = {}\n",
"tpr = {}\n",
"roc_auc = {}\n",
"\n",
"for i in range(n_classes):\n",
" fpr[i], tpr[i], _ = roc_curve(y_test_bin[:, i], y_pred_probs[:, i])\n",
" roc_auc[i] = auc(fpr[i], tpr[i])\n",
"\n",
"# Plot all ROC curves\n",
"plt.figure()\n",
"colors = ['blue', 'green', 'red', 'cyan', 'magenta']\n",
"for i in range(n_classes):\n",
" plt.plot(fpr[i], tpr[i], color=colors[i], lw=2,\n",
" label=f'Class {i} (AUC = {roc_auc[i]:.2f})')\n",
"\n",
"# Add random guess line\n",
"plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive Rate')\n",
"plt.title('Receiver Operating Characteristic for Multi-Class')\n",
"plt.legend(loc='lower right')\n",
"plt.savefig('vit roc curve.png')\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 524
},
"id": "PQ8gHuUISNdr",
"outputId": "ed4c37aa-eb86-4633-b2b0-dcad0cf18907"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 779ms/step - accuracy: 0.6788 - loss: 0.8523\n",
"Test Accuracy: 70.91%\n",
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 636ms/step\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from google.colab import files\n",
"files.download('vit roc curve.png')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"id": "8TPOT0e6b4qg",
"outputId": "17852944-3fc9-44e6-8c72-d5ca52bcf3b8"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"download(\"download_9aa4e716-98b7-40aa-8c25-912effb77065\", \"vit roc curve.png\", 52573)"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import classification_report\n",
"y_pred = np.argmax(y_pred_probs, axis=1) # Predicted class labels\n",
"print(classification_report(y_test, y_pred, target_names=test_batches.class_indices.keys()))\n",
"from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
"cm = confusion_matrix(y_test, y_pred)\n",
"disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=test_batches.class_indices.keys())\n",
"disp.plot(cmap=\"Blues\")\n",
"plt.savefig('vit conmat.png')\n",
"plt.show()\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 782
},
"id": "cylFWecC1oWl",
"outputId": "5008e945-d4fd-47f1-d673-98ee9f5aea0f"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
"/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1531: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" Mild 0.14 0.02 0.03 56\n",
" Moderate 0.27 0.44 0.33 150\n",
" No_DR 0.49 0.54 0.51 271\n",
"Proliferate_DR 0.00 0.00 0.00 44\n",
" Severe 0.00 0.00 0.00 29\n",
"\n",
" accuracy 0.39 550\n",
" macro avg 0.18 0.20 0.18 550\n",
" weighted avg 0.33 0.39 0.35 550\n",
"\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from google.colab import files\n",
"files.download('vit conmat.png')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"id": "giMldUx97Zgm",
"outputId": "6f25925d-1166-4d61-c307-3096192fb297"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"download(\"download_8c2a7add-24a3-4b36-8a36-46fd83e3d2a5\", \"vit conmat.png\", 27943)"
]
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"test_loss, test_accuracy = model.evaluate(test_batches)\n",
"print(f\"Test Accuracy: {test_accuracy * 100:.2f}%\")\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "CnBQWkox081u",
"outputId": "4f3f4c77-d776-4d13-8503-88d6a1057f1a"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1m18/18\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 499ms/step - accuracy: 0.7363 - loss: 0.7401\n",
"Test Accuracy: 71.82%\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from math import ceil, pow\n",
"src_dir = r'../root/.cache/kagglehub/datasets/sovitrath/diabetic-retinopathy-224x224-2019-data/versions/4/colored_images'\n",
"for i in range(5):\n",
" total_files = len(train)\n",
" denom = pow(2, i)\n",
" print(denom)\n",
" files_to_copy = ceil(total_files / denom) # Half the files, rounded up if odd\n",
" print(files_to_copy)\n",
"\n",
" if os.path.exists(train_dir):\n",
" shutil.rmtree(train_dir)\n",
" os.makedirs(train_dir, exist_ok=True)\n",
"\n",
" # Counter to track the number of files copied\n",
" copied_files = 0\n",
"\n",
" for index, row in train.iterrows():\n",
" if copied_files >= files_to_copy:\n",
" break # Stop when half of the files are copied\n",
"\n",
" diagnosis = row['type']\n",
" binary_diagnosis = row['binary_type']\n",
" id_code = row['id_code'] + \".png\"\n",
" srcfile = os.path.join(src_dir, diagnosis, id_code)\n",
" dstfile = os.path.join(train_dir, binary_diagnosis)\n",
" os.makedirs(dstfile, exist_ok=True)\n",
"\n",
" if os.path.exists(srcfile):\n",
" shutil.copy(srcfile, dstfile)\n",
" copied_files += 1 # Increment the counter\n",
"\n",
" for index, row in val.iterrows():\n",
" diagnosis = row['type']\n",
" binary_diagnosis = row['binary_type']\n",
" id_code = row['id_code'] + \".png\"\n",
" srcfile = os.path.join(src_dir, diagnosis, id_code)\n",
" dstfile = os.path.join(val_dir, binary_diagnosis)\n",
" os.makedirs(dstfile, exist_ok=True)\n",
" if os.path.exists(srcfile):\n",
" shutil.copy(srcfile, dstfile)\n",
"\n",
" for index, row in test.iterrows():\n",
" diagnosis = row['type']\n",
" binary_diagnosis = row['binary_type']\n",
" id_code = row['id_code'] + \".png\"\n",
" srcfile = os.path.join(src_dir, diagnosis, id_code)\n",
" dstfile = os.path.join(test_dir, binary_diagnosis)\n",
" os.makedirs(dstfile, exist_ok=True)\n",
" if os.path.exists(srcfile):\n",
" shutil.copy(srcfile, dstfile)\n",
"\n",
"\n",
"\n",
"\n",
"\n",
" for subdir in [train_dir, val_dir, test_dir]:\n",
" print(f\"\\nContents of {subdir}:\")\n",
" for root, dirs, files in os.walk(subdir):\n",
" print(f\"{root}: {len(files)} files\")\n",
"\n",
" train_path = train_dir\n",
" val_path = val_dir\n",
" test_path = test_dir\n",
"\n",
" from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"\n",
" # Define image size for ViT\n",
" IMG_SIZE = 224\n",
"\n",
" # ImageDataGenerator for preprocessing and augmentation\n",
" train_datagen = ImageDataGenerator(\n",
" rescale=1./255,\n",
" rotation_range=15,\n",
" width_shift_range=0.1,\n",
" height_shift_range=0.1,\n",
" shear_range=0.1,\n",
" zoom_range=0.2,\n",
" horizontal_flip=True,\n",
" fill_mode='nearest'\n",
" )\n",
"\n",
" val_datagen = ImageDataGenerator(rescale=1./255)\n",
"\n",
" train_batches = train_datagen.flow_from_directory(\n",
" train_dir,\n",
" target_size=(IMG_SIZE, IMG_SIZE),\n",
" batch_size=32,\n",
" class_mode='categorical'\n",
" )\n",
"\n",
" val_batches = val_datagen.flow_from_directory(\n",
" val_dir,\n",
" target_size=(IMG_SIZE, IMG_SIZE),\n",
" batch_size=32,\n",
" class_mode='categorical'\n",
" )\n",
"\n",
" test_batches = val_datagen.flow_from_directory(\n",
" test_dir,\n",
" target_size=(IMG_SIZE, IMG_SIZE),\n",
" batch_size=32,\n",
" class_mode='categorical'\n",
" )\n",
"\n",
" model = build_vit(input_shape=(224, 224, 3), num_classes=2)\n",
"\n",
"\n",
" model.compile(\n",
" optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy']\n",
" )\n",
"\n",
" history = model.fit(\n",
" train_batches,\n",
" epochs=10,\n",
" validation_data=val_batches\n",
" )"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "a0CI73EAS85J",
"outputId": "55ab3593-0c29-48e2-fcf6-77ba18ab7687"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1.0\n",
"2562\n",
"\n",
"Contents of .\\dataset/train:\n",
".\\dataset/train: 0 files\n",
".\\dataset/train/No_DR: 1263 files\n",
".\\dataset/train/DR: 1299 files\n",
"\n",
"Contents of .\\dataset/val:\n",
".\\dataset/val: 0 files\n",
".\\dataset/val/No_DR: 271 files\n",
".\\dataset/val/DR: 279 files\n",
"\n",
"Contents of .\\dataset/test:\n",
".\\dataset/test: 0 files\n",
".\\dataset/test/No_DR: 271 files\n",
".\\dataset/test/DR: 279 files\n",
"Found 2562 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Epoch 1/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m166s\u001b[0m 2s/step - accuracy: 0.5473 - loss: 0.6990 - val_accuracy: 0.5236 - val_loss: 0.6295\n",
"Epoch 2/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 2s/step - accuracy: 0.6094 - loss: 0.6367 - val_accuracy: 0.6527 - val_loss: 0.5635\n",
"Epoch 3/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m204s\u001b[0m 2s/step - accuracy: 0.6892 - loss: 0.5631 - val_accuracy: 0.8673 - val_loss: 0.2989\n",
"Epoch 4/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 2s/step - accuracy: 0.8897 - loss: 0.2919 - val_accuracy: 0.9418 - val_loss: 0.2133\n",
"Epoch 5/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 2s/step - accuracy: 0.8993 - loss: 0.2678 - val_accuracy: 0.9236 - val_loss: 0.2149\n",
"Epoch 6/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 2s/step - accuracy: 0.9125 - loss: 0.2261 - val_accuracy: 0.9418 - val_loss: 0.1868\n",
"Epoch 7/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 2s/step - accuracy: 0.9165 - loss: 0.2209 - val_accuracy: 0.9436 - val_loss: 0.1829\n",
"Epoch 8/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 2s/step - accuracy: 0.9066 - loss: 0.2540 - val_accuracy: 0.9436 - val_loss: 0.1968\n",
"Epoch 9/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m203s\u001b[0m 2s/step - accuracy: 0.9159 - loss: 0.2397 - val_accuracy: 0.9309 - val_loss: 0.2041\n",
"Epoch 10/10\n",
"\u001b[1m81/81\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m143s\u001b[0m 2s/step - accuracy: 0.9167 - loss: 0.2311 - val_accuracy: 0.9273 - val_loss: 0.2115\n",
"2.0\n",
"1281\n",
"\n",
"Contents of .\\dataset/train:\n",
".\\dataset/train: 0 files\n",
".\\dataset/train/No_DR: 633 files\n",
".\\dataset/train/DR: 648 files\n",
"\n",
"Contents of .\\dataset/val:\n",
".\\dataset/val: 0 files\n",
".\\dataset/val/No_DR: 271 files\n",
".\\dataset/val/DR: 279 files\n",
"\n",
"Contents of .\\dataset/test:\n",
".\\dataset/test: 0 files\n",
".\\dataset/test/No_DR: 271 files\n",
".\\dataset/test/DR: 279 files\n",
"Found 1281 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Epoch 1/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m102s\u001b[0m 2s/step - accuracy: 0.5381 - loss: 0.7419 - val_accuracy: 0.5945 - val_loss: 0.6350\n",
"Epoch 2/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 2s/step - accuracy: 0.5926 - loss: 0.6564 - val_accuracy: 0.6309 - val_loss: 0.6514\n",
"Epoch 3/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 2s/step - accuracy: 0.5882 - loss: 0.6618 - val_accuracy: 0.6345 - val_loss: 0.6493\n",
"Epoch 4/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 2s/step - accuracy: 0.6420 - loss: 0.6208 - val_accuracy: 0.6127 - val_loss: 0.5946\n",
"Epoch 5/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m77s\u001b[0m 2s/step - accuracy: 0.6443 - loss: 0.6133 - val_accuracy: 0.6200 - val_loss: 0.5749\n",
"Epoch 6/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 2s/step - accuracy: 0.7071 - loss: 0.5551 - val_accuracy: 0.8836 - val_loss: 0.3864\n",
"Epoch 7/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 2s/step - accuracy: 0.8003 - loss: 0.4265 - val_accuracy: 0.8909 - val_loss: 0.2268\n",
"Epoch 8/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 2s/step - accuracy: 0.8699 - loss: 0.3068 - val_accuracy: 0.9091 - val_loss: 0.2272\n",
"Epoch 9/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m76s\u001b[0m 2s/step - accuracy: 0.8995 - loss: 0.2755 - val_accuracy: 0.9327 - val_loss: 0.2326\n",
"Epoch 10/10\n",
"\u001b[1m41/41\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m79s\u001b[0m 2s/step - accuracy: 0.8821 - loss: 0.2698 - val_accuracy: 0.9400 - val_loss: 0.1942\n",
"4.0\n",
"641\n",
"\n",
"Contents of .\\dataset/train:\n",
".\\dataset/train: 0 files\n",
".\\dataset/train/No_DR: 316 files\n",
".\\dataset/train/DR: 325 files\n",
"\n",
"Contents of .\\dataset/val:\n",
".\\dataset/val: 0 files\n",
".\\dataset/val/No_DR: 271 files\n",
".\\dataset/val/DR: 279 files\n",
"\n",
"Contents of .\\dataset/test:\n",
".\\dataset/test: 0 files\n",
".\\dataset/test/No_DR: 271 files\n",
".\\dataset/test/DR: 279 files\n",
"Found 641 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Epoch 1/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 2s/step - accuracy: 0.5198 - loss: 0.7469 - val_accuracy: 0.6400 - val_loss: 0.6782\n",
"Epoch 2/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 2s/step - accuracy: 0.5440 - loss: 0.6979 - val_accuracy: 0.6309 - val_loss: 0.6504\n",
"Epoch 3/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 2s/step - accuracy: 0.5974 - loss: 0.6670 - val_accuracy: 0.6291 - val_loss: 0.6494\n",
"Epoch 4/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 2s/step - accuracy: 0.6265 - loss: 0.6647 - val_accuracy: 0.5509 - val_loss: 0.6311\n",
"Epoch 5/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 2s/step - accuracy: 0.6058 - loss: 0.6498 - val_accuracy: 0.6055 - val_loss: 0.6200\n",
"Epoch 6/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 2s/step - accuracy: 0.5676 - loss: 0.6684 - val_accuracy: 0.6309 - val_loss: 0.6235\n",
"Epoch 7/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 2s/step - accuracy: 0.6281 - loss: 0.6268 - val_accuracy: 0.6073 - val_loss: 0.5969\n",
"Epoch 8/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 2s/step - accuracy: 0.6578 - loss: 0.5855 - val_accuracy: 0.6327 - val_loss: 0.5996\n",
"Epoch 9/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 2s/step - accuracy: 0.6610 - loss: 0.6098 - val_accuracy: 0.6382 - val_loss: 0.5984\n",
"Epoch 10/10\n",
"\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m44s\u001b[0m 2s/step - accuracy: 0.7020 - loss: 0.5637 - val_accuracy: 0.6927 - val_loss: 0.5528\n",
"8.0\n",
"321\n",
"\n",
"Contents of .\\dataset/train:\n",
".\\dataset/train: 0 files\n",
".\\dataset/train/No_DR: 160 files\n",
".\\dataset/train/DR: 161 files\n",
"\n",
"Contents of .\\dataset/val:\n",
".\\dataset/val: 0 files\n",
".\\dataset/val/No_DR: 271 files\n",
".\\dataset/val/DR: 279 files\n",
"\n",
"Contents of .\\dataset/test:\n",
".\\dataset/test: 0 files\n",
".\\dataset/test/No_DR: 271 files\n",
".\\dataset/test/DR: 279 files\n",
"Found 321 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Epoch 1/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 2s/step - accuracy: 0.5432 - loss: 0.7991 - val_accuracy: 0.4927 - val_loss: 0.7263\n",
"Epoch 2/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 2s/step - accuracy: 0.5097 - loss: 0.7377 - val_accuracy: 0.5709 - val_loss: 0.6773\n",
"Epoch 3/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 2s/step - accuracy: 0.4834 - loss: 0.7112 - val_accuracy: 0.4927 - val_loss: 0.6949\n",
"Epoch 4/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 2s/step - accuracy: 0.5774 - loss: 0.6837 - val_accuracy: 0.4927 - val_loss: 0.6664\n",
"Epoch 5/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 2s/step - accuracy: 0.5739 - loss: 0.7198 - val_accuracy: 0.6382 - val_loss: 0.6522\n",
"Epoch 6/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m42s\u001b[0m 2s/step - accuracy: 0.5698 - loss: 0.6767 - val_accuracy: 0.6291 - val_loss: 0.6477\n",
"Epoch 7/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 2s/step - accuracy: 0.5697 - loss: 0.6826 - val_accuracy: 0.5582 - val_loss: 0.6470\n",
"Epoch 8/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 2s/step - accuracy: 0.5692 - loss: 0.6906 - val_accuracy: 0.6418 - val_loss: 0.6660\n",
"Epoch 9/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 2s/step - accuracy: 0.5299 - loss: 0.6941 - val_accuracy: 0.4545 - val_loss: 0.6325\n",
"Epoch 10/10\n",
"\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 2s/step - accuracy: 0.5234 - loss: 0.7074 - val_accuracy: 0.6309 - val_loss: 0.6422\n",
"16.0\n",
"161\n",
"\n",
"Contents of .\\dataset/train:\n",
".\\dataset/train: 0 files\n",
".\\dataset/train/No_DR: 82 files\n",
".\\dataset/train/DR: 79 files\n",
"\n",
"Contents of .\\dataset/val:\n",
".\\dataset/val: 0 files\n",
".\\dataset/val/No_DR: 271 files\n",
".\\dataset/val/DR: 279 files\n",
"\n",
"Contents of .\\dataset/test:\n",
".\\dataset/test: 0 files\n",
".\\dataset/test/No_DR: 271 files\n",
".\\dataset/test/DR: 279 files\n",
"Found 161 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Found 550 images belonging to 2 classes.\n",
"Epoch 1/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m37s\u001b[0m 3s/step - accuracy: 0.5475 - loss: 0.7219 - val_accuracy: 0.4927 - val_loss: 0.6887\n",
"Epoch 2/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 3s/step - accuracy: 0.6052 - loss: 0.7085 - val_accuracy: 0.4927 - val_loss: 0.7314\n",
"Epoch 3/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 3s/step - accuracy: 0.4888 - loss: 0.7350 - val_accuracy: 0.5073 - val_loss: 0.6983\n",
"Epoch 4/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m22s\u001b[0m 3s/step - accuracy: 0.4843 - loss: 0.7229 - val_accuracy: 0.5073 - val_loss: 0.7072\n",
"Epoch 5/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 3s/step - accuracy: 0.4714 - loss: 0.7491 - val_accuracy: 0.5073 - val_loss: 0.7004\n",
"Epoch 6/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 3s/step - accuracy: 0.4828 - loss: 0.7400 - val_accuracy: 0.5255 - val_loss: 0.6820\n",
"Epoch 7/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 3s/step - accuracy: 0.5376 - loss: 0.7210 - val_accuracy: 0.6309 - val_loss: 0.6734\n",
"Epoch 8/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 3s/step - accuracy: 0.6096 - loss: 0.6666 - val_accuracy: 0.5291 - val_loss: 0.6792\n",
"Epoch 9/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m18s\u001b[0m 3s/step - accuracy: 0.4550 - loss: 0.7474 - val_accuracy: 0.6218 - val_loss: 0.6675\n",
"Epoch 10/10\n",
"\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 3s/step - accuracy: 0.6348 - loss: 0.6555 - val_accuracy: 0.6309 - val_loss: 0.6581\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# create data\n",
"x = np.arange(5)\n",
"y1 = [80, 21, 19, 93, 73]\n",
"y2 = [80, 20, 20, 74, 72]\n",
"\n",
"width = 0.2\n",
"\n",
"# plot data in grouped manner of bar type\n",
"plt.bar(x-0.2, y1, width, color='#849abb')\n",
"plt.bar(x, y2, width, color='#bcdaf5')\n",
"\n",
"plt.xticks(x, ['F1 Score', 'Sensitivity', 'Specifity', 'Train acc', 'Val acc'])\n",
"plt.xlabel(\"Metrics\")\n",
"plt.ylabel(\"Scores\")\n",
"plt.legend([\"CNN\", \"ViT\"])\n",
"plt.savefig(\"CNN vs ViT Comparison.png\")\n",
"plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 449
},
"id": "tn-CR47VXZ_t",
"outputId": "73c72a85-1ca1-4a90-f87e-177c1e3b9b99"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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lEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlEWQAAIBlOSXIFBYWKiMjQzk5Oc7oDgAAoEpqFGT69++vl19+WZL022+/qWPHjurfv7/atm2rpUuXOrVAAACAitQoyHz66ae6/vrrJUnLli2TMUZHjhzRzJkzNWXKFKcWCAAAUJEaBZmCggKFhoZKklatWqWkpCT5+/urd+/e2rVrl1MLBAAAqEiNgkxkZKQ2btyooqIirVq1Srfccosk6fDhw/L19XVqgQAAABWpU5MbjRw5UsnJyQoMDFSTJk3UtWtXSX8ccoqNjXVmfQAAABWqUZB57LHHdM0112j//v26+eab5eHxx8RO8+bNWSMDAAAumBoFGUnq2LGj2rZtq9zcXLVo0UJ16tRR7969nVkbAABApWq0Rub48eMaPHiw/P391aZNG+3bt0+SNGLECE2fPt2pBQIAAFSkRkEmNTVV2dnZysrKcljcm5CQoLfffttpxQEAAFSmRoeWMjIy9Pbbb6tz586y2Wz29jZt2mjPnj1OKw4AAKAyNZqROXTokMLCwsq0FxUVOQQbAACA2lSjINOxY0etXLnSfv10ePnnP/+p+Ph451QGAABwDjU6tPTMM8+oZ8+e+uabb/T777/rpZde0jfffKMNGzZo7dq1zq4RAACgXDWakbnuuuuUnZ2t33//XbGxsVq9erXCwsK0ceNGxcXFObtGAACAclV7RubkyZN6+OGHNXbsWM2bN682agIAAKiSas/IeHl5aenSpbVRCwAAQLXU6NBSv379lJGR4eRSAAAAqqdGi32jo6M1adIkrV+/XnFxcQoICHDY/uc//9kpxQEAAFSmRkHm9ddfV926dbVt2zZt27bNYZvNZiPIAACAC6JGQSY3N9fZdQAAAFRbjdbInMkYI2OMM2oBAAColhoHmUWLFik2NlZ+fn7y8/NT27Zt9cYbbzizNgAAgErV6NDSCy+8oLFjx2r48OG69tprJUn//e9/9cgjj+jnn3/WqFGjnFokAABAeWoUZGbNmqU5c+ZowIAB9rbbbrtNbdq00YQJEwgyAADggqjRoaW8vDx16dKlTHuXLl2Ul5d33kUBAABURY2CTMuWLfXOO++UaX/77bcVHR193kUBAABURY0OLU2cOFF33323Pv30U/samfXr1yszM7PcgAMAAFAbajQjk5SUpM2bN6tBgwbKyMhQRkaGGjRooC1btuj22293do0AAADlqtGMjCTFxcXpzTffdGYtAAAA1VKjGZn3339fH374YZn2Dz/8UB988MF5FwUAAFAVNQoyY8aMUWlpaZl2Y4zGjBlz3kUBAABURY2CzK5du9S6desy7TExMdq9e/d5FwUAAFAVNQoyISEh+v7778u07969WwEBAeddFAAAQFXUKMj07dtXI0eO1J49e+xtu3fv1uOPP67bbrvNacUBAABUpkZB5rnnnlNAQIBiYmIUFRWlqKgoxcTEqH79+nr++eedXSMAAEC5anT6dUhIiDZs2KA1a9YoOztbfn5+ateuna6//npn1wcAAFChas3IbNy4UStWrJAk2Ww23XLLLQoLC9Pzzz+vpKQkDR06VCUlJbVSKAAAwNmqFWQmTZqkr7/+2n79yy+/1JAhQ3TzzTdrzJgxeu+99zRt2jSnFwkAAFCeagWZHTt2qHv37vbrixcv1jXXXKN58+Zp9OjRmjlzJt+1BAAALphqBZnDhw8rPDzcfn3t2rXq2bOn/frVV1+t/fv3O686AACASlQryISHhys3N1eSdOLECW3fvl2dO3e2bz969Ki8vLycWyEAAEAFqhVkevXqpTFjxmjdunVKTU2Vv7+/w5lKX3zxhVq0aOH0IgEAAMpTrdOvJ0+erDvuuEM33nijAgMDtXDhQnl7e9u3z58/X7fccovTiwQAAChPtYJMgwYN9Omnn6qgoECBgYHy9PR02L5kyRIFBgY6tUAAAICK1PgD8coTGhp6XsUAAABUR42+ogAAAMAdEGQAAIBlEWQAAIBlEWQAAIBluU2QmT59umw2m0aOHGlvKy4u1rBhw1S/fn0FBgYqKSlJBw4ccF2RAADArbhFkNm6dateffVVtW3b1qF91KhReu+997RkyRKtXbtWP/30k+644w4XVQkAANyNy4PMsWPHlJycrHnz5qlevXr29oKCAr3++ut64YUXdNNNNykuLk5paWnasGGDNm3aVGF/JSUlKiwsdLgAAICLk8uDzLBhw9S7d28lJCQ4tG/btk0nT550aI+JiVGTJk20cePGCvubNm2aQkJC7JfIyMhaqx0AALiWS4PM4sWLtX37dk2bNq3Mtvz8fHl7e6tu3boO7eHh4crPz6+wz9TUVBUUFNgvfBs3AAAXrxp9sq8z7N+/X3/5y1+0Zs0a+fr6Oq1fHx8f+fj4OK0/AADgvlw2I7Nt2zYdPHhQHTp0UJ06dVSnTh2tXbtWM2fOVJ06dRQeHq4TJ07oyJEjDrc7cOCAIiIiXFM0AABwKy6bkenevbu+/PJLh7YHH3xQMTEx+tvf/qbIyEh5eXkpMzNTSUlJkqSdO3dq3759io+Pd0XJAADAzbgsyAQFBenKK690aAsICFD9+vXt7YMHD9bo0aMVGhqq4OBgjRgxQvHx8ercubMrSgYAAG7GZUGmKl588UV5eHgoKSlJJSUlSkxM1CuvvOLqsgAAgJtwqyCTlZXlcN3X11ezZ8/W7NmzXVMQAABway7/HBkAAICaIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLquPKO582bZreffddffvtt/Lz81OXLl307LPP6oorrrDvU1xcrMcff1yLFy9WSUmJEhMT9corryg8PNyFlcPdTZ33kVP7e2pIglP7AwA4h0tnZNauXathw4Zp06ZNWrNmjU6ePKlbbrlFRUVF9n1GjRql9957T0uWLNHatWv1008/6Y477nBh1QAAwF24dEZm1apVDtcXLFigsLAwbdu2TTfccIMKCgr0+uuvKz09XTfddJMkKS0tTa1atdKmTZvUuXNnV5QNAADchFutkSkoKJAkhYaGSpK2bdumkydPKiHh/6b1Y2Ji1KRJE23cuLHcPkpKSlRYWOhwAQAAFyeXzsic6dSpUxo5cqSuvfZaXXnllZKk/Px8eXt7q27dug77hoeHKz8/v9x+pk2bpokTJ9Z2ubjEZO467vQ+u0f7O73PS5Gzx4ZxAazFbWZkhg0bpq+++kqLFy8+r35SU1NVUFBgv+zfv99JFQIAAHfjFjMyw4cP14oVK/Tpp5/qsssus7dHREToxIkTOnLkiMOszIEDBxQREVFuXz4+PvLx8antkgEAgBtw6YyMMUbDhw/XsmXL9PHHHysqKsphe1xcnLy8vJSZmWlv27lzp/bt26f4+PgLXS4AAHAzLp2RGTZsmNLT0/Wf//xHQUFB9nUvISEh8vPzU0hIiAYPHqzRo0crNDRUwcHBGjFihOLj4zljCbAgZ3++jyR17trF6X0CsA6XBpk5c+ZIkrp27erQnpaWpkGDBkmSXnzxRXl4eCgpKcnhA/EAAABcGmSMMefcx9fXV7Nnz9bs2bMvQEUAAMBK3OasJQAAgOoiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMsiyAAAAMuyRJCZPXu2mjVrJl9fX3Xq1ElbtmxxdUkAAMANuH2QefvttzV69GiNHz9e27dvV7t27ZSYmKiDBw+6ujQAAOBibh9kXnjhBQ0ZMkQPPvigWrdurblz58rf31/z5893dWkAAMDF6ri6gMqcOHFC27ZtU2pqqr3Nw8NDCQkJ2rhxY7m3KSkpUUlJif16QUGBJKmwsNDp9RX/VuT0PouOObfOwsLfndqfVTh7bJw9LtKlOTa8Z9wX7xn3dCm/Z07/3TbGVLqfWweZn3/+WaWlpQoPD3doDw8P17ffflvubaZNm6aJEyeWaY+MjKyVGgEAQO05evSoQkJCKtzu1kGmJlJTUzV69Gj79VOnTunXX39V/fr1ZbPZXFiZ8xQWFioyMlL79+9XcHCwq8vBGRgb98S4uC/Gxj25w7gYY3T06FE1bty40v3cOsg0aNBAnp6eOnDggEP7gQMHFBERUe5tfHx85OPj49BWt27d2irRpYKDg3njuynGxj0xLu6LsXFPrh6XymZiTnPrxb7e3t6Ki4tTZmamve3UqVPKzMxUfHy8CysDAADuwK1nZCRp9OjRGjhwoDp27KhrrrlGM2bMUFFRkR588EFXlwYAAFzM7YPM3XffrUOHDmncuHHKz8/XVVddpVWrVpVZAHwp8fHx0fjx48scQoPrMTbuiXFxX4yNe7LSuNjMuc5rAgAAcFNuvUYGAACgMgQZAABgWQQZAABgWQQZwAkGDRqkfv36VbpPVlaWbDabjhw5UqU+9+7dK5vNph07dpx3fSifzWZTRkaG/fq3336rzp07y9fXV1dddRVj4ATNmjXTjBkzXF0GKtG1a1eNHDnS1WXUGEHGCQYNGiSbzVbmsnv3bknSp59+qj59+qhx48ZlfnFWpLS0VNOnT1dMTIz8/PwUGhqqTp066Z///GctPxprOnTokB599FE1adJEPj4+ioiIUGJiotavX39B7v+ll17SggUL7NfL+8XQpUsX5eXlVekDnqQ/vlYjLy9PV155paTqByF35+oxk6S8vDz17NnTfn38+PEKCAjQzp07lZmZedGPwZnK+x125mXChAk16nfr1q0aOnSoc4uFJKlPnz7q0aNHudvWrVsnm82mL7744gJXdeG5/enXVtGjRw+lpaU5tDVs2FCSVFRUpHbt2iklJUV33HFHlfqbOHGiXn31Vb388svq2LGjCgsL9dlnn+nw4cNOr/20EydOyNvbu9b6r01JSUk6ceKEFi5cqObNm+vAgQPKzMzUL7/8ckHuvyrhxNvbu8JPpC6Pp6dntfa3GlePmaQyz++ePXvUu3dvNW3atMJ9LlZ5eXn2n99++22NGzdOO3futLcFBgbafzbGqLS0VHXqnPtPyOnfg3C+wYMHKykpST/++KMuu+wyh21paWnq2LGj2rZt66LqLiCD8zZw4EDTt2/fKu0rySxbtuyc+7Vr185MmDCh0n1KS0vNs88+a1q0aGG8vb1NZGSkmTJlin37F198Ybp162Z8fX1NaGioGTJkiDl69GiZuqdMmWIaNWpkmjVrZowxZt++feauu+4yISEhpl69eua2224zubm5VXp8rnD48GEjyWRlZVW6z+DBg02DBg1MUFCQ6datm9mxY4d9+/jx4027du3MokWLTNOmTU1wcLC5++67TWFhoX2fJUuWmCuvvNL+fHbv3t0cO3bMGOP4Ghg4cKCR5HDJzc01n3zyiZFkDh8+bAoKCoyvr695//33Hep89913TWBgoCkqKjK5ublGkvn888/tP595GThwoFm4cKEJDQ01xcXFDv307dvX3H///ef71NaaqoyZJPPKK6+YHj16GF9fXxMVFWWWLFnisE9VXquvv/66ad26tfH29jYRERFm2LBhDvdx+v149vM7fvz4i3oMKpOWlmZCQkLs10+/dt9//33ToUMH4+XlZT755BOze/duc9ttt5mwsDATEBBgOnbsaNasWePQV9OmTc2LL75ovy7JzJs3z/Tr18/4+fmZli1bmv/85z+V1rNo0SITFxdnAgMDTXh4uLn33nvNgQMHHPb56quvTO/evU1QUJAJDAw01113ndm9e7d9e2WvA6s6efKkCQ8PN5MnT3ZoP3r0qAkMDDRz5swxP//8s7nnnntM48aNjZ+fn7nyyitNenq6w/433nij+ctf/lLh/VRlnIuLi82TTz5pLrvsMuPt7W1atGhh/vnPf9q3n2t8zgeHltxURESEPv74Yx06dKjCfVJTUzV9+nSNHTtW33zzjdLT0+0fFFhUVKTExETVq1dPW7du1ZIlS/TRRx9p+PDhDn1kZmZq586dWrNmjVasWKGTJ08qMTFRQUFBWrdundavX6/AwED16NFDJ06cqNXHXFOBgYEKDAxURkaGSkpKyt3nrrvu0sGDB/XBBx9o27Zt6tChg7p3765ff/3Vvs+ePXuUkZGhFStWaMWKFVq7dq2mT58u6Y//Vu+9916lpKQoJydHWVlZuuOOO8r9evmXXnpJ8fHxGjJkiPLy8pSXl1fm29eDg4N16623Kj093aH9rbfeUr9+/eTv7+/QHhkZqaVLl0qSdu7cqby8PL300ku66667VFpaquXLl9v3PXjwoFauXKmUlJRqPIsXVlXGTJLGjh2rpKQkZWdnKzk5Wffcc49ycnIkqUqv1Tlz5mjYsGEaOnSovvzySy1fvlwtW7Ys977y8vLUpk0bPf7448rLy9MTTzzhsP1iG4OaGDNmjKZPn66cnBy1bdtWx44dU69evZSZmanPP/9cPXr0UJ8+fbRv375K+5k4caL69++vL774Qr169VJycrLDe/FsJ0+e1OTJk5Wdna2MjAzt3btXgwYNsm//3//+pxtuuEE+Pj76+OOPtW3bNqWkpOj333+XVL3XgZXUqVNHAwYM0IIFCxx+Fy1ZskSlpaW69957VVxcrLi4OK1cuVJfffWVhg4dqgceeEBbtmyp8v1UZZwHDBigf/3rX5o5c6ZycnL06quv2mfxzjU+580pcegSN3DgQOPp6WkCAgLslzvvvLPcfVXFGZmvv/7atGrVynh4eJjY2Fjz8MMPO/z3XlhYaHx8fMy8efPKvf1rr71m6tWrZ58xMMaYlStXGg8PD5Ofn2+vOzw83JSUlNj3eeONN8wVV1xhTp06ZW8rKSkxfn5+5sMPPzxn3a7y73//29SrV8/4+vqaLl26mNTUVJOdnW2MMWbdunUmODi4zH/MLVq0MK+++qox5o8ZGX9/f4cZmL/+9a+mU6dOxhhjtm3bZiSZvXv3lnv/Z8/KlfcfzpkzMsYYs2zZMvvsizHGPkvzwQcfGGOMw2xAebc/7dFHHzU9e/a0X//HP/5hmjdv7jCG7qiyMTPmj/fKI4884nCbTp06mUcffdQYU7XXauPGjc1TTz1VYQ1nvx/btWtnxo8fb79+sY9BRSqakcnIyDjnbdu0aWNmzZplv17ejMzTTz9tv37s2DEjyf66r4qtW7caSfYZ5tTUVBMVFWVOnDhR7v7neh1YWU5OjpFkPvnkE3vb9ddfX+lsYO/evc3jjz9uv36uGZnynDnOO3fuNJLKzNKcdq7xOV/MyDhJt27dtGPHDvtl5syZ59Vf69at9dVXX2nTpk1KSUnRwYMH1adPHz300EOSpJycHJWUlKh79+7l3j4nJ0ft2rVTQECAve3aa6/VqVOnHI57x8bGOqyLyc7O1u7duxUUFGT/rzk0NFTFxcXas2fPeT2m2pSUlKSffvpJy5cvV48ePZSVlaUOHTpowYIFys7O1rFjx1S/fn37YwoMDFRubq7DY2rWrJmCgoLs1xs1aqSDBw9Kktq1a6fu3bsrNjZWd911l+bNm3fe65V69eolLy8v+3/yS5cuVXBwsBISEqrVz5AhQ7R69Wr973//kyQtWLDAvgDdnVU2Zqed/eWw8fHx9hmZc71WDx48qJ9++qnC94gzWXUMqqtjx44O148dO6YnnnhCrVq1Ut26dRUYGKicnJxzzsicuW4jICBAwcHB9vdaebZt26Y+ffqoSZMmCgoK0o033ihJ9vvZsWOHrr/+enl5eZW57YV8HbhCTEyMunTpovnz50uSdu/erXXr1mnw4MGS/jhxZPLkyYqNjVVoaKgCAwP14YcfnnOMznSucd6xY4c8PT3t43K2ysbHGVjs6yQBAQFOn6r08PDQ1VdfrauvvlojR47Um2++qQceeEBPPfWU/Pz8nHIfZwYd6Y8XbFxcnN56660y+7r7oj1fX1/dfPPNuvnmmzV27Fg99NBDGj9+vB577DE1atRIWVlZZW5Tt25d+89nv8lsNptOnTol6Y+Ft2vWrNGGDRu0evVqzZo1S0899ZQ2b96sqKioGtXr7e2tO++8U+np6brnnnuUnp6uu+++u0oLKM/Uvn17tWvXTosWLdItt9yir7/+WitXrqxRTRdaRWN25mGDipzrterhceH+T7PyGFTH2b8vnnjiCa1Zs0bPP/+8WrZsKT8/P915553nPAxd2XvtbKcPkycmJuqtt95Sw4YNtW/fPiUmJtrvp7Lfh876XenOBg8erBEjRmj27NlKS0tTixYt7KHi73//u1566SXNmDFDsbGxCggI0MiRI6u1VOBc43yu57i2x4AZGQtp3bq1pD/e2NHR0fLz81NmZma5+7Zq1UrZ2dkqKiqyt61fv14eHh664oorKryPDh06aNeuXQoLC1PLli0dLlU9bdhdtG7dWkVFRerQoYPy8/NVp06dMo+pQYMGVe7PZrPp2muv1cSJE/X555/L29tby5YtK3dfb29vlZaWnrPP5ORkrVq1Sl9//bU+/vhjJScnV7jv6Zmz8vp96KGHtGDBAqWlpSkhIaHMmhyrOD1mp23atMlh+6ZNm9SqVStJ536tBgUFqVmzZhW+R2riUhiD6li/fr0GDRqk22+/XbGxsYqIiNDevXudeh/ffvutfvnlF02fPl3XX3+9YmJiyszetG3bVuvWrdPJkyfL3L42Xgfupn///vLw8FB6eroWLVqklJQU+2zg+vXr1bdvX91///1q166dmjdvru+++65a/Z9rnGNjY3Xq1CmtXbu23NtXNj7OQJC5AI4dO2Y/5CRJubm52rFjR6VTe3feeadefPFFbd68WT/88IOysrI0bNgwXX755YqJiZGvr6/+9re/6cknn9SiRYu0Z88ebdq0Sa+//rqkP/5A+vr6auDAgfrqq6/0ySefaMSIEXrggQcq/ebw5ORkNWjQQH379tW6deuUm5urrKws/fnPf9aPP/7o1OfFWX755RfddNNNevPNN/XFF18oNzdXS5Ys0XPPPae+ffsqISFB8fHx6tevn1avXq29e/dqw4YNeuqpp/TZZ59V6T42b96sZ555Rp999pn27dund999V4cOHbL/UT1bs2bNtHnzZu3du1c///xzhf9t3nDDDYqIiFBycrKioqLUqVOnCmto2rSpbDabVqxYoUOHDunYsWP2bffdd59+/PFHzZs3zxILTM81ZqctWbJE8+fP13fffafx48dry5Yt9gXrVXmtTpgwQf/4xz80c+ZM7dq1S9u3b9esWbNqXPfFNAbOEB0drXfffVc7duxQdna27rvvvgpf6zXVpEkTeXt7a9asWfr++++1fPlyTZ482WGf4cOHq7CwUPfcc48+++wz7dq1S2+88Yb9MLqzXwfuJjAwUHfffbdSU1OVl5fnMKMZHR1tn03OycnRww8/rAMHDlSr/3ONc7NmzTRw4EClpKQoIyPD/l585513JJ17fM5bray8ucSc6/Tr0wvlzr4MHDiwwtu89tprplu3bqZhw4bG29vbNGnSxAwaNMhhsWlpaamZMmWKadq0qfHy8jJNmjQxzzzzjH17VU+/PlteXp4ZMGCAadCggfHx8THNmzc3Q4YMMQUFBdV6Xi6U4uJiM2bMGNOhQwcTEhJi/P39zRVXXGGefvppc/z4cWPMH4ujR4wYYRo3bmy8vLxMZGSkSU5ONvv27TPG/N/p12d68cUXTdOmTY0xxnzzzTcmMTHRNGzY0Pj4+JjLL7/cYUHj2c/lzp07TefOnY2fn1+5p1+f6cknnzSSzLhx4xzaz15oaowxkyZNMhEREcZms5V5/TzwwAPlngbsjqoyZpLM7Nmzzc0332x8fHxMs2bNzNtvv+3QT1Veq3PnzjVXXHGF8fLyMo0aNTIjRoywb1M1F/sac/GMQWUqWux79ms3NzfXdOvWzfj5+ZnIyEjz8ssvl1k4Wt5i37NPeAgJCTFpaWkV1pOenm6aNWtmfHx8THx8vFm+fHmZccnOzja33HKL8ff3N0FBQeb66683e/bssW+v7HVwMdiwYYORZHr16uXQ/ssvv5i+ffuawMBAExYWZp5++mkzYMCAc56ccKaqjPNvv/1mRo0aZRo1amS8vb1Ny5Ytzfz58+3bzzU+58NmTDnnjwKwnO7du6tNmzbnvdDcXdhsNi1btuycX/3gTi62MQCsgMW+gMUdPnxYWVlZysrK0iuvvOLqci5JjAHgOgQZwOLat2+vw4cP69lnn610ITdqD2MAuA6HlgAAgGVx1hIAALAsggwAALAsggwAALAsggwAALAsggwAALAsggyAi5bNZlNGRoarywBQiwgyAGrVoEGDZLPZ9Mgjj5TZNmzYMNlstip927UkZWVlyWaz6ciRI1XaPy8vTz179qxGtQCshiADoNZFRkZq8eLF+u233+xtxcXFSk9PV5MmTZx+fydOnJAkRUREyMfHx+n9A3AfBBkAta5Dhw6KjIzUu+++a29799131aRJE7Vv397edurUKU2bNk1RUVHy8/NTu3bt9O9//1uStHfvXnXr1k2SVK9ePYeZnK5du2r48OEaOXKkGjRooMTEREllDy39+OOPuvfeexUaGqqAgAB17NhRmzdvliRlZ2erW7duCgoKUnBwsOLi4qr87egAXIevKABwQaSkpCgtLU3JycmSpPnz5+vBBx9UVlaWfZ9p06bpzTff1Ny5cxUdHa1PP/1U999/vxo2bKjrrrtOS5cuVVJSknbu3Kng4GD5+fnZb7tw4UI9+uijWr9+fbn3f+zYMd14443605/+pOXLlysiIkLbt2/XqVOnJEnJyclq37695syZI09PT+3YsUNeXl6194QAcAqCDIAL4v7771dqaqp++OEHSdL69eu1ePFie5ApKSnRM888o48++kjx8fGSpObNm+u///2vXn31Vd14440KDQ2VJIWFhalu3boO/UdHR+u5556r8P7T09N16NAhbd261d5Py5Yt7dv37dunv/71r4qJibH3B8D9EWQAXBANGzZU7969tWDBAhlj1Lt3bzVo0MC+fffu3Tp+/Lhuvvlmh9udOHHC4fBTReLi4irdvmPHDrVv394eYs42evRoPfTQQ3rjjTeUkJCgu+66Sy1atKjCIwPgSgQZABdMSkqKhg8fLkmaPXu2w7Zjx45JklauXKk//elPDtuqsmA3ICCg0u1nHoYqz4QJE3Tfffdp5cqV+uCDDzR+/HgtXrxYt99++znvG4DrsNgXwAXTo0cPnThxQidPnrQvyD2tdevW8vHx0b59+9SyZUuHS2RkpCTJ29tbklRaWlrt+27btq127NihX3/9tcJ9Lr/8co0aNUqrV6/WHXfcobS0tGrfD4ALiyAD4ILx9PRUTk6OvvnmG3l6ejpsCwoK0hNPPKFRo0Zp4cKF2rNnj7Zv365Zs2Zp4cKFkqSmTZvKZrNpxYoVOnTokH0WpyruvfdeRUREqF+/flq/fr2+//57LV26VBs3btRvv/2m4cOHKysrSz/88IPWr1+vrVu3qlWrVk59/ACcjyAD4IIKDg5WcHBwudsmT56ssWPHatq0aWrVqpV69OihlStXKioqSpL0pz/9SRMnTtSYMWMUHh5uP0xVFd7e3lq9erXCwsLUq1cvxcbGavr06fL09JSnp6d++eUXDRgwQJdffrn69++vnj17auLEiU55zABqj80YY1xdBAAAQE0wIwMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACzr/wF8CgGRmUZLpAAAAABJRU5ErkJggg==\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from google.colab import files\n",
"files.download('CNN vs ViT Comparison.png')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 17
},
"id": "8MlOReVlYpko",
"outputId": "effe9bd1-3639-490f-dce1-4fa23a35ced3"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"\n",
" async function download(id, filename, size) {\n",
" if (!google.colab.kernel.accessAllowed) {\n",
" return;\n",
" }\n",
" const div = document.createElement('div');\n",
" const label = document.createElement('label');\n",
" label.textContent = `Downloading \"${filename}\": `;\n",
" div.appendChild(label);\n",
" const progress = document.createElement('progress');\n",
" progress.max = size;\n",
" div.appendChild(progress);\n",
" document.body.appendChild(div);\n",
"\n",
" const buffers = [];\n",
" let downloaded = 0;\n",
"\n",
" const channel = await google.colab.kernel.comms.open(id);\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
"\n",
" for await (const message of channel.messages) {\n",
" // Send a message to notify the kernel that we're ready.\n",
" channel.send({})\n",
" if (message.buffers) {\n",
" for (const buffer of message.buffers) {\n",
" buffers.push(buffer);\n",
" downloaded += buffer.byteLength;\n",
" progress.value = downloaded;\n",
" }\n",
" }\n",
" }\n",
" const blob = new Blob(buffers, {type: 'application/binary'});\n",
" const a = document.createElement('a');\n",
" a.href = window.URL.createObjectURL(blob);\n",
" a.download = filename;\n",
" div.appendChild(a);\n",
" a.click();\n",
" div.remove();\n",
" }\n",
" "
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Javascript object>"
],
"application/javascript": [
"download(\"download_4d6a4db2-6739-4101-b179-153971f0b32d\", \"CNN vs ViT Comparison.png\", 13851)"
]
},
"metadata": {}
}
]
}
]
}