{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"

Convolutional Neural Network

\n\n\n* [Selamlar! / Greetings!](#0)\n\n\n* [CNN Nedir? / What is CNN?](#1)\n\n\n* [Genel Anlamda CNN / CNN in General](#2)\n\n - [Convolution Nedir? / What is Convolution?](#2.1)\n - [CNN ve ANN (Düzenli) karşılaştırması! / Comparison of CNN and ANN (Regular)!](#2.2)\n \n\n* [CNN İşleyişi / The Process of CNN](#3)\n\n - [Noktaların Seviyeleri / Feature Levels](#3.1)\n - [Kaç Katmana İhtiyaç Var? / How Many Layers Do You Need?](#3.2)\n - [Convolution İşlemi / Convolution Process](#3.3)\n - [PADDING Nedir? / What is PADDING?](#3.4)\n - [POOLING Katmanı / POOLING Layer](#3.5)\n - [MAX POOLING](#3.6)\n - [FLATTEN İşlemi / FLATTEN Process](#3.7)\n - [Fully-Connected Katman / Fully-Connected Layer](#3.8)\n - [Bütün Sürecin Görseli / İllustration of the Whole Process](#3.9)\n\n\n* [Projeler / Projects](#4)\n\n - [MNIST Veri Seti ile CNN / CNN with MNIST Data](#4.1)\n - [KERAS \"to categorical\"](#4.1.1)\n - [Earlystopping](#4.1.2)\n - [Batch Size / Epochs / Learning Rate](#4.1.3)\n - [Confusion Matrix / Classification Report](#4.1.4)\n - [Hadi Deneyelim / Let's Predict](#4.1.5)\n - [CIFAR-10 Veri Seti ile CNN / CNN with CIFAR-10 Data](#4.2)\n - [Image Classification / Data Augmentation](#4.3)\n - [ImageDataGenerator](#4.3.1)\n - [Data Augmentation](#4.3.2)\n - [Veri setlerini Oluşturma / Generating Datasets *pip install split-folders*](#4.3.3)\n - [flow_from_directory](#4.3.4)\n - [Hadi Deneyelim / Let's Predict](#4.3.5)\n \n \n* [Sonuç / Conclusion](#5)","metadata":{}},{"cell_type":"markdown","source":"# Merhaba Millet!!! \n\nBu projemde sizlere CNN'nin ne olduğunu, ne işe yaradığını ve bir CNN derin öğrenme algoritmasını aşama aşama nasıl inşa ettiğimizi anlatmaya çalışacağım. **Sakın panik yapmayın!** Bu notebookta ihtiyacınız olacak bütün açıklamaları bulacaksınız ve hatta CNN hakkında elinizin altında bir kitap olarak bile bulundurabileceksiniz.
\n\nKaggle platformu teorik kısım için, notebookumda çok fazla gömülü resim olduğundan dolayı kaydetmeye müsaade etmiyor. O yüzden teorik kısım için --> [lütfen buraya tıklayınız.](https://github.com/emir1031/03_PROJECTS/blob/main/05_CNN_Guide_From-Start-to-Finish_All-in-One/CNN_Guide_From-Start-to-Finish_All-in-One.ipynb)
\n\nDilerseniz bütün bir notebooku, bir üst satırda linkini verdiğim GitHub sayfamdan indirebilirsiniz.\n\n--> [Lütfen GitHub Sayfama Göz Atın](https://github.com/emir1031)\n\n# Hi Everyone! \n\nIn this project, I will try to explain to you what CNN is, what it does, and how we built a CNN deep learning algorithm step by step. **Don't panic!** In this notebook you will find all the explanations you will need and you will even have a book about CNN.
\n\nFor the theoretical part, kaggle platform does not allow saving because there are too many embedded images in my notebook. So please --> [click here for the theoretical part.](https://github.com/emir1031/03_PROJECTS/blob/main/05_CNN_Guide_From-Start-to-Finish_All-in-One/CNN_Guide_From-Start-to-Finish_All-in-One.ipynb)\n\nIf you wish, you can download a whole notebook from my GitHub page, which I have given the link above.
 \n\n--> [Please Browse My Github Profile](https://github.com/emir1031)","metadata":{}},{"cell_type":"markdown","source":"#
PROJECTS
","metadata":{}},{"cell_type":"markdown","source":"# CNN - MNIST ","metadata":{}},{"cell_type":"code","source":"# Gerekli kütüphaneler\n# Necessary Libraries\n\nimport pandas as pd \nimport seaborn as sns\nimport tensorflow as tf \nimport numpy as np \nimport matplotlib.pyplot as plt \n%matplotlib inline","metadata":{"id":"pyjqyftAzSfT","execution":{"iopub.status.busy":"2022-01-19T18:39:49.603801Z","iopub.execute_input":"2022-01-19T18:39:49.604418Z","iopub.status.idle":"2022-01-19T18:39:54.616471Z","shell.execute_reply.started":"2022-01-19T18:39:49.604320Z","shell.execute_reply":"2022-01-19T18:39:54.615695Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.datasets.mnist import load_data # To load the MNIST digit dataset (handwriting numbers)\n # MNIST rakamlar datasetinin yükleyelim (el yazısı numaralar)\n \n(X_train, y_train) , (X_test, y_test) = load_data() # Loading data (tuple unpacking ile ayırırz)\n # Verileri tuple olarak açarak yükleriz.","metadata":{"id":"xmHOOUQUzYbu","outputId":"0989903e-bfcd-4765-c0ee-7b8f28aceece","execution":{"iopub.status.busy":"2022-01-19T18:39:58.364158Z","iopub.execute_input":"2022-01-19T18:39:58.364411Z","iopub.status.idle":"2022-01-19T18:39:59.709297Z","shell.execute_reply.started":"2022-01-19T18:39:58.364382Z","shell.execute_reply":"2022-01-19T18:39:59.708590Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Toplamda \", len(X_train), \"adet eğitim veri seti mevcuttur.\") \nprint(\"Toplamda \", len(X_test), \"adet test veri seti mevcuttur.\",\"\\n\")\n\nprint(\"There are \", len(X_train), \"images in the training dataset\") \nprint(\"There are \", len(X_test), \"images in the test dataset\") ","metadata":{"id":"JeiYe7OuzdN6","outputId":"5bb81996-947d-4cd9-dad1-2b7ea71d922d","execution":{"iopub.status.busy":"2022-01-19T18:40:01.839984Z","iopub.execute_input":"2022-01-19T18:40:01.840252Z","iopub.status.idle":"2022-01-19T18:40:01.848910Z","shell.execute_reply.started":"2022-01-19T18:40:01.840221Z","shell.execute_reply":"2022-01-19T18:40:01.848182Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Checking the shape of one image\n# Görüntünün şeklini kontrol etme\n\nX_train[5].shape","metadata":{"id":"9FNlcgiTzj1X","outputId":"dfe6af36-c0e9-4d19-f004-9778f4e76aeb","execution":{"iopub.status.busy":"2022-01-19T18:40:05.663497Z","iopub.execute_input":"2022-01-19T18:40:05.663800Z","iopub.status.idle":"2022-01-19T18:40:05.681641Z","shell.execute_reply.started":"2022-01-19T18:40:05.663767Z","shell.execute_reply":"2022-01-19T18:40:05.680904Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train[5]","metadata":{"id":"fo95HaLhzn9h","outputId":"6cfe6b54-7886-4f1b-eff6-91d4e985cc3d","scrolled":true,"execution":{"iopub.status.busy":"2022-01-19T18:40:58.528952Z","iopub.execute_input":"2022-01-19T18:40:58.529364Z","iopub.status.idle":"2022-01-19T18:40:58.549843Z","shell.execute_reply.started":"2022-01-19T18:40:58.529326Z","shell.execute_reply":"2022-01-19T18:40:58.549189Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Viewing the image\n# Resmi görüntüleme\n\nplt.imshow(X_train[5]);\n# plt.matshow(X_train[5])","metadata":{"id":"nAlrzyEHzr68","outputId":"d722219c-d558-4698-dfc9-5a490977c35b","execution":{"iopub.status.busy":"2022-01-19T18:41:21.671151Z","iopub.execute_input":"2022-01-19T18:41:21.671450Z","iopub.status.idle":"2022-01-19T18:41:21.854977Z","shell.execute_reply.started":"2022-01-19T18:41:21.671406Z","shell.execute_reply":"2022-01-19T18:41:21.854287Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.matshow(X_train[3]);","metadata":{"execution":{"iopub.status.busy":"2022-01-19T18:41:24.305517Z","iopub.execute_input":"2022-01-19T18:41:24.305768Z","iopub.status.idle":"2022-01-19T18:41:24.557004Z","shell.execute_reply.started":"2022-01-19T18:41:24.305739Z","shell.execute_reply":"2022-01-19T18:41:24.556045Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train[3]","metadata":{"id":"zP9FMA-D0Dho","outputId":"538c51cb-82a2-4984-d30a-b17b89b9a1fe","execution":{"iopub.status.busy":"2022-01-19T18:41:26.864930Z","iopub.execute_input":"2022-01-19T18:41:26.865472Z","iopub.status.idle":"2022-01-19T18:41:26.870620Z","shell.execute_reply.started":"2022-01-19T18:41:26.865416Z","shell.execute_reply":"2022-01-19T18:41:26.869679Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Data setindeki resimlerin for düngüsü ile sample alınarak gösterimi. --> (c * 2) + r ya da bu denklem de olur (r * 5 + c)\n# Representation of the pictures in the data set by sampling with the for loop. --> (c * 2) + r or you can use (r * 5 + c)\n\nrows, columns = 2, 5\nfig, ax = plt.subplots(rows, columns, gridspec_kw={'hspace':0.3}, figsize=(12,5), squeeze=True)\n\nfor r in range(rows):\n for c in range(columns): \n image_index = (c * 2) + r # random not in exact rule\n ax[r,c].axis(\"off\")\n ax[r,c].matshow(X_train[image_index], cmap='Blues')\n ax[r,c].set_title('No. %d' % y_train[image_index])\nplt.show()\nplt.close()","metadata":{"id":"IPKvujQwz00v","outputId":"8db1b972-98e8-4da1-f055-df4378b58ba9","execution":{"iopub.status.busy":"2022-01-19T18:41:42.248695Z","iopub.execute_input":"2022-01-19T18:41:42.248953Z","iopub.status.idle":"2022-01-19T18:41:43.231033Z","shell.execute_reply.started":"2022-01-19T18:41:42.248926Z","shell.execute_reply":"2022-01-19T18:41:43.230218Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data Preprocessing","metadata":{"id":"-_pXVdp-y8Mo"}},{"cell_type":"code","source":"# En düşük pixel değeri [0-255]\n# min pixel value\n\nX_train[5].min()","metadata":{"id":"_XspA3CDLpm6","outputId":"912c6fd2-54b8-4c0b-b174-0626b2ec2eb8","execution":{"iopub.status.busy":"2022-01-19T18:41:46.103776Z","iopub.execute_input":"2022-01-19T18:41:46.104030Z","iopub.status.idle":"2022-01-19T18:41:46.109412Z","shell.execute_reply.started":"2022-01-19T18:41:46.104001Z","shell.execute_reply":"2022-01-19T18:41:46.108763Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# En yüksek pixel değeri [0-255]\n# max pixel value\n\nX_train[5].max()","metadata":{"id":"XJefIibxLqf2","outputId":"1e80007e-86b8-4151-e0d5-21bdd9ce5d7a","execution":{"iopub.status.busy":"2022-01-19T18:42:00.868791Z","iopub.execute_input":"2022-01-19T18:42:00.869339Z","iopub.status.idle":"2022-01-19T18:42:00.874710Z","shell.execute_reply.started":"2022-01-19T18:42:00.869300Z","shell.execute_reply":"2022-01-19T18:42:00.873987Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# İleride işlem yapabilmek için float'a çevirelim. Ve scale edelim. 255 --> 1 ve 0 --> 0\n# Let's convert it to float for further processing. And scale them. \n\nX_train = X_train.astype('float32')\nX_test = X_test.astype('float32')\nX_train /= 255\nX_test /= 255","metadata":{"id":"m_eSMmhJFqRA","execution":{"iopub.status.busy":"2022-01-19T18:42:02.749514Z","iopub.execute_input":"2022-01-19T18:42:02.750251Z","iopub.status.idle":"2022-01-19T18:42:02.831664Z","shell.execute_reply.started":"2022-01-19T18:42:02.750211Z","shell.execute_reply":"2022-01-19T18:42:02.830890Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train[5].min()","metadata":{"id":"-NqymetKL1Lb","outputId":"25584cff-c2ae-4a6e-a3d1-8371f6a73b87","execution":{"iopub.status.busy":"2022-01-19T18:42:04.401913Z","iopub.execute_input":"2022-01-19T18:42:04.402431Z","iopub.status.idle":"2022-01-19T18:42:04.407411Z","shell.execute_reply.started":"2022-01-19T18:42:04.402393Z","shell.execute_reply":"2022-01-19T18:42:04.406773Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train[5].max()","metadata":{"id":"SOU_kW9nL1OD","outputId":"be60382c-4843-48f5-eafb-066163accb3e","execution":{"iopub.status.busy":"2022-01-19T18:42:06.032545Z","iopub.execute_input":"2022-01-19T18:42:06.033135Z","iopub.status.idle":"2022-01-19T18:42:06.039348Z","shell.execute_reply.started":"2022-01-19T18:42:06.033100Z","shell.execute_reply":"2022-01-19T18:42:06.038656Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.imshow(X_train[5])\nplt.axis(\"off\");","metadata":{"id":"Jrp0SbiHL7aA","outputId":"403bf3cd-b431-44bc-a332-2e4df2118027","execution":{"iopub.status.busy":"2022-01-19T18:42:10.625578Z","iopub.execute_input":"2022-01-19T18:42:10.625885Z","iopub.status.idle":"2022-01-19T18:42:10.704720Z","shell.execute_reply.started":"2022-01-19T18:42:10.625848Z","shell.execute_reply":"2022-01-19T18:42:10.703796Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train.shape, X_test.shape","metadata":{"execution":{"iopub.status.busy":"2022-01-19T18:42:17.533847Z","iopub.execute_input":"2022-01-19T18:42:17.534127Z","iopub.status.idle":"2022-01-19T18:42:17.540409Z","shell.execute_reply.started":"2022-01-19T18:42:17.534098Z","shell.execute_reply":"2022-01-19T18:42:17.539632Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Reshape: Görüntümüzün boyut ayarlarını değiştirir. Yukarıda orjinal boyutları bakın ve aşağıda işlem sonucu ne hale geldiğini görün. \n# Reshape: Changes the sizes (width,length etc.) of our image. See the original dimensions above and see what happens as a result of the process below.\n\nX_train = X_train.reshape(X_train.shape[0],28, 28, 1) # --> [0] satıra karşılık gelir. 1 --> siyah beyaz. 3 --> RGB renkler\nX_test = X_test.reshape(X_test.shape[0], 28, 28, 1) # --> [0] corresponds to the row. 1 --> black and white. 3 --> RGB colors","metadata":{"id":"uk7qzToCFp-s","execution":{"iopub.status.busy":"2022-01-19T18:42:19.925464Z","iopub.execute_input":"2022-01-19T18:42:19.925921Z","iopub.status.idle":"2022-01-19T18:42:19.931049Z","shell.execute_reply.started":"2022-01-19T18:42:19.925883Z","shell.execute_reply":"2022-01-19T18:42:19.930085Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train.shape, X_test.shape","metadata":{"id":"5nLub1tMFqOc","outputId":"f67a656f-c61d-490c-b7bd-c8f2a964d142","execution":{"iopub.status.busy":"2022-01-19T18:42:25.905688Z","iopub.execute_input":"2022-01-19T18:42:25.906503Z","iopub.status.idle":"2022-01-19T18:42:25.912581Z","shell.execute_reply.started":"2022-01-19T18:42:25.906457Z","shell.execute_reply":"2022-01-19T18:42:25.911482Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(X_train)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T18:42:27.945588Z","iopub.execute_input":"2022-01-19T18:42:27.946042Z","iopub.status.idle":"2022-01-19T18:42:27.951349Z","shell.execute_reply.started":"2022-01-19T18:42:27.946005Z","shell.execute_reply":"2022-01-19T18:42:27.950406Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(y_train)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T18:42:29.291825Z","iopub.execute_input":"2022-01-19T18:42:29.292460Z","iopub.status.idle":"2022-01-19T18:42:29.297990Z","shell.execute_reply.started":"2022-01-19T18:42:29.292386Z","shell.execute_reply":"2022-01-19T18:42:29.297182Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train # 0 dan 9 a kadar olan rakamlar. Bu rakamları kategorik yani class a dönüştürmem lazım ve bunu \"keras\" yapar.\n # Numbers 0 to 9, I need to convert them to categorical aka. class. And \"keras\" does it.","metadata":{"id":"7r8BUJwuL-a6","outputId":"725bd26c-dac3-4c76-8722-3caf5a50d60b","execution":{"iopub.status.busy":"2022-01-19T18:42:31.224009Z","iopub.execute_input":"2022-01-19T18:42:31.224544Z","iopub.status.idle":"2022-01-19T18:42:31.230601Z","shell.execute_reply.started":"2022-01-19T18:42:31.224500Z","shell.execute_reply":"2022-01-19T18:42:31.229912Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## KERAS to_categorical \n\nBir sınıf(class) vektörünü (yani tam sayıları) ikili sınıf matrisine dönüştürür. Daha fazlasını öğrenmek istiyorsanız lütfen [tıklayın](https://www.tensorflow.org/api_docs/python/tf/keras/utils/to_categorical).
\nConverts a class vector (integers) to binary class matrix. If you wanna learn more please [click](https://www.tensorflow.org/api_docs/python/tf/keras/utils/to_categorical)
","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.utils import to_categorical","metadata":{"id":"z3CBRoZ1GWHh","execution":{"iopub.status.busy":"2022-01-19T18:42:55.586612Z","iopub.execute_input":"2022-01-19T18:42:55.587509Z","iopub.status.idle":"2022-01-19T18:42:55.591850Z","shell.execute_reply.started":"2022-01-19T18:42:55.587464Z","shell.execute_reply":"2022-01-19T18:42:55.591123Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Y_train = to_categorical(y_train, 10) # --> y_traini alır ve kategorik hale getirir. Class sayısını 10 olarak ayarladım.\nY_test = to_categorical(y_test, 10) # --> takes y_test like above and makes it categorical. Number of classes is 10.\nY_test.shape","metadata":{"id":"rwav3vH5FqT9","outputId":"c964f06b-3346-4b27-f1ea-67250df38b57","execution":{"iopub.status.busy":"2022-01-19T18:42:58.936021Z","iopub.execute_input":"2022-01-19T18:42:58.936813Z","iopub.status.idle":"2022-01-19T18:42:58.945259Z","shell.execute_reply.started":"2022-01-19T18:42:58.936773Z","shell.execute_reply":"2022-01-19T18:42:58.944596Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential # Sequential model.\nfrom tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten\n\n# 1 --> Model\n# 2 --> conv. layer, \n# 3 --> maxpool, \n# 4 --> padding\n# 5 --> flatten layer\n# 6 --> Hidden layer and it's activation function\n# 7 --> Output layer and it's activation function / dense layer","metadata":{"id":"EAvdD1-IHYaV","execution":{"iopub.status.busy":"2022-01-19T18:43:00.809484Z","iopub.execute_input":"2022-01-19T18:43:00.810023Z","iopub.status.idle":"2022-01-19T18:43:00.815833Z","shell.execute_reply.started":"2022-01-19T18:43:00.809986Z","shell.execute_reply":"2022-01-19T18:43:00.814956Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# 1) Model seçimi / Model selection\nmodel = Sequential()\n\n# 2) FILTER - KERNEL\n# 32 times --> 3X3 kernel(filter), activation --> relu ve input --> 1X28X28\nmodel.add(Conv2D(filters=32, kernel_size=(3,3),input_shape=(28, 28, 1), activation='relu',)) \n\n# 3) POOLING\n# 28 ve 32 lik boyutlar için 2x2 lik pooling yeterli. (For sizes 28 and 32, 2x2 pooling is fairly enough.) \nmodel.add(MaxPool2D(pool_size=(2, 2))) \n\n# 4) PADDING\n# Padding kurmadık çünkü köşelerde veri yok, olsaydı parametreyi same ayarlarık. (No Padding --> no data at the edges) \n# stride --> 2 (default)\n\n# 5) FLATTEN \n# matris --> vektöre dönüşümü / matris --> vector transformation\nmodel.add(Flatten())\n\n# 6) Activation Function\n# nöron sayısı(quantity) --> 128 ve activation --> relu\nmodel.add(Dense(128, activation='relu'))\n\n# 7) ANN deki gibi 2. hidden layer isteğe bağlı kuruldu.\n# As in ANN, the 2nd hidden layer was optionally setted.\nmodel.add(Dense(10, activation='softmax'))\n\nmodel.compile(loss='categorical_crossentropy',\n optimizer='adam', # OPTIMIZER !!!\n metrics=['accuracy'])","metadata":{"id":"1XKYFc5cGD92","execution":{"iopub.status.busy":"2022-01-19T18:43:15.639059Z","iopub.execute_input":"2022-01-19T18:43:15.639784Z","iopub.status.idle":"2022-01-19T18:43:18.346675Z","shell.execute_reply.started":"2022-01-19T18:43:15.639746Z","shell.execute_reply":"2022-01-19T18:43:18.344767Z"},"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**A sample example showing the conversion of 3D data to 2D**
\n![3Dto2D](https://dphi-courses.s3.ap-south-1.amazonaws.com/Deep+Learning+Bootcamp/3D+to++2D.png)","metadata":{"id":"d4GuNNrC1ihO"}},{"cell_type":"code","source":"# Yukarıda kurmuş olduğumuz Deep Learning modelinin özeti. (Padding yok, çünkü resimlerdeki veriler resimlerin merkezinde)\n# Summary of the Deep Learning model we have established above. (No padding, because the data in the images is in the center of the images)\n\nmodel.summary()","metadata":{"id":"7dlzbf8cLVu0","outputId":"9818ec5e-9b63-4d51-d41d-3e5237b3b113","execution":{"iopub.status.busy":"2022-01-19T18:43:36.076523Z","iopub.execute_input":"2022-01-19T18:43:36.076812Z","iopub.status.idle":"2022-01-19T18:43:36.086480Z","shell.execute_reply.started":"2022-01-19T18:43:36.076780Z","shell.execute_reply":"2022-01-19T18:43:36.085619Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Earlystopping \n\nİzlenen bir metrik iyileştirmeyi durdurduğunda eğitimi durdurur. Daha fazlasını öğrenmek istiyorsanız lütfen [tıklayın](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping).
\nEğer görsel ve uygulamalı olarak izleyerek öğrenmek isterseniz lütfen videoya [buradan](https://www.youtube.com/watch?v=2UHCjhyNLKw) erişin.
\nIt stops training when a monitored metric has stopped improving. If you wanna learn more please [click](https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/EarlyStopping)
\nIf you want to learn by watching visually and practically, please access the video [here](https://www.youtube.com/watch?v=2UHCjhyNLKw)","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.callbacks import EarlyStopping","metadata":{"id":"hOuwgRVZGEAu","execution":{"iopub.status.busy":"2022-01-19T18:46:59.460321Z","iopub.execute_input":"2022-01-19T18:46:59.461048Z","iopub.status.idle":"2022-01-19T18:46:59.466283Z","shell.execute_reply.started":"2022-01-19T18:46:59.461008Z","shell.execute_reply":"2022-01-19T18:46:59.465354Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Düşüşünü yani öğrenmesini takip etmemiz gereken değeri monitor değişkenine atarız\n# We assign the value we need to follow to the variable \"monitor\".\n\nearly_stop = EarlyStopping(monitor='val_loss',patience=2)","metadata":{"id":"rtIURzRFGEDN","execution":{"iopub.status.busy":"2022-01-19T18:47:01.957054Z","iopub.execute_input":"2022-01-19T18:47:01.957308Z","iopub.status.idle":"2022-01-19T18:47:01.961694Z","shell.execute_reply.started":"2022-01-19T18:47:01.957279Z","shell.execute_reply":"2022-01-19T18:47:01.960825Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Batch Size / Epochs / Learning Rate \n\nİterasyonlar yani tekrarlar, bir epoch'u tamamlamak için gereken batch sayısıdır. Batch sayısı, bir epoch içindeki iterasyon sayısına eşittir.
\nKısaca: Bizim datamız 60000 satır idi. Aşağıda her bir batch içinde 1875 satır var ve batch sayımız 32'dir. 1875x32=60000
\nVe her bir epoch'ta hata kayıbımız global point'e düşmesi için eğitimler tekrarlanır.
\nHatanın en düşük olacağı global point'i yakalayabilmek için püf noktalar (learning rate etc.) [buradadır](https://towardsdatascience.com/estimating-optimal-learning-rate-for-a-deep-neural-network-ce32f2556ce0).
\nIterations is the number of batches needed to complete one epoch.The number of batches is equal to number of iterations for one epoch.
\nIn short: Our data was 60000 rows. Below are 1875 rows in each batch and our batch number is 32. 1875x32=60000.
\nAnd in each epoch, the trainings are repeated so that our error loss falls to the global point.
\nTricks to catch the global point where the error will be lowest (learning rate etc.) [here](https://towardsdatascience.com/estimating-optimal-learning-rate-for-a-deep-neural-network-ce32f2556ce0)","metadata":{}},{"cell_type":"code","source":"model.fit(X_train, Y_train, batch_size=32, epochs=10, validation_data=(X_test,Y_test),callbacks=[early_stop])","metadata":{"id":"-IDJimxdKq-k","outputId":"6896f2b6-cd97-470e-c6bc-c5d24e15cddf","execution":{"iopub.status.busy":"2022-01-19T18:47:52.590339Z","iopub.execute_input":"2022-01-19T18:47:52.590880Z","iopub.status.idle":"2022-01-19T18:48:25.134101Z","shell.execute_reply.started":"2022-01-19T18:47:52.590843Z","shell.execute_reply":"2022-01-19T18:48:25.133378Z"},"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.metrics_names","metadata":{"id":"wNmOLqBv1Kqn","outputId":"4f7fafc1-4bd9-49b5-e2fb-cebecba1beb7","execution":{"iopub.status.busy":"2022-01-19T18:50:02.303811Z","iopub.execute_input":"2022-01-19T18:50:02.304080Z","iopub.status.idle":"2022-01-19T18:50:02.311380Z","shell.execute_reply.started":"2022-01-19T18:50:02.304050Z","shell.execute_reply":"2022-01-19T18:50:02.310457Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.history.history","metadata":{"id":"Brzdl3zN2wrh","outputId":"2a36d800-a41a-4750-c2db-6a63d69e9402","execution":{"iopub.status.busy":"2022-01-19T18:50:13.428400Z","iopub.execute_input":"2022-01-19T18:50:13.428935Z","iopub.status.idle":"2022-01-19T18:50:13.436450Z","shell.execute_reply.started":"2022-01-19T18:50:13.428895Z","shell.execute_reply":"2022-01-19T18:50:13.435508Z"},"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"summary = pd.DataFrame(model.history.history)\nsummary.head()","metadata":{"id":"2zYPEYGK2qyc","outputId":"0d52231e-11a0-4757-eecc-83b06297ca62","execution":{"iopub.status.busy":"2022-01-19T18:50:19.436759Z","iopub.execute_input":"2022-01-19T18:50:19.437014Z","iopub.status.idle":"2022-01-19T18:50:19.460183Z","shell.execute_reply.started":"2022-01-19T18:50:19.436986Z","shell.execute_reply":"2022-01-19T18:50:19.459351Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(10,6))\nplt.plot(summary.loss)\nplt.plot(summary.val_loss)\nplt.ylabel(\"Loss\")\nplt.xlabel(\"Epoch\")\nplt.gca().legend(('loss',\"val_loss\"));","metadata":{"id":"XelPrR8324J7","outputId":"3b4707b3-7db0-4aeb-abc7-4117ac3c2785","execution":{"iopub.status.busy":"2022-01-19T18:50:41.013642Z","iopub.execute_input":"2022-01-19T18:50:41.014019Z","iopub.status.idle":"2022-01-19T18:50:41.263879Z","shell.execute_reply.started":"2022-01-19T18:50:41.013986Z","shell.execute_reply":"2022-01-19T18:50:41.263204Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(10,6))\nplt.plot(summary.accuracy, label=\"accuracy\")\nplt.plot(summary.val_accuracy, label=\"val_accuracy\")\nplt.legend(loc=\"upper left\")\nplt.ylabel(\"Accuracy\")\nplt.xlabel(\"Epoch\");","metadata":{"id":"R4sKogEX3KKr","outputId":"84bf2744-647c-409d-f969-91804dcba7a9","execution":{"iopub.status.busy":"2022-01-19T18:50:45.630051Z","iopub.execute_input":"2022-01-19T18:50:45.630311Z","iopub.status.idle":"2022-01-19T18:50:45.870960Z","shell.execute_reply.started":"2022-01-19T18:50:45.630280Z","shell.execute_reply":"2022-01-19T18:50:45.870292Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(model.evaluate(X_test,Y_test,verbose=1)) # soldaki loss sağdaki accuracy / left one is loss and the other accuracy","metadata":{"id":"1naFYlgT35-S","outputId":"eda1a44e-6d3f-4ebf-b41f-f06bf6142651","execution":{"iopub.status.busy":"2022-01-19T18:50:54.954097Z","iopub.execute_input":"2022-01-19T18:50:54.954349Z","iopub.status.idle":"2022-01-19T18:50:55.684480Z","shell.execute_reply.started":"2022-01-19T18:50:54.954320Z","shell.execute_reply":"2022-01-19T18:50:55.683758Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Confusion Matrix / Classification Report \n\nMakine öğreniminde klasifikasyon, supervised(denetimli) öğrenmenin bir parçasıdır ve bu; modeli eğitmek için kullanılan verilerin her kategoriyi tanımlayan etiketlere(label) sahip olduğu anlamına gelir.
\nBir makine öğrenimi modelinin yaşam döngüsündeki kritik bir adım, performansının değerlendirilmesidir.
\nBir klasifikayson modelini değerlendirmek için kullanılan iki teknik, confusion matrisi ve klasifikasyon raporudur.
\nDaha fazlası için [makale](https://medium.com/swlh/confusion-matrix-and-classification-report-88105288d48f) ve [video](https://www.youtube.com/watch?v=O-eH80oi2P4).
\nIn machine learning, classification is part of supervised learning, which means that the data used to train the model have labels that identify each category.
\nA critical step in the life cycle of a machine learning model is the evaluation of its performance.
\nTwo techniques used to evaluate a classification model are the confusion matrix and the classification report.
\nTo learn more here is the [article](https://medium.com/swlh/confusion-matrix-and-classification-report-88105288d48f) and [video](https://www.youtube.com/watch?v=O-eH80oi2P4)","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import classification_report,confusion_matrix","metadata":{"id":"3qZywTaf4Nay","execution":{"iopub.status.busy":"2022-01-19T18:52:35.604905Z","iopub.execute_input":"2022-01-19T18:52:35.605168Z","iopub.status.idle":"2022-01-19T18:52:35.745020Z","shell.execute_reply.started":"2022-01-19T18:52:35.605137Z","shell.execute_reply":"2022-01-19T18:52:35.744317Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"preds = model.predict(X_test)\n# preds --> ihtimalleri alırız. Ama predict classes artık kaldırıldı. Bu yüzden artık aşağıdaki yöntem uygulanır.\n# # preds --> we get probabilities. But predict classes are now removed. So now the following method is applied.","metadata":{"id":"IOAxLbRQ4cMe","execution":{"iopub.status.busy":"2022-01-19T18:52:42.420246Z","iopub.execute_input":"2022-01-19T18:52:42.420515Z","iopub.status.idle":"2022-01-19T18:52:42.834618Z","shell.execute_reply.started":"2022-01-19T18:52:42.420485Z","shell.execute_reply":"2022-01-19T18:52:42.833886Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"preds # Kullanışsız / Useless","metadata":{"execution":{"iopub.status.busy":"2022-01-19T18:52:45.023967Z","iopub.execute_input":"2022-01-19T18:52:45.024747Z","iopub.status.idle":"2022-01-19T18:52:45.030389Z","shell.execute_reply.started":"2022-01-19T18:52:45.024698Z","shell.execute_reply":"2022-01-19T18:52:45.029745Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Doğru olan yol / Correct Way\npredictions= np.argmax(preds, axis=1)","metadata":{"id":"P54bs5fIQ1EU","execution":{"iopub.status.busy":"2022-01-19T18:52:47.329061Z","iopub.execute_input":"2022-01-19T18:52:47.329624Z","iopub.status.idle":"2022-01-19T18:52:47.333477Z","shell.execute_reply.started":"2022-01-19T18:52:47.329586Z","shell.execute_reply":"2022-01-19T18:52:47.332616Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predictions","metadata":{"id":"HzB8GiCOMr2l","outputId":"9cb18311-0d90-498f-a30b-8782c42cb9a9","execution":{"iopub.status.busy":"2022-01-19T18:52:49.120410Z","iopub.execute_input":"2022-01-19T18:52:49.121124Z","iopub.status.idle":"2022-01-19T18:52:49.126297Z","shell.execute_reply.started":"2022-01-19T18:52:49.121087Z","shell.execute_reply":"2022-01-19T18:52:49.125556Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(classification_report(y_test, predictions))","metadata":{"id":"HUl6w6Jf423N","outputId":"730a26df-e8d5-4d62-feeb-00a729f328f9","execution":{"iopub.status.busy":"2022-01-19T18:52:50.651243Z","iopub.execute_input":"2022-01-19T18:52:50.651824Z","iopub.status.idle":"2022-01-19T18:52:50.680731Z","shell.execute_reply.started":"2022-01-19T18:52:50.651782Z","shell.execute_reply":"2022-01-19T18:52:50.679865Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(confusion_matrix(y_test, predictions))","metadata":{"id":"weQW_anSNmj8","outputId":"813f45f0-ee06-4456-e736-6323e2f2a49a","execution":{"iopub.status.busy":"2022-01-19T18:52:57.836966Z","iopub.execute_input":"2022-01-19T18:52:57.837239Z","iopub.status.idle":"2022-01-19T18:52:57.882731Z","shell.execute_reply.started":"2022-01-19T18:52:57.837209Z","shell.execute_reply":"2022-01-19T18:52:57.882002Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"score = model.evaluate(X_test, Y_test, verbose=0)\nprint('Test loss:', score[0])\nprint('Test accuracy:', score[1])","metadata":{"id":"3DyaGeFU46Hv","outputId":"1b4d6194-9d31-46aa-9692-97f58255cf4a","execution":{"iopub.status.busy":"2022-01-19T18:52:59.777332Z","iopub.execute_input":"2022-01-19T18:52:59.778052Z","iopub.status.idle":"2022-01-19T18:53:00.481147Z","shell.execute_reply.started":"2022-01-19T18:52:59.778007Z","shell.execute_reply":"2022-01-19T18:53:00.480377Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"score # index 0 --> loss / index 1 --> accuracy","metadata":{"execution":{"iopub.status.busy":"2022-01-19T18:53:02.006708Z","iopub.execute_input":"2022-01-19T18:53:02.007331Z","iopub.status.idle":"2022-01-19T18:53:02.016350Z","shell.execute_reply.started":"2022-01-19T18:53:02.007291Z","shell.execute_reply":"2022-01-19T18:53:02.014881Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Hadi Deneyelim / Let's Predict ","metadata":{}},{"cell_type":"code","source":"# Boyutlara göre reshape edilmiş örneklemin her bir pixel'inin renk kodları ile gösterimi.\n# Color codes of each pixel of the reshaped sample according to dimensions.\n\nX_test[20].reshape(28,28)","metadata":{"scrolled":true,"execution":{"iopub.status.busy":"2022-01-19T18:53:03.808982Z","iopub.execute_input":"2022-01-19T18:53:03.809673Z","iopub.status.idle":"2022-01-19T18:53:03.824927Z","shell.execute_reply.started":"2022-01-19T18:53:03.809634Z","shell.execute_reply":"2022-01-19T18:53:03.823970Z"},"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"my_number = X_test[20]","metadata":{"id":"WduIUJj64rIp","execution":{"iopub.status.busy":"2022-01-19T18:53:15.367839Z","iopub.execute_input":"2022-01-19T18:53:15.368584Z","iopub.status.idle":"2022-01-19T18:53:15.372865Z","shell.execute_reply.started":"2022-01-19T18:53:15.368535Z","shell.execute_reply":"2022-01-19T18:53:15.372015Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.imshow(my_number.reshape(28,28));","metadata":{"id":"18_nBUbAN1fh","outputId":"2a575827-dc7d-4fe8-87dd-1398772e800f","execution":{"iopub.status.busy":"2022-01-19T18:53:26.437392Z","iopub.execute_input":"2022-01-19T18:53:26.438096Z","iopub.status.idle":"2022-01-19T18:53:26.627023Z","shell.execute_reply.started":"2022-01-19T18:53:26.438058Z","shell.execute_reply":"2022-01-19T18:53:26.626296Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.matshow(my_number.reshape(28,28));","metadata":{"execution":{"iopub.status.busy":"2022-01-19T18:53:29.669266Z","iopub.execute_input":"2022-01-19T18:53:29.669542Z","iopub.status.idle":"2022-01-19T18:53:29.914092Z","shell.execute_reply.started":"2022-01-19T18:53:29.669512Z","shell.execute_reply":"2022-01-19T18:53:29.913285Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Modele sokabilmek için resim sayısı eklenmesi ve resmin siyah beyaz olarak değerlendirilmesi.\n# Adding the number of pictures and evaluating the picture as black and white in order to be able to put it into the model.\n\nmodel.predict(my_number.reshape(1,28,28,1))","metadata":{"id":"1DCFzSFtOGZF","outputId":"e820db6e-ce10-40f4-e67a-af22438260a5","execution":{"iopub.status.busy":"2022-01-19T18:53:33.110179Z","iopub.execute_input":"2022-01-19T18:53:33.110431Z","iopub.status.idle":"2022-01-19T18:53:33.167992Z","shell.execute_reply.started":"2022-01-19T18:53:33.110402Z","shell.execute_reply":"2022-01-19T18:53:33.167333Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# SHAPE --> (num_images,width,height,color_channels)\nnp.argmax(model.predict(my_number.reshape(1,28,28,1)), axis=1)","metadata":{"id":"zetmO1KEN4ro","outputId":"566a3264-50ec-4b38-9a99-46c90da0410e","execution":{"iopub.status.busy":"2022-01-19T18:53:39.225832Z","iopub.execute_input":"2022-01-19T18:53:39.226081Z","iopub.status.idle":"2022-01-19T18:53:39.270509Z","shell.execute_reply.started":"2022-01-19T18:53:39.226052Z","shell.execute_reply":"2022-01-19T18:53:39.269859Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
Doğru Tahmin Bravo!!! / Correct Prediction Congrats!!!

\n\"acorrect_pred\"","metadata":{}},{"cell_type":"code","source":"# Modeli daha sonra hazır halde kullanabilmek için kaydedelim ve istersek denemelerimizi hazır model üzerinden yapalım.\n# Let's save the model so that we can use it later. And if we want, we can make our predictions on the ready model.\n\nmodel.save(\"cnn-1.h5\")","metadata":{"id":"OuF9luxd1p56","execution":{"iopub.status.busy":"2022-01-19T18:53:46.239734Z","iopub.execute_input":"2022-01-19T18:53:46.240000Z","iopub.status.idle":"2022-01-19T18:53:46.277806Z","shell.execute_reply.started":"2022-01-19T18:53:46.239972Z","shell.execute_reply":"2022-01-19T18:53:46.277024Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___","metadata":{}},{"cell_type":"markdown","source":"# CNN - CIFAR-10 Data
\n**Dictionary of Data** --> [Kaggle](https://www.kaggle.com/c/cifar-10/data)","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport seaborn as sns\nimport tensorflow as tf \nimport numpy as np \nimport matplotlib.pyplot as plt \n%matplotlib inline","metadata":{"id":"pyjqyftAzSfT","execution":{"iopub.status.busy":"2022-01-19T18:54:04.621957Z","iopub.execute_input":"2022-01-19T18:54:04.622639Z","iopub.status.idle":"2022-01-19T18:54:04.632066Z","shell.execute_reply.started":"2022-01-19T18:54:04.622597Z","shell.execute_reply":"2022-01-19T18:54:04.631220Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.datasets import cifar10\n\n(X_train, y_train), (X_test, y_test) = cifar10.load_data()","metadata":{"id":"xmHOOUQUzYbu","execution":{"iopub.status.busy":"2022-01-19T18:54:06.242244Z","iopub.execute_input":"2022-01-19T18:54:06.242515Z","iopub.status.idle":"2022-01-19T18:54:13.176892Z","shell.execute_reply.started":"2022-01-19T18:54:06.242486Z","shell.execute_reply":"2022-01-19T18:54:13.176128Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Toplamda \", len(X_train), \"adet eğitim veri seti mevcuttur.\") \nprint(\"Toplamda \", len(X_test), \"adet test veri seti mevcuttur.\",\"\\n\")\n\nprint(\"There are \", len(X_train), \"images in the training dataset\") \nprint(\"There are \", len(X_test), \"images in the test dataset\") ","metadata":{"id":"JeiYe7OuzdN6","outputId":"03863ed0-38bd-4a62-cbba-8aefcc2eeab6","execution":{"iopub.status.busy":"2022-01-19T18:54:26.431870Z","iopub.execute_input":"2022-01-19T18:54:26.432643Z","iopub.status.idle":"2022-01-19T18:54:26.440638Z","shell.execute_reply.started":"2022-01-19T18:54:26.432606Z","shell.execute_reply":"2022-01-19T18:54:26.439852Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Resimlerin boyutlarını kontrol edelim\n# Checking the shape of one image\n\nX_train[5].shape","metadata":{"id":"9FNlcgiTzj1X","outputId":"02b17d4a-b2df-481b-ac01-9222bb024e83","execution":{"iopub.status.busy":"2022-01-19T18:54:28.311700Z","iopub.execute_input":"2022-01-19T18:54:28.312135Z","iopub.status.idle":"2022-01-19T18:54:28.317987Z","shell.execute_reply.started":"2022-01-19T18:54:28.312098Z","shell.execute_reply":"2022-01-19T18:54:28.317317Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train[5]","metadata":{"id":"fo95HaLhzn9h","outputId":"ad5e225a-b1aa-4c9b-d207-4425dcb0b7fc","scrolled":true,"execution":{"iopub.status.busy":"2022-01-19T18:54:32.394958Z","iopub.execute_input":"2022-01-19T18:54:32.395512Z","iopub.status.idle":"2022-01-19T18:54:32.401248Z","shell.execute_reply.started":"2022-01-19T18:54:32.395469Z","shell.execute_reply":"2022-01-19T18:54:32.400503Z"},"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image-2.png](attachment:image-2.png)","metadata":{},"attachments":{"image-2.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQAAAAEoCAYAAAC3jS89AAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAADVxSURBVHhe7d0NXFRV/j/wjyADOhM6RI6ZqDGZuLajbbPuT2yDHrAHYBNbH7aCXY111W11K6hNa6PatJLtv7L7EysfXiuWQpvQKpXgz8AMWx0zZyPGjCkdU0fRUWQEBmH+59w5wAwDDM8+3O+71+Scc+69c+cO93vPvXPnfPs5GRBCZMlP/EsIkSEKAITIGAUAQmSMAgAhMkYBgBAZowBAiIxRACBExtoOAPUO2O32Nh+OetdkDrfnPaKGLb9GPPdFWkeHKHSRg7+fLi6jO/NeMnaY95nZ/3tQH22HHv9bI+0EgG/zMGvWrDYfed/yiUzIa3reM0x5bPl5JlHyga/jP3bDJopdYfs8o8vL6M68l8zhImSmZaLosCh3mh0Woxk2t/29b7aDDbv/0Zm/NQdsh4ywVIpiH3BUmGE82qOhtde1HQDGzMCWLVtcj3UpiGT/pawTZfaYMUZMR64sI2OxfMtyxI4U5U6zYPeSbBj7cMfqGjuMm5dg93FR7AP2r7OxZJdFlK4M/mmMeN62C4ex6wMLwqb+HKMGijpJBUo3FgJ3Pwj/Axvw7rqPsf+sPzQ3hmFQfzEJZ7egZNv7yN3wKUrt9QgdztoVoq2Fiq82ohBT8PAtoa6KehuMOz5G/pYN+LSsBjWqGzDqWjHz6VJsNKkQq6vB7nffxdqdpahxhuKGsEHwWHw7r19zeBdyj4Yh4fZRGOCq8mIzFuJfm3PwoeF71ASOgFbjmtJr3vbWlbNbYdiZg3/x7XS8BqpRWoQGijY2r2nXx/jXpg0oOVSD+utGISzYXzQyPrah/bgBxf/6F9YW7MexGhVuCg/13AaN7CYUbt2PuhHstRVsfTbvRuUNQ1HxKf/82LIb/DFs2DCo3D+/RtK8Rdj/5fc4paiBtTYEY29QNW2HmFHHsOOdDdhQcggNwTdDe53nGrS1HVtnh3VfMXLeW8u2ZQMGaUNhN/wb9oiHMU78abAFovCjfOS/y7dJDQZdPwpqaXvy9/URSr4qxbHzA2A71w8jGrdHm/O4+NqObb0H655cfPT5Vyj9wY4BZ2zoJ21fqakF1oPaXYh/b33ftQy/oQgbpoL0SUvb93sobqjEHv73fPQ63D1GLc3V3rZznDThWH0oBrm9j47qkYuAe99ZA6NSj+jpMdBW5GLBiuLm7mBFCTKeyIBxgI61RyOirhgvPpEFY4fO883IfSYJWWYVdPGPIU7HXuvlNOQccut/Vu1FVmYRoIvGzDsjYN++BGmbTKwDKHTr9R0ws6NIarYFQyfGInbyCJzITsWy7VbR7s7XurL2F1KRb4tA3B/mIFpTjjUv5cAkrYcdhrcWYQ0LZpGPLESCzo7ClzJQfFKa0fd7MOci7Zl82CLisHB2NIaa13huA3fVbOdYZ4C1mhdsMK8rwtZ1jZ+fHqrdGUhl7a12ZAM00N4UBjVU0IRroWV/uE2ObcWGj+0ImxyLmRNVKHl1MXIONq6B2I4f2KBl7bETh8LS5nbkHDBtSkPqhzZE3DkT0WxbFmVmYW+VaObYe05NykJ5sA5xs+Ogw16kse1pll6Srd9NWmiCAfVItp4jNQjwOY+rve3t2P57UA1jr6Nh2yM0jG0j9trSC7bEPufM+Viy3Q7tXTMx8y7297QxFRm7xN4ifTb5WLuuAI4I9vd8q4ZV+tp2ZuQ/l4oF60pa/8x84T8G8ulUkXNp3FJn0SlRblLmzI6Lc2YaakWZqT3gXOM2bfn7C51pH59wFQSprtCzrlHZxjhn3MYyUap1VllOOKtEiTvx8WJnYk65q2DKdsbFpTkLjrmKkmr2+onNdb5e/0zxUmfcq0XOM1KphXOfOVfErXB+dk6UuVMHnFvz9jr53J7z+lhXaRuuZ1usEZv+VBX7P8e3I9tmVqkgqTpzwll70fW8Q+9hQ/OSnbVVzjPn3D4Tdx6fZSufn9Se6dzr/kY8iHV1+1uQXv+5rc4joszx9970OfLtmLjGeaDaVZRIdeudB1pbzWMFzrSW0/M6tq7ZJlFm7/HIMY+t7Sx4LtGZ/Y0osk+l6FW36Tkf87S7HTvwHqT5m/52W1drPeI84baM2v1rnHF//cz1NyRt+8XOrRapyaUDr1trLXOWt/oH7FuP9ABCVG59HYWCxd9GVpTvr0OYvxVGo7HpcaJaBYPFKiJrexRQDmfR22aByci6Zltykb/HAlu925zhOmivF8+5IC10kwwot/B42L3Xd5hZl+yBSIxnR5ImoTrEPqgHj82efKwrOzJEjMlB+ms5KN5nhpWtnjJUKbqXYQh/oAxrVmQgf7cJFpsDSrUGCqlf6Ps9qIdHIGJTOpZtKobBbGVHAiXUwa32P1vl8fmFsqM86xnYpR5CJ6hUbp872xoDmkvSdhynhuOb5vU3fs+OejYTrKfFRG4cVgsMk9jnGiQquOt1iJwgnnMKJcKGBMB21AQjO1XI35yPksNsmQ2ivTU+5mlvO3b2PbRFMYT1oOqtMLP5S7bnIru4DKirE60c245u77sjr6sYEoFw15lCp/VIAGibHbbDAThzrBzl3zY/rAP0mBOmgPvbblWNCTlLktgpRT72fsu6PMPGQj9hrGgUNCHsY3KnhIrtsLYavmt07/XtVWdYQFG4uo+++FzXcCS8th6L71DjyJ5spD/xEJJXlLDdm1NCPz8Ty2eOB0wFWPV8MpKWNJ8e+HwP4QlYvn4xokKPwPBeOhY9lMy6lY1dxEtP2o6se2txW//ybx0Im61npxPe2truCrdLBo6DOVgyewEytuxF+TH2ZzBOD32EaGyDz3na2Y6dfQ+ts6JkRTKSn8lCEZvfrtRi8q3s/KYdPfO6bWvtUk8PUmP4BDOqdMuRMKHjR6RGdmMRsobMw6ZFkU07ubWCnQi6n+zsZjuDIwqapsWzjWVSQzvRtXl8vX57vQA1O68Lf68cR9nZYrio43PY+dFb6bm8Dq2rvxrhk2KkB7+anv/8AuTu2YR5E/kcSmh0UYjlj9kOGN56CJnb9FjxYAe3oTockffwB3v+SD6WzM+F4dZ50HtGx0tCrRnBjnpqRE6LaaXn5E2avuV2d7Ce0G52fHyIF+ww7siCZv4mLJzUtLVRyHot7pvbUwfnaWs7dvI9tMpcgmxzLNL+kdD0vhxfGsSz1nV223VWL/cA1NDflYCiDXkwNW5l1v0pfiMZ6TtZN8YH5aAQqG02nGm8+aPSiIJCoygI6iJkbzY1fYjWT7KQVZ0A/Whe6t7rIzwScepcbP6k8WjqgOXDv2LWau+LZD7XlR19klPYUb1xRjubtlKNkEHsj7GiGMuS0psv+tXbwBaFoWoexHy/B9OmZKRuat4G9jNnUKUOgarXdv4qVLXcAO0ZHYVEf/a5NG1Hto5f5yD1cbY9WruxZ7QeCQHu290O0+ZsFDUd8lgvj20bm40dHQX7lwUo+FIU3Jw517iivudpdzt29D1UVnn9bTQJDsFQ/jfSOEGNBcU7ikShDT5f1wHz9izkftnc3hm93ANg3bYJj+KVyg1YNT8e1uAwOA47oJ02D/NYV9inMVFYODwDS6bmQjOG7dyBU/DYXZFsI4t2btxjSBxegEVJGVAEWVB1XQIWPt0cYbv1+izmxjyZAtsbSxC/jpfYRg5PxCtPuo7yHr0HX+s6ZioWR/0VS2clQ61TwGp0YOzcNDwl3U8RhTmPlyMjJQnZYRo4jFZoHnwBC8U6+noPEVMXI3rFUsyaq4ZOYYXRMRbz0p6Cjx5xF0Ug6hkNljwej1UPvIBN8/Wivj18Oy5GVSbfjgqE8c9JEYWZTy5EhNs3nc3Y6dLTC7F2RSriN6oQxk7nIuam4LFxqfwTkETctRBhK9jy8jRsjaxQxDyG6NvZziHaeeCcNDURBa/MQrx6DlasSPA5T/vb0fd7UE9MQOI2Nn+8GnNWrEBCc7fRJXQSZk4vYX8DSVCMUaGqMgLzfsV6g3tEe6t8va4N5btykMtOOadM0DT1PjuqD4cE413nOgQEKcXFrU7gt5rWBXh1uz3w24LZOXPb03Tj9Tl+izLbvEr3C1Ot8bWu0nqy9VA2XgB052sdfbTzdaxv57UvB51cR377b10A2+5tTO6rvTU+5/G1jt3dzj7/VtvQC58vjQlIiIz18jUAQsjljAIAITJGAYAQGaMAQIiMUQAgRMYoABAiYxQACJExCgCEyBgFAEJkjAIAITJGAYAQGaPfAvS2ixdR/e9/42J5OZw1NXBWV6Ofvz/81Gr4Dx8O/7AwBEyYgH4D2hsgk5DeQQGglzirqlC1ciUuZGUhMCYGip/8RNrJ+wUGAuxRf/So6/H996grK0Pw4sUIiosTcxPSNygA9BLb/PkIGDMGAx56SDrKt8fxn//gfHo6+o8aBdXChT6nJ6SnUADoBZV//jMfbhnBL78sajrG/tZbcOzfD3VmpqghpHfRRcAeVlNQgPoffuj0zs8FxcaizthiyDNCehEFgB5Wk5eHAVOnilLn+N9wA1tADRoqKkQNIb2LAkAP4lf6HQYDguLjRU3nBeh0uHjokCgR0rsoAPSgutJSBN17ryh1jbO+nn0q9LGQvnEJ/tIcsLLzXEtjXrsuuhxzxTttNvSThvLuuroDB9B/DKVeJn2j7wOA3Yjc9HTkl7Y5enoHmJA3qzO54vtGAwsA/Aafrqo/cgR+gwbBb/BgUUNI7+r7AKDUY9769Zh3W69lrbhk/IYORb3ZLEqdx8/9+TUAQvqKfxojnvegdnKgg+duL8SxwWMx7BpW2pOL3VUhUBwqxMZ3dwPjbsWgI4XIN6ugdRrx73c2IM8rJ3oFSjcWAlOac8XbD5egMD8f739kwPfs9GKoW45712uMwNCTxdjwrivfvP8NYRimdBtc30f+/Y7wu+46nHvySekiYFd6ArXbt0tHf8XPfiZqCOldXj2Aw4cP45lnnkE8+yPm//Jy5/jIgS7lpF8Ls8iYYzOvRdHGtdhwKAT6+6OhZR0Dh9WAtZ9kYdU7R6EROdHL1y9C+s7W0x/Z96zC/OcLYQ+Pxszp0RhxLBupK0vYK7lIr5G/BmtKldDfORN6VQkyUtbC0Ji1x1f+/Q7y12gQ/NxzOL9smajpnOq8PATefrsoEdIH+J2A7p5++mlnXFxc04OXO6vdHOgiJ31j3vayjex13twr8uS7SHnWW8sP35QT3XMZzuoTziNWtyXUHnCuiVvu/EzkTPd+DVfu+Mz/uHLF+8q/3xmOL75wnnvpJeeZ3/7WefFEx+ev+fRTZ8X06aJESN/w6gF8/fXX4plLy3JH+M6B3kKwyjtN1oQIhLXMDz/SgPKjouwuSIOwwXWwmo0wslOP3PeKUIY61Ll/S+DxGmpoRrKegZRC3Hf+/c5osNthX7NG+vHPyZ/+FJWvvIKGSvdkht4aTp3C+RdfxMBHHhE1hPQNrwDwox/9SDxzaVn2rfM50FulVrVIdKiAQmVuNY5Yd2UgOTkVWcXlKLcroZ00Hh1/xQ7k3+8E3oUPycrChffeg/J3v0P/G28ULa1z7NuHc08/jQG//CUGPPigqCWkb3gFgAULFjTt9PxfXu6UphzoKZgzLQExk3QYHtzZ3YgxW5vO4SUOC8p3RWH4EFFuYkbJe+WITVuJlNkJSLgnErrh6k7suK78+yG6WCSw9fV43BPR6WyrXODPf47QrVvhN3AgKl9+GTWbN+Pit83fWfJbfR2ff45zqak4l5KCoPvvl4IFIX3NKwCMHDkSr732GrZs2SL9y8ud0pUc6K350j0nOs8Pz3oUD0RjvNfFdTVChvF8+o0v6IDlkwJ0/BV959/vCv7bf9UTTyD0gw9Qx06jzv7+9zg+ahROseBwKiYG9jffRMAtt+C6Tz7BgBkzxFyE9C2vANBtUg50K1bNSsKClAVIWpgL5a0xorETbp+Jn1ZkIGnuAixImoX0w9FIm61v5YisxqTpM2F9cxaSHk9l0y5ArvKn6MwrSvn3f1GFrPnxSH6crfPUJShSz0OiyL/fHf1vvhmDXn8dodu2Yeg33yBk40Zo+E9+163DwF//WkxFyKXRe+MBdDUHOmPbuQxJuyOx/pkoqHm+/YYO5OVn+O3BaDO3fkf4ys9PyNWl53sAjfwVXdr5vSg6tvNzCmV3d1y+zrTzE/novQDQHUEa6K69+m4VJuRyQ0OCESJjl2cPgBDSJygAECJjFAAIkTEKAFcSulxDehgFgCsI/81A9ebNokRI91EAuFKwo3/11q1w1taKCkK6jwLAFaJ2504E/s//sCcUAEjPoQBwhajdtQsBt93mGjackB5CAeAKwAcLvbBhA5x2uyu7MCE9hALAFcC+ejWuSU1FvcXiSh9GSA+hAHCZ46nDebox5Zw5uPjddxQASI+iANAOn9mHKs0wHGpOcNLT2Yr4WIHnnn0W16SkSGV+KuAfFiY9J6QnXJ4BoNIC4yFbpwfk7Fk27P5H+9mHLMWZePHNIlikUs9mK6r74gtUxMVBmZwsDRlWb7XC79prpZGGCOkpl2cAOL4bSzYb0XxsvTyFxS/HlvRY9MoxuX9/DH7jDQx8+GGpyHsD1P0nPe2SZwaS2E0o3Po9VGOHod/BQuTv2I/9351CQLUVdW7TSdl/cnPxz52lqHGG4oawQU1Dfdv5fGYFRlwsRd4/V+H/yhowSKtFaK0ZxfnZ2FhgRM3AUW7ZhZh6G4w7/oXc9z6E4XANFGFaNDfX4PBnubBH/BLBh/6Nje/meU0jveaXdRgRHsrWwztbUXeyDfEkI/4jRogS6/6XlaH+8GEExcaKGkK6z6sH0NeZgSRsRzesM0ujAAdcq4V2pBoI1kB7E9vZxLggtt0ZWPR3IxS6aMy8UwfFfzMw/43m7D+ubELZyN5Vh4g7Z2K8fwFS38hAxnoDAm6KROxEFUrS05BzUJxYOMzI/XMqcm1aRN4fi8hwG3IXpSLnkOeJR3n+Kmw4qpGm0WvKkbUoHcUnXW3Sa+5rI3dAD2UbatRw+jT8hngNiUxIt3gFgJUrVzYlA+H/8nLnKKF76BUsT52ByDHhCB8ThZmPRqN4T5nnMN9tUISGQ6cdCqhGYKxOh7BgXst6CJnliF0wDzG36aDT6REzNwWJlauw1ei2+1WNR+wjUaxdh6ikxzBjXznCHmDrwcq6STOQGG/Dge9da2H7PBtrh8xByvRIaXre/uyTY7H1nWLWR2lWpo7GU2Ia/T3zkPKIHWs+Nvq8PmHemQ3bQymYd49emjdy2rNYfJ8BubtaT2/mCz8F4LkHCelJXgHgkmQG8uW4BQcwGePDRVnCegi3qmH4zi1VkErhlv0HCMRQhEgBxEWtGQujjQcAByzlJYi6bazHKMOK0WxHZUHD4nbxIXKc1mOZGjaNmk3TWoKiZj2bbYirO3gQ/d1OCQjpCV4B4LLJDOSuygZLhMZrSHBl8FCYq7uyO9n5IqEJaTHst5JnI7LBXi3KjFLV4lVVIRhqrvOxE/dstiGudts2KPhvAQjpQV4BoPczA6mhniCeNmoQ/7ZlWDgid5d5HJn5Udz6PTuKD9eIcmeoMVQbDsOhFrn8WU/DpNYirPEiHmNiR2x3DrZTl9w5nPU/2tO9bEP8hp+qjAzX429/w/nXX0fAhAmwr1+P8+npOL98uVR3/rXXcP7VV7ucjZgQrwDQ+5mB1KwbrcZnJQbY+E0zlWYUbswCO0nwVFWFqsabapT8HJoFli2mpq8G7V/nIeuTWETrupa8I3xiHELe39x0QU/KBsTWw/GQHu5nGpb3s5unsZuQt6EIsbePZ++iPd3LNtQvKAj9VCrpUffVV2g4cwZBU6ZItwJX5+Sgdvt21B89Kv00uN8113h8W0BIZ3gFgG7zmRlIAd19CzH+2wwkTY1HfMpWKGMSMVa0SsZE4dnQXCxg7av28D2IzfOrVxBXlYX58ez0Ym485q88gSlL50Dvdo7fKdfHIOVPWpQ8F4+kucmubEDDUpD2oMeFBsT9bgqsK5JcGYNmpaM8Kg1zJvo6hrM17ka2If/rr5du/eVZhh27d6Nu/37UHz+OoHvvxbUffCBlGRrMegfBf/4zVL//PWUVJl12WWYGahfPFFQX0KPL7UhGoa5nHep6tiHnhQtwOhzwGzxY1BDSsygvACEy1vOnAISQKwYFAEJkjAIAITJGAYAQGaMAQIiMUQAgRMYoABAiYxQACJExCgCEyBgFAEJkTBYBgI+tX1daKkqEkEZX9W8B7OvWoXrjRvgNHSqNqdf/xhsxaNky6Se0hJCrOABUrVyJi19/LY2rzwfTkOr+9jdpaC11ZqZUJkTueuUUwHeGHDvM+8xNg3t0WI0d9g6MAMYHzKjetAnBaWlNOz+n+uMfUV9eLqXaIoT0SgDwnVEHh4uQmZaJok6OOG7Km4WMz32PqFPJuvm8q+8X6ja2lzBg1iw4du0SJULk7dJcBBwZi+VbliO2k6ONdQQfT4+PQKyYPFnUeOLZdfhwWoSQXg0ADlh252LVa8uw6p1CGN0P3DwT0OZCMV6eK1OQ6aQFJZtXYdlqQ1P+ANvBYuRmLsOyzFyUHO3Y6L91e/ciMCpKlLz5DxuGi0eOiBIh8uYVALqfGcilvYw6rkxABlil4bd5pqAiZK/egPJQPWKjtNKoudad6Vj0dhlUE2MRO1mDo++swtYOrEr/MWMwQOTTa42zspKG2CJE8AoA3c8M5NK5jDpGaO59Col36KEbrYbCYUTBajsSn2rMBBSJGYuiEdKBr/IDxo9HwFiPIUY9NJw/D79Bg0SJEHnzCgA9kRmI62xGnRCV29SnrTCpddBeL8pcUAT0d4rn3cC/AeBBghDSSgDofmYgl65l1BF4JqBgnqXHnQKKbg4E3FBRgdodO6D42c9EDSHy5hUAup0ZSOhaRh0hdDh0X3rm6OMXCy0m8bSLzv3pTxg4fTr6jx4tagiRN68A0O3MQELXMuoI6vGIfsAzE5CVp/7u2vVINJw9i7N//KP0DYBy/nxRSwjxCgA9pasZdVyU0M9OQ/ThdMxKWoAFc5OQUTEF8x4QzZ1Q/8MPOMOP+lotgl96SdR64gGitrgYVW+8gYsHD4paQq5+vf5bgK5n1BH47b8sICiDRLmTKvlOX1+P/uHhQGAgUFuLBrZOUtYd9i9Pu3Xxm2+g+PnPofjJT6D87W9ZWOy1uEjIZeWqzwzEU2vxhJr1Vqu080uJN5VK12PgQOl6QMCPfyymJkRervoAQAhpG/V1CZExCgCEyBgFAEJkjAIAITJGAYAQGaMAQIiMUQAgRMYoABAiYxQACJExCgCEyNhVdStw/ZEjUgowfo9/P5UKATod+gUEiFZCSEtXRQBw1tXB/tZbOL98OQbOnAlnVZU09h//BSBPDkIIad0VHwBqPvoIlX/5C4KmTJF+yssH/eCcLABYJ07Eddu2wX/ECKmOEOLpsgwAjgozTDUa6Ia3P4BIzccfo/K556B++20E3HqrqG1WtWKF9HPga1JTRQ0hxN1leRHQ/nU2luyyiFLreDffNncuBq9a1erOz/mPGiXlASCEtM4/jRHPe5T9cAkKc3Pxz52lqHGG4oawQc3DhNfbYNzxMfK3bMCnZTWoUd2AUde6Wq17cvHR51+h9Ac7Bpyxod8ILUJbGQ24Jj8ffsHBUP7mN6LGW53BIPUAAttIE0aI3Hn1AHoiM5BtdwYWvVIMR0Q0Zt6pA/a8iEXrG5OCmJH7TBKyzCro4h9DHGve+3Iacg65WlXDtNBqVEBoGLQ3aaFp4yJ+f3Z0l4bvage/DuB3zTWiRAhpyesaAN/p3ZOB8KHB+ejAHcd28EUrAPZICBdVDjOKt1oQ9kAUwoMcsB+1AcM1TeP+W7ctQWrlY1g/3TWDbecyJB1LwJZZEVK5q86//jr8hw/HwHZShREiZ149gG5nBjpeDgOiMb5x5+cU4Yiaxnd+qQAl2/kDbBaYjAYUb8lF/h4LbPUdS/7ZWXzEX0JI67wCQLczA7Wa1cdNjQk5S5KwYEU+9n5rBYaNhX5C27n8umPgo49KvYC6//5X1BBC3HkFgG5nBhoWjsgvy1De4uI7Hx6cH+PtxiJkDZmHFWnzkDgtFlG3RUCjqHJN1MP4PQGDV6yQvi2gngAh3rwCQLczAyl1iJxmQO6HzVl97PvWInVxgZQYVDkoBGqbDWfqXW2oNKKg0CgKbiqrmubvjgEPPgjlY4/BnpkpagghjXrnRqAaCwpXL0PWNgcU11vhUMQg8U/zEDOcf59nhWF1BjI+sEIzhpUCp+CxyeVIr3S76MdPE15eiiyjGnPYEbzpYmIX1R8+jNPTp2PInj2ihhDC9e6dgA477HUBUCpb+SK/vbZeYJs9GwMTExF4112ihhDidQrQoxTKtnfw9tp6AU8Hxn8lSAhpdsX/GKilC++8w8Iai2t1ddKvBJ21tXDs3CmlAlOvWSOmIoRwV1UA4Hf+VfGLffX1UiLQfgqFNB4A/01A0L33iqkIIY2uuh4AIaTjevcaACHkskYBgBAZowBAiIxRACBExigAECJjFAAIkTEKAITIGAUAQmSMAgAhMiabAFC3fz/qjK2MO0CIjPVdAKixw947w/51SO2nn8JhMIiSJ8fevRQciCz1WQAw5c1Cxuc2Uep7zupq9BswQJQ88QDg+M9/RIkQ+ZDNKYDzwoU2AwAhctVrAcB2sBi5mcuwLDMXJUdb7/vbjIXIkqbJQqHRu3fQdrsVhs2FMJ20oGTzKixbbYCvvoXUAwgMFCVCCOcVAHoiM5B1ZzoWvV0G1cRYxE7W4Og7q7DVYzEOmDcvQeoHNmgns2kmDoUlOxXLtls72G6DeV0RsldvQHmoHrFR2raHIRf8hg5Fg7VxfkII5xUAVq5c2ZQMhP/Ly53iMKJgtR2JT81DzG066HSRmLEoGiGlop2rNGBrnhYLU2cgUsemuS0Gc56ZA+X6Ahh5Z8FXu8QIzb1PIfEOPXSj1c15B9vgP2QI6k+eFCVCCOcVALqdGei0FSa1DtrrRZkLioD+TvGccZhNKBynhuMbI4xG8fiedeJtJlhP+25vFKLq+JiCfiwAUA+AEE9eAaB3MgMpoHDbV+1VZ4BqKyzflqO86eFA2Gw91B1o74qgKVOk6wDnnn4a1Zs3o6awsOlx8ZtvUFdaKtVf2LgRF9avh33tWimXQD0FDXIV8xoSjJ/zN54GNGYG6lRyEFsJ0pOMiN40D/qmKGBF4fPJMMSsx7N3sF34UA6S1qux/OUYaMQUHny1w4Sc+FQgfQtmjBFVHVSdl+f6ys/RfGGy7uBBaRDR/qNHS8lEERDQNJ7ggF/+En6hoWJKQq4yPAD0rCrn3pWJzpSNZeyZy4kdK5yPJcY5lxafaaxxFryQ6Fy+44Qos7lKs50pv892ll3kJV/tZc7suDhntklq6rbz//u/TtvChc6zqamihhB56J1BQWvMyF+xFKtKFQgLqoI6ZjES6lNROEz0ALhKE3Iz07FWmsaCKkUUZj65ELHh4lyh3fau9wBaU3/kCOrPnMHZ3/0OQ+iGICIjvTsqML/9F0oopbTgbeDT1LeTIchXew+qiIvDoFdfRcAtt4gaQq5uNCy4m9qdOxF4xx2iRMjVjwIAITLWa7cCE0IufxQACJExCgCEyBgFAEJkjAIAITJGAYAQGaMAQIiMUQAgRMYoABAiYxQACJExCgCEyBgFAEJkrO9+DMR/1uunRG//qre2uBj2t95Cw6lTfLQTqc7Z0CD9q161Shr1hxDi0mcBwLQpHrnuA4L0Aj7cFx/LTzV/PgL4WIb9+okWF/8RI8QzQgh3VZ0C1G7fLu38QfffD/+RI6Ud3v1BCPHUaz0AnhmoaEcJTIhAVHwsQnY95NUD4Jl/tn5mwFEMh35yHGJ0nr2D5nbXMrTH8mEcNAUxY1pPA3Li5puhOXCAUoAR0kFePYBLkhlosha2DxYh9T0za3GRlrG+HEPdlpH1yVoYrK2nGav/4Qf4azS08xPSCV4BoE8yA1XsRvY6DeYsEpl/+DSpKRi7JQvFx1l7a8t4Ihoa92W0UG+xoD/rAXB8rP/Kl17CudRU2H73O5x5+GGcW7wYF/nw34SQJl4BoE8yAx0tR8mdeowNFhVckBa6SQaUW+ytL0MRgfFuy2ip4exZ+IWE4Owf/gD7qlUIGDcOgXffjYGJibjm6afR/6abcHrWLOlbAkKIi1cA6LPMQJqQFll+lFCxgGCrYV38DiyjJZ7+u2b7dih+9jNc+/77GPDQQwi67z4E3n47AiZMgHLOHAz+29+kXoHz/HkxFyHy5hUAeCagxp2+MTNQp4QOh+7LcvADeTMrLCbxlFEP0yJ8TxnMouzCp1Gzoz4LC8PCEfllGcorRZPEjLI94mkrBkybBs2+fRj46KOixltgVJQUIGo/+0zUECJvXgGApwF77bXXsGXLFunfTqUF49TjEf1ACbK3mNAYA6yfZCPb/SJgeCTi1LnY/Elz3j3rJ1nIqk6Ant+no9RhSpIFq1bnw3TSDrvdCsP6bJQEuKZtk5/X2/GiYD2Cui++ECVC5K13vgbsVGYgdjbAjv4IT8TCJ2dA13RdwA7z9ixkF1tQxabQ/+oxjDXO6vbNRBfefRf1R49K1wUIkbvevROwo5mBfE0jccC47iEUjF6PlNvbDwCnf/lLBP7851BMngyFXi9qgYaTJ3Fm9myo/vAH6foAIXLnu8/cHUEd2LFbncaOkn8kI32bGfZ6XnbAfjAfuZv1GK/1ffQPfv55KcPv+VdfxcmJE2FjOz1/nIyKkr4ZoJ2fEJe++zFQZ7FThPx3spH7oYGdIKgRNjESCbMSETO69bsA28K7+/XHjknPFT/5CdC/v/ScEHI5BwBCSK/r3VMAQshljQIAITJGAYAQGaMAQIiMUQAgRMYoABAiYxQACJExCgCEyBgFAEJkTBYBoP7ECdQfPw6I/ACEEJer+lbgBpsNFffdh35iKKGLhw8j6N57oX77balMiNz1WgCwHy5B0T4rHEP0SLg9TNT2Ldv8+dJgoaFbt7oqLl5kfR7W6enAwCGEyEHv7AmH85H2fCHs12sRNkwlKvvW2SefhP8NNzTv/Bz/JSDt/IQ06Z29ocYO07hoxEzSQR/e9dF7uurcs8/CT6lE8HPPiRpCSGv80xjxvEdY9+SisKQU+787hYBqK076jYA21AbD5t2ovFaBb7ZtxIbPgFt+Mgw8hQc/VSjMzcU/d5aixhmKG8IGwWPwX5sRhZtzkfORARX+Gmj7H8K//1OJEeGhntMJ9nXrcLG8HIP/+ldRQwhpi1cPoLuZgVTDtNCOZEf9YLaz3sSeX8tH8rTBvK4I2as3oDxUj9gorTTkt213Bhb93QiFLhoz79RB8d8MzH+jhE0tnCxG+qIslGvYPPdHQnN0A1ZtLMRafm1BTOKubv9+nH/9dTQcO4aKBx5ARWwszj7xBC5kZ4spCCHuvAJAdzMDKYfroNMOZZFgBMbqdAgPbTxOG6G59ykk3qGHbrSaHb1NKMwsR+yCxuw/esTMTUFi5SpsNfLd2wHjx2tgfyQF8+5h87BlRU5/CtGhZa7FtcK+fj0CbrlFGiJ88N//jsErVkhX/WuLinDmN78RUxFCGnkFgG5nBmpHiMqt037cggOYjPHhoixhvYZb1TB8d5Q9t8F6SM2ChcbVJFEggvUW2lKTnw/V449j4K9/jf5arZQNiI//p87MlO4BoKxAhHjyCgDdzgzUUTz7T4SmRfYf1oMIHgpzNe8B2GE7rIKqRa5PhaL15AB1ZWUIYOvKk3+0JoidEtR8+KEoEUI4rwDQ7cxAHcWz/+wua5FByAHr9yWIGs6P+hqETzKi7HuPCWA2GcQzT84zZ+AXGipK3vwGD6Y7AQlpwSsAdDszUEcp2Tn9NM8MQvav85D1SSyidfyrQyV09yTCwrrv+QetsNvtsO7LQnZJa9f+GYUCzro6UfDGbwjyHzFClAghnFcA6DsK6H71CuKqsjA/PhnJc+Mxf+UJTFk6B3qRHUgxegbSnh8PS14Glr66FNmHf4qFc37qamzBT61Gw6lTouTNsWcP+o8ZI0qEEO7y+C2Aww57XQCUyjaO7m4cX67FQzsisP7JyBbZhQHb3LnoP2oUrlm8WNS41P7f/6EyLQ3XffqpqCGEcJewB+BGoWx157fvzkDyG4UwiyzBDp4sJC8Xep3Wa+fn1G+9hXrWCzgxdiwqYmJw5tFHpYxA/M7A4GXLxFSEkEaX+a8B7TBtYef9W/JhOM528JF6RE5NROI94V7fHrhzVldLeQDrKyqkXwIG/PjHooUQ4o4yAxEiY5fHKQAh5JKgAECIjFEAIETGKAAQImMUAAiRMQoAhMgYBQBCZIwCACEyRgGAEBmjAECIjMn2VmDnhQtoqKhAw+nTrMA2QWAgAsaNE62EyEOvBACH3Q4EKaHwFxWXmcq//AXV774Lv+uvh//QoWwr9MPFb75BSFYWjRlAZKUXAoAJOfGpQPoWzLiC9iWeRmzAL36BoPvvFzWEXP3oGgDDRwu+aDLRzk9kp8czAwEVKN1YCNz9IPwPbMC76z7G/rP+0NwYhkH9xSSc3YKSbe8jd8OnKLXXI3Q4a28cE8RuQuHW76G4oRJ7WFd97dHrcPcYNau3wrAzB//iyzxeA9UoLUIDxTxce8tsAx9H8Cw7+gc/+yz6h3uMUU7IVc+rB9DdzECN9r6zBkalHtHTY6CtyMWCFcXNGX8qSpDxRAaMA3SsPRq6AUZkzE9HSYVor2Y7+rp8rF1XAEdENGbeykcJNiP3hVTk2yIQ94c5iNaUY81LOTDVuGZpucyIumK8+EQWjI3tLfD0YfzIf27JEviPHg2/a68VLYTICL8G4O7pp592xsXFNT14uXPKnNlsvkxDrSgztQeca+KWOotOuYplGxOdC98vdxWEEx+nORP/ecApzXWqyLk0brFzq0VqcpHq1rOlN6p1Vp2qck3PlL+/0Jn28QlRcpHqCj3rzqakOI/feKPzVEyM0zZvnvNYeLjz9KxZztO/+pXzQl6emIoQefDqAfRUZiCPLEAKBZqThFthKQUm/9izu60ZrYN6Xzl4TiAXFVRB4ikXGoaIMTlIfy0HxfvMsNoBZahSJAi1onx/HcL8rTAajU2PE9UqGCyeeQT7jx0LDTvfDy0ogOL226F8+GGEbNyIEHaqMeDBB8VUhMiDVwDo/cxAPOMP2wkHiWIjVQiGmutaTfrpEo6E19Zj8R1qHNmTjfQnHkLyihK263N8mQE4c6wc5d82P6wD9JgTpoB7tgDlnDnSOIHcxYMHEXDrrdJzQuTIKwD0fmYgnvGnxCvjj4PtvCV3Dmet7fBXs3ljkDj/WSx/ayUSKpYhdw9fjhrDJ5gRootFwrQEz8c9EW0OIFpXWor+7PyfELnyCgC9nxlICd2kBJTk5MPUlBLIhLwNRYi9fXyrw31LDuYgOSXHbR4bzlSqETKI795q6O9KQNGGvOb2eiuK30hG+s6mS4+enE449u5FADslIESuvAJAX1BMeBSv/KIKWfPjkTw3GfHzM3DivlcwZ2I7g32PmYrFUeVIn5WM1CULkDQrA0fvScNUcbORxzIfZ+1Tl6BIPQ+J7JShVf36YXBGBtDf/btJQuTlEv8WwAG7vQ4BysaLeR1Qz+apaW8esczL+FZkQi4XlBeAEBm7JKcAhJDLAwUAQmSMAgAhMkYBgBAZowBAiIxRACBExigAECJjFAAIkTEKAITIGAUAQmTsqgoA9UePou6LL9Bw8qSoIYS056r4LQAf2KPqzTdRW1iIfoGBgL8/lL/9LZTJyWIKQkhrrooeQOWyZQi8/XZo9u+HevVq9q786Hf+hHRA3weAGjvsbYzUK6k0w3DIc7QgXxqOHUOATif9tj9gwgQEp6Xhwvvvi1ZCSFv6PACY8mZhVp5JlLxZijPx4ptFsIhyRwyYNg1Vb7whSuyUoLTUlfKLENKuy+4UICx+ObakxyJMlDtCOW8eEBSEqowMOAwGXNi0Ccq5c0UrIaQtvXMRsN4G02dFKNptgGNIJPQxMYgc7hq/x7QpHqlYjk06C7ILi2BV6BEVH9vUbj9YiAJLGKZIg3laYdhshOqeSGBPNgqKq6CaqEfMXZEIazF6WENlJU4nJMBZU4NrnnlGyvNHCGmfVw+g+5mB7DC8tQhrTCpEPrIQCTo7Cl/KQLH7N3OGLKwpVUJ/50zEjLYid/5fUSyyAjmsBqzd1ziWvw3mdUXYujoDRRAZf2oKseQFt4xAgvPcOTjPnmWRoAFOWxsDgRJCPHgFgJUrVzYlA+H/8nLnWGD+cCzipsZAN1yDsNtmIOW1RExyz7x1cwLmTY+ETqeD/p45SJxWgpKv29ppjbCPm4N59+il6SOnP4uUcVuRvcuVEaDRhaws6au/a7OzUVNQgPPLlokWQkhbvAJA9zMDhSH8gTKsWZGB/N0mWGwOKNUazwE6g1VuA3oqoBggnrZKjbHh7tkCFIi4NRqGQxbW12jWcPo0/LVa+I8YIeX5b2C9AJ7yu+HUKTEFIaQlrwDQ/cxArGs/PxPLZ45nJ/wFWPV8MpKWeHfZOy4M6ua8YhLFAFZRaffIIuQfFoZ6i/juwM8Pg15/HQrWYzg9axZQX++qJ4R48AoAPZMZSAmNLgqxsxfilX+sxsKRWcjcZhZtnWWExeqZMMz6/QGow1lgEGXOPzxcyvHvTsl6AIGTJ0t3CRJCvHkFgG5nBqooxrKk9OaLfvWsK85O74eq28z541Oue8afk8XIeseBhNs8k4sOiI1F7Y4duPjNN6LGZeDs2ahh74UQ4s0rAHRbaBTmPB6CgpQkLFiSiuSpqSi69gXMaStDTwfMiB2OgifY8h5PRnxKAUIeT0GC5/7vuv9/7lzY335bVLg4L1yg6wCEtKEXfwzUExl6TMiJTwXSt2DGGLZEO+sG+MgidOaRR9Bfq0X/MWOkU4I6oxEDWd2AGTPEFISQRj3fA2iiYPtqz6bnUnQghVjIhg3w02hQf+SIlPo7eOlS2vkJacNl/nNgM/KfXwMkvYJYyuJNSI+j3ICEyFgvngIQQi53FAAIkTEKAITIGAUAQmSMAgAhMkYBgBAZowBAiIzJ5j6Auv37UfXWW65xA4YPR/Cf/gS/IUNEKyHyJI8AwN7iqbvuwjVPPSUNGFK7axeqc3JwXVGRmIAQeZLFKUDN9u3oP3o0guLipPwBQffeK40YRAOFELmTRQBw7NmDwLvvFiXWIaiqYucEdahrMYAIIXJzZQcAX1mGhIaKCviFhIgSm+2jj9Bv0CBRIkS+eicAVFpgPGTzGLOvN/jKMtSIZwvu5+eH6rw8V/IQ1iPwHzZMtBIiX70TAI7vxpLNRo9Rey+lwHvuge3xx1G3bx/6DRyIkH/+U7QQIm89HgB4Zp/cHWXAsb3YujkXhuO8lmf4KYTppAUlm1dh2WoDeBYA655c5O5xH9/fDtP2xnmEehuM27Ow6rVlWPVOIYzt5fyoNKGQvY6xwrPvoZw9G5ovvkDwyy9LKcP7XXONaCFE3rwCQHczAwVcq4V2pBoI1kB7kxYaKYWXK8NP9uoNKA/VIzZKC15tM6/FWrP7Hu2Add9amCsbi2bk/jkV2d8Phf7+WESGnUD2M8tQ6B4gGtlNyHlpDWwRkdCFeo8b1G9Au8kHCJElrwDQ3cxAitBw6LRDAdUIjNXpEBYsGmCE5t6nkHiHHrrRap9De3H2fVuxdsgcLE6OgZ4tS3dHIlIe16HqmGdWIGnnf2ENah9Nw4wftUgaSAhpk1cA6H5moLaFqDqy2zdyoNxUiNhJ46XeQiO1LhYJt7llCqo0sp0/FeX3pSBxAu38hHSGVwDofmagnmJHFTs7UCgCRLkNu8pQq4tC2cfF3cg+RIg8eQWAnskM1DFqtU48a+QAmm7OU2OoNhwHzEdFWXC0+O7/gZlITFqIxfq9WLqyGC1ODggh7fAKAN3ODNSoqgpVPu60VV8fAfXuEhj4dcB6O8zbspHl9rV++MQ4hORtbs4yVGNB/v+bhTX7W37BqEDEtPmIO7YGWTs9Q8D55culJKEXDx5EvdWKi999h9pPPkHdV1+h/803i6kIkSevANAjxkTh2dBcLJgaj1V72r4bQKGbgoU/LkdGUjzipy7CVuUUJI4Tjdz1MUh5cgSKnotH0txkJE1/EYaRr+CxSa2c6yvCMePpx4DV6cj5uvk1r0lNlfIDnmX/np42DWdZj8a+ejUGr1iBfgE+Ti8IucpdMb8GlLICdSvLECGkJcoLQIiM9c4pACHkikABgBAZowBAiIxRACBExigAECJjFAAIkTEKAITIGAUAQmSMAgAhsgX8fzPwckLQzxieAAAAAElFTkSuQmCC"}}},{"cell_type":"code","source":"plt.imshow(X_train[5])\nplt.axis('off');\n\n# plt.matshow(X_train[5]);","metadata":{"id":"nAlrzyEHzr68","outputId":"32ae390d-4ea2-47b6-998f-b6c78a6e8b6f","execution":{"iopub.status.busy":"2022-01-19T18:54:44.658404Z","iopub.execute_input":"2022-01-19T18:54:44.659121Z","iopub.status.idle":"2022-01-19T18:54:44.740708Z","shell.execute_reply.started":"2022-01-19T18:54:44.659085Z","shell.execute_reply":"2022-01-19T18:54:44.739837Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train[5] # index 1 --> autonobile","metadata":{"execution":{"iopub.status.busy":"2022-01-19T18:54:46.569337Z","iopub.execute_input":"2022-01-19T18:54:46.569900Z","iopub.status.idle":"2022-01-19T18:54:46.574983Z","shell.execute_reply.started":"2022-01-19T18:54:46.569861Z","shell.execute_reply":"2022-01-19T18:54:46.574303Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.matshow(X_train[28])\nplt.axis('off');","metadata":{"execution":{"iopub.status.busy":"2022-01-19T18:54:51.588693Z","iopub.execute_input":"2022-01-19T18:54:51.589211Z","iopub.status.idle":"2022-01-19T18:54:51.746358Z","shell.execute_reply.started":"2022-01-19T18:54:51.589172Z","shell.execute_reply":"2022-01-19T18:54:51.745511Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train[28] # index 4 --> deer","metadata":{"id":"zP9FMA-D0Dho","outputId":"e52793b4-f166-4820-b599-48d377bdd969","execution":{"iopub.status.busy":"2022-01-19T18:54:53.719109Z","iopub.execute_input":"2022-01-19T18:54:53.719641Z","iopub.status.idle":"2022-01-19T18:54:53.726541Z","shell.execute_reply.started":"2022-01-19T18:54:53.719604Z","shell.execute_reply":"2022-01-19T18:54:53.725762Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"classes = [\"airplane\",\"automobile\",\"bird\",\"cat\",\"deer\",\"dog\",\"frog\",\"horse\",\"ship\",\"truck\"]","metadata":{"id":"2CvLychSAaB5","execution":{"iopub.status.busy":"2022-01-19T18:54:57.888270Z","iopub.execute_input":"2022-01-19T18:54:57.888536Z","iopub.status.idle":"2022-01-19T18:54:57.892880Z","shell.execute_reply.started":"2022-01-19T18:54:57.888507Z","shell.execute_reply":"2022-01-19T18:54:57.891981Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Datasetinden for düngüsü ile 15x15=225 adet resim. (Data içini görme amaçlı)\n# # 15x15=225 images from the Dataset with the for loop. (For seeing inside data)\n\nplt.figure(figsize= (16,16))\nfor i in range(225):\n plt.subplot(15,15,i+1)\n plt.axis('off')\n plt.imshow(X_train[i])","metadata":{"id":"Xu3eRR-oAbf1","outputId":"9c749790-7452-4d20-e4b4-0a5764e981a1","execution":{"iopub.status.busy":"2022-01-19T18:55:00.329134Z","iopub.execute_input":"2022-01-19T18:55:00.329601Z","iopub.status.idle":"2022-01-19T18:55:07.869631Z","shell.execute_reply.started":"2022-01-19T18:55:00.329564Z","shell.execute_reply":"2022-01-19T18:55:07.868972Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"classes","metadata":{"id":"mFD9acgwA50l","outputId":"2c16f872-6090-4215-abb8-a1c4e7cab631","execution":{"iopub.status.busy":"2022-01-19T18:55:12.797426Z","iopub.execute_input":"2022-01-19T18:55:12.798278Z","iopub.status.idle":"2022-01-19T18:55:12.803884Z","shell.execute_reply.started":"2022-01-19T18:55:12.798227Z","shell.execute_reply":"2022-01-19T18:55:12.803043Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data Preprocessing","metadata":{"id":"-_pXVdp-y8Mo"}},{"cell_type":"code","source":"X_train[5].min()","metadata":{"id":"_XspA3CDLpm6","outputId":"cf87408b-6a11-4b48-d492-62affba3085a","execution":{"iopub.status.busy":"2022-01-19T18:55:15.026931Z","iopub.execute_input":"2022-01-19T18:55:15.027205Z","iopub.status.idle":"2022-01-19T18:55:15.033389Z","shell.execute_reply.started":"2022-01-19T18:55:15.027175Z","shell.execute_reply":"2022-01-19T18:55:15.032615Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train[5].max()","metadata":{"id":"XJefIibxLqf2","outputId":"063cab6b-fbb6-4425-b8f3-0be0cd17036b","execution":{"iopub.status.busy":"2022-01-19T18:55:16.465995Z","iopub.execute_input":"2022-01-19T18:55:16.466548Z","iopub.status.idle":"2022-01-19T18:55:16.472795Z","shell.execute_reply.started":"2022-01-19T18:55:16.466508Z","shell.execute_reply":"2022-01-19T18:55:16.472114Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train = X_train.astype('float32')\nX_test = X_test.astype('float32')\nX_train /= 255\nX_test /= 255","metadata":{"id":"m_eSMmhJFqRA","execution":{"iopub.status.busy":"2022-01-19T18:55:23.365146Z","iopub.execute_input":"2022-01-19T18:55:23.365401Z","iopub.status.idle":"2022-01-19T18:55:23.617004Z","shell.execute_reply.started":"2022-01-19T18:55:23.365372Z","shell.execute_reply":"2022-01-19T18:55:23.616259Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train[5].min()","metadata":{"id":"-NqymetKL1Lb","outputId":"e03c10a5-7030-48e9-a60b-67b5989fd1fd","execution":{"iopub.status.busy":"2022-01-19T18:55:24.517089Z","iopub.execute_input":"2022-01-19T18:55:24.517336Z","iopub.status.idle":"2022-01-19T18:55:24.524480Z","shell.execute_reply.started":"2022-01-19T18:55:24.517309Z","shell.execute_reply":"2022-01-19T18:55:24.523620Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train[5].max()","metadata":{"id":"SOU_kW9nL1OD","outputId":"f21e831f-f7b7-4188-f221-bafa6d2df8f5","execution":{"iopub.status.busy":"2022-01-19T18:55:26.063674Z","iopub.execute_input":"2022-01-19T18:55:26.063928Z","iopub.status.idle":"2022-01-19T18:55:26.069505Z","shell.execute_reply.started":"2022-01-19T18:55:26.063899Z","shell.execute_reply":"2022-01-19T18:55:26.068832Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.imshow(X_train[5])\nplt.axis('off');","metadata":{"id":"Jrp0SbiHL7aA","outputId":"770af6bc-1ea7-46ca-ec1a-d0933ffa49a6","execution":{"iopub.status.busy":"2022-01-19T18:55:27.695395Z","iopub.execute_input":"2022-01-19T18:55:27.697604Z","iopub.status.idle":"2022-01-19T18:55:27.826773Z","shell.execute_reply.started":"2022-01-19T18:55:27.697562Z","shell.execute_reply":"2022-01-19T18:55:27.826128Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.imshow(X_train[28])\nplt.axis('off');","metadata":{"execution":{"iopub.status.busy":"2022-01-19T18:55:29.403612Z","iopub.execute_input":"2022-01-19T18:55:29.404020Z","iopub.status.idle":"2022-01-19T18:55:29.495617Z","shell.execute_reply.started":"2022-01-19T18:55:29.403979Z","shell.execute_reply":"2022-01-19T18:55:29.494845Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train.shape, X_test.shape","metadata":{"id":"5nLub1tMFqOc","outputId":"5e893146-96ea-4746-861c-cca17d2a9a48","execution":{"iopub.status.busy":"2022-01-19T18:55:38.141005Z","iopub.execute_input":"2022-01-19T18:55:38.141266Z","iopub.status.idle":"2022-01-19T18:55:38.148114Z","shell.execute_reply.started":"2022-01-19T18:55:38.141236Z","shell.execute_reply":"2022-01-19T18:55:38.147345Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train.shape, y_test.shape","metadata":{"id":"7r8BUJwuL-a6","outputId":"b09f2fa9-7ffb-43b1-c0ab-1281fa52df58","scrolled":true,"execution":{"iopub.status.busy":"2022-01-19T18:55:39.553420Z","iopub.execute_input":"2022-01-19T18:55:39.553980Z","iopub.status.idle":"2022-01-19T18:55:39.560169Z","shell.execute_reply.started":"2022-01-19T18:55:39.553943Z","shell.execute_reply":"2022-01-19T18:55:39.559294Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.utils import to_categorical","metadata":{"id":"z3CBRoZ1GWHh","execution":{"iopub.status.busy":"2022-01-19T18:55:41.238063Z","iopub.execute_input":"2022-01-19T18:55:41.238706Z","iopub.status.idle":"2022-01-19T18:55:41.244189Z","shell.execute_reply.started":"2022-01-19T18:55:41.238662Z","shell.execute_reply":"2022-01-19T18:55:41.243124Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"Y_train = to_categorical(y_train, 10) \nY_test = to_categorical(y_test, 10)\nY_test.shape","metadata":{"id":"rwav3vH5FqT9","outputId":"7866ea20-4df5-4a33-f832-adb210f3f036","execution":{"iopub.status.busy":"2022-01-19T18:55:43.448245Z","iopub.execute_input":"2022-01-19T18:55:43.448516Z","iopub.status.idle":"2022-01-19T18:55:43.455815Z","shell.execute_reply.started":"2022-01-19T18:55:43.448486Z","shell.execute_reply":"2022-01-19T18:55:43.455040Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Conv2D, MaxPool2D, Flatten, Dropout","metadata":{"id":"EAvdD1-IHYaV","execution":{"iopub.status.busy":"2022-01-19T18:55:45.216426Z","iopub.execute_input":"2022-01-19T18:55:45.216967Z","iopub.status.idle":"2022-01-19T18:55:45.220796Z","shell.execute_reply.started":"2022-01-19T18:55:45.216930Z","shell.execute_reply":"2022-01-19T18:55:45.220112Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = Sequential()\n\nmodel.add(Conv2D(filters=32, kernel_size=(4,4),input_shape=(32, 32, 3), activation='relu',))\n\nmodel.add(MaxPool2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(filters=32, kernel_size=(4,4),input_shape=(32, 32, 3), activation='relu',))\n\nmodel.add(MaxPool2D(pool_size=(2, 2)))\n\nmodel.add(Flatten())\n\nmodel.add(Dense(256, activation='relu'))\n\nmodel.add(Dense(10, activation='softmax'))\n\nmodel.compile(loss='categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy'])","metadata":{"id":"1XKYFc5cGD92","execution":{"iopub.status.busy":"2022-01-19T18:55:46.866762Z","iopub.execute_input":"2022-01-19T18:55:46.867346Z","iopub.status.idle":"2022-01-19T18:55:46.920157Z","shell.execute_reply.started":"2022-01-19T18:55:46.867309Z","shell.execute_reply":"2022-01-19T18:55:46.919494Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"id":"7dlzbf8cLVu0","outputId":"aa4ea919-1690-4618-a9e1-644e62be1767","execution":{"iopub.status.busy":"2022-01-19T18:55:48.549014Z","iopub.execute_input":"2022-01-19T18:55:48.549791Z","iopub.status.idle":"2022-01-19T18:55:48.560997Z","shell.execute_reply.started":"2022-01-19T18:55:48.549751Z","shell.execute_reply":"2022-01-19T18:55:48.560207Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.callbacks import EarlyStopping","metadata":{"id":"hOuwgRVZGEAu","execution":{"iopub.status.busy":"2022-01-19T18:55:53.845841Z","iopub.execute_input":"2022-01-19T18:55:53.846568Z","iopub.status.idle":"2022-01-19T18:55:53.850768Z","shell.execute_reply.started":"2022-01-19T18:55:53.846522Z","shell.execute_reply":"2022-01-19T18:55:53.849955Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"early_stop = EarlyStopping(monitor='val_loss',patience=5)","metadata":{"id":"rtIURzRFGEDN","execution":{"iopub.status.busy":"2022-01-19T18:55:55.544479Z","iopub.execute_input":"2022-01-19T18:55:55.545075Z","iopub.status.idle":"2022-01-19T18:55:55.548972Z","shell.execute_reply.started":"2022-01-19T18:55:55.545035Z","shell.execute_reply":"2022-01-19T18:55:55.548165Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.fit(X_train, Y_train, batch_size=32, epochs=20, validation_data=(X_test,Y_test),callbacks=[early_stop])","metadata":{"id":"-IDJimxdKq-k","outputId":"18c3743a-f6af-4d22-a687-88e9d5991fc4","execution":{"iopub.status.busy":"2022-01-19T18:55:57.517751Z","iopub.execute_input":"2022-01-19T18:55:57.518003Z","iopub.status.idle":"2022-01-19T18:56:55.404896Z","shell.execute_reply.started":"2022-01-19T18:55:57.517973Z","shell.execute_reply":"2022-01-19T18:56:55.404232Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.metrics_names","metadata":{"id":"wNmOLqBv1Kqn","outputId":"68ae4626-78fb-43fe-e860-fe3897ca719f","execution":{"iopub.status.busy":"2022-01-19T18:57:11.168330Z","iopub.execute_input":"2022-01-19T18:57:11.168898Z","iopub.status.idle":"2022-01-19T18:57:11.174683Z","shell.execute_reply.started":"2022-01-19T18:57:11.168858Z","shell.execute_reply":"2022-01-19T18:57:11.173870Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.history.history","metadata":{"id":"Brzdl3zN2wrh","outputId":"f096090d-72e9-4054-936f-19690a09e20e","scrolled":true,"execution":{"iopub.status.busy":"2022-01-19T18:57:17.237155Z","iopub.execute_input":"2022-01-19T18:57:17.237871Z","iopub.status.idle":"2022-01-19T18:57:17.244226Z","shell.execute_reply.started":"2022-01-19T18:57:17.237822Z","shell.execute_reply":"2022-01-19T18:57:17.243361Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"summary = pd.DataFrame(model.history.history)\nsummary.head()","metadata":{"id":"2zYPEYGK2qyc","outputId":"190da5cb-5799-4c40-8683-30ce1c3e0be3","execution":{"iopub.status.busy":"2022-01-19T18:57:22.946043Z","iopub.execute_input":"2022-01-19T18:57:22.946619Z","iopub.status.idle":"2022-01-19T18:57:22.960671Z","shell.execute_reply.started":"2022-01-19T18:57:22.946579Z","shell.execute_reply":"2022-01-19T18:57:22.959755Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(10,6))\nplt.plot(summary.loss, label=\"loss\")\nplt.plot(summary.val_loss, label=\"val_loss\")\nplt.legend(loc=\"upper right\")\nplt.ylabel(\"Loss\")\nplt.xlabel(\"Epoch\")\nplt.show();","metadata":{"id":"XelPrR8324J7","outputId":"b0447078-65cb-4d6b-de5d-6d995f96c884","execution":{"iopub.status.busy":"2022-01-19T18:57:28.563984Z","iopub.execute_input":"2022-01-19T18:57:28.564239Z","iopub.status.idle":"2022-01-19T18:57:28.760177Z","shell.execute_reply.started":"2022-01-19T18:57:28.564211Z","shell.execute_reply":"2022-01-19T18:57:28.759501Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(10,6))\nplt.plot(summary.accuracy, label=\"accuracy\")\nplt.plot(summary.val_accuracy, label=\"val_accuracy\")\nplt.legend(loc=\"upper left\")\nplt.ylabel(\"Accuracy\")\nplt.xlabel(\"Epoch\")\nplt.show()","metadata":{"id":"R4sKogEX3KKr","outputId":"ff9f7769-1958-448f-dd9b-7b5f7bc19128","execution":{"iopub.status.busy":"2022-01-19T18:57:36.150978Z","iopub.execute_input":"2022-01-19T18:57:36.151235Z","iopub.status.idle":"2022-01-19T18:57:36.351579Z","shell.execute_reply.started":"2022-01-19T18:57:36.151205Z","shell.execute_reply":"2022-01-19T18:57:36.350902Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(model.evaluate(X_test,Y_test,verbose=1))","metadata":{"id":"1naFYlgT35-S","outputId":"84796239-f595-4eaa-b23b-7d6c3513166b","execution":{"iopub.status.busy":"2022-01-19T18:57:41.554334Z","iopub.execute_input":"2022-01-19T18:57:41.554991Z","iopub.status.idle":"2022-01-19T18:57:43.248556Z","shell.execute_reply.started":"2022-01-19T18:57:41.554941Z","shell.execute_reply":"2022-01-19T18:57:43.247804Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import classification_report,confusion_matrix, plot_confusion_matrix","metadata":{"id":"3qZywTaf4Nay","execution":{"iopub.status.busy":"2022-01-19T18:57:46.991347Z","iopub.execute_input":"2022-01-19T18:57:46.992185Z","iopub.status.idle":"2022-01-19T18:57:46.997530Z","shell.execute_reply.started":"2022-01-19T18:57:46.992133Z","shell.execute_reply":"2022-01-19T18:57:46.996759Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"preds = model.predict(X_test)\n","metadata":{"id":"IOAxLbRQ4cMe","execution":{"iopub.status.busy":"2022-01-19T18:57:48.068402Z","iopub.execute_input":"2022-01-19T18:57:48.068841Z","iopub.status.idle":"2022-01-19T18:57:49.060487Z","shell.execute_reply.started":"2022-01-19T18:57:48.068804Z","shell.execute_reply":"2022-01-19T18:57:49.059645Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predictions= np.argmax(preds, axis=1)","metadata":{"id":"P54bs5fIQ1EU","execution":{"iopub.status.busy":"2022-01-19T18:57:49.254692Z","iopub.execute_input":"2022-01-19T18:57:49.254938Z","iopub.status.idle":"2022-01-19T18:57:49.259225Z","shell.execute_reply.started":"2022-01-19T18:57:49.254909Z","shell.execute_reply":"2022-01-19T18:57:49.258093Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predictions","metadata":{"id":"HzB8GiCOMr2l","outputId":"6155498d-ed5f-44d8-e430-e8e298d5dbd7","execution":{"iopub.status.busy":"2022-01-19T18:57:50.992906Z","iopub.execute_input":"2022-01-19T18:57:50.993488Z","iopub.status.idle":"2022-01-19T18:57:50.999899Z","shell.execute_reply.started":"2022-01-19T18:57:50.993430Z","shell.execute_reply":"2022-01-19T18:57:50.999238Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(classification_report(y_test, predictions))","metadata":{"id":"HUl6w6Jf423N","outputId":"701a4d8a-ee39-481c-a5b1-7cb994232cba","execution":{"iopub.status.busy":"2022-01-19T18:57:52.732879Z","iopub.execute_input":"2022-01-19T18:57:52.733449Z","iopub.status.idle":"2022-01-19T18:57:52.763820Z","shell.execute_reply.started":"2022-01-19T18:57:52.733401Z","shell.execute_reply":"2022-01-19T18:57:52.762920Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(confusion_matrix(y_test, predictions))","metadata":{"id":"weQW_anSNmj8","outputId":"47ee00e3-ca40-4723-bbe6-f2a942c3428c","execution":{"iopub.status.busy":"2022-01-19T18:57:57.818605Z","iopub.execute_input":"2022-01-19T18:57:57.819085Z","iopub.status.idle":"2022-01-19T18:57:57.864704Z","shell.execute_reply.started":"2022-01-19T18:57:57.819048Z","shell.execute_reply":"2022-01-19T18:57:57.863874Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"score = model.evaluate(X_test, Y_test, verbose=0)\nprint('Test loss:', score[0])\nprint('Test accuracy:', score[1])","metadata":{"id":"3DyaGeFU46Hv","outputId":"1a470699-2494-4245-d1aa-562b81add3ed","execution":{"iopub.status.busy":"2022-01-19T18:58:05.162667Z","iopub.execute_input":"2022-01-19T18:58:05.163381Z","iopub.status.idle":"2022-01-19T18:58:06.075238Z","shell.execute_reply.started":"2022-01-19T18:58:05.163345Z","shell.execute_reply":"2022-01-19T18:58:06.074499Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"my_image = X_test[17]","metadata":{"id":"WduIUJj64rIp","execution":{"iopub.status.busy":"2022-01-19T18:59:39.354836Z","iopub.execute_input":"2022-01-19T18:59:39.355096Z","iopub.status.idle":"2022-01-19T18:59:39.359066Z","shell.execute_reply.started":"2022-01-19T18:59:39.355067Z","shell.execute_reply":"2022-01-19T18:59:39.358199Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.imshow(my_image)\nplt.axis(\"off\");","metadata":{"id":"18_nBUbAN1fh","outputId":"1b3bc931-1cc7-4bde-b365-2301b73b1208","execution":{"iopub.status.busy":"2022-01-19T18:59:41.139125Z","iopub.execute_input":"2022-01-19T18:59:41.139369Z","iopub.status.idle":"2022-01-19T18:59:41.220949Z","shell.execute_reply.started":"2022-01-19T18:59:41.139341Z","shell.execute_reply":"2022-01-19T18:59:41.220242Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image_prediction=model.predict(my_image.reshape(1,32,32,3))","metadata":{"id":"1DCFzSFtOGZF","execution":{"iopub.status.busy":"2022-01-19T18:59:44.923510Z","iopub.execute_input":"2022-01-19T18:59:44.924254Z","iopub.status.idle":"2022-01-19T18:59:44.967485Z","shell.execute_reply.started":"2022-01-19T18:59:44.924209Z","shell.execute_reply":"2022-01-19T18:59:44.966578Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# SHAPE --> (num_images,width,height,color_channels)\nnp.argmax(image_prediction, axis=1)","metadata":{"id":"zetmO1KEN4ro","outputId":"8ccd781a-13a6-4bb9-c114-a4bf874ac01c","execution":{"iopub.status.busy":"2022-01-19T18:59:47.039010Z","iopub.execute_input":"2022-01-19T18:59:47.039609Z","iopub.status.idle":"2022-01-19T18:59:47.045172Z","shell.execute_reply.started":"2022-01-19T18:59:47.039565Z","shell.execute_reply":"2022-01-19T18:59:47.044419Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"result=np.argmax(image_prediction, axis=1)\nclasses[result[0]]","metadata":{"id":"ytupOpKKYLPY","outputId":"7d861c89-952f-43e8-d262-cba6c5359613","execution":{"iopub.status.busy":"2022-01-19T18:59:48.836431Z","iopub.execute_input":"2022-01-19T18:59:48.836998Z","iopub.status.idle":"2022-01-19T18:59:48.843399Z","shell.execute_reply.started":"2022-01-19T18:59:48.836959Z","shell.execute_reply":"2022-01-19T18:59:48.842506Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
Doğru Tahmin Bravo!!! / Correct Prediction Congrats!!!

\n\"acorrect_pred\"","metadata":{}},{"cell_type":"code","source":"model.save(\"cnn-2.h5\")","metadata":{"id":"OuF9luxd1p56","execution":{"iopub.status.busy":"2022-01-19T18:59:53.202897Z","iopub.execute_input":"2022-01-19T18:59:53.203359Z","iopub.status.idle":"2022-01-19T18:59:53.236616Z","shell.execute_reply.started":"2022-01-19T18:59:53.203321Z","shell.execute_reply":"2022-01-19T18:59:53.235956Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Şimdi Modelimizi Geliştirelim / Let's improve our model","metadata":{}},{"cell_type":"code","source":"early_stop = EarlyStopping(monitor='val_loss',patience=15)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:00:09.309106Z","iopub.execute_input":"2022-01-19T19:00:09.309367Z","iopub.status.idle":"2022-01-19T19:00:09.314221Z","shell.execute_reply.started":"2022-01-19T19:00:09.309337Z","shell.execute_reply":"2022-01-19T19:00:09.313501Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model= Sequential()\nmodel.add(Conv2D(filters=32,kernel_size=(3,3),activation='relu',input_shape=(32,32,3)))\nmodel.add(MaxPool2D((2,2)))\n \nmodel.add(Conv2D(filters=64,kernel_size=(3,3),activation='relu'))\nmodel.add(MaxPool2D((2,2)))\nmodel.add(Dropout(0.3))\n \nmodel.add(Conv2D(filters=128,kernel_size=(3,3),activation='relu'))\nmodel.add(MaxPool2D((2,2)))\nmodel.add(Dropout(0.3))\n \nmodel.add(Flatten())\nmodel.add(Dense(512, activation='relu'))\nmodel.add(Dense(10, activation='softmax'))\n\nmodel.compile(loss='categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy'])","metadata":{"id":"c4nSjrxc28pa","execution":{"iopub.status.busy":"2022-01-19T19:00:10.665508Z","iopub.execute_input":"2022-01-19T19:00:10.666356Z","iopub.status.idle":"2022-01-19T19:00:10.737258Z","shell.execute_reply.started":"2022-01-19T19:00:10.666316Z","shell.execute_reply":"2022-01-19T19:00:10.736620Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"id":"Q7V-q5WV5vly","outputId":"d8b84803-4dd7-4423-b0e1-2d7beeeb3c47","execution":{"iopub.status.busy":"2022-01-19T19:00:12.685562Z","iopub.execute_input":"2022-01-19T19:00:12.686118Z","iopub.status.idle":"2022-01-19T19:00:12.695822Z","shell.execute_reply.started":"2022-01-19T19:00:12.686084Z","shell.execute_reply":"2022-01-19T19:00:12.695082Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.fit(X_train, Y_train, batch_size=32, epochs=100, validation_data=(X_test,Y_test), callbacks=[early_stop])","metadata":{"id":"tgKtp0Nf5p_g","outputId":"78b472b9-4e51-491e-8d45-1f290caeadf8","execution":{"iopub.status.busy":"2022-01-19T19:00:14.993166Z","iopub.execute_input":"2022-01-19T19:00:14.994618Z","iopub.status.idle":"2022-01-19T19:06:18.100976Z","shell.execute_reply.started":"2022-01-19T19:00:14.994562Z","shell.execute_reply":"2022-01-19T19:06:18.100274Z"},"scrolled":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"score = model.evaluate(X_test, Y_test, verbose=0)\nprint('Test loss:', score[0])\nprint('Test accuracy:', score[1])","metadata":{"id":"IOG8VaRqMF-L","outputId":"759dc1d8-4a9e-456d-a3be-0e1d0abda53c","execution":{"iopub.status.busy":"2022-01-19T19:06:22.119749Z","iopub.execute_input":"2022-01-19T19:06:22.120151Z","iopub.status.idle":"2022-01-19T19:06:23.814636Z","shell.execute_reply.started":"2022-01-19T19:06:22.120118Z","shell.execute_reply":"2022-01-19T19:06:23.813859Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"preds = model.predict(X_test)\npredictions= np.argmax(preds, axis=1)","metadata":{"id":"f51oR_6C5yxU","execution":{"iopub.status.busy":"2022-01-19T19:06:42.239526Z","iopub.execute_input":"2022-01-19T19:06:42.240166Z","iopub.status.idle":"2022-01-19T19:06:43.196957Z","shell.execute_reply.started":"2022-01-19T19:06:42.240127Z","shell.execute_reply":"2022-01-19T19:06:43.196210Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(classification_report(y_test, predictions))","metadata":{"id":"-QFUL2Zi84vG","outputId":"7fb0c5e1-b3af-4062-832d-0bd5a51d72b9","execution":{"iopub.status.busy":"2022-01-19T19:06:45.231088Z","iopub.execute_input":"2022-01-19T19:06:45.231857Z","iopub.status.idle":"2022-01-19T19:06:45.260825Z","shell.execute_reply.started":"2022-01-19T19:06:45.231805Z","shell.execute_reply":"2022-01-19T19:06:45.260086Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(confusion_matrix(y_test, predictions))","metadata":{"id":"nM5RPyN0872W","outputId":"de0f7460-79d4-4472-e4ad-ab75cfcb6e19","execution":{"iopub.status.busy":"2022-01-19T19:06:51.079732Z","iopub.execute_input":"2022-01-19T19:06:51.080025Z","iopub.status.idle":"2022-01-19T19:06:51.127080Z","shell.execute_reply.started":"2022-01-19T19:06:51.079995Z","shell.execute_reply":"2022-01-19T19:06:51.125956Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(10,6))\nsns.heatmap(confusion_matrix(y_test, predictions), annot=True);","metadata":{"id":"SVGLO8ve-m9H","outputId":"24fe695f-6eff-4d61-9dec-8952897a391e","execution":{"iopub.status.busy":"2022-01-19T19:07:01.543981Z","iopub.execute_input":"2022-01-19T19:07:01.544803Z","iopub.status.idle":"2022-01-19T19:07:02.208799Z","shell.execute_reply.started":"2022-01-19T19:07:01.544761Z","shell.execute_reply":"2022-01-19T19:07:02.208119Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.layers import BatchNormalization, MaxPooling2D","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:07:04.841315Z","iopub.execute_input":"2022-01-19T19:07:04.841965Z","iopub.status.idle":"2022-01-19T19:07:04.845410Z","shell.execute_reply.started":"2022-01-19T19:07:04.841922Z","shell.execute_reply":"2022-01-19T19:07:04.844709Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"early_stop = EarlyStopping(monitor='val_loss',patience=5)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:07:06.675639Z","iopub.execute_input":"2022-01-19T19:07:06.675893Z","iopub.status.idle":"2022-01-19T19:07:06.680492Z","shell.execute_reply.started":"2022-01-19T19:07:06.675865Z","shell.execute_reply":"2022-01-19T19:07:06.679659Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = Sequential()\n\nmodel.add(Conv2D(32, (3,3), padding='same', activation='relu', input_shape=(32,32,3)))\nmodel.add(BatchNormalization())\nmodel.add(Conv2D(32, (3,3), padding='same', activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.3))\n\nmodel.add(Conv2D(64, (3,3), padding='same', activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Conv2D(64, (3,3), padding='same', activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.5))\n\nmodel.add(Conv2D(128, (3,3), padding='same', activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Conv2D(128, (3,3), padding='same', activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.5))\n\nmodel.add(Conv2D(256, (3,3), padding='same', activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Conv2D(256, (3,3), padding='same', activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.5))\n\n\nmodel.add(Flatten())\nmodel.add(Dense(256, activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Dropout(0.5))\n\nmodel.add(Dense(128, activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Dropout(0.5))\nmodel.add(Dense(10, activation='softmax')) # num_classes = 10\n\n# Checking the model summary\nmodel.summary()","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:07:08.768665Z","iopub.execute_input":"2022-01-19T19:07:08.769434Z","iopub.status.idle":"2022-01-19T19:07:09.002117Z","shell.execute_reply.started":"2022-01-19T19:07:08.769392Z","shell.execute_reply":"2022-01-19T19:07:09.001431Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:07:17.675727Z","iopub.execute_input":"2022-01-19T19:07:17.675979Z","iopub.status.idle":"2022-01-19T19:07:17.687524Z","shell.execute_reply.started":"2022-01-19T19:07:17.675953Z","shell.execute_reply":"2022-01-19T19:07:17.686829Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.fit(X_train, Y_train, batch_size=32, epochs=20, validation_data=(X_test,Y_test), callbacks=[early_stop])","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:07:22.462350Z","iopub.execute_input":"2022-01-19T19:07:22.462903Z","iopub.status.idle":"2022-01-19T19:11:53.382594Z","shell.execute_reply.started":"2022-01-19T19:07:22.462864Z","shell.execute_reply":"2022-01-19T19:11:53.381916Z"},"scrolled":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"score = model.evaluate(X_test, Y_test, verbose=0)\nprint('Test loss:', score[0])\nprint('Test accuracy:', score[1])","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:12:03.944219Z","iopub.execute_input":"2022-01-19T19:12:03.944491Z","iopub.status.idle":"2022-01-19T19:12:05.269911Z","shell.execute_reply.started":"2022-01-19T19:12:03.944458Z","shell.execute_reply":"2022-01-19T19:12:05.269189Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"preds = model.predict(X_test)\npredictions= np.argmax(preds, axis=1)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:12:22.214017Z","iopub.execute_input":"2022-01-19T19:12:22.214722Z","iopub.status.idle":"2022-01-19T19:12:24.114486Z","shell.execute_reply.started":"2022-01-19T19:12:22.214682Z","shell.execute_reply":"2022-01-19T19:12:24.113708Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(classification_report(y_test, predictions))","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:12:26.157934Z","iopub.execute_input":"2022-01-19T19:12:26.159830Z","iopub.status.idle":"2022-01-19T19:12:26.186949Z","shell.execute_reply.started":"2022-01-19T19:12:26.159780Z","shell.execute_reply":"2022-01-19T19:12:26.186256Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.save(\"cnn-3.h5\")","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:12:28.780494Z","iopub.execute_input":"2022-01-19T19:12:28.781150Z","iopub.status.idle":"2022-01-19T19:12:28.914785Z","shell.execute_reply.started":"2022-01-19T19:12:28.781114Z","shell.execute_reply":"2022-01-19T19:12:28.914002Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"___","metadata":{}},{"cell_type":"markdown","source":"# Image Classification / Data Augmentation
\n**Dictionary of Data** --> [Kaggle](https://www.kaggle.com/c/dogs-vs-cats/data)
\n\nÇok az veri kullanarak güçlü görüntü sınıflandırma modelleri oluşturmak için:
\nTo build powerful image classification models using very little data:
\n\n**Explanatory Video** --> [Youtube](https://www.youtube.com/watch?v=hxLU32zhze0)
\n**Explanatory Blog** --> [Blog Keras](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html)","metadata":{}},{"cell_type":"markdown","source":"Köpekler ve Kediler veri seti, resimlerin köpek veya bir kedi mi olduğunun sınıflandırıldığı yaygın kullanılan bir görsel veri setidir.
\nVeri seti ile iyi bir şekilde çalışılması, görüntü sınıflandırması için evrişimli sinir ağlarının (CNN) nasıl tasarlanacağını, değerlendirileceğini ve uygulanacağını anlamak ve uygulamak için faydalı olacaktır.
\nGörüntülerle bir sınıflandırma oluşturup ve CNN kullanarak kedileri ve köpekleri tespit etmeye çalışacağız.
\n\nThis example shows how to do image classification from scratch, starting from JPEG image files on disk, without leveraging pre-trained weights or a pre-made Keras Application model. We demonstrate the workflow on the Kaggle Cats vs Dogs binary classification dataset.
","metadata":{}},{"cell_type":"markdown","source":"Bu örnek, önceden eğitilmiş ya da önceden yapılmış bir Keras Uygulama modelinden yararlanmadan, lokaldeki JPEG görüntü dosyalarından başlayarak sıfırdan görüntü sınıflandırmasının nasıl yapıldığını gösterir. Kaggle Cats vs Dogs ikili sınıflandırma veri kümesindeki işleyişi görselleştireceğiz.
\nVeri kümelerini oluşturmak için image_dataset_from_directory yardımcı programını kullanıyoruz ve görüntü standardizasyonu ve veri geliştirme(augmentation) için Keras görüntü ön işleme katmanlarını kullanıyoruz.
\n\nThis example shows how to do image classification from scratch, starting from JPEG image files on disk, without leveraging pre-trained weights or a pre-made Keras Application model. We demonstrate the workflow on the Kaggle Cats vs Dogs binary classification dataset.
\nWe use the image_dataset_from_directory utility to generate the datasets, and we use Keras image preprocessing layers for image standardization and data augmentation.
","metadata":{}},{"cell_type":"code","source":"import os\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom matplotlib.image import imread\nimport warnings\nwarnings.filterwarnings('ignore')","metadata":{"id":"7YcSALh_Tqpg","execution":{"iopub.status.busy":"2022-01-19T19:13:13.833269Z","iopub.execute_input":"2022-01-19T19:13:13.833888Z","iopub.status.idle":"2022-01-19T19:13:13.839937Z","shell.execute_reply.started":"2022-01-19T19:13:13.833847Z","shell.execute_reply":"2022-01-19T19:13:13.839142Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pwd # Mevcut dosya lokasyonumuz / Our current location","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:13:16.567914Z","iopub.execute_input":"2022-01-19T19:13:16.568222Z","iopub.status.idle":"2022-01-19T19:13:16.574760Z","shell.execute_reply.started":"2022-01-19T19:13:16.568189Z","shell.execute_reply":"2022-01-19T19:13:16.573950Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Öncelikle ham verileri (786M ZIP arşivi)'ni indirelim:\n# First, let's download the 786M ZIP archive of the raw data:\n!curl -O https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:13:18.619659Z","iopub.execute_input":"2022-01-19T19:13:18.619917Z","iopub.status.idle":"2022-01-19T19:13:37.354593Z","shell.execute_reply.started":"2022-01-19T19:13:18.619887Z","shell.execute_reply":"2022-01-19T19:13:37.353768Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Şimdi indirdiğimiz dosyayı zip'ten çıkaralım. Ve PetImages klasörüne erişelim.\n# Now let's unzip the downloaded file. And let's access the PetImages folder.","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:13:45.575990Z","iopub.execute_input":"2022-01-19T19:13:45.576312Z","iopub.status.idle":"2022-01-19T19:13:45.580112Z","shell.execute_reply.started":"2022-01-19T19:13:45.576277Z","shell.execute_reply":"2022-01-19T19:13:45.579399Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import zipfile\nwith zipfile.ZipFile(\"./kagglecatsanddogs_3367a.zip\", 'r') as zip_ref:\n zip_ref.extractall(\"./\")","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:13:48.817832Z","iopub.execute_input":"2022-01-19T19:13:48.818511Z","iopub.status.idle":"2022-01-19T19:13:57.425702Z","shell.execute_reply.started":"2022-01-19T19:13:48.818467Z","shell.execute_reply":"2022-01-19T19:13:57.424940Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](attachment:4c822d63-cc7e-4f04-8007-8d69ece10754.png)","metadata":{},"attachments":{"4c822d63-cc7e-4f04-8007-8d69ece10754.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAATYAAADsCAYAAAD+bopUAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAEo0SURBVHhe7b0LXFTl2vf/e7XZyGiM7cCNGGIIGyEQUXSL9Ed9wTb0JvWE7ujZYoWHPOQhi2izKbLYPBqPpCkeMLAcqzEdt6GPsBPehB1igJBIIAkkosjLYDqkgzTl/O+1Zg2sGYaDggeG6/v5rA/ca92z1rrX4beuax3u3/9qarqsg4jmn3/Go2OchBJBEMT9zeXLPwn/tTNI+EsQBGExkLARBGFxkLARBGFxkLARBGFxkLARBGFxkLARBGFxkLARBGFx9F7YrhxDwlOeeNTZ1WSYiYT8VqHSwEZzOgOH8hqgFcrEPaSpBIf2FEIlFAnLpNcv6Kr2L8KUN44JJVMewcwXQvGYjVAUYefyZ4Q86QG7wcIIS+VmGRSr0+GwNgYBDwnjOLQt0GgAqcxaGCGCn2ZOBiWQPGgNifhydFMLTVMdai8BtmMdYTdMIkwQaGXzuiGel8T8Ms2hLkPmkRZMen4y7IRRHeisHdx6XaxBrfZhuDjaQmKyWubQXmPtqNZC5sraIe3kB5pm1J+rhOZBNzjZ25jMVwutusX4AjLEBlIr4X8eLU5ufg2FvhuwxK8HK0Xc95h7QfcOC1s3/HUnzrw3A0bHXZ+jher0d9A8MhlOYmHpNc2oLTgPqbcn7LpogCY7EcvKpmHnKn8mKXrUBXuQtCuPnfQ+iP5oMdyF8W0Up+DFrSVCQYw9IuLjEGivL2lOK7B+aw5qYc0EDbh6pYVVmY7omHC4SPV1VIfXIupgg75gQGKD4EXvIHxi1wKnzUvGolNT8PGyycIYYzptx8UMrI9PR5WVDYYPbsHVn2UIXBGDcK9OlnezCSe3J2L7aS2GM2G+rm6G3ePLET3fE0IzWJ0GnNj4AVLLmyGR2WDojWaotDaY9NyrWGHYICiBfGEKsoWSAalzKNa+GQI7wwWBbd9FR5zx37FBkAmjiP6LOWEDJ2zioebHc7pboXHfQt2YR110/zgujOgRxbp/sN+M+c8vdOeFMXeOS7qsvy/R7T4pFPuMYt3uBe/osi4JRbOodfnrlug2ZKnbyqe2vaFbuOYDXY5yi+6FBTt05cKU7vjl5A7d0lVy3dnfhBHV+3V/W/CqbluOShjB+OW8LifxVd3Cd47oGoV6jYfe0b3wd1bWF3muf7tDt3LRFl3RDWFEJ5RvE6+7mC7a8dsZ3eerluje+2f7nr3O1p1f3nVhhAncOorXWXe1QJe64lXd56VCWafRFW1cwurs1527KoxiXP9Ornt70ats32qEMdw+MdnXbJtkvLNE97d9oiPtxje6bd3uO6K/YKph3NAvHh5ozmRBuSkBUfHJUB5lachNYQKj/lga5MfqhBJHMyoOpOHEOe7/OpxIVSK/Gag4yuodKAPLmqA5lc7/RnM2C4r1axG3PgWH8pu4HwhwvzPMQ4BLy1JzUN/2fxYqoEb+PtPlizmPs9U2cH3UkIv/ArhGYFPiagSMuZU0qAG5B0rgMjsULvwea8aJfVnArFewJMCWr8EjcUTAiucx6WI6Mr8TxplBOmUyfG9W49x5YYRZKnGq3B7e5u4jdNWOc2U4ec0HoaGOwgi2vIkvYf5jZcjhdkQHanAiR4PQCFFEJZuMsNXPw+NBoX65Eqnfe2JJVBicRCGW1HseXn5iKLIPHOv8nhnbJpMm2bP0tYbf9zxWY+Fq38D2Dd31tFTue2FTZSZg1fYyDPWZhYgnJmDoqR1Y9V5G24GsPlOI7DNiUdKgvrgQZ/noVIaRXm5wYKmibLQXvF1t+XTwel0JsrPk2PzJeTjMfBJhfg/j3N63EH/YkLI14Wy+YR4CLXUoyq9kUsYYYgsnL0c2dwkcXNl8R3eS0DTUofamLeweFsqwxfhAll7d4lbXFqdDec0foTMFkblZifKztgh43FlfFmM1GUtStiFiolA2x9lKVAwaizGjhbI5LrI6Vp5wN2R5RnTRjhstuM7+GN0HFKgwp6SNTEDVbnBxbELFYQXk7IKiPFyCXxwnY/wYfXtVP7CL2YQpmNSWl7bjMOc9fBzPRFEod+BmM6qrG+Awxrk9rWUpvcMjbC9fuSyUCUvj/ha2myXIPNCEwAWrERzADvQp/ghe8wpCW9iJnt+Tq60NnKZ4wokJm4Mb+72Xfdt9LjTKEBQTiQAWvYwPCMOKlUHQpCtxsicPcq3s4T6FCSY7VZwmsN87m4tqDMgg69WNnDpkfyGO1hiNTSxydITTKKHcHY0ZeD8qGlH8sAaLki8jNG4xJnV1b/BMJVRenqyNt8i4CQiQnkG+ODq7mIGM00ygtWb2WdNl1MtakJPwLhQXZHB1HQn1iV2ISWAXLyEyb2pogsvo9giwO3J3G9rKhhXvIGvEcsQ+2/PfE/2f+1vYWPpQCk94ewlljkHOcPewRunZGmHEbTLKue0GO4+zD/xk1Sw9Ecp9iFaUOt8q2nwm4jdE0ZoIo/lyDxwWLm0b2qNPhswTs+fOQQQ/hCH8jw1ITZKjoi03M0WLirIa+Hm6CeVbYJAnQl/0RMWuWMSwFF++aS2WJZTBaaIoZRZzQwPNlTJoHo/H2iUhmBoQgsh3YxCmTYf863Zx1P4q/MPTgOzY9ra+uDAFFcIUDpc/GdrKhmemQPJtCjZnmjxAISya+1vYflbjqq0tS3yMkdkMhYZ7x6A3DLcRpSYcUkitWqC9IRT7gods2LpfgqpRKN8yLFr7sgzu4miNY4Q9nAZVo1Z8D3DiYnz80TZ+WGOahlo5sgiTi3j1UW/gshgsGV0I+ZFO7g3eLEP5957weEwo3yLSiZFI3ByDyJk+8J4Rgf/aGAO/YdfNR10OjnBhF6/pj4uEe5A9JvnasouX/ipj+4g9amurRa9x2CMwXt/Wj9eGwPQZPh+dG9o7KxzRK6dDfVAcjTdDzd1TGPSAvkhYHPe3sI0ZC/fGSlTzN7YMaHG+tj01kTHxMKVHEdKFBuMbzq0sWmtw5O+9cOnjcNNXQ37Tik6sHmI1FmNGNKC2y5v0ndNptMaiIg+vZmRkFrbfEDegycPx76zh4mz25piANUY72aK+SXxvUsSZ75E/whmjb+c9HP7BSgaqmPi4cMLi7QzZ4BpUlEvgPc5M1MaL9HnUXxTKAlz6afeQPoe382Yp8eksZJvU4aj/phC1rm4dxM2IMaPhcrMJV68IZXaxOXeRbaMxnUSRRL+nz4TtuOIDbPigp4McJ4XfdYlsCqZ71eDzz9kJLIiVpmAXdn/vjCA//UFpN3o0JOXHcaKRyY62GVV706A0c75evWLyRO5KFtIOChHLzWaU7mJXdFd/+I7gRjCBGw3k/zsHKk7NrlTi0K4s/RNRIzS4erUrubNnJ7YNis5UCuXO0ZxWIi01r+2+UqfRGo8Ek+aFw+W7NKxPLUS98DKv9mIJlIl7cHLULAR68KM60toCVTlL8/5vE6YywTBH/enTGOrl1vkN+a6Q2ULyYzoUB4Sn12zb1u5TIP2hJxEgPOswausgH/g9rkW6XIla/gKmhTo/BduLbBE4TfiBYxgiA9RQJG5EdnmT/sLF9nXt0WSsP3odgcHTTKJvA9wLu3UoTUlHrq3oQUhDDRPeifAdJ5QJi6P3XYNzn1RFvIKd5bf4+ZTUA4s2fYSYwG5OH00NsrfvgKK8hZ3P7AQe5Iyw1a8g2FV42VP04qaWnfDuz0bA+3gaVM+2PxnUFKchbnshVEP8EfvhPMi4l1ZPuCHi0RIoilog4aKxEcYvtuJiDjYnKXCSO9kkjoh4yRP5KQ0IE72IqjqSgJgDddC6hmNr9HTzJ1dDBuITLiN84zxjgeJfwkXbi631+99CzFcPY0niakxlgYomLxmr9skQnWTyOzFNhVBslrNIRogmB0ng4heBl+dNhp3wlMTcC7qSYY7weyYCkTPM3VBvQvbaBKjmJiG8M3EUY9IOHm69NsqR2aAXXKlzEKJXh8FJ2ECmbeVe0C39JBmbDZ+dSewR/NJyhE8RR1QtqD2chh3/U4Z6/WwhecgT4QsiEDjOENGaeUGXbROHsdMRsSQM7sJDHG75W7AYCXPogYIlcEe+PLhrcJ8GaR+A1PSToduAP9lPTEEi95oAN9/f2Hw7+4Sn12hRkRqLHNe/Y0lAx7S5T+jq86xbpTUP21d8j+nbmVD1Np7XNEMz2PSTpi7gPsPi2tHNPtZeYxcxyS3MV4ymEGkxBfBOWG729RGi/9G/PQ+srPtE1DrAzfeOiRoHiyIXrL9zosYhYW3oC1HjaASGPzO98yjxVpDeoviw6Kon+1gy7DZFjUM6GZEbSdQsnf4jbH3I76xlsPu9FL8TyoQIR3+EP+nW/r4fQfRDyH6PIIh+DdnvEQQxICBhIwjC4iBhIwjC4iBhIwjC4iBhIwjC4iBhIwjC4iBhIwjC4iBhIwjC4rj/hY18Swmi//JbM6qyU7F6fjC84/OBonUdzuW/Ki4IlfuO+/7Lg7vlW8oZvGS0TkPYFBnvTcl959gTL8w7wm1+8N/ehrvQzxj3wfrPv3b0ORXQ1BWi6oYnxht6YSEGHppybHnxL9hQ1Aorex8ER/0XNjruw6N/SRUqABOXJuPvf5mOiVz//beJBX55cAFff7IVWzZ3HOJWPY0p7xxDz2I6LSqOZUDN99VbBsVr0VCc1k+5F6iOvo9l67Ju0a1c3Ia7QGMWkl57H7lmewduRuneNGz+sqBjR5jEwOC3C9i7ihM1YGbclyj49xfY+B9jhYmMGU8g+CGgeNs+nB18+6LWGf1G2BZ9ehY/1vR0+AKLuB/VqLjOKrqH7wrbGR630cX/fcN91QYbTH19G3a+3kkfdYTF0/yvJMRlt8JlsRzbXvCAjWnm5DwP2+SvwgXH8Oa6Q+xS2Lf0sbC1ou7YHjM95grDJ8dQdz/eFuO7wvbE2E7cpNQFSsj35kFlWPfGQhxKTcb6qARs35OBCtMee9WVyN0jTN9fCPUVkScpjxaqfCXSeE/TNOSebeH9UTNPdb57u/JW5RG1Qcvqyo9U6jtt5NH7pGaXt4/RlmdAflDvs8ojXuc9WUJvtnr0Pqw1/HbYHh8NpdlotgW1RxVQHGbrxkpGfq8GT1ZNDXJTExEXm8jqlUEtbgPX0y77PTf/9ZuUKG0S+8MS/YtmHP/XIaYGT+CVBT4wisfGzsWnu3fj02ddAI95eOVJNu5IOjL62Gunj4XNCo7/3ww8dulfHdPDI82Y+OcZcOz7qLPXcL6VnXWFrSlIwdufXYZ3sD/suHUv34NVMZ/jnGwCwl4KwwSUICk+DRUG0dOUQB63EdmtbgiZO4tNz0PSh+nINniSMlSHExGztw4Ofk8ibOZo1H7yAdKyClFUZz5xM/ZWdYM2Jxmrko39DsRtkAzXovZgDqoMwlFTAGV+IRS5ZcIIoCo3HaWtUn1E1ZCF9W/uwKkhE/TrPKQMSUzgsoWDjfdhzdyF7V9p4fHEHPjyvhBitKg/+AHij0sR8Gc3fp5Gfq+8J+sxpG1Ph8Z1BsKecIYqKxlv7zF0md6CipR3EP+1hp9/iA+Qs3ELv85G3q5EP6EaxYfYHz8/TPy9fkwbD43FtMf9MM2DO1JtMJH9Dxa1Vf/IT+0z+j4VHfwIgv/rI2ybI8qnx85DmjwOM7vyF7lnNKO68jomeXU0H+ZE7W+faTE/djHGG6K5scFYExuDFXP84cLyvqnzwhEqYaJ0Rj+5/sgB5D46D9ELgniXpKlzVuPlx1ra75e1FkKZ3oLQlQav1CBEvDULtp3lzCzFzD5iXD/83cUIPJuO3DaTKZM22HvC9/clKBLc4FXlZZD5+sCp/HtU8WM4l3cbBEzW1684qETtlEi+TYZ1jg5sgWJfYXvU99tjCI8J531YnYyMbjhRY1HYKTfERoXCodPnHRq4zxbawPm4zveBpqhQvz51RyAvHoslsSKf15fdoGkzXyH6JdzB08OHd33NnbnHJha3+1rUGK2n8V31OHibGHtc5UQtpQ4hry3HJPH9eCtbOI0aClVNGUpzM6BMTUcRF4r9xk3U4tLFJrh7ehndW3KYOKE9Grx4HtVD3OAu1lGrCZjcmdXduUqc/O1hSJoKUVogDEUN0Fg14dx5QXY6tMER431skF/GRURM9L7XwPuJEPhKT6OCs2Nl634SXsI61KCqml1cJxobuziw6E9ythK1QhlWMjPO77+yyJCJ2r9kWBHV7mlgHimGPyj8y+HgCCeNhj/2tecbUD/WE+7i3zv6wM/UKYzoJzyCxwLZn6J8FHNX9NYLKP4mH8d/ML3V0syPB0tZH+tjY507I2wcvLh9iVNf3seixvH99zj52GMdusIuPQ94OzYh439KjFI+zhgmank0Pth3HKdYnVG+LMJoEz41rl5lp/AQk1ccrCTtQsd5pXLdkQtFPZLOXy3h6g/W8M5Rp9qG85CMm4wxw1r0dcy0wcHLCzhdhnp1CY5f5ESMEzvg7I/NfAR33WcCXPiaGlxV28LO9GGqzAZ2gvB0ThNOXbPFeKsypP/f279JouFMPoc8YNJrr/TevW5D9BI7zPyP2bDCV9igKAeu5GPL/Pn46xffC9P1tJakYssRdnrMDsa0Pu45/84JG8dgK9h0eRW/91SVnYHT2LEdusIOmLMYkVGL4XsmBUltrurNKD1aCNu58UiIXoyIeSGY6j0Sv2s7+1k096g1Sn8wtttTMyFpi3w4r9QrlagSp1ncE00hle3AyJEsspHBe14kIhYYD8He+qPBbBs8JiOglUVmmWWo8GCix0ZxYldVlofS7xvg12a9NxquY5vY8o2vptpzdah1dISDUDaPPULnLWbRWiiQ/j5LJwWhvUVkY0ZDWv0jasUPE66U4dRtG00T9xqbP6/B2kAr1H3wF0TuKO/wdkJz+R4sjdiKKukMrF0zu8fvm/aUOytsfcgd8S1laVjFaQm8PToJKaU+CH9pMlTpaYJZrw1kMiZUIqNhdV4GskQi5fLELDjkp2H70Rqo1Fx0pMT2zKb2CE02DSF+TZB/qEBFXTOLVupwIlmJ/M72xIgZCPEqw+5d7d6qqEtH/FLDzf3O2uDGxjUgPbsMk7wEERv3GPzOpENRLXZ5t8H4xz1RcUiBUsPTDXUhFPvK2Hh/dPKg2JhRIXj5GVvk7pajwvzzj67xCELYw3nYnJyF2ka2TRorcSj5KOrv84si0QXXVWi1n8hit1Z8/cketMVqtYew+i/TMOWptfgaPnhNvgHP3YHvAe57YbML/CsWeVjh+0MmT1m7HA6hmPMtjZyBLp0j2Ql0qtVwr8k8Eu9IxLKARJGYwp/47rPDMbIoES8uXYNVS5ci/owzAsSaYh+C2Leexu++3YH34/+BLUelCF/CxE6YzKWd7i+9gzWP1UHx4T8QFy/HuT8tx4JO7zFYY9KiKIRq9mPV4pVYtnwpXkzIg+2cSARyy+2iDe5TWO550xkTPIVYjnOQZ8vRPjaBpY/6URxSf9bGIA3kUUuxaOlKvBglh+rxKCwJ7Hl+YPfkq1gz7gySEtPbfD97jj0CY2LwvKQAWxLZNvkwC9LnXkboHTT2Iu4wmioc+zS/40vmqu/x9RUXBL+5E0f//QVe8bkzO3lAm7lo85Kx7PQ07FzCBOAW6dLbkousxJeMGiWi1qkRmRKpNxU2nQ4tTm5aiQzX9xD7ZBdfDnCfWt1gQiSy2utNGzrQzWdSd5QO26QGh6IScXXeNkR4C6MIwgxk5mKC6jcZwgOMnwb2lM68LbXlexC1JhknLgphC+dkvy8PV728hJv1TchdtxLxeyuh5qowMVEX74XytC18vbr5HIp76GDiH9qbNnSA8/Vk87/rotZaCUXUGmzPbYCWEzhtC2qPKJGh1keYBHGrkP1en8O9gS+H/GAJqriXdiXWcPd+GhELpre/49VUCGXqfmSeZVEfK0ptnRHy/MuYLTwMGIhozmZBsScd+eyCoGXpumyMJ55/4SVMdTR9rEMQxpiL2EjYCILo11AqShDEgICEjSAIi4OEjSAIi4OEjSAIi4OEjSAIi4OEjSAIi4OEjSAIi4OEjSAIi8PyhY18SQni3kG+oneGO+1Lyn8Mz/eeKyCxhlTaB58B3aa3KEHcN9xDX9H7VthUR7bi+PhleLqDccit0bWwdcNfd+LMezOMXXaMaEB27FrITTqPldhPx5qocLj3qDMzxpUalF6Qwt3Lvq2zSNXhtYg6MQWJ8SFmTWYI4r6G8xVdEow3szlf0S+wcZ5gwcdFbJywcb6ip75C5pUZWJe7E8/14jzvX59UaUqw+j/X4Ms+ilLvpC+pyzNx+PijbfohZT1WPFKA9Zuz2lypuuXHLCTtLcFVoUgQ/R0L8xXtYy4c6lNxuysMssH4Z2fB5VwJKtqUTewjmoJD+e098PKenUfr2JFQgkOpnflocr6g6ahobELpHsGX82gNNGhBfS7n9bkWSanGXqAGn862ZebWGXk3GK9TJ96mWra8/SlIYlFp0lYlShtNepDkev/dz3mRCtNN/VWJAYrF+YreAfqjuPFocZ1/NqFFxa5YxBxSw2Xmkwjzexjn9r+LuP2cXRRLW22d4T3aBpz7lYuXF0aaTV+bcDY/D8qUPTg7mvPldETVvkQkxe+AstEe054IgseVI4hLzBB6LG3GicRo3qfTJTgcEcGOuHggAUlH2o+ebr1NW8ugePNdKNXOCAp9EtNG1EEe9y4y9avNhLMSirgEZKjdELYyEiFsemp8yu11DU5YGJboK3onsJLB5gHh//seLeq/KUCVrRvcR7DilRyk54829syMDYX0qwycZMInGeWJ8W5Mzawc4d7Bs1NMM2xnLkcY78sZjpeftEWVxAcLeC9QfwTPZ1FiQw3O82JqjUn/GYW13DK93eDiHYIFcz1RVViiT4974G2qzk1H9ujnjPxRY5+1hvJ/BK/RxhpUXfNB2EtBcHFk6z5nOd6NehYuQ/ifEwR3KliYr2hfcr/7kjJqMz5AVFS0fli+EnF5MixZEcb7HGjLK1ExwhraMpEv6FmWs908j3reIKbnDLUWPSF9gCm9zKbdJGaEPZxYZHeVN5aRQOLoDLubdahiy8vdnwb519X8gfYLN7lbb1MtqspZNDfsV/73hvWubZJAe/68Pioc4QyXYSXY/HYKsnMrUX+Nrc4o27vf+y5xH2LJvqJ9QT8QNQ6HgEgWHf1dPyQkYeeG1Zg6Sj+N98z89TLOGvmCalgK6Aa7O7X1bzYgO34llkWnIYstTy3zQsBEkTN/t96men9UbVO1aJ3ZoHkYgeOE7ssHuSE88T1ET38YtfkKrI9aiUXxLBU2OGkRAxhL9xXtDVIfrPvo/hc1DslQGaRc9MQPxp4EslH2kPzmjEATT1BumDpGqNTXnMmCsnEKojfHYQlbzuxZLMUV37vr1tvUFg6PMGEbM6PDOvNdnAu1MNgWLrPCEBkdh03JUQhWp7PIsK+fbxH9EfIV7QS7J5f1qd/gnfEl7QFeMxA6OAtpB+uEESyKK0jBquVpqBA/ZGxVQ2Py0PG2+f3DcLihRtPPQllTg8ysMqHA6IG3qfvMIEiyFcisE1bqZjNKt6/BspQy/taJtigFi9boLQl5fr4MdSvbbw8NXN8GQsQ99hW1fM8D7pOqiFews/wWP5/ifEk3fYSYwK5ej9W/oJs/NQ6xT3URWjYVQrFRjkx22ZIO1kI7yB7BS95AmJcQ3XGpY0IC5Oe0GP/8eqwJtDF5QbcE8oUpwLJtiJio/wk//UIoPm6z3ePqpMMhPg6B9i2o2vM+1h9rgGSYBNpWe4T/2RbyIsf2F36ZUFUc2AHFt03QQIZJcxfDo/gtpD/S3hZ1wR4k7cpD/SCWo7ZqIRk1HWuiw+HC57CGZVxmUSqXcrOUfMZiRM/zNElxiQFJwz5EToth4iUicjd+DM6B99/KMXNuJF6ZOwMunT4s6zlk5nKv0bDo6Kb13ftMStsCjcbYh7SNHnubMiFWt0A7pBMPVWEZ98SLlCAYZOZyr5Eycbib335y362aE7Vb8jaVQMLdO+zsuzJhGSRqxP0ERWwDFfI2JSwESkUJgrA4KBUlCGJAQMJGEITFQcJGEITFQcJGEITFQcJGEITFQcJGEITFMXBe92iuRnFpI24IRWNkcPXr3riFIIj7jwH8HtsF7A6bibgSoWiOGQnI3TkXjiRuBNGvGMDvsalwgRM1PyZepsYtXyzQVzkWg4BF+1AnttIjCKJfQvfYxPRHcdO2QFVThoqaJmi77PaI+5i9uZs6rNY1rhsjk+EWO0YhiHsNCZsp/UjcNKcViFu5BnHJcqQlv4tlK9dCebpFmGpKGRSvRUNxWiiapQaZa6OxjNUTD0lH+9hCiCDuMCRsg+3wmJdH+2BvxYubvECYfr/SWojdmwvgsigJWzesR+KGD7HpeVtkfnoE9UKVW0eNq1fsEREveKQKQ5d9zRHEfQgJm88CHP7yy7Zh1xrODuzW0HBdcW9KQFR8MpRHK6Ex9PuvLkNmag7qNTXITRX8QA+XQd3mCyD4hV7h/Ds5f84EbN+fx+oLk7vihj2mLXwFoRPbuyWS/tEZTk2XuzZq/tWwLMGLVLyshgbUYiQcSMeIfg4JWy9RZSZg1aY8aD1mIeKJCcC3yViVXKg3J26pQ1H+MaRtT4fGlfMDdYYqKxlv76nkf9vmF5q8C6dkUxAydwqGFu9B3Oac7l3kZY4YP8UZYisDVVEJal3d2j0JzFC017AswYs0weBFyvjpMuofsoEmXwl5ahrknMh2uyIEcf9BwtYbbpYh+0gTApfFIXwW58/pj7Co5QiXXkLtNaEOkzj32YJ/J+cpOt8HmqJCVAlTgWbIAl5BBP/7IESsDIXT2QKRi3x3lEHJ2f6tXon47z0Ru2K6kdiZ4vK0YVn+CF4RhqkNBSgVbqFpNb8A10qQUwI4eY2G9Id0xLEoMvsWbQIJ4l5DwtYbzlXiJCbC10soc1i5IXBBKNyHCWVIMfxB4V8OB0c4aTR8544Ghg8T9XJrz/mDanBdeAZQsX0pXlwoGmJFERbPaPjOnYOI/wyF389ZSNqa06UFntGyrKQYKvzLIfGNxNZt67FmWRgCmMiGxaxH9ONNUBwQTJIJop9AwtYbzPpz9i3u89fzDwfahjeD9GYsbdjAiXOYZ0IU/u5i+J1XIPM7YVIf4OI5DtqzlagVygTRHyBh6w3m/DkN5id9ZRzM+SS0eZa2eyZoTqdDfqBMfy/PwCBb2NkAqiu35+2pKlBAfqzdJpBD28JiNZuHu0xvCeJ+g4StN8imYLpXHZR7C9uehKpzU7DqLeUdj3Ckw7WoOJKG3blNwhgtVLlKpDc6Y9pEzregBVX7U6DI7fk7aMMfaEb+Z3JknhMSzyuFUOwrg8s0H5MokSDub0jYeoU1Ji2KQshPcqxavBKLli7F63s1CF0RBpc7vWUdwxC7Ygqa9r4l3H9biZgvtQiNegVTea/GJlQVlSC7oNI4qusCycSXEDtXiuyElfp5RstR67sca56k9z+I/sUA+Qi+GjtnBSOhWih2iw/WHv0C88cKxZ7Q2gKN9oG7a69ngPMrZSIrlfbVsrl0+leAvEKJfsDAdqnqstsiY4aMfAwTx5INHUH0B8h+jyAIi4Ps9wiCGBCQsBEEYXGQsBEEYXGQsBEEYXGQsBEEYXGQsBEEYXGQsBEEYXHQC7o85CtKEP0V8hUlX1GCsDjIV5R8RQliQED32MTchrjxPpyaLvqX1bZAY65/NsEPtLS8rqNvJ/8bka/nLfXv1jP/0Da4D+i7Wv9eoL1Wh6qCStRf637+/HbsrJ6mCbWnzMyH63jAaDsZhs4sCI3hltnj7UT0KwZIKlqCBOe/YCcXsX3K0k1hLE/ROjz6l1ShINDjtLQB2bFrIW/2R+zGeWa7Kqrf/xZiMh9ARHwcAoXef1TZG/H+3hr8IrPG75gQXb3yK1yCV2PFHGd9b7zFKXhxq0nePEiC8U+sxhJDnU4pgXxhCrBsGyImCqM6pfv1vy1uNuDExg+QelaL4cMk+IUJyO9cw/HG6umwM7eMhgzEv52OqhGhSIwPEfX91oLa/R8g/qsGDGfbipsPxoYjdo1+PqrDaxF10Fx/cz6I/mgx3IWSefRtr3+2J9uJuJ+hb0XN0Re+opo8ZH1r5tJ/swy5bR1BCtQp8cFeDQLf2oBNieuRmJiEneuegyx3C9KNzIy5k1Pk7xkfBimroyzuwxCjLg/ZjbZwkuYhvw+7E68/kIzUn2cgITmJtW89Nn0YhamNCijNbSM048QnGcCojn2+aYvlWP+NLdZs/FCYTwxCriqQlq3vIdjuqTgj/1NuSHzGHpJunLoIy4eErQ98Rd3HOOJEdkfLPO23OciW2hqdZNoLDagf4YNJjqK+02z98fzq5+H9+y5Ea8R0TPduQW6Jwbqv99R/+x2u+j2Hl31tkZvXV4YtWkg8QrFmYVB7dCZxhscYoOnyZWFEO5o8OVJvhODl2SOFMQa0KP13CcbPfQnuhhBV4ojApcsRMsrYDKeN1hJkfqVG4GwzTl03m1Gfq8T2+Gis36REqcn1hkfLea6mIIlFcklbWZ1G06VoocpXIm39WsStT8Gh/Pbei2uP7oFS3FtxayWyUxU4Udc3W5W4NUjY+gCHmbMw9XwO8o3sApqRn10Gl8DpRimR5BF7ODQcR3axsS+BzHky3Ed101EkO0ckkr7qTLKSRZPXEeDrCYfHp8HpdAFKTe/13RYS2HmYtEVTgqIz1nBxNonKNIVQ7G1C2EJx+mmgEuXfO8J1bAsvSAafU9UwT4z3YFGZUEuM6mg6ckc9jRAPYUQbLahIeQdxmWp4PDEHIT5AzsY05Ivb21oGxZvvQql2RlDokwhyVUMZGw35KYMwaVGxKxYx++vg4PckwmY6QrX/XcTtr2HTJHDysEHFHjlOCP4X9Qf3QHHNEePFFzDirkHC1hdYT0aQ33WkZ5UJIxh1R5F53hNBAQ8LIwQcwxC92A0VO6OxaLk+esgtNxc+iNCwaKOYpXLF1kyI3PhR9cfYic6d7IbB1NilO04XItdgHWjvj4DRZcj5pgsTGN7V3niZJ84J0zpBnb0RUVHRWPbmAWBuDMKNBKcFpR9/jir/CASPEkaJUV+G6qYEZ/f9Ax98o233OY1LQYW5hrYWQpneSbRWdwTy4rFYEhuJAM7Ri/N3jXSDRmTCozqqROZDLNJcEITxXJ1ZkYh+YSxyP09HPVfhSg7S82SIiBU8YqeEIDIqAuN/vqSfPioUkYHNSN2dB01DBtKO2WPJIv876mBGdA4JWx/hEuSP4fk5OClEAVVf57E0bzomWenLYmRT5mHttg+x9c1I+I1qQG7yW1gUq0CV0QlbgvUGL9GV0YjZ/j2cFraLw9DRXvD2Eg2utmajmM6oyMvDUP9pcOFLNvD+kyNK8ws6d6AfYsvExXiZI7uxrpKOC0LE3DmYz8S9dM8mKE63P63UnvocqT9MRORcZ2GMCS0aJtQ1qJBFIjEmXPA5fQcr/ngGm/d3TMfrD6Xj5Fhz0Rpb1nmW/o/1bE9pOZx94Md7Q3Bocb66AS4TJxiJotST/aapBufZRtGWV6LC1R++bb9h2E9G2Ev+bbcaHObOQ+CPSsQlZkA6JwKTSNXuGSRsfYXjLASzqCcrl0U9N8tQVDQUoUGewkRzSCBxdEPAnOWI3RyHMOQg7Yg4lxU9PFjjz044G7gy8TLApa58ZGEYvMynZ2Zh0U1OMRelJApGMEuxai9b9rk8FHVmamVlD3fx8tjgJD7JzSAZxdJGVm/qnNWIfdYamXuP6c2eb1ZCues7eM8NhsPPwisa3KscN5mYGV5t4Y2jbRD0uD5C1WON8b7joPmhxtg0mrUn/avr5qM1hkbNlGnIAybbR8rSeuFfJudXrzJhsm3fvjwPSjGcyStnXm1+HiYMGg2PP7Ltqn4YHuOoa/l7CQlbn2EDv0BPVGUfRRX30GDEdPgZvVeih0shlfkm6jHIHi5/tGYnRCfxkkcY5j9Wg917S7jbbL1GW1yAk7+fjlixEfOG9xA5rgHZ3xj7it46dTixq2OaKnuYiUbzZX1E2FiDKiZkubvewrLXWKrKDbtZGt+YhbjX3kduI1dpJBzsm3HxknGLVY2XwJmnDhfKHF1FaxyyUUz0q380tkRsrcY5fjkcLBp91BpFZ0wiwXPnUTHIka0Hm8cYlgpz8xC/T3hTa/TOnD4K9cSSZ6RQbkvv0pGfuLOQsPUhkj9NR6AmC+t3lWFSoPnowc6qBZnsxD90xnA/Swt1uRKKb4AAH3F0IsYak2YHYXjRAWRy96p7RTNO5pbBztcfLmIjZpkt/Pw9oSoq0N8zum2YiFwrROonStQadFpdiUOHSiDznaxPfe1DECt6RYMflvmw8aFI/Mjwvp89fGc64+S+FJwQnk5qz6VjR3oDpk6Z0h45mY3WTDxVvfwROCgPioOCaN9sRukuJoaio9/lcX8M/0aJTMNTTG0dMuVZ+IWN5x/+jJuGECmbR7pB+FtQuzcByzbn6cWaj0IL4T73eUx9KgLhkizsyOws/CXuNCRsfckgTwQE2EI7xB9BfzKftEj8IhEbKkP+pmghDVyJVZsLYDv3VURM7CLRcQ5FhO91pO/r+FqJObK3Cvfn2oYUVHATrhQg56w9Ah/vGE5KJk7BpJ/ykGv0Pt2tIsGkJTGIGF6A+NeEZb+WjKLRkXh3XmfCbR5Z4CuIDdJCGav3OV2UkAdZaBTm+7Vvp/qD5qI1E0/VQW4IjwqH7N8JeHHpSixbEouM0XMQNoKvrMc5DNELRyM/gfOH5YZE5P5hXvs6D3LG7NfnwSGPpe+ch+ziNYgvHokVS4J4Qa3aswPZI8PxvL81K7HtuzAIOJiG7Iv8r4m7zAD58uAu+IreMtynTy3QDra+N16kdwMuVfv5V0h67U+q9zmVyDjR6B38Z1QSFqGaeaijR79fILUR3YMzoc99XIneMLDt98hXlCAskoEtbARBWCT0rShBEAMCEjaCICwOEjaCICwOEjaCICwOEjaCICwOEjaCICwOEjaCICwOekGXh3xFCaK/MoBf0CVfUYKwVAbwC7rkK0oQAwm6xyamP4pbZ56bpnAenN3V6eBnyg098+gkiPsJEjZT+o24taAiNRqLXkvAlj1pWB+1ElFJOZ12bqg6+j6Wrcsy7nnWBG1BWnvHj22DXN/dEUH0I0jY+sJX9B6gzU9D0umxWLNB8O5MjkPgTwrIv+7CkKUbrl5pAnwXG3cA2a3xMEHcf5Cw9YGvqOZMFpSbEhAVnwzl0UpoDFET7+yUg3pNDXJTExEXmwjF4TKo26KqOpxITUcFE5TS/cks6krA9v15rL4wuQu0UkcEPBPSblAyyB6Tptqj9GzXnc5pzmZBsX4tv66HCozdsZouNMDlEVN/T4Lof5Cw9RJVZgJWbcqD1mMWIp6YAHybjFXJhfqeW1vqUJR/DGnb06FxnYGwJ5yhykrG23sMfes34Wx+HpTJu3BKNgUhc6dgaPEexG3uvpdcqXcoImaIe8FtRsV3DRjv2kXvmM1HkXZIjTEzn0TEn6xR9FEC5G3O8s3gLBdkgy4hl6W28lQFsou7sQUkiPsUErbecLMM2UeaELgsDuGzOOcmf4RFLUe49BJqrwl1mMS5zxa8KDk/y/k+0BQVokqYygmKLOAVRPC/D0LEylA4nS1ARU/6/xahOrwF8iZ/hM7sooNMqymIXB2GqZzL1KxIvBwoQXaBwQtVg+s3JCg9fhyq4W5w/UMzcne+hVXbS/QiTRD9CBK23nCuEicNpsMGrNwQuCAU7sOEMqQY/qDwL4eDI5w0GiO3qeHDRF1e87Zzess3jortYt8CNsRmdHgAoClOQXw6EB41Dy5d7VErGaSi6ZKhYuNLewTGfYid8csR9pQ/pj61GGsTwuFUfAC5vTWuIoi7DAlbb/hZjatW1nfU7dt9vtgijw1vBsFOmMZTl46knU0IinoVgeYc1XvDQ27wHtGE8h9u/4EEQdwLSNh6w5ixcL9SiaorQplHMGnpK09Jqdgijw1i45eLGYh/7xicFr2K2a69NDpprUQu9yDDKAXWQNMK2D1E/g9E/4KErTfIpmC6Vx2UewvbnoSqc1Ow6i2lsTnvnUBdCHniUTi89A4iJpoRtZsNOJmagkOne/iCLUtTtT9mYPvnhVBzefJNLeoPKpCunQzfx/RVCKK/QMLWK6wxaVEUQn6SYxXnNbl0KV7fq0HoirCu73X1ARWfpyH7WguLsgz+pCb34LR1KPy2BFmnLvH1u8cegVHLEXCBtYW1g/POjMuTYUFUJNw7taojiPsT8hU1y234inKfLGkfsAyPUK4tv7G2kG8m0Q8gX1HyFSUIi4N8RQmCsDjIV5QgiAEBCRtBEBYHCRtBEBYHCRtBEBYHCRtBEBYHCRtBEBYHCRtBEBYHCRtBEBbHwHhBV5WPLW+vQ2a9UO6WRxD85jt4xc+ogyCCIO5DBuwLusWp87HhX9VoFDtPqarx/ely1JnpHrax+its+OtWHCePUYLolwygVHQiXtve0bTluXfbx+mHj/DaFG5KNeq67RiD63utGdpu7DrvKD3xC7U0NM18P3Fdob12j/eLGG0LVDVlqKhp6nyd2H6sLy9EVV0XffkJ8+lQx6wfLDf0Yb+ABprKcOLU/e+FMWDvsdnN3oBTJ3cCrz2Np2K+6tJvs3PKoHgtGorTQvEe0BO/0L5Ee7EMpTX3tkfdit3RSDraIJTM0YDcdfd2vxjQnFYgbuUaxCXLkZb8LpatXAulSR95quyNiFoRjfW79mPHhmgsi9qIExeFiQKas0qsF+bD1Vm0RlTntNyMHyw3vI/cRqFOH1GbmYbtu7LufH+DvYQeHgxlQ2s3l3+ijaslSiR91eP+nwY2rYXYvbkALouS+G7dEzd8iE3P2yLz0yNou91bp8QHezUIjmPTElmdjRsQ63sZqQcK230xWkug3JwHB8N8WJ2EADUvMHyHxxNNvWC3YecyH0iHjYXTCH4OfYbTvCR8vDEc9/td+MFvvBH9jvA/T+svv+Ch4cOFkmVw6ZvN+OKkI4Je+g94Cr0RqQ6+gklzN2PSluPYtsCN1zc9Gnz/ZSqyzhnXN08DStNPApOfgrdgx6kuUGLfN1fg4DoaQx9gIxoLcUhxEAc/z0JZ4zUMdXCFndgkQV2J3P17oZCz6c1D4DryCo59VgHpxDHQe8Booco/iL2f7cPBvBoM/sM4SNgVOk89Fi72VtD8kIOjF0bhif/t2tYGzuc0/VMFdmWV4udWGcY8agvJ/xImMrqcrm1C6T8V+OcBBbJLLqPV2h7O9voVrj+WhiP5dahtUuHn2sv43Vg32A1hE9R1OHF4Dz7dlYGCqssY+qgH/mBo481m1Gb9Ewf2K9n6/4BrN0fA0UkGfU9vnK9qDn5+9GFc+p9d+JidqGXNg+AwZjQebOsKjrW/+Csc2v0Z9gntH/rjv1BhMwMBfxQcc9gy6v99GJ/KP2Hr3Awbl9HQnGDzdW/fL/p2sXX87DDyz7B1dHLFH4YOFiYyhPVUfC4X5vEIfsr8FNXWPnhEOB005/KQ+ckefHT4BC789Ds4jxuFIaLt2oHrN/E7J1/8yXcEuM3EIbG6jNLDDXAOncR7V2hbBsPe0w++LsOgX5vBGK6twoEiCQKEfar99gA23fjfiPkP57Y6DzqPg9swK1iNsIW4GXoacCxZAe2fX8H/GavvJZTf518osffL4/jxJ+APRttYDLdPlPi6pASl4qHZFt5jZNCcSofiuBau7iPYPjSz/xpbYefkjOGGBt8FWlo69hJNqWivUlFjNAUpePuzy/AO9ocddzyV78GqmM9xTjYBYS+FYQJKkBSfhgpDgKgpgTxuI7Jb3RAydxabnoekD9ORnV/Z5iuqOpyImL11cPB7EmEzR6P2kw+QllWIInNPPRi8z+n2Mgz14XxO3aDNEfmcGqZ35oN6sxKKqLeQek6GCc9GIswHyE9OgPyUPnYYOtoLLnbsbLBxhLeXM2y5g5f7TVwCMtRuCFsZiZARdUiNT0EFP8NmnEiMRvzXGrgEhyMi2BEXDyQg6YghjezeV5Vv/+5KDBe1X3lBmMjTgoqUdxCXqYbHE3MQwtY5Z2Ma8sVBeGsZFG++C6XaGUGhTyLIVQ1lbHRbuwzz4NazfR5boMwvxFnDAze2L/+WkImrHmF49eUgOJz7HG+ndGNNKHPE+CnOkAlFDlVRCWpd3eAglCX2bhjvZS8IPQdbl4IzkP3Ruc20p+pMGVxcx0LLhPVQKuf5qsSJS1K4T2EXFjPipC1Oh/JauxWjOjsRq5JyoHENQcR/hsDpkhJxiR3dzvTIMNLLi+1fYfCQsQtLIWo1+gVdrytBdnEdrvMlYf99mIwcdvRyx7BHaw7i4wz7/x7Cve4hHmp+PKezNE7+l4tuzKMROkWdMILjhlqn/ulr3T+CQnX/Z026rlEYrWP/KV4yU98sxbrdC5bodp/U6a5/u0O3ctUWXZFKmMRxQ6U796N4RLUu/XVW/zt96eK+WN3Cjd/oruuLPNy4Fxbs0JVzhRsFum2LYnXp1fwkPfy4Jbr3Dl3ii42H3tG98Pcj+vX/7bTu8xUm9YVxGecN/7+q+7xUP4nnxhld1kdf6sp/Zv//9ovuavV53dXf9JM4Lire0C386LRQEpa3rVgoMS4d0b3H1vdU22/YPC6odL/w5V90v5yv1l0UNfCXb7boXnjniO4qX9Jvvw9zNHyJh5/f+7p8roK59l//xqj9uvP7dX9bxLa7eCNWs3HCfuHg1/m9o8Iy9Vxn67Ewer/uIlcwNw9unOk8kgv0BQ62bxtVvwiF7jit2//6G7rXV63QrVz3pe6ceDkG/t8x3YdcnWWv6v624xvRPlDr8tct0a37cIvu9TXv6/Z/VaDLkb+ve33Rq2zdRNutjfO6jOglug1ZaqHMuH5ed+68qO4Ntg0X/EOX9f+Echc0/vMd3UK2vxqF9TE63oT99zeFeAdpdEUb2bh93AF3dzDVMG4YsBGb6tBr8J60COCeim6YbWxpd4tcZZHa31LqEPLackyyFUZyWNnCadRQ/klWaW4GlKnpKOJCEf41Ei0uXWyCu6eXkX2fw8QJ7ety8Tyqh7jB3Vkoc1hNwOTOzFU4n9PfHoakqRClBcJQ1ACNVRPOnWfRSXc+qIMkkDnbQ9pUw6KGPGTvSYPyFPd0sbNHeYwRznAZVoLNb6cgO7cS9dfYNX8US235I0sCiSOLPG7WoYqtS+5+Fm18Xc01Hb/wP9bTqa+qufZLpxi1X3u+AfVjPeEu3ojOPvB7SPifLex8dQNc2HYVR05ST/Yb1s7zbH+YnYejeB4swnceC1mxHHHbM3CivI6toS3sbM3mcmYYDd+5c1i0FAq/n7OQtDUHKtOnlTI3BHF1nvPHyMq9SNpTJkSDbFv8DFRUy/ByYhTCZk1GwLworF04DvkfK0XG23q0+Sxau2FinC115O+1cU9dS48qWcSXgwpuJ3TzxJT3qz02EmuiQmDXhVK4jxPvIGuM9/VEfXWNUL43UCraB6lo6XnA27EJGf9jnJpoitMQtTwaH+w7jlOszijfyRjfJnxqXL3KjrkhJg5TVpJ2oTPrW8rEorPzias/WIP606dxqm04D8m4yRgzjClFdz6oXGr82kosS1Qi/3QdOx+nYJqHWKnNMMgN4YnvIXr6w6jNV2B91EosimdpDnfS3GxAdjybX3Qasti6qGVeCJh4C0YSPWi/Rs2UacgDolSOQyqqo9/ODrYm7XhQiuGCgHY/D4bHPGxKWI5A2zoc/+wDrFq+EuvbUurusIET574/JQjh7y6G33kFMr8TJhmwsmepJasTEIYVsaGQ5rJ9wM/eHg6PsOPVb5qRQZDU1we+mmrUGq1CHbK/LIP77FCjuqojCVi0IhppR9nxoJbBe4YP3IVpnXIxA0nbz8Bv8WJjwe+AjbEhOENizTbcz/c2Fx2wwgYrG9g8JIMdFx718qlowJzFiIxaDN8zKUg6bDjSmtnVsRC2c+OREL0YEfNCMNV7JH7XFvywaO5Ra5T+UCmU9ajLy9ofpZvzLb1ZhvIzwv+mjBwJJw07cOdFImKB8RDsza7g3figavKzkP1QGP6bRQaRC8IRGOCJkRJxbNUJg23hMisMkdFx2JQchWB1OovMmoEzWVA2TkH05jgsYeswm0UbTuKwqTu49lzRR1Xt1KD2R+FfhmyUPSTVPxq/ftBajXNtrznot3PRGePtzEJYVAxyhIM9m8eY0ZBy8xBHMFfKcMr0VQlbN35fr4lPws7oaWg6IMcJo3UzRnOabYcDhshLYBCL9NiuUF3RvzJTn7sH8mMmVvsPPQwHJsgq4f6e7SP2UDVc4mKsdhqbUM/aNlwUVZqN1lCJ7MwmBK74ELGr2LEwJwjju/Pz4C5IH2UAoW8g3EMY1ynNuHjJOKKvP1sNyaOOQuneMHCFjccHi7iXcnuZivJIfRD+0mSo0tOQzb9fZAMZO4nVTe0vM6rzMpAlEhWXJ2bBIT8N24/WQKVuhqpcie3sIGy7QMqmIcSvCfIPFaio4164rMOJZHYl72yvjZiBEK8y7N7V7nPKOcXHL01ANqe33figSpnQS6414arhOG3KQ3q+mXfWrrF1EX6vLUrBojUpKDWc4D9fhppdJ3iT5d+zE/SGGk0sleLR1CAzq0wo9IAR/ghyrcHnnxvWV/A6FYuJlz8CB+VBcVAQh5vsgrIrHSdF28jlcX8M/0aJzDqhYdo6ZMqz8Asbz0cuHkEIezgPm5OzUNvI2tZYiUPJR1HftiO0KN2+EqtS2rebpqkZ1wfJIDOJVsRIh2tRcSQNu3MNx4AWKhaJpTc6Y9pEvbjYSdTI/UyOQ2eE7cw9nd2bgdxhE+E7Tj/Kzn8Gxn+vRKphPq1s/Xeko9Z7Csa3WSOaj9bYTmdC2oKrgpByDydqD+bgpFDi0JxWIi01T0iPuQcp70NpF4E1TzHV7wEn9u1q3//seEvL1iLQz1MYcW8YEN+KFq9zRVgKk5qxHnDsMqzW01hdDpVmHj49G4dpHR6li2Gp20I242XbEDFRP0Z1OAExWbZYsXYxxl/LweYkBU5qrNnh1YLf+YZi6o/p0DzbXh91OUj75AjvwC59ZAYinwTk6+sQ9hFLAbjp7ECvOLADim+b2JVfhklzF8Oj+C2kPxKHWHbgqQ6vRdSJKUiMD9GLMxOP7O2sfnkLJFZadiLaYOqcV7EkUDhIOXHZuBFK7haIhJ3og5wRtvoVBHNO8uxKfWLjB0hlv5UOY1UHT0C4bx3k6lB8vMRH+D1r81spyFZbI3hNEruit6Bqz/tYf+wypEzINawdDjMWI3qeJxNow7QGSIZJoG21R/ifbSEvchTWt+P202/TdDjEx4FfZU0ZlAkpOMSiJ+lgLYb6LkaYNgVZQvt5Lhq2M0vjfwOcnomA9/E0qETbWV2wB0m78liUw9Ik1mw73+cQvdgfMoMIMLE7kSqHspo1wGo0gl8IAT5JRL1hHob1uPwA7Aa1MBGwx+xlbyDMq2sHfs0pBZJSclAlJAUSGUvdl7yMwDbnfibWR1OwhUV29YLuSuz9sWL1PNFtCzafsxlI3c4EWxAQO49QvLIkBE7C8azJS8aqfTJEJ83r4GnLvSS8fmsO6gdZs13+K9s+QbA7UAInYRvX738LMV89jCWJqzG1JQPxsekd7t3BPpTfZzA63vT7T/tUKFT/YkI7SAKN1hqTnnsVKwzH211g4LpUqfKxc902fFlluKx0g2wsnl78Nyx6vNdxHA//eY/EBlJzxsPcVVJ8INYoEbVOjciUSEHY2GB0oGpxctNKZLi+h9gnu7j/xX1qdYOJgayTE68rH9SupnUG91kPy7kkD7KTx+TEMkzrdF16AvcZFay79Drtcjvz6NNuSG063qfssJ1rcCgqEVfnMWHzFkZxcOtxk62HYds0dCIEsEeEQZw5+mT9GWw+XJ1O77N2Shdtv23EFyZu/r8C5vb/HYbs9+4ztOV7EJOiRljUYkwdxY42LtravAUK6fPYumIyiy2akJvwLnLHLseKZ90gYxGL+ru97OpbiYC49xB8b29jWA6tlVDE7MDVp9/AgsftIfmNpWtHt2D9QSkWbF6OSV0JzYDGXMR99yFhu+/gTiA55AdL9KmKxBru3k8jYsF0OBiuqk2FUKbuR+ZZdqVmRamtM0KefxmzuYcBRJ+hOZsFxZ505F9kkQe7pMjGeOL5F17CVMc+C28sEJaeR8mB+etZSi6MugeQsBEEYXEM2P7YCIIYWJCwEQRhcZCwEQRhcZCwEQRhcZCwEQRhcZCwEQRhcZCwEQRhcQyYT6rIV5QgLJMB+x6bWV/RLiBfUYLo3wyIiE3fu4cf1uXuxnOPCCM7RYW9kdPw5rGe1r9LcB9RD+7mA+necC4H8mzBfWrsLETMEH2IavQBN9cLxlGc5SeMRSD3+Rf/P0HcG+jLAyZamTFP46mnOxtikHm3DDpvke69NHvJT5XIPvMrnDgDj9FCb5A3m1C6ay0WrYzGst2GftQEs49Rv6JUZDrTES1UpwtRa9SpZXc0o7agDKre9ftJEPTwwJgbaB3I6aeVo757amcbQF2ItKh3Ib/ijzBxtz2Gbq4nOBp5CHTkMkr3piFX1Ntt91QjN0WJ0lsSQ4LoyAATNjsEJ3yJw1yvuWaHJDx9S/3jcWlZOiou1iA3NRFxSe22cZyPo3JTAqLik6E8WtnW86oBfvpW9pvYRKTtz0O9Uf/RnJdmBhTr1yJuPROHs5yziQh1GTJTc1DLeZJu1y/jUEETH2FVHE5DUmwCtu8vhErouNBAd+tkBIuaPObHI3FNEJxuuYMLbrsowXW+W3E0ra17bG0N14OGoadWDi2qjqRBmVsHLd+mLFSwLZi/j/3GtLtsgrgFKBU1Gm41FdX7KsqT5aj6wzSEzXLju/XuztuzO5/Hbr00W+pQlH+MdxvSTmTLmABkpWzC+nW7UGTlhaBQzp8zDXGpJXxXRxzdrVMHRkzG1NvuGolLV93gYMX+G83SVldb3ihFMsYTw8/s0fshcNSkI+1gC8b4OkIyxJalwVwUKIGDqygdJojbgFJRI24nFW3GyOA3EPmUv9749mYZso+0IHTlagQHtDsTBZ5NBwtMeGR+4Yh9KwYRszzh4uGJwMVhmFRXglLOPKS1EMp0499HxEyHramxCJMk72fYfLm08KnlWDCxCbWPhLJ5Cr95bjK01TV6sezBOvUtXLrqCScmbA5ubHkGQ+BB9giODELTXjlOahqQLc/B6BciMYm7GvAuTZyRsBROE9hvuHSYIG4TSkWNhltNRfUY+WJ25+3J0ZXPYw+8NPVIIRX5N3DdMTv8XhTl/IFzeLqsT417sk53C+cwRD5ejd1vfQDFkDA879+L7sIJohMoFeWGXvqKGtGdtyejS5/HW/US7Qk9WKe7icO4cdCqm+Hk5tbNAwiCuD0oFeXopa+oEd15e3bn89gDL81bptt1uou0lkH52Rn4PR8K6ZEdyOStCgmib6FUlBv6wlfUQHfenixG6dLnsSdemrdKt+vUB9xswMnUFBw6bRwBtrdTTxX3CsijYQgLDEHEMxIoP2p/aKJHg6ttxqYEcXsMIGHLR8JLZtLQDsNCbCjg6o+F40j+h7eINSYtikKoZj9WLV6JZcuX4sWEPNjOiRSs2Ozh99x0XPo8GouWr8GyxdFQPOgJP/63HDaYumI5Ai7IsWwJ+/3SlfjgcggW9MoFqLt16gO0dSj8tgRZpy4JI1g7n52M83uj8eLKPXp7uvI92PzNSCyY78+n2nbBkQjTpmNHm3u+D4KflSHzv1fixfU5wjiCuHUGzEfw98RXtEtvzx74PPbAi/KW6WydilPw4gGDkXEP4P00RcbOBHGPIJcqonM4Ydtaov/fd3G783sH9F6S2fz/PogmYSPuMSRsBEFYHPQRPEEQAwISNoIgLA4SNoIgLA4SNoIgLA4SNoIgLA4SNoIgLA4SNoIgLI6B8x5bczWKSxtxQygaI4OrnwfsBgtFgiD6DQP4Bd0L2B02E3HCi/VmmZGA3J1z4UjiRhD9igH8gq4KFzhR82PiVXMWP4qHLxboqxyLQcCifagjL1GC6PfQPTYx/VTctNeaoemsSznuo/dr3XQDpGV11GweRsPd74CSIPoKEjZT+pO4aWqQnRSNZas79xxVHX0fy9ZlddlDsLYgDcteY/MxGuSoEKYTRH+DhG2wHR7z8mgf7K14cZPzfbLdx5xLR9xrG5E7YgaCRwjjbpOrV5r0PXp8tE00UK8dRP+FhM1ngVFvurvWtHf52FM69esU/D/rWWTF+47GJkJxuAxqw3SDLykTltL9yVgfxfmBmnqMdoJWhsCYDVg7zwfDe7AXNWezeJ/SNg9SEU0XGuDyyG31qkkQ9yUkbL2E9+vclAetB+fXOQH4VuTXKfh/pm1Ph8Z1BsKecIYqKxlv76nkf2vwJVUm78Ip2RSEzOX8QPcgbnO78XKnuE5HgGMPO6BsPoq0Q2qMmfkkIv5kjaKPEiAvNtx3a4aaLUw26BJy96RBnqpAdrGx8BFEf4OErTfwfp1NCFwWh3Dez9MfYVHLES69hNprQh0mce6zBT/PgDCsmO8DTVGhvqtsnmbIAl5p9wNdGQqnswWo6I3HgSlWUxC5OgxTOQ/SWZF4OVCC7IIyYaIG129IUHr8OFTD3eD6h2bk7nwLq7aX6MWZIPohJGy9gfPrxET4egllDis3BC4IhfswoQwphj8o/Mvh4AgnjabNoZ3DyJfU3h5OnNgIDyUrti/FiwtFQ6yp+UkPsJJBKtrTkqFicz97BMZ9iJ3xyxH2lD+mPrUYaxPC4VR84A6ZKRPEnYeErTeY9QDtW9znr8fWDaLhzaC+c9TqjIfc4D2iCeU/GDtMEUR/gYStN4wZC/crlai6IpR59CYt2rYHBL1EagOpTDQM60NjF47WSuRyDzCMUl8N/16c3UN32XOUIPoIErbeIJuC6V51UO5t9+tU56Zg1VtK1OqLd5+LeVBsVaKqpzfIWJqq/TED2z8vhJrLj28KPqbayfB9TF+FIPobJGy9Qu/XGfKTnPfrXLR0KV7fq0HoijC43KMtqz3/HbKL83Cqxw7r9giM0vuYrmLr/yJrR1yeDAuiIuFuJVQhiH7GAPkIvho7ZwUjoVoodosP1h79AvPHCsWewH26pH2g71PFuwnXht9YG/rSx5Qg7jAD236vy26LjBky8jFMHEv3lwiiP0C+ogRBWBzkK0oQxICAhI0gCIuDhI0gCIuDhI0gCIuDhI0gCIuDhI0gCIuDhI0gCIuDhI0gCIuDhI0gCIuDhI0gCIuDhI0gCIuDhI0gCIuDhI0gCIuDhI0gCIuDhI0gCIuDhI0gCIuDhI0gCIuDhI0gCIuDhI0gCIuDhI0gCIuDhI0gCAsD+P8B4YmJn8j1MNYAAAAASUVORK5CYII="}}},{"cell_type":"code","source":"# Gereksiz dosyaları silelim\n# Let's delete unnecessary folders","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\nos.remove('./kagglecatsanddogs_3367a.zip')","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:14:08.094019Z","iopub.execute_input":"2022-01-19T19:14:08.094295Z","iopub.status.idle":"2022-01-19T19:14:08.212753Z","shell.execute_reply.started":"2022-01-19T19:14:08.094267Z","shell.execute_reply":"2022-01-19T19:14:08.211498Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.remove('./MSR-LA - 3467.docx')","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:14:09.515761Z","iopub.execute_input":"2022-01-19T19:14:09.516488Z","iopub.status.idle":"2022-01-19T19:14:09.520555Z","shell.execute_reply.started":"2022-01-19T19:14:09.516427Z","shell.execute_reply":"2022-01-19T19:14:09.519671Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.remove('./readme[1].txt')","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:14:11.423458Z","iopub.execute_input":"2022-01-19T19:14:11.424077Z","iopub.status.idle":"2022-01-19T19:14:11.429760Z","shell.execute_reply.started":"2022-01-19T19:14:11.424027Z","shell.execute_reply":"2022-01-19T19:14:11.428905Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# güncel klasörüm\n# updated current folder\nos.listdir('./')","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:14:13.749027Z","iopub.execute_input":"2022-01-19T19:14:13.749308Z","iopub.status.idle":"2022-01-19T19:14:13.758169Z","shell.execute_reply.started":"2022-01-19T19:14:13.749278Z","shell.execute_reply":"2022-01-19T19:14:13.757331Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](attachment:28c06f68-c1f8-43d2-9525-b053de4cb9c2.png)","metadata":{},"attachments":{"28c06f68-c1f8-43d2-9525-b053de4cb9c2.png":{"image/png":"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"}}},{"cell_type":"code","source":"my_data_dir = './PetImages/'","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:14:19.907837Z","iopub.execute_input":"2022-01-19T19:14:19.908296Z","iopub.status.idle":"2022-01-19T19:14:19.911719Z","shell.execute_reply.started":"2022-01-19T19:14:19.908257Z","shell.execute_reply":"2022-01-19T19:14:19.910987Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.listdir(my_data_dir)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:14:21.547332Z","iopub.execute_input":"2022-01-19T19:14:21.547913Z","iopub.status.idle":"2022-01-19T19:14:21.553370Z","shell.execute_reply.started":"2022-01-19T19:14:21.547873Z","shell.execute_reply":"2022-01-19T19:14:21.552693Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Bozuk resimleri filtreleme**
\nÇok sayıda gerçek dünya görüntü verisi ile çalışırken, bozuk görüntüler yaygın bir durumdur.
\nBaşlıklarında \"JFIF\" dizesini içermeyen kötü kodlanmış görüntüleri filtreleyelim.
\n\n**Filter out corrupted images**
\nWhen working with lots of real-world image data, corrupted images are a common occurrence.
\nLet's filter out badly-encoded images that do not feature the string \"JFIF\" in their header.","metadata":{}},{"cell_type":"code","source":"num_skipped = 0\nfor folder_name in (\"Cat\", \"Dog\"):\n folder_path = os.path.join(\"PetImages\", folder_name)\n for fname in os.listdir(folder_path):\n fpath = os.path.join(folder_path, fname)\n try:\n fobj = open(fpath, \"rb\")\n is_jfif = tf.compat.as_bytes(\"JFIF\") in fobj.peek(10)\n finally:\n fobj.close()\n\n if not is_jfif:\n num_skipped += 1\n # Delete corrupted image\n os.remove(fpath)\n\nprint(\"Deleted %d images\" % num_skipped)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:14:23.684619Z","iopub.execute_input":"2022-01-19T19:14:23.684875Z","iopub.status.idle":"2022-01-19T19:14:24.243981Z","shell.execute_reply.started":"2022-01-19T19:14:23.684848Z","shell.execute_reply":"2022-01-19T19:14:24.243134Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(os.listdir(my_data_dir + '/Cat'))","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:14:27.469398Z","iopub.execute_input":"2022-01-19T19:14:27.470630Z","iopub.status.idle":"2022-01-19T19:14:27.483634Z","shell.execute_reply.started":"2022-01-19T19:14:27.470578Z","shell.execute_reply":"2022-01-19T19:14:27.482893Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(os.listdir(my_data_dir + '/Dog'))","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:14:29.199768Z","iopub.execute_input":"2022-01-19T19:14:29.200239Z","iopub.status.idle":"2022-01-19T19:14:29.214684Z","shell.execute_reply.started":"2022-01-19T19:14:29.200198Z","shell.execute_reply":"2022-01-19T19:14:29.213981Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**data_augmentation veya ImageDataGenerator'ı Kullanalım**
\nBüyük bir görüntü veri kümeniz olmadığında, eğitim görüntülerine rastgele yatay çevirme veya küçük rastgele döndürmeler gibi rastgele ancak gerçekçi dönüşümler uygulayarak örnek çeşitliliğini yapay olarak tanıtmak iyi bir uygulamadır. Bu, overfitting'i azalatırken, modelin, eğitim verilerinden generate edilmiş farklı resimlere maruz kalmasına yardımcı olur.
\nUsing image data_augmentation or ImageDataGenerator
\nWhen you don't have a large image dataset, it's a good practice to artificially introduce sample diversity by applying random yet realistic transformations to the training images, such as random horizontal flipping or small random rotations. This helps expose the model to different aspects of the training data while slowing down overfitting.
","metadata":{}},{"cell_type":"markdown","source":"## ImageDataGenerator ","metadata":{}},{"cell_type":"code","source":"# ImageDataGenerator'ü kullanalım.\n# Let's use ImageDataGenerator.\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:15:35.972598Z","iopub.execute_input":"2022-01-19T19:15:35.973513Z","iopub.status.idle":"2022-01-19T19:15:35.980045Z","shell.execute_reply.started":"2022-01-19T19:15:35.973461Z","shell.execute_reply":"2022-01-19T19:15:35.979335Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# help(ImageDataGenerator)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:15:37.717258Z","iopub.execute_input":"2022-01-19T19:15:37.717527Z","iopub.status.idle":"2022-01-19T19:15:37.721193Z","shell.execute_reply.started":"2022-01-19T19:15:37.717496Z","shell.execute_reply":"2022-01-19T19:15:37.720230Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Görüntü Manipülasyonu**\n\nGörüntüleri döndürme, yeniden boyutlandırma ve ölçekleme ile işlemek için ImageDataGenerator'ı kullanabiliriz, böylece model, veri setimizin sahip olmadığı farklı görüntülere karşı daha sağlam hale gelir. ImageDataGenerator aşağıdakileri yapar.\n\n\n* Eğitim için kullanılan batch görüntüleri kabul eder.\n* Batch/grup içindeki her görüntüye bir dizi rastgele dönüşüm uygular.\n* Orijinal batch/grup ile rastgele dönüştürülmüş grup resmi değiştirir.\n* CNN'yi bu rastgele dönüştürülmüş batchler ile eğitir.\n\n\nVeri büyütmeyi uygulamanın amacı, daha genelleştirilmiş bir modele sahip olmaktır.\n\nImage Manipulation\n\n
\nWe can use the ImageDataGenerator to manipulate the images with rotation, resizing, and scaling so the model becomes more robust to different images that our data set doesn't have. ImageDataGenerator does the followings.\n\n
\n *Accepts a batch of images used for training.
\n *Applies a series of random transformations to each image in the batch.
\n *Replaces the original batch with randomly transformed batch.
\n *Training the CNN on this randomly transformed batch.\n\n
\nThe goal of applying data augmentation is to have a more generalized model.","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.preprocessing.image import ImageDataGenerator","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:16:43.653853Z","iopub.execute_input":"2022-01-19T19:16:43.654135Z","iopub.status.idle":"2022-01-19T19:16:43.659494Z","shell.execute_reply.started":"2022-01-19T19:16:43.654100Z","shell.execute_reply":"2022-01-19T19:16:43.658433Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image_gen = ImageDataGenerator(\n rotation_range=20, # Resmi 20 derece döndürür / rotate the image 20 degrees\n width_shift_range=0.10, # Resim eninin maksimum %5 kaydırır / Shift the pic width by a max of 5%\n height_shift_range=0.10, # Resim boyunu maksimum %5 kaydırır / Shift the pic height by a max of 5%\n rescale=1/255, # Resmi normalleştirerek yeniden ölçeklendirir / Rescale the image by normalzing it.\n shear_range=0.1, # Görüntünün bir kısmını keser max 0.1 / Shear means cutting away part of the image (max 10%)\n zoom_range=0.1, # Maksimum %10 yakınlaştır / Zoom in by 10% max\n horizontal_flip=True, # Yatay çevirir / Allow horizontal flipping\n fill_mode='nearest' # Eksik pikselleri en yakındaki ile doldurur / Fill in missing pixels with the nearest filled value\n )","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:18:25.184567Z","iopub.execute_input":"2022-01-19T19:18:25.185149Z","iopub.status.idle":"2022-01-19T19:18:25.189802Z","shell.execute_reply.started":"2022-01-19T19:18:25.185111Z","shell.execute_reply":"2022-01-19T19:18:25.189113Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pwd","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:18:27.083472Z","iopub.execute_input":"2022-01-19T19:18:27.084157Z","iopub.status.idle":"2022-01-19T19:18:27.089846Z","shell.execute_reply.started":"2022-01-19T19:18:27.084118Z","shell.execute_reply":"2022-01-19T19:18:27.089115Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"my_data_dir","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:18:28.471038Z","iopub.execute_input":"2022-01-19T19:18:28.471770Z","iopub.status.idle":"2022-01-19T19:18:28.479617Z","shell.execute_reply.started":"2022-01-19T19:18:28.471729Z","shell.execute_reply":"2022-01-19T19:18:28.478878Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.listdir(my_data_dir)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:18:29.856234Z","iopub.execute_input":"2022-01-19T19:18:29.856728Z","iopub.status.idle":"2022-01-19T19:18:29.862158Z","shell.execute_reply.started":"2022-01-19T19:18:29.856689Z","shell.execute_reply":"2022-01-19T19:18:29.861506Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.listdir(my_data_dir + 'Cat')[0]","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:18:31.529232Z","iopub.execute_input":"2022-01-19T19:18:31.529798Z","iopub.status.idle":"2022-01-19T19:18:31.543898Z","shell.execute_reply.started":"2022-01-19T19:18:31.529757Z","shell.execute_reply":"2022-01-19T19:18:31.543231Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.listdir(my_data_dir + 'Dog')[0]","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:18:33.537153Z","iopub.execute_input":"2022-01-19T19:18:33.537417Z","iopub.status.idle":"2022-01-19T19:18:33.551817Z","shell.execute_reply.started":"2022-01-19T19:18:33.537386Z","shell.execute_reply":"2022-01-19T19:18:33.551176Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cat_image_path = my_data_dir + '/Cat' + '/0.jpg'\ndog_image_path = my_data_dir + '/Dog' + '/0.jpg'","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:18:45.509928Z","iopub.execute_input":"2022-01-19T19:18:45.510194Z","iopub.status.idle":"2022-01-19T19:18:45.515375Z","shell.execute_reply.started":"2022-01-19T19:18:45.510165Z","shell.execute_reply":"2022-01-19T19:18:45.514711Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cat_img=imread(cat_image_path)\ndog_img=imread(dog_image_path)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:18:47.295400Z","iopub.execute_input":"2022-01-19T19:18:47.295981Z","iopub.status.idle":"2022-01-19T19:18:47.308275Z","shell.execute_reply.started":"2022-01-19T19:18:47.295941Z","shell.execute_reply":"2022-01-19T19:18:47.307503Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.imshow(cat_img)\nplt.axis('off');","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:18:48.837007Z","iopub.execute_input":"2022-01-19T19:18:48.837599Z","iopub.status.idle":"2022-01-19T19:18:49.018928Z","shell.execute_reply.started":"2022-01-19T19:18:48.837558Z","shell.execute_reply":"2022-01-19T19:18:49.018279Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.imshow(dog_img)\nplt.axis('off');","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:18:51.490784Z","iopub.execute_input":"2022-01-19T19:18:51.491046Z","iopub.status.idle":"2022-01-19T19:18:51.671830Z","shell.execute_reply.started":"2022-01-19T19:18:51.491017Z","shell.execute_reply":"2022-01-19T19:18:51.670110Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**ImageDataGenerator ile Dönüştürelim**
\nLet's Transform the images with ImageDataGenerator
","metadata":{}},{"cell_type":"code","source":"plt.imshow(image_gen.random_transform(cat_img));","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:19:43.976352Z","iopub.execute_input":"2022-01-19T19:19:43.976932Z","iopub.status.idle":"2022-01-19T19:19:44.253749Z","shell.execute_reply.started":"2022-01-19T19:19:43.976891Z","shell.execute_reply":"2022-01-19T19:19:44.253104Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.imshow(image_gen.random_transform(dog_img));","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:19:38.043023Z","iopub.execute_input":"2022-01-19T19:19:38.043882Z","iopub.status.idle":"2022-01-19T19:19:38.333924Z","shell.execute_reply.started":"2022-01-19T19:19:38.043832Z","shell.execute_reply":"2022-01-19T19:19:38.333170Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Data_Augmentation ","metadata":{}},{"cell_type":"markdown","source":"**Data_Augmentation ile Dönüştürelim**
\n\nVeri kümesindeki ilk görüntüye art arda data_augmentation uygulayarak artırılmış örneklerin nasıl göründüğünü görselleştirelim:\n\nLet's Transform the images with Data_Augmentation
\n\nLet's visualize what the augmented samples look like, by applying data_augmentation repeatedly to the first image in the dataset:","metadata":{}},{"cell_type":"code","source":"import tensorflow_datasets as tfds\n(train_ds, val_ds, test_ds), metadata = tfds.load(\n 'CatsVsDogs',\n split=['train[:80%]', 'train[80%:90%]', 'train[90%:]'],\n with_info=True,\n as_supervised=True,\n)","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-01-19T19:20:14.168866Z","iopub.execute_input":"2022-01-19T19:20:14.169591Z","iopub.status.idle":"2022-01-19T19:20:52.436822Z","shell.execute_reply.started":"2022-01-19T19:20:14.169541Z","shell.execute_reply":"2022-01-19T19:20:52.436114Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# labels --> cats and dogs --> 2\nnum_classes = metadata.features['label'].num_classes\nprint(num_classes)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:21:04.320408Z","iopub.execute_input":"2022-01-19T19:21:04.321024Z","iopub.status.idle":"2022-01-19T19:21:04.325486Z","shell.execute_reply.started":"2022-01-19T19:21:04.320985Z","shell.execute_reply":"2022-01-19T19:21:04.324721Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"get_label_name = metadata.features['label'].int2str\n\nimage, label = next(iter(train_ds))\n_ = plt.imshow(image)\n_ = plt.title(get_label_name(label))","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:21:05.977467Z","iopub.execute_input":"2022-01-19T19:21:05.978118Z","iopub.status.idle":"2022-01-19T19:21:06.837783Z","shell.execute_reply.started":"2022-01-19T19:21:05.978081Z","shell.execute_reply":"2022-01-19T19:21:06.837125Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow import keras\nfrom tensorflow.keras import layers","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:21:12.082316Z","iopub.execute_input":"2022-01-19T19:21:12.082909Z","iopub.status.idle":"2022-01-19T19:21:12.086551Z","shell.execute_reply.started":"2022-01-19T19:21:12.082866Z","shell.execute_reply":"2022-01-19T19:21:12.085842Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data_augmentation = tf.keras.Sequential([\n layers.RandomFlip(\"horizontal_and_vertical\"),\n layers.RandomRotation(0.2),\n])","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:21:14.235263Z","iopub.execute_input":"2022-01-19T19:21:14.235909Z","iopub.status.idle":"2022-01-19T19:21:14.252688Z","shell.execute_reply.started":"2022-01-19T19:21:14.235867Z","shell.execute_reply":"2022-01-19T19:21:14.252016Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image = tf.expand_dims(image, 0)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:21:16.273949Z","iopub.execute_input":"2022-01-19T19:21:16.274216Z","iopub.status.idle":"2022-01-19T19:21:16.278772Z","shell.execute_reply.started":"2022-01-19T19:21:16.274186Z","shell.execute_reply":"2022-01-19T19:21:16.278026Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(10, 10))\nfor i in range(9):\n augmented_image = data_augmentation(image)\n ax = plt.subplot(3, 3, i + 1)\n plt.imshow(augmented_image[0])\n plt.axis(\"off\")","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:21:19.068929Z","iopub.execute_input":"2022-01-19T19:21:19.069604Z","iopub.status.idle":"2022-01-19T19:21:19.879934Z","shell.execute_reply.started":"2022-01-19T19:21:19.069558Z","shell.execute_reply":"2022-01-19T19:21:19.879288Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Veri Setlerini Oluşturalım / Generating the DataSets ","metadata":{}},{"cell_type":"markdown","source":"Datamızı train, test ve validation olarak bölelim.
\nDiskinizdeki verileri eğitme, test etme ve doğrulama olarak ayırmanın kolay yolu.
\n\nLet's split our data into train, test and validation.
\nEasy way to split data on your disk into train, test, and validation.\n \nSource = [Youtube](https://www.youtube.com/watch?v=C6wbr1jJvVs)","metadata":{}},{"cell_type":"code","source":"!pip install split-folders","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:21:32.338379Z","iopub.execute_input":"2022-01-19T19:21:32.338955Z","iopub.status.idle":"2022-01-19T19:21:41.360018Z","shell.execute_reply.started":"2022-01-19T19:21:32.338918Z","shell.execute_reply":"2022-01-19T19:21:41.358988Z"},"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import splitfolders","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:21:41.363617Z","iopub.execute_input":"2022-01-19T19:21:41.363851Z","iopub.status.idle":"2022-01-19T19:21:41.373009Z","shell.execute_reply.started":"2022-01-19T19:21:41.363823Z","shell.execute_reply":"2022-01-19T19:21:41.372272Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.makedirs('./PetImages2')","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:21:48.910241Z","iopub.execute_input":"2022-01-19T19:21:48.910527Z","iopub.status.idle":"2022-01-19T19:21:48.914762Z","shell.execute_reply.started":"2022-01-19T19:21:48.910493Z","shell.execute_reply":"2022-01-19T19:21:48.913990Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Şimdi PetImages2 adında bir boş klasör oluşturalım ve split edilmiş datasetlerini oraya atalım.
\n\nNow let's create an empty folder named PetImages2 and put the split datasets there.
\n![image.png](attachment:f3e6bc51-9051-4c5b-a226-ad5843bf1903.png)","metadata":{},"attachments":{"image.png":{"image/png":"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"},"f3e6bc51-9051-4c5b-a226-ad5843bf1903.png":{"image/png":"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"}}},{"cell_type":"code","source":"pwd","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:22:01.422284Z","iopub.execute_input":"2022-01-19T19:22:01.422668Z","iopub.status.idle":"2022-01-19T19:22:01.430404Z","shell.execute_reply.started":"2022-01-19T19:22:01.422630Z","shell.execute_reply":"2022-01-19T19:22:01.429643Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"input_folder = './PetImages/'\noutput_folder = './PetImages2/'","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:22:03.753786Z","iopub.execute_input":"2022-01-19T19:22:03.754348Z","iopub.status.idle":"2022-01-19T19:22:03.758106Z","shell.execute_reply.started":"2022-01-19T19:22:03.754307Z","shell.execute_reply":"2022-01-19T19:22:03.757318Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# SpLit with a ratio.\n# To onLy spLit into training and vaLidation set, set a tupLe to ‘ratio‘, i.e,\n# Train, vaL, test\n# If you don't want to use ratio, I mean if you want to split with fixed number of images:\n# use \"fixed\" instead of \"ratio\"\nsplitfolders.ratio(input_folder, output= output_folder, seed = 42, ratio = (.7, .2, .1), group_prefix = None)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:22:05.732923Z","iopub.execute_input":"2022-01-19T19:22:05.733225Z","iopub.status.idle":"2022-01-19T19:22:09.343481Z","shell.execute_reply.started":"2022-01-19T19:22:05.733195Z","shell.execute_reply":"2022-01-19T19:22:09.342736Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](attachment:41a0b355-e416-4f3d-9944-bd9b5e94184c.png)","metadata":{},"attachments":{"image.png":{"image/png":"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"},"41a0b355-e416-4f3d-9944-bd9b5e94184c.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"**Hadi Klasör Uzantılarını Ayarlayalım**
\nLet's Specify the Folder Paths
","metadata":{}},{"cell_type":"code","source":"# PetImages klasörünü silebiliriz\n# we can delete PetImages folder\nimport shutil\nshutil.rmtree('./PetImages')","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:22:47.280886Z","iopub.execute_input":"2022-01-19T19:22:47.281144Z","iopub.status.idle":"2022-01-19T19:22:47.358104Z","shell.execute_reply.started":"2022-01-19T19:22:47.281112Z","shell.execute_reply":"2022-01-19T19:22:47.357270Z"},"_kg_hide-output":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](attachment:32baab3d-82d9-40c8-aa17-f9da5144123d.png)","metadata":{},"attachments":{"32baab3d-82d9-40c8-aa17-f9da5144123d.png":{"image/png":"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"}}},{"cell_type":"code","source":"my_data_dir = './PetImages2'\nmy_data_dir","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:23:39.125006Z","iopub.execute_input":"2022-01-19T19:23:39.125293Z","iopub.status.idle":"2022-01-19T19:23:39.132511Z","shell.execute_reply.started":"2022-01-19T19:23:39.125259Z","shell.execute_reply":"2022-01-19T19:23:39.131775Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.listdir(my_data_dir)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:23:40.484342Z","iopub.execute_input":"2022-01-19T19:23:40.484904Z","iopub.status.idle":"2022-01-19T19:23:40.492227Z","shell.execute_reply.started":"2022-01-19T19:23:40.484866Z","shell.execute_reply":"2022-01-19T19:23:40.491413Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"val_path = my_data_dir + '/val/'\ntrain_path = my_data_dir + '/train/'\ntest_path = my_data_dir + '/test/'","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:23:41.746770Z","iopub.execute_input":"2022-01-19T19:23:41.747302Z","iopub.status.idle":"2022-01-19T19:23:41.751225Z","shell.execute_reply.started":"2022-01-19T19:23:41.747265Z","shell.execute_reply":"2022-01-19T19:23:41.750428Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.listdir(train_path)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:23:47.689452Z","iopub.execute_input":"2022-01-19T19:23:47.690142Z","iopub.status.idle":"2022-01-19T19:23:47.695787Z","shell.execute_reply.started":"2022-01-19T19:23:47.690107Z","shell.execute_reply":"2022-01-19T19:23:47.695050Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Train\nlen(os.listdir(train_path+'/Cat')), len(os.listdir(train_path+'/Dog'))","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:23:49.037187Z","iopub.execute_input":"2022-01-19T19:23:49.037777Z","iopub.status.idle":"2022-01-19T19:23:49.054151Z","shell.execute_reply.started":"2022-01-19T19:23:49.037739Z","shell.execute_reply":"2022-01-19T19:23:49.053486Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Test\nlen(os.listdir(test_path+'/Cat')), len(os.listdir(test_path+'/Dog'))","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:23:50.942818Z","iopub.execute_input":"2022-01-19T19:23:50.943612Z","iopub.status.idle":"2022-01-19T19:23:50.950714Z","shell.execute_reply.started":"2022-01-19T19:23:50.943574Z","shell.execute_reply":"2022-01-19T19:23:50.949943Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Val\nlen(os.listdir(val_path+'/Cat')), len(os.listdir(val_path+'/Dog'))","metadata":{"execution":{"iopub.status.busy":"2022-01-19T12:24:45.868081Z","iopub.execute_input":"2022-01-19T12:24:45.868326Z","iopub.status.idle":"2022-01-19T12:24:45.877559Z","shell.execute_reply.started":"2022-01-19T12:24:45.868299Z","shell.execute_reply":"2022-01-19T12:24:45.876854Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## flow_from_directory \n### Bir klasörden çok sayıda manipüle edilmiş görüntü oluşturma\nflow_from_directory işlevi, alt dizinlerde düzenlenen resimlerle çalışır. Dizinleriniz yalnızca bir görüntü sınıfı içermelidir, bu nedenle görüntü sınıfı başına bir klasör.\n\n### Generating many manipulated images from a directory\nflow_from_directory function works with images organized in sub-directories. Your directories should include only one class of images, so one folder per class of images.","metadata":{}},{"cell_type":"code","source":"# image generator'ü hatırlayalım\n# let's remember image generator\nimage_gen","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:23:58.768491Z","iopub.execute_input":"2022-01-19T19:23:58.769039Z","iopub.status.idle":"2022-01-19T19:23:58.776261Z","shell.execute_reply.started":"2022-01-19T19:23:58.769001Z","shell.execute_reply":"2022-01-19T19:23:58.775379Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image_gen.flow_from_directory(train_path)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:24:04.195703Z","iopub.execute_input":"2022-01-19T19:24:04.196432Z","iopub.status.idle":"2022-01-19T19:24:05.070583Z","shell.execute_reply.started":"2022-01-19T19:24:04.196382Z","shell.execute_reply":"2022-01-19T19:24:05.069757Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image_gen.flow_from_directory(val_path)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:24:07.569609Z","iopub.execute_input":"2022-01-19T19:24:07.570108Z","iopub.status.idle":"2022-01-19T19:24:07.887344Z","shell.execute_reply.started":"2022-01-19T19:24:07.570071Z","shell.execute_reply":"2022-01-19T19:24:07.886656Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image_gen.flow_from_directory(test_path)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:24:09.037503Z","iopub.execute_input":"2022-01-19T19:24:09.037956Z","iopub.status.idle":"2022-01-19T19:24:09.252946Z","shell.execute_reply.started":"2022-01-19T19:24:09.037918Z","shell.execute_reply":"2022-01-19T19:24:09.251103Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Modeli Oluşturalım / Creating the Model","metadata":{}},{"cell_type":"code","source":"image_shape = (128,128,3)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:24:14.406104Z","iopub.execute_input":"2022-01-19T19:24:14.406572Z","iopub.status.idle":"2022-01-19T19:24:14.411320Z","shell.execute_reply.started":"2022-01-19T19:24:14.406524Z","shell.execute_reply":"2022-01-19T19:24:14.410479Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Activation, Dropout, Flatten, Dense, Conv2D, MaxPooling2D","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:24:16.170908Z","iopub.execute_input":"2022-01-19T19:24:16.171592Z","iopub.status.idle":"2022-01-19T19:24:16.178069Z","shell.execute_reply.started":"2022-01-19T19:24:16.171542Z","shell.execute_reply":"2022-01-19T19:24:16.177323Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\nmodel = Sequential()\n\nmodel.add(Conv2D(filters=32, kernel_size=(3,3),input_shape=image_shape, activation='relu',))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(filters=64, kernel_size=(3,3),input_shape=image_shape, activation='relu',))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\nmodel.add(Conv2D(filters=64, kernel_size=(3,3),input_shape=image_shape, activation='relu',))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\n\n\nmodel.add(Flatten())\n\n\nmodel.add(Dense(128))\nmodel.add(Activation('relu'))\n\n\nmodel.add(Dropout(0.5))\n\n\nmodel.add(Dense(1))\nmodel.add(Activation('sigmoid'))\n\nmodel.compile(loss='binary_crossentropy',\n optimizer='adam',\n metrics=['accuracy'])","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:24:19.144748Z","iopub.execute_input":"2022-01-19T19:24:19.145004Z","iopub.status.idle":"2022-01-19T19:24:19.218262Z","shell.execute_reply.started":"2022-01-19T19:24:19.144975Z","shell.execute_reply":"2022-01-19T19:24:19.217482Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:24:28.530806Z","iopub.execute_input":"2022-01-19T19:24:28.531067Z","iopub.status.idle":"2022-01-19T19:24:28.541127Z","shell.execute_reply.started":"2022-01-19T19:24:28.531038Z","shell.execute_reply":"2022-01-19T19:24:28.540450Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Early Stopping","metadata":{}},{"cell_type":"code","source":"from tensorflow.keras.callbacks import EarlyStopping","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:24:31.080192Z","iopub.execute_input":"2022-01-19T19:24:31.080742Z","iopub.status.idle":"2022-01-19T19:24:31.086919Z","shell.execute_reply.started":"2022-01-19T19:24:31.080697Z","shell.execute_reply":"2022-01-19T19:24:31.085925Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"early_stop = EarlyStopping(monitor='val_loss',patience=2)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:24:32.648721Z","iopub.execute_input":"2022-01-19T19:24:32.648974Z","iopub.status.idle":"2022-01-19T19:24:32.654092Z","shell.execute_reply.started":"2022-01-19T19:24:32.648945Z","shell.execute_reply":"2022-01-19T19:24:32.653278Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"batch_size = 16","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:24:34.119600Z","iopub.execute_input":"2022-01-19T19:24:34.119858Z","iopub.status.idle":"2022-01-19T19:24:34.124506Z","shell.execute_reply.started":"2022-01-19T19:24:34.119829Z","shell.execute_reply":"2022-01-19T19:24:34.123458Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_image_gen = image_gen.flow_from_directory(train_path,\n target_size=image_shape[:2],\n color_mode='rgb',\n batch_size=batch_size,\n class_mode='binary')","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:24:35.766150Z","iopub.execute_input":"2022-01-19T19:24:35.766717Z","iopub.status.idle":"2022-01-19T19:24:36.623016Z","shell.execute_reply.started":"2022-01-19T19:24:35.766677Z","shell.execute_reply":"2022-01-19T19:24:36.622265Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"val_image_gen = image_gen.flow_from_directory(val_path,\n target_size=image_shape[:2],\n color_mode='rgb',\n batch_size=batch_size,\n class_mode='binary',shuffle=False)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:24:37.672734Z","iopub.execute_input":"2022-01-19T19:24:37.673481Z","iopub.status.idle":"2022-01-19T19:24:37.896825Z","shell.execute_reply.started":"2022-01-19T19:24:37.673414Z","shell.execute_reply":"2022-01-19T19:24:37.896021Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_image_gen = image_gen.flow_from_directory(test_path,\n target_size=image_shape[:2],\n color_mode='rgb',\n batch_size=batch_size,\n class_mode='binary',shuffle=False)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:24:39.602193Z","iopub.execute_input":"2022-01-19T19:24:39.602475Z","iopub.status.idle":"2022-01-19T19:24:39.813039Z","shell.execute_reply.started":"2022-01-19T19:24:39.602426Z","shell.execute_reply":"2022-01-19T19:24:39.812328Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_image_gen.class_indices","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:24:41.799389Z","iopub.execute_input":"2022-01-19T19:24:41.799700Z","iopub.status.idle":"2022-01-19T19:24:41.804675Z","shell.execute_reply.started":"2022-01-19T19:24:41.799670Z","shell.execute_reply":"2022-01-19T19:24:41.803952Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_image_gen.class_indices","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:24:43.144241Z","iopub.execute_input":"2022-01-19T19:24:43.144516Z","iopub.status.idle":"2022-01-19T19:24:43.149982Z","shell.execute_reply.started":"2022-01-19T19:24:43.144485Z","shell.execute_reply":"2022-01-19T19:24:43.149310Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Model Training","metadata":{}},{"cell_type":"code","source":"import warnings\nwarnings.filterwarnings('ignore')","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:24:44.918256Z","iopub.execute_input":"2022-01-19T19:24:44.918692Z","iopub.status.idle":"2022-01-19T19:24:44.925322Z","shell.execute_reply.started":"2022-01-19T19:24:44.918653Z","shell.execute_reply":"2022-01-19T19:24:44.924237Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"results = model.fit_generator(train_image_gen,epochs=10,\n validation_data=val_image_gen,\n callbacks=[early_stop])","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:24:49.190520Z","iopub.execute_input":"2022-01-19T19:24:49.190947Z","iopub.status.idle":"2022-01-19T19:45:27.661333Z","shell.execute_reply.started":"2022-01-19T19:24:49.190904Z","shell.execute_reply":"2022-01-19T19:45:27.660623Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Modelin Değerlendirilmesi / Evaluating the Model","metadata":{}},{"cell_type":"code","source":"summary = pd.DataFrame(model.history.history)\nsummary.head()","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:46:07.382744Z","iopub.execute_input":"2022-01-19T19:46:07.383008Z","iopub.status.idle":"2022-01-19T19:46:07.399004Z","shell.execute_reply.started":"2022-01-19T19:46:07.382979Z","shell.execute_reply":"2022-01-19T19:46:07.395683Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(10,6))\nplt.plot(summary.loss, label=\"loss\")\nplt.plot(summary.val_loss, label=\"val_loss\")\nplt.legend(loc=\"upper right\")\nplt.ylabel(\"Loss\")\nplt.xlabel(\"Epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:46:10.037668Z","iopub.execute_input":"2022-01-19T19:46:10.037915Z","iopub.status.idle":"2022-01-19T19:46:10.228945Z","shell.execute_reply.started":"2022-01-19T19:46:10.037889Z","shell.execute_reply":"2022-01-19T19:46:10.228284Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(10,6))\nplt.plot(summary.accuracy, label=\"accuracy\")\nplt.plot(summary.val_accuracy, label=\"val_accuracy\")\nplt.legend(loc=\"upper left\")\nplt.ylabel(\"Accuracy\")\nplt.xlabel(\"Epoch\")\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:46:12.802291Z","iopub.execute_input":"2022-01-19T19:46:12.802829Z","iopub.status.idle":"2022-01-19T19:46:12.996229Z","shell.execute_reply.started":"2022-01-19T19:46:12.802789Z","shell.execute_reply":"2022-01-19T19:46:12.995582Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.metrics_names","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:46:15.542180Z","iopub.execute_input":"2022-01-19T19:46:15.542996Z","iopub.status.idle":"2022-01-19T19:46:15.550133Z","shell.execute_reply.started":"2022-01-19T19:46:15.542949Z","shell.execute_reply":"2022-01-19T19:46:15.549366Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.evaluate_generator(val_image_gen)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:46:17.925868Z","iopub.execute_input":"2022-01-19T19:46:17.926122Z","iopub.status.idle":"2022-01-19T19:46:43.971032Z","shell.execute_reply.started":"2022-01-19T19:46:17.926093Z","shell.execute_reply":"2022-01-19T19:46:43.970346Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pred_probabilities = model.predict_generator(val_image_gen)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:46:52.563923Z","iopub.execute_input":"2022-01-19T19:46:52.564399Z","iopub.status.idle":"2022-01-19T19:47:18.393180Z","shell.execute_reply.started":"2022-01-19T19:46:52.564352Z","shell.execute_reply":"2022-01-19T19:47:18.392483Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pred_probabilities","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:47:20.657955Z","iopub.execute_input":"2022-01-19T19:47:20.658509Z","iopub.status.idle":"2022-01-19T19:47:20.663854Z","shell.execute_reply.started":"2022-01-19T19:47:20.658469Z","shell.execute_reply":"2022-01-19T19:47:20.663136Z"},"scrolled":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"val_image_gen.classes","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:47:29.574338Z","iopub.execute_input":"2022-01-19T19:47:29.575134Z","iopub.status.idle":"2022-01-19T19:47:29.583342Z","shell.execute_reply.started":"2022-01-19T19:47:29.575099Z","shell.execute_reply":"2022-01-19T19:47:29.582485Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predictions = pred_probabilities > 0.5","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:47:31.026341Z","iopub.execute_input":"2022-01-19T19:47:31.027012Z","iopub.status.idle":"2022-01-19T19:47:31.030885Z","shell.execute_reply.started":"2022-01-19T19:47:31.026975Z","shell.execute_reply":"2022-01-19T19:47:31.029703Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predictions","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:47:32.754812Z","iopub.execute_input":"2022-01-19T19:47:32.755190Z","iopub.status.idle":"2022-01-19T19:47:32.762852Z","shell.execute_reply.started":"2022-01-19T19:47:32.755156Z","shell.execute_reply":"2022-01-19T19:47:32.762081Z"},"scrolled":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import classification_report,confusion_matrix","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:47:38.354953Z","iopub.execute_input":"2022-01-19T19:47:38.355204Z","iopub.status.idle":"2022-01-19T19:47:38.358699Z","shell.execute_reply.started":"2022-01-19T19:47:38.355175Z","shell.execute_reply":"2022-01-19T19:47:38.357957Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(classification_report(val_image_gen.classes,predictions))","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:47:39.915755Z","iopub.execute_input":"2022-01-19T19:47:39.916017Z","iopub.status.idle":"2022-01-19T19:47:39.931987Z","shell.execute_reply.started":"2022-01-19T19:47:39.915988Z","shell.execute_reply":"2022-01-19T19:47:39.931215Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"confusion_matrix(val_image_gen.classes,predictions)","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:47:42.808703Z","iopub.execute_input":"2022-01-19T19:47:42.809262Z","iopub.status.idle":"2022-01-19T19:47:42.833515Z","shell.execute_reply.started":"2022-01-19T19:47:42.809224Z","shell.execute_reply":"2022-01-19T19:47:42.832784Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.models import load_model\nmodel.save('cat_dog_detector.h5')","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:47:45.350364Z","iopub.execute_input":"2022-01-19T19:47:45.350708Z","iopub.status.idle":"2022-01-19T19:47:45.418345Z","shell.execute_reply.started":"2022-01-19T19:47:45.350680Z","shell.execute_reply":"2022-01-19T19:47:45.417639Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](attachment:cc376ccc-458b-441f-a808-0672fc3071f8.png)","metadata":{},"attachments":{"image.png":{"image/png":"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"},"cc376ccc-458b-441f-a808-0672fc3071f8.png":{"image/png":"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"}}},{"cell_type":"code","source":"# PetImages klasörünü silebiliriz\n# we can delete PetImages folder\nimport shutil\nshutil.rmtree('./PetImages2')","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:48:00.828975Z","iopub.execute_input":"2022-01-19T19:48:00.829237Z","iopub.status.idle":"2022-01-19T19:48:01.585369Z","shell.execute_reply.started":"2022-01-19T19:48:00.829207Z","shell.execute_reply":"2022-01-19T19:48:01.584317Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Hadi Deneyelim / Let's Predict ","metadata":{}},{"cell_type":"code","source":"# Modelimin doğru çalışıp çalışmadığını öğrenmek için modeli kullanabilir ve resminizin kedi mi köpek mi olduğunu tahmin edebilirsiniz.\n# To find out if my model is working correctly, you can use the model and guess whether your picture is a cat or a dog.","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.preprocessing import image","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:48:24.712828Z","iopub.execute_input":"2022-01-19T19:48:24.713087Z","iopub.status.idle":"2022-01-19T19:48:24.717222Z","shell.execute_reply.started":"2022-01-19T19:48:24.713058Z","shell.execute_reply":"2022-01-19T19:48:24.716253Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model=load_model('cat_dog_detector.h5')","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:48:32.964906Z","iopub.execute_input":"2022-01-19T19:48:32.965157Z","iopub.status.idle":"2022-01-19T19:48:33.093389Z","shell.execute_reply.started":"2022-01-19T19:48:32.965130Z","shell.execute_reply":"2022-01-19T19:48:33.092631Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from PIL import Image","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:48:40.614496Z","iopub.execute_input":"2022-01-19T19:48:40.615031Z","iopub.status.idle":"2022-01-19T19:48:40.618202Z","shell.execute_reply.started":"2022-01-19T19:48:40.614994Z","shell.execute_reply":"2022-01-19T19:48:40.617507Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"path_pred = '../input/image-to-predict-cnn'","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:49:02.941018Z","iopub.execute_input":"2022-01-19T19:49:02.941282Z","iopub.status.idle":"2022-01-19T19:49:02.946239Z","shell.execute_reply.started":"2022-01-19T19:49:02.941252Z","shell.execute_reply":"2022-01-19T19:49:02.945208Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"img=image.load_img(path_pred+'/newimage1.jpg')\nimg","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:50:33.503228Z","iopub.execute_input":"2022-01-19T19:50:33.503503Z","iopub.status.idle":"2022-01-19T19:50:33.546282Z","shell.execute_reply.started":"2022-01-19T19:50:33.503471Z","shell.execute_reply":"2022-01-19T19:50:33.545668Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"resized_img = img.resize((128, 128))\nresized_img","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:50:36.196476Z","iopub.execute_input":"2022-01-19T19:50:36.197020Z","iopub.status.idle":"2022-01-19T19:50:36.220973Z","shell.execute_reply.started":"2022-01-19T19:50:36.196975Z","shell.execute_reply":"2022-01-19T19:50:36.220389Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"resized_img1=np.array(resized_img)\nresized_img1.shape","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:50:39.499611Z","iopub.execute_input":"2022-01-19T19:50:39.500430Z","iopub.status.idle":"2022-01-19T19:50:39.506506Z","shell.execute_reply.started":"2022-01-19T19:50:39.500378Z","shell.execute_reply":"2022-01-19T19:50:39.505511Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"resized_img1=np.expand_dims(resized_img1, axis=0)\nresized_img1.shape","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:50:41.781569Z","iopub.execute_input":"2022-01-19T19:50:41.782121Z","iopub.status.idle":"2022-01-19T19:50:41.789214Z","shell.execute_reply.started":"2022-01-19T19:50:41.782083Z","shell.execute_reply":"2022-01-19T19:50:41.788454Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](attachment:52f7b0ff-96f8-4ad0-94dc-80fea13e40fb.png)","metadata":{},"attachments":{"52f7b0ff-96f8-4ad0-94dc-80fea13e40fb.png":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAVUAAABmCAYAAAB7lQKZAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAABu1SURBVHhe7d0JWFRV/wfwrwoOIIgmCApIqLk8IpILaCLFm2haamqYvaGW66tiuWWZu7m+KqZg5pK5VSam5r7hBkWgKeLu6xIqCgl/hDFgGNT/OXfOwIAgM3hnlOH3eZ6puede5i7C955tZio9ZkAIIUQWlcX/CSGEyIBClRBCZEShSgghMqJQJYQQGVGoEkKIjChUCSFERhSqhBAiIwpVQgiREYUqIYTIqMK8oyonR4VM5QNkZecgT50HehsZIcQYKkSo/n0vDdnZKtjZVYOFZRVWUkmzghBCZGb2oZp0JwVVq1pCYaUQJYQQYjxmHaq8hlqpUiUKVEKIyZjtQBXvQ+VNfgpUQogpmW2o8kEp3odKCCGmZLahykf5NYNShBBiOmYbqnzaFI3yE0JMzWxDleahEkKeB7MNVUIIeR70nlJ169YtrFmzRnreqlUrvPXWW7CwsJCWjSEvLw/79u3Dn3/+KS0PHDgQbm5u0nN9/O9aIpycHMQSIYSYhkGheubMmWLDVDdwGzRogN69e8Pa2lpaTktLQ0REBFJSUljIOSEoKAi1atWS1mmdPXsWW7duLTY4teHaokULClVCyAvvmZv/2dnZUthOmDAB06ZNQ7169XDixIn8dUePHpWClK/j/+fLvFyLB/KVK1fg6ekpSgghpPx65lDlNdJ33nknv2bq4eGBzMxMqYZ59epVODo65tdM79y5g3PnziE1NVVa5uEaFxeH9u3bQ6EwbJI+/9mNGzfi+PHjooQQQp4/2Qeqbty4AXd3d6mLID09HTVr1pTKefjx5YCAANy/f18q4zXaRo0awcGBmumEEPMga6jyvlFeS23atKkoAXJzc7Fr1y4pXP39/UWpptnPNW/eXPq/oXjNODg4uNBrEkLI8yZbqPJATUxMLDSQxYOUByofZOLhybsEeOja2tpK/bBHjhzBjBkzMHv2bGmUnw928e35doQQUh7JEqq8aV80ULm6detKA1Da5v3du3elpr+zs7PUD8sHr/hj0qRJ0jQtPvrPy/WZqkV9qoSQF9EzhyqfMnXhwgWppslrnLzmyR+8ec8HqHx8fPDf//5XKjt27Fih6VaEEGJuZJmnakw0T5UQUp7IPvpPCCEVmUGhqm3im2Iwib8+3492EIsQQsoDs/06FWr+Czd/Qv93duPtXRvxfj1RRuSXGo9+E+5gh1hEQCNk9GsoFl5EahxfcxCD0RAJAxvBSpSWVdyGPQir44sNHQu/BT0nPhpey4HV4X7wtxSFZo5CtSxStiIk8BDePPgNejqJMtmlYtuIAER2OoLwd+nmUJ7wgAnEix6q8iopVCsi6lMlhBAZUaga5AxCvZrDO3AaohGFGYHsOV/2WogEsYUk6zq2zQpGB77OvwfGrb0IlVgleZiKuBVj0NOf/2xrdBy1EnGaj0OQJITy8gDMiAaipwaIfTRH6GmxgR40r6F9jMC2FLFCwmvBzREydRr6tm2ODsEr8fPawex42bF8voetFXTPw+s19J+1FTeyxDpBmbCO1dpb569fP5W97nadkyntWujj5h7MDX6N7UNzrbYtGwzv0DNiJafCje1z0F+6nq+h55h1uKRznKnbR7DtY5G0V2zTNgAhBh+HGonR0Qj6ZA/sB+6Bz9wYHLirFuv0lHULESsOwov9PH+NoBXxuFzkeub8FY/JU/ahEd9m+EFMPnQLOWKd5FE64jYdReBw7XHEIi79kVipB95NIfYvPTZcFSu0rmLhwEhE/HUDq+ZqjsNrUjQO/F14Hzn/O4HB4lrw8ziTIVZI0hAxW2cfA2MQJ9YU0ON66l6v4fswaluivNfCSChUDdICYxPOIv7gDPihA6YdZM/5csJ4eIkt8DAJ2z4PRmT9Sdj2x0nEbhwPh239MG5nQdAk/TIaQ894Y24EW//HPoR6HsfQQetwQ6z3Gstf8wim+QF+M4+IfZzF2FfFBnrQvAZ7SMdaAs8BWLVrMbpeDUOUzZfYc3geOuxdi0jpQJSInNEH6y2GYRM/j9824gPlSvRdGlsQRilbMTF4K1wm/8rO4whC307Foe1iHafHtSiVit3Ihk5FUtfvcOiP37DtU1fE7YwVKzWSto9B/0gPfCldzwiMc9qKAbo3B27zFwhX9kTovpOI2jgK+LYfQmP0j9WcEzEI3JyH/uP9kbK8HVZ7ZiFk7knE6f0SmTiw4ixWVnHFzuVvIiXcG/2zktHtxysFQaG6ggVzk4Eu3jix/A3cnOiMnB1nMTk2U2wAJB44icBr1bDoqzfYcfgi3CMTgbP+xGWxvlQO3tiwpisy2ONggCh7ggpfrUqC+0f+SOD7aPIAQWHxSBRrkXoWIQvT4P5hS3YMbyDcNxubT4l1kloImqTZR8Y4zWd/FFXq9Xx0DxFLzmJ/3UY4yPaRMtUdtaPPI+RImthAhmthJBSqMlPFrcO81FEY9++mcLBRQFGvA8Z+1gvRvx5Hktgm7fYZuLTwQRMntt7GAV4fzcf6L7xR1bCq0zOr/0p92LE/siYttc8dYYeLSJI+78YOfhOPYNNnHeDCz8OuPrr07QjV9rj8X9obkZsQ3WkYQl53YeehgMOrQxHST6xk9LkWpVGd2I31YK/7vuY17Bp2RcgQX7GWUcVi/bxUhIz9QFxPF/iNGY93j21F1B2xDec5CiF9tK/RC/17qxB9/rpYWZo07DzwAJ37tES3erawUtSEV7dmmO36AGduik1KZQ3/IX7YObAp3BUKWNnURbdO9kg5kYYrYgsos3BFbQXfZs6wV9jAvl5zTBnfEN3riPVMyj01vBrUhpeDDTuOWvDp6Y2ofg6oIfPvTlDf19CpjmYf/p2d0T0pE5cfaNYlxiQjopELPvF1Zutt4OTti9kl3rmLU/r1zIm/jEkZzpjSzR1ObB9WdZris762iDh2Kz/cTXUtDEWhKjNlym2ozs9Bz/ymd3P4jvgJiLvOfpU0vPrOR5Mf+rGm7ByEb96DS2ksWNu1gIthn35odIoqt3F4yRj0lZr37FwGrGNNsoLfWGXyRRbITVj8FrDS+QJbfa5FaZSpt1l1vSk8dF5XUaWqeMbcT0JS1kXM7VGwD+82I/AzYnFDt8uD/Yju5eXHmZSZK5ZKk47Ea4B7zepimXNG0IRADHlFLJbKElbIwMENR9FNNHntF6XzVnBBTdWhEUZ1VGPcFwcxft0JRMSzu4JrI/jXK9ivT2c3eB87z5q60QjbfR4JGdXh5e3BgkdsIBPryjrRID19iAxxoCn31fBvVAv2mkWGbWDQyH7p1zPj/1RI+Ts5v6uEP5yWs1S/mQ3tP6uproWhKFSNoc9iHDr+O6IKPUYVdBHU7YpFR37Duv+wWuC9Y5jTtz36LjtjeF+jUaWypnsfrLzfAdM3HtGcQ9gHYp0BSrsWsvgAiw4X3cfvGCnvTp5RmtScXaqsgdlT/XBzKXsM0w0VzgY+/w7ElXnN0KdBFZzZm4A2I49ip25/Zu3mCFv2Jnb2qQVnZRq+mhaJbj9eLdzXaC583TXXqdCjBXzE6hf1WlColoVN9UK1M112Dq5AdDzuWNvBrobOw057+0xC5LIwRCYp4NKsA3qOZE3/HTPhsmIF9hQaTFKgekk7MYXUOERGd0D/kb1Ys1qcg7VODZGp5doCl05dglIsczkPxROm9GtROjsnVyiiL+KGzuuqHurUMGs4wgXHceZW1SL7sGM1WrHNM6sJ9was2Zte0LfJ+0jjdsfigL79GOl3sP+aAkP7erPmanXY27KHjVgnZFw6hbB9N5BhXxc+fi0xa2InbHk9B8H7td0UyTjwI9vnXQWcGjRlTXR/RMxzw8uHEmFIN/WzcnK0xPEraazercVC36Axu9Kvp709q/qez8RlS3GtpIctu2bai/ZiXIviUKiWhZ0LGtSNQuSx61CpVFDeT4VSVDMV7XoiRLEO02ZvxaVUFVTKJMQtC0bHJdoBHgfYKdfhywU/4VIKW69SIunPeFyycYVLDWkDwQ4ur7gg+sBB3GBNbpVSiVTtTkzBwQPe/BwPXEQq27/yZhRCF24SKzVcOn2ELtErEH4sCSq2TerplQjfIFYypV+L0il8emKIgr3uz+I4ru5B+CqdgSoFuzF9osD66XOw7XwqOw52PWPC0P+thQYMIhW4tP5DeHVdgCjdOwVqoVsnW+zffAoR19KRo0pHwqF4TDr8CM76TiFmTd0WtirsiEpEhioLGXcv4qsNuqHCgwQ4vvkylsYmI0OtQk7qLcRee4ROjrZii5qwykpDyPpTSEjNQo76ARLPpyPekgWLCW/A7u3qYsiVJOk4c9i5pJw/gQXRYqVeSr+eVq3cMcsyHZNWxbNmPTvXrGQc/zESPhu0NdEX41oUh0K1TJqi36LxwKo+8G3TGl0HhCFO82UGQJWmGLxxM/pX3Yoh/2oN38BghKf3wqr/+Io+PQV8PvsVizyiENKDrW8TgAHrwX5mHHyKVOCa/HsxxlZZib5t2XZvB2NZTKG/9KfKn1JVZPpXoelOT8XOMXwGXPYPQse27dFzShQ8Ph6FN5uxOoX2MGp0xPTveiFpVg/4tg3A2N0O6PiuWMeVei30wF9j+Ty47BHHseQ2fLrpDFQxTT7eiE0fK7BteIB0HAO+VaLn8lFPXM9nYdWmHQ72scDmJTFwGh6DwScUmD3ZF17afehMVQo8wpaPXBF9gdrpRO4YMrYu3E+eR73hRxH4TRqadHdE/9oPWWBIGwB1vLF6Qm1k7DiFesMi4TTlIhIaNMLqTs5iAwX8B7ZEeN10DJ50FE7DjiNob2V8MrEl/PU911KPUw/2zTBrfC0k/nCKXYujCIlWoFNLsU6iM6WK9xsjHYFinwvPa7Yo9XpWdseoGc0x1PIegsewcx0TjzClIzYENRTv/pLhWhgJvaOqHOLzLjtOjRJLOvxm4NA3vVhd2IR4szy/mS3eBdblCMK76XMUfN5vMNg95Qn91+lMISu0D37DaM1qr98hdmQLUUI0+BzTK/hKLOmaMq4rxrMbIjE+CtXySKWEsuALaQtUqWpQf+UzS92DiaMv4O05w9CGXeo7xxZi7IwchOybjzcLdWWUTHVfieLG4Ktq+0RVZ7B60FrYTZyEdz3soLy8DmOH74b/2l8xuIlmW6KlRs6D7GK7VhQ21WFF7VKToFAlzySJBWlo6FZE3lDCzusDjJ0+Dj0byhvsyoSfMGfRShw+nQo4dcCQ2TMx2If+bcmLiUKVEEJkRA0CQshz8fDYUWT5tUf26/54GG3Q9IEXGoUqIcTkHl2+jJxhw/A4NRWPUlKQu2SJWFP+UagSQkwud+EC8UzQfVtsOUehSggxKfWKb/EwqvCUQMsPPxTPyj8KVUKIyTw6dw65ixeLJQ3L4GBYdO8ulso/ClVCiMmoly0TzzQqN2yIqqPHiCXzQKFKCDGJvMhDyDtyWCxpVP1iImCr/WwD80ChSggxibzvvxfPNCzfC0IVP4M+3bpcoFAlhBhd3o4deHjypFjSsAgOFs/MC4UqIcSoHicnI3fObLGkYfnxx6jcxDw/vIFClRBiVOrwcDy+r/1sTA1zmkJVlNmGaiXxf0LI85O3axfUWyLEkoZi+nRUcnUTS+bHbEPVwtKC/dcsPyuGkHKBvwU1d95csaRh0TEQFn3L8F1n5YjZhqqNtRXy1DpfbEQIMancsDApWHVZfvqpeGa+zDZUq9vZQqn8RywRQkwp9+uvkfdz4e80qzp5Ciq/ovd3epdbZhuqVlYKWFsroMox4ZflEUKQGxoK9bfLxZKGRcC/pLejVgRmG6pcbcdayM1VU7ASYiK5CxZAvXKFWNKo5OoKy88/F0vmz6xDlXOp6wT+5Qbp/5eBPHUeK6HBK0KMIXf+PKi/Wy2WNCq5ucEqfBkqv/yyKDF/Zvt1KkXlsNpqpvIBsrJzpHClaCVEHpX+/huKld/C4uB+UaLxyN0dOTNm4ZFHfVFSMVSYUCWEyC9n505kTJmCR0VG+S0aN0bNFStg0bChKKk4KFQJIQZ7nJ0N5fz5+Gd14eY+Z9msGWp8y2quHh6ipGKhUCWEGEQVFQXlzJlQX7woSgpYv/suaixdCnP6ehRDUagSQvT2YMkSKBcU+X4pwX7WLNh89JFYqrgoVAkhpcresQNZa9Ygt8jH93EKPz/YffklLL28REnFRqFKCCnR08KUsxs/HrajR4slwlGoEkKeUFqY8tH96tOmQeHvL0qIFoUqISRfaWHK2X3xBaoNGoRK1taihOiiUCWkguNzTLN37kT25s1Qnz0rSp9UbehQ2PCvk65fsSbzG4pClRATyo2JQW5cHB6r1bDu0QMWz/FTm3JjYzU103XrREnxKEwNQ6FKiAmo4+OlqUiqY8dECVO5MurcvCkWTONRZqb0LqjsrVulUH0aCtOyoVAlxMh4eKW9/z6Qxz/Qp7CXfvgBitdfF0vGw48hZ9cu/MNrpY8eidLiUZg+GwpVQoxIfeoU0j78EI+VSlFSWO3ff0eVevXEkrzUCQnI/fNPqVaqPn1alBbP0tsbNkFBsOrRA5Vr1BClpCwq7HvJ7u0cjvrsTlw/7JQo0bqHHSHFlRvR6WXsWIZjR7JYfoGdCmPXJmQHu0qmYtp/D+n82O/F8J3Pfobqc+eQPmRIiYFq3aePrIHKQzRr7VrcDwlBSvPmSO3aFZlTpjw1UG0++AAvbdoEB1aLtRkwgAJVBhUzVJN3YOqnnthy/Tquj2opCrUc4dpYPC1KCj/NH13xgVwC/nNPC6I6bugsnspFGw4GHace3F5+8kjzb1D5j2WQb49P+fcos1NYJo51WZG8aTmK/U5c3wLPT6c+800uc9o0PExJEUuFWbLQqz51qlgyXN7Vq8jZvx8PvvlGE6ItWkghmjF5MrK3b8ej9HSx5ZOkfc+cidonT8J+wQLpHVFEPmUK1by8PERHR+PXX3+VHnfv3hVrypGubjDkS3Kl4Oh9Dl//zv/oxOOJQH4x8GN9D1vyj3MLW5Kj5vVUYwr2F7vkHN6TNVhlxG6ow+uvgtvvWzBOFD2J/W50FU/L6MHSpU8dCLKfP1+vWuHDmzelwS1eA+W1zrTevZHcsCHuvfEG0gcNgnLOHE2IpqWJnygZ7yeVaqV796LawIGo4uws1hA5lalPlYfqH3/8gQYNGqBOnTqiVEO7Lk38Izdp0gSNGxdUNU6yu2NSUpL03MXFBa1bt5aea+Xk5CAqKgrW1tZo27YtLCz4V03LjP9hzQJmhndn9SB98CaoL24Nuo6Rr4qiQnjN5z0sEktgf65bro9EyyfKBR5A+gYyr+X2XoRxv5S076L4PnloLEd36W9GHEPXrxGr9/kahoe4719DdM6pyPXi1/u10dB+hHHnJbFY3k0cSZF1Er2vjzg3Q65nPs3Potjryo+f1SIna6+hYfiUqbRevcTSk3jtkDe7tXj3QN6NG3iYmIg8FqIP2XP1lSvIu3ABj9nfQ1lVcXOTaqGWrVrBqlMnVH7pJbGGGJPszf9r167B0dERPXr0gL+/PxLZL0q6aIpcvnxZ+j9fxx+ctozjgcxDt2bNmqLESO7ewv7GrgYEzC3c2tMZboXvHzpaYqSopWlraqukmqEo/4XViXioabcxZg03+TbOwROuPAy0tbJfvkbnPewcNFuYgKbJfu42uwZSaO5Fl/waPm9a+4pmNws2FqieLNikddrrZMzroxd+/Ptxq4wNMN4kL0m1jz9GpWrV8GDxYqQPH457HToguWlTqenOl5Vz5yKL1Sb5AJehgcpDlIe1/cKFcDx8GLVjYjQB3rcvBaoJyR6qvFaqrZna2dnBxsZGqn3yx507d6TaLceXedjeu3dPClOOB7KVlZVUgzUOXjupj/rR7QwLNimonkYMpoh+Ot9P92P/XzJF2KsjpcDRr5aqg9dwX7uFIddZbavEm4EJ8BvYmCE6Nb6WaDdGBG5xDAp/cdMyQgjzvtV20Yb3D/NpS6pDh8TSk/75/nvcHzECykWLpPmivIZaFlXc3WH19tuwHTsWNZYtg2NkZKEQtWjUSGxJTM2oA1VK1qxRq9VSzTM7O1sq4816HqbHjx+XApav56HKy3joenp6StsZh/gj9IsxbPDG2ZXV/UquudzbORWjUVATjV0i97CTAaRjXYT3vnNjx8O7IBgebAb2IT+be7jNGiCerqW1BTQBu6i3uCFJfdbimJ8zPtAX48f/PQ07Hh6acpKa8B07wm7CBCkwa/3yC5wSElD7t9+kryuxY6EqvTNLp4uNPF9GC1VeE+VNed6nymufWjw4eTnvGtA283mo8m4Ab2/vQtsaDR9tv3yb/enrqyX6sKBc1Lv4Wsutv/azKrq2O+EUNrOaaiF8f2VtfosZB0VHqUumCarOnduJ42G16O8W6SxzosZupKlRp8J8pZtMH1675ue+eFXBSHryDqxa3Bld2rCj4c8v63SL8Fq1QX2Y4jxkn27FbwpP6+4pXh77HS7tXUrFsrCQ5onyKVY8PHnNk09xcjp3Tqp9vrR2LWw/+URq2lf19aWm/AvOKKGqHWzitU7tQBavofLw5KEaEBAghSffztLSEvfv30dKSopUe+WzCeJ4R39aGnbv3v3CzCxw7LZcjGqLWpXOH3PLoK9ZcLwnylfBbUmRcWXn7hgyhtUei/ycsbQcFYsu+33F8fhib2edgSGJGN2Ws581//zra2YeaAfF2Lkv/8UTo18T5y71oYrw5Nel8Wj4ip/TPIw9X1cEsRhA1NSS5dnn06YxcZXt7VHVx0dqnvNPeqq5erXU91nnr7+kEK0RGiqFJ6958pClOaPlk+yj/7wZH8Purq+++uoT63gNlfez8j5X7WvwQS3d2QEcD1Lev8pH//l22oDmswZ4EPPtT58+jTZt2pRtUIsPnhg0+m9+pBH7/V2MNiNAL9IgFu/3LWhi82a3FMrPdbCqbKP/fE7q361aiaUCVp07w5q/W+mtt0QJMWdGGf3n/aS8tqmdx8rntPJw5M17PjDFy3gttLhALQ2v4coyzWrPXsSUg3cwyY4HGaupPfdA5Xhfr3iqoemL7fyy6Xp/i8dne4inBqji5CQ134vik/TTBw9GaqdOyN6yRZQScyV7TbW8kGpqvO+zTHMciVykmuliscA9538P7fEUmktroMzp04v96mYt/hXO/ENLrHv3FiXEnFTYUCXEmFSHD0u1Uv55pSXhg0589L5q+/aihJiDZwpV7bumfHx8KFwJKYb6wgVkb9uGf5YvFyVPsh0xQhqgqmRrK0pIeVamUCWEGObh7dvI+uEHPAgLEyWFWXh4SDMC+IR+Ur5RqBJiQlK4rl9f4ltZ+VxUm4EDYdm0qSgh5Q2FKiHPgSo6Gg9CQ6UPXylO9RkzpG8sJeUPhSohzxHva82cPVssFahcqxaczpwRS6Q8Mep7/wkhT1dt+HA4HDgAqy5dRIkG/8AUUj5RTZWQFwT/+up/1qyRnvM3ETzPr68mZUehSgghMqLmPyGEyIhClRBCZAP8P2A7jlV2/vgrAAAAAElFTkSuQmCC"}}},{"cell_type":"code","source":"# BRAVO!!!!\nmodel.predict(resized_img1) # --> class 1 is dog","metadata":{"execution":{"iopub.status.busy":"2022-01-19T19:50:46.050824Z","iopub.execute_input":"2022-01-19T19:50:46.051332Z","iopub.status.idle":"2022-01-19T19:50:46.096213Z","shell.execute_reply.started":"2022-01-19T19:50:46.051294Z","shell.execute_reply":"2022-01-19T19:50:46.095581Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
Doğru Tahmin Bravo!!! / Correct Prediction Congrats!!!

\n\"acorrect_pred\"","metadata":{}},{"cell_type":"markdown","source":"___","metadata":{}},{"cell_type":"markdown","source":"# Sonuç / Conclusion ","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}