{"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":"\n\n**CIFAR-10  is an established computer-vision dataset used for object recognition. It is a subset of the 80 million tiny images dataset and consists of 60,000 32x32 color images containing one of 10 object classes, with 6000 images per class. It was collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton**","metadata":{"id":"l__gp7eIZ-B_"}},{"cell_type":"markdown","source":"\nIn this notebook, we will classify small images cifar10 dataset from tensorflow keras datasets. There are total 10 classes as shown below. We will use CNN for classification\n\n![small_images.jpg](data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABgAAD/4RDyRXhpZgAATU0AKgAAAAgABAE7AAIAAAANAAAISodpAAQAAAABAAAIWJydAAEAAAAaAAAQ0OocAAcAAAgMAAAAPgAAAAAc6gAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAERoYXZhbCBQYXRlbAAAAAWQAwACAAAAFAAAEKaQBAACAAAAFAAAELqSkQACAAAAAzg4AACSkgACAAAAAzg4AADqHAAHAAAIDAAACJoAAAAAHOoAAAAIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA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Libraries**","metadata":{"id":"-EVbyDLLZtyx"}},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras import datasets,layers,models\nimport matplotlib.pyplot as plt\nimport numpy as np","metadata":{"id":"4f9HS6yeRpdF","execution":{"iopub.status.busy":"2021-08-01T16:03:46.393455Z","iopub.execute_input":"2021-08-01T16:03:46.393873Z","iopub.status.idle":"2021-08-01T16:03:50.728179Z","shell.execute_reply.started":"2021-08-01T16:03:46.393771Z","shell.execute_reply":"2021-08-01T16:03:50.727298Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"(X_train,y_train),(X_test,y_test)=datasets.cifar10.load_data()","metadata":{"id":"jqfOzUhySKLA","execution":{"iopub.status.busy":"2021-08-01T16:03:50.729752Z","iopub.execute_input":"2021-08-01T16:03:50.730139Z","iopub.status.idle":"2021-08-01T16:03:59.373621Z","shell.execute_reply.started":"2021-08-01T16:03:50.730102Z","shell.execute_reply":"2021-08-01T16:03:59.372686Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train.shape","metadata":{"id":"fZJsV21fSXjS","outputId":"dda24850-6fe8-464e-e1e9-044290b2d593","execution":{"iopub.status.busy":"2021-08-01T16:03:59.376914Z","iopub.execute_input":"2021-08-01T16:03:59.377327Z","iopub.status.idle":"2021-08-01T16:03:59.384827Z","shell.execute_reply.started":"2021-08-01T16:03:59.377287Z","shell.execute_reply":"2021-08-01T16:03:59.383928Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_test.shape","metadata":{"id":"D-cp0GINScZl","outputId":"cf1fe709-96ac-429b-8fcb-94c27b7ace08","execution":{"iopub.status.busy":"2021-08-01T16:03:59.386361Z","iopub.execute_input":"2021-08-01T16:03:59.386727Z","iopub.status.idle":"2021-08-01T16:03:59.396403Z","shell.execute_reply.started":"2021-08-01T16:03:59.386688Z","shell.execute_reply":"2021-08-01T16:03:59.395287Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train.shape","metadata":{"id":"NmZBsX9qSz0Y","outputId":"db7f11fc-b6db-44b0-d227-020bc97cf616","execution":{"iopub.status.busy":"2021-08-01T16:03:59.398229Z","iopub.execute_input":"2021-08-01T16:03:59.399067Z","iopub.status.idle":"2021-08-01T16:03:59.406052Z","shell.execute_reply.started":"2021-08-01T16:03:59.399011Z","shell.execute_reply":"2021-08-01T16:03:59.405139Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_train[:5]","metadata":{"id":"na8Ahc5CSefy","outputId":"f5f44b04-4087-49a3-bca4-1b5875e88435","execution":{"iopub.status.busy":"2021-08-01T16:03:59.407483Z","iopub.execute_input":"2021-08-01T16:03:59.408118Z","iopub.status.idle":"2021-08-01T16:03:59.416772Z","shell.execute_reply.started":"2021-08-01T16:03:59.408077Z","shell.execute_reply":"2021-08-01T16:03:59.415583Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\ny_train is a 2D array, for our classification having 1D array is good enough. so we will convert this to now 1D array","metadata":{"id":"ouzwHDRzS2jN"}},{"cell_type":"code","source":"y_train = y_train.reshape(-1,)\ny_train[:5]","metadata":{"id":"Pyiz9-AdSh3P","outputId":"d93c60e1-9b32-4068-dcee-1599920c432b","execution":{"iopub.status.busy":"2021-08-01T16:03:59.418661Z","iopub.execute_input":"2021-08-01T16:03:59.419143Z","iopub.status.idle":"2021-08-01T16:03:59.427007Z","shell.execute_reply.started":"2021-08-01T16:03:59.419085Z","shell.execute_reply":"2021-08-01T16:03:59.425797Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_test = y_test.reshape(-1,)","metadata":{"id":"B4XMKPQ8S8Tz","execution":{"iopub.status.busy":"2021-08-01T16:03:59.430560Z","iopub.execute_input":"2021-08-01T16:03:59.431078Z","iopub.status.idle":"2021-08-01T16:03:59.435436Z","shell.execute_reply.started":"2021-08-01T16:03:59.431040Z","shell.execute_reply":"2021-08-01T16:03:59.434483Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"classes = [\"airplane\",\"automobile\",\"bird\",\"cat\",\"deer\",\"dog\",\"frog\",\"horse\",\"ship\",\"truck\"]","metadata":{"id":"VCi5HoCUS94s","execution":{"iopub.status.busy":"2021-08-01T16:03:59.437639Z","iopub.execute_input":"2021-08-01T16:03:59.438351Z","iopub.status.idle":"2021-08-01T16:03:59.443111Z","shell.execute_reply.started":"2021-08-01T16:03:59.438307Z","shell.execute_reply":"2021-08-01T16:03:59.442093Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Let's plot some images to see what they are","metadata":{"id":"bL2g6FwyTBis"}},{"cell_type":"code","source":"def plot_sample(X, y, index):\n    plt.figure(figsize = (15,2))\n    plt.imshow(X[index])\n    plt.xlabel(classes[y[index]])","metadata":{"id":"GDrGy_NnS_ba","execution":{"iopub.status.busy":"2021-08-01T16:03:59.444507Z","iopub.execute_input":"2021-08-01T16:03:59.445226Z","iopub.status.idle":"2021-08-01T16:03:59.450433Z","shell.execute_reply.started":"2021-08-01T16:03:59.445172Z","shell.execute_reply":"2021-08-01T16:03:59.449462Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_sample(X_train, y_train, 0)","metadata":{"id":"jU8ES3XmTDrH","outputId":"ef779b9e-ae01-42ce-acbb-33d537161a88","execution":{"iopub.status.busy":"2021-08-01T16:03:59.451809Z","iopub.execute_input":"2021-08-01T16:03:59.452437Z","iopub.status.idle":"2021-08-01T16:03:59.589377Z","shell.execute_reply.started":"2021-08-01T16:03:59.452404Z","shell.execute_reply":"2021-08-01T16:03:59.588236Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_sample(X_train, y_train, 1)","metadata":{"id":"-nvWLs6lTH97","outputId":"3adc5042-04f3-4239-fc8e-71fed5be574c","execution":{"iopub.status.busy":"2021-08-01T16:03:59.591054Z","iopub.execute_input":"2021-08-01T16:03:59.591558Z","iopub.status.idle":"2021-08-01T16:03:59.708867Z","shell.execute_reply.started":"2021-08-01T16:03:59.591507Z","shell.execute_reply":"2021-08-01T16:03:59.707898Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for i in range(10):\n  plot_sample(X_train, y_train, i)","metadata":{"id":"8kB_TUC-TJiv","outputId":"7a27e4d5-9e7a-48e7-93f4-9f2f36b178f1","execution":{"iopub.status.busy":"2021-08-01T16:03:59.710306Z","iopub.execute_input":"2021-08-01T16:03:59.710699Z","iopub.status.idle":"2021-08-01T16:04:00.855117Z","shell.execute_reply.started":"2021-08-01T16:03:59.710655Z","shell.execute_reply":"2021-08-01T16:04:00.853927Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Normalize the images to a number from 0 to 1. Image has 3 channels (R,G,B) and each value in the channel can range from 0 to 255. Hence to normalize in 0-->1 range, we need to divide it by 255","metadata":{"id":"r5AVXGO1T0hr"}},{"cell_type":"code","source":"X_train[0]","metadata":{"id":"g2GVeJnHTQFv","outputId":"865b8ed8-6f3d-4cba-de30-9702ae2b5aee","execution":{"iopub.status.busy":"2021-08-01T16:04:00.856889Z","iopub.execute_input":"2021-08-01T16:04:00.857434Z","iopub.status.idle":"2021-08-01T16:04:00.865133Z","shell.execute_reply.started":"2021-08-01T16:04:00.857391Z","shell.execute_reply":"2021-08-01T16:04:00.864073Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Normalizing the training data**","metadata":{"id":"TCOKvET9UO0-"}},{"cell_type":"code","source":"X_train=X_train/255\nX_test=X_test/255","metadata":{"id":"aD5yGv2BUB47","execution":{"iopub.status.busy":"2021-08-01T16:04:00.866606Z","iopub.execute_input":"2021-08-01T16:04:00.867221Z","iopub.status.idle":"2021-08-01T16:04:01.356599Z","shell.execute_reply.started":"2021-08-01T16:04:00.867159Z","shell.execute_reply":"2021-08-01T16:04:01.355685Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Build simple artificial neural network for image classification**","metadata":{"id":"pL5UksKrUjD5"}},{"cell_type":"code","source":"ann=models.Sequential([\n                      layers.Flatten(input_shape=(32,32,3)),\n                      layers.Dense(3000,activation='relu'),\n                      layers.Dense(1000,activation='relu'),\n                      layers.Dense(10,activation='softmax')\n])\n\nann.compile(optimizer='SGD',\n            loss='sparse_categorical_crossentropy',\n            metrics=['accuracy']\n            )\nann.fit(X_train,y_train,epochs=5)","metadata":{"id":"DbED30uOUJt3","outputId":"fd7dfbec-1915-4ab4-da0e-c95c3025b3b9","execution":{"iopub.status.busy":"2021-08-01T16:04:01.358052Z","iopub.execute_input":"2021-08-01T16:04:01.358484Z","iopub.status.idle":"2021-08-01T16:04:26.303989Z","shell.execute_reply.started":"2021-08-01T16:04:01.358437Z","shell.execute_reply":"2021-08-01T16:04:26.303226Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**You can see that at the end of 5 epochs, accuracy is at around 49%**","metadata":{"id":"qpw1r3jkVbRz"}},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix , classification_report\nimport numpy as np\ny_pred = ann.predict(X_test)\ny_pred_classes = [np.argmax(element) for element in y_pred]\n\nprint(\"Classification Report: \\n\", classification_report(y_test, y_pred_classes))","metadata":{"id":"8jU8iZ51VP3X","outputId":"0f118c82-e37d-4eef-b20e-0903841643eb","execution":{"iopub.status.busy":"2021-08-01T16:04:26.306671Z","iopub.execute_input":"2021-08-01T16:04:26.306939Z","iopub.status.idle":"2021-08-01T16:04:27.527295Z","shell.execute_reply.started":"2021-08-01T16:04:26.306912Z","shell.execute_reply":"2021-08-01T16:04:27.525717Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Now let us build a convolutional neural network to train our images**","metadata":{"id":"vm-3FWSVVjxM"}},{"cell_type":"code","source":"cnn=models.Sequential([\n                       #cnn\n                      layers.Conv2D(filters=32,kernel_size=(3,3),activation='relu',input_shape=(32,32,3)),\n                      layers.MaxPool2D((2,2)),\n                       \n                      layers.Conv2D(filters=64,kernel_size=(3,3),activation='relu',input_shape=(32,32,3)),\n                      layers.MaxPool2D((2,2)),\n\n\n                      layers.Flatten(),\n                      layers.Dense(64,activation='relu'),\n                      layers.Dense(10,activation='softmax')\n])","metadata":{"id":"3KXklBBwVgXc","execution":{"iopub.status.busy":"2021-08-01T16:04:27.529157Z","iopub.execute_input":"2021-08-01T16:04:27.529744Z","iopub.status.idle":"2021-08-01T16:04:27.634447Z","shell.execute_reply.started":"2021-08-01T16:04:27.529675Z","shell.execute_reply":"2021-08-01T16:04:27.633432Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cnn.compile(optimizer='adam',\n            loss='sparse_categorical_crossentropy',\n            metrics=['accuracy']\n            )\ncnn.fit(X_train,y_train,epochs=15)","metadata":{"id":"uJ2W9T_XXT9J","outputId":"2e9bbc51-b37b-4f1b-d455-48ac7344ebba","execution":{"iopub.status.busy":"2021-08-01T16:04:27.635729Z","iopub.execute_input":"2021-08-01T16:04:27.636241Z","iopub.status.idle":"2021-08-01T16:05:51.890566Z","shell.execute_reply.started":"2021-08-01T16:04:27.636204Z","shell.execute_reply":"2021-08-01T16:05:51.889693Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**With CNN, at the end 5 epochs, accuracy was at around 70% which is a significant improvement over ANN. CNN's are best for image classification and gives superb accuracy. Also computation is much less compared to simple ANN as maxpooling reduces the image dimensions while still preserving the features**","metadata":{"id":"ekg13QYfYNMU"}},{"cell_type":"code","source":"cnn.evaluate(X_test,y_test)","metadata":{"id":"rxINoAIYXd8r","outputId":"d1a09cab-f164-49a0-92c6-72255afdcaa4","execution":{"iopub.status.busy":"2021-08-01T16:05:51.893556Z","iopub.execute_input":"2021-08-01T16:05:51.893834Z","iopub.status.idle":"2021-08-01T16:05:52.972096Z","shell.execute_reply.started":"2021-08-01T16:05:51.893804Z","shell.execute_reply":"2021-08-01T16:05:52.971283Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred = cnn.predict(X_test)\ny_pred[:5]","metadata":{"id":"OqD0nGtQXrSJ","outputId":"1a2f2872-a140-468d-88df-6e04a8dc1109","execution":{"iopub.status.busy":"2021-08-01T16:05:52.974948Z","iopub.execute_input":"2021-08-01T16:05:52.975240Z","iopub.status.idle":"2021-08-01T16:05:53.598014Z","shell.execute_reply.started":"2021-08-01T16:05:52.975212Z","shell.execute_reply":"2021-08-01T16:05:53.597200Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_classes = [np.argmax(element) for element in y_pred]\ny_classes[:5]","metadata":{"id":"W-b0prakYTpf","outputId":"ec4e973c-8142-42da-99b7-8368fa1be83c","execution":{"iopub.status.busy":"2021-08-01T16:05:53.599540Z","iopub.execute_input":"2021-08-01T16:05:53.599903Z","iopub.status.idle":"2021-08-01T16:05:53.641451Z","shell.execute_reply.started":"2021-08-01T16:05:53.599865Z","shell.execute_reply":"2021-08-01T16:05:53.640722Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_test[:5]","metadata":{"id":"3wPWnH_fYWUH","outputId":"8dc2b93d-e7e3-4f8a-bf50-321e6aad1f76","execution":{"iopub.status.busy":"2021-08-01T16:05:53.645728Z","iopub.execute_input":"2021-08-01T16:05:53.645984Z","iopub.status.idle":"2021-08-01T16:05:53.650685Z","shell.execute_reply.started":"2021-08-01T16:05:53.645958Z","shell.execute_reply":"2021-08-01T16:05:53.649862Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_sample(X_test, y_test,3)","metadata":{"id":"h984zYXKYZGo","outputId":"b2738ea2-4fcf-40c0-c51c-db6672855be5","execution":{"iopub.status.busy":"2021-08-01T16:05:53.651997Z","iopub.execute_input":"2021-08-01T16:05:53.652515Z","iopub.status.idle":"2021-08-01T16:05:53.784929Z","shell.execute_reply.started":"2021-08-01T16:05:53.652477Z","shell.execute_reply":"2021-08-01T16:05:53.783683Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"classes[y_classes[3]]","metadata":{"id":"TGYB083CYZ4F","outputId":"46b9bd4f-d0cb-4b17-eceb-b5805025bc99","execution":{"iopub.status.busy":"2021-08-01T16:05:53.786602Z","iopub.execute_input":"2021-08-01T16:05:53.787135Z","iopub.status.idle":"2021-08-01T16:05:53.792919Z","shell.execute_reply.started":"2021-08-01T16:05:53.787092Z","shell.execute_reply":"2021-08-01T16:05:53.792038Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plot_sample(X_test, y_test,10)","metadata":{"id":"_J_r_9CvYlSS","outputId":"6f8ea265-f718-4c63-c988-458cab7a6845","execution":{"iopub.status.busy":"2021-08-01T16:05:53.794480Z","iopub.execute_input":"2021-08-01T16:05:53.795192Z","iopub.status.idle":"2021-08-01T16:05:53.905052Z","shell.execute_reply.started":"2021-08-01T16:05:53.795113Z","shell.execute_reply":"2021-08-01T16:05:53.903956Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"classes[y_classes[10]]","metadata":{"id":"XoC1UKbTY_Jd","outputId":"3dc5b582-e3f9-478c-8a2c-a8baaf3bff4e","execution":{"iopub.status.busy":"2021-08-01T16:05:53.906634Z","iopub.execute_input":"2021-08-01T16:05:53.907013Z","iopub.status.idle":"2021-08-01T16:05:53.913237Z","shell.execute_reply.started":"2021-08-01T16:05:53.906975Z","shell.execute_reply":"2021-08-01T16:05:53.912257Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import confusion_matrix , classification_report\nimport numpy as np\ny_pred = cnn.predict(X_test)\ny_pred_classes = [np.argmax(element) for element in y_pred]\n\nprint(\"Classification Report: \\n\", classification_report(y_test, y_pred_classes))","metadata":{"id":"tXa49ziqZBQI","outputId":"f4e2b378-073b-4a5a-d344-a043f41c2ecb","execution":{"iopub.status.busy":"2021-08-01T16:05:53.914659Z","iopub.execute_input":"2021-08-01T16:05:53.915087Z","iopub.status.idle":"2021-08-01T16:05:54.554249Z","shell.execute_reply.started":"2021-08-01T16:05:53.915045Z","shell.execute_reply":"2021-08-01T16:05:54.552760Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"id":"lAMJ16cAZh0n"},"execution_count":null,"outputs":[]}]}