{"cells":[{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import keras\nfrom keras.datasets import cifar10\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.layers import Dense,Dropout,Activation,Flatten\nfrom keras.layers import Conv2D,MaxPooling2D\nimport os\nimport numpy as np\nimport seaborn as sns\nimport matplotlib\nimport matplotlib.pyplot as plt\n\nfrom sklearn.metrics import confusion_matrix, classification_report\nimport itertools\n\n%matplotlib inline","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"(x_train, y_train), (x_test, y_test) = cifar10.load_data()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_train=x_train.astype(\"float32\")\ny_train=y_train.astype(\"float32\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"xtrain=x_train/255\nxtest=x_test/255\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ytrain=keras.utils.to_categorical(y_train,10)\nytest=keras.utils.to_categorical(y_test,10)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ytrain","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ytrain[1].argmax(axis=0)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"xtrain[0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"labels = ['Airplane', 'Automobile', 'Bird', 'Cat', 'Deer', 'Dog', 'Frog', 'Horse', 'Ship', 'Truck']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.imshow(xtrain[1])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"modelt=Sequential()\nmodelt.add(Conv2D(32,(3,3),activation=\"relu\",kernel_initializer=\"he_uniform\",padding=\"same\",input_shape=(32, 32, 3)))\nmodelt.add(MaxPooling2D((2,2)))\nmodelt.add(Conv2D(64,(3,3),activation=\"relu\",kernel_initializer=\"he_uniform\",padding=\"same\"))\nmodelt.add(MaxPooling2D((2,2)))\nmodelt.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\nmodelt.add(MaxPooling2D((2, 2)))\nmodelt.add(Flatten())\nmodelt.add(Dense(128,activation=\"relu\",kernel_initializer=\"he_uniform\"))\nmodelt.add(Dropout(0.5))\nmodelt.add(Dense(10,activation='softmax'))\nmodelt.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"modelt.compile(loss=\"categorical_crossentropy\",optimizer='adam',metrics=[\"accuracy\"])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history=modelt.fit(xtrain,ytrain,batch_size=32,epochs=25,verbose=2,validation_data=(xtest,ytest))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def summary(history): \n    plt.subplot(211)\n    plt.title(\"Loss\")\n    plt.plot(history.history['loss'],color=\"blue\",label=\"train\")\n    plt.plot(history.history['val_loss'],color=\"orange\",label=\"test\")\n    plt.subplot(212)\n    plt.title('Classification Accuracy')\n    plt.plot(history.history['accuracy'], color='blue', label='train')\n    plt.plot(history.history['val_accuracy'], color='orange', label='test')\n    plt.tight_layout()\n    plt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"summary(history)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sc=modelt.evaluate(xtest,ytest)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(\"loss \",sc[0])\nprint(\"accuracy \",sc[1])","execution_count":null,"outputs":[]}],"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":4,"nbformat_minor":4}