{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\nfrom keras.models import Sequential\nfrom keras.layers import Conv2D\nfrom keras.layers import MaxPooling2D\nfrom keras.layers import Flatten\nfrom keras.layers import Dense\nfrom keras.layers import Dropout\n\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import shutil\nfrom shutil import copyfile","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!unzip ../input/dogs-vs-cats-redux-kernels-edition/train.zip -d train","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"!unzip ../input/dogs-vs-cats-redux-kernels-edition/test.zip -d test","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(os.listdir(\"../working/train/train\"))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"os.mkdir('../working/dogs-v-cats/')\nos.mkdir('../working/dogs-v-cats/training/')\nos.mkdir('../working/dogs-v-cats/validation/')\nos.mkdir('../working/dogs-v-cats/training/cats/')\nos.mkdir('../working/dogs-v-cats/training/dogs/')\nos.mkdir('../working/dogs-v-cats/validation/dogs/')\nos.mkdir('../working/dogs-v-cats/validation/cats/')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"BASE_DIR = '../working/'\ntrain_dir = os.path.join(BASE_DIR,'train/train/')\ntest_dir = os.path.join(BASE_DIR,'test/test/')\ntrain_cats_dir = os.path.join('../working/dogs-v-cats/training/cats/')\ntrain_dogs_dir = os.path.join('../working/dogs-v-cats/training/dogs/')\nvalidation_cats_dir = os.path.join('../working/dogs-v-cats/validation/cats/')\nvalidation_dogs_dir = os.path.join('../working/dogs-v-cats/validation/dogs/')\ntraining_dir = os.path.join('../working/dogs-v-cats/training/')\nvalidation_dir = os.path.join('../working/dogs-v-cats/validation')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.keras.optimizers import RMSprop","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\n\nmodel.add(Conv2D(32, (3,3), input_shape=(150, 150, 3), activation='relu'))\nmodel.add(MaxPooling2D((2,2),(2,2)))\n\nmodel.add(Conv2D(64, (3,3), activation='relu'))\nmodel.add(MaxPooling2D((2,2),(2,2)))\n\nmodel.add(Conv2D(128, (3,3), activation='relu'))\nmodel.add(MaxPooling2D((2,2),(2,2)))\nmodel.add(Dropout(0.2))\n\nmodel.add(Conv2D(256, (3,3), activation='relu'))\nmodel.add(MaxPooling2D((2,2),(2,2)))\nmodel.add(Dropout(0.2))\n\nmodel.add(Conv2D(256, (3,3), activation='relu'))\nmodel.add(MaxPooling2D((2,2),(2,2)))\nmodel.add(Dropout(0.4))\n\nmodel.add(Flatten())\n\nmodel.add(Dense(units=512, activation='relu'))\nmodel.add(Dense(units=1, activation='sigmoid'))\n\nmodel.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for fname in os.listdir(train_dir)[:int(0.9*len(os.listdir(train_dir)))]:\n    if(os.path.getsize(train_dir + fname)>0):\n        if(fname.split('.')[0] == 'cat'):\n            copyfile(train_dir + fname,train_cats_dir + fname)\n        elif(fname.split('.')[0] == 'dog'):\n            copyfile(train_dir + fname,train_dogs_dir + fname)\nfor fname in os.listdir(train_dir)[int(0.9*len(os.listdir(train_dir))):]:\n    if(os.path.getsize(train_dir + fname)>0):\n        if(fname.split('.')[0] == 'cat'):\n            copyfile(train_dir + fname,validation_cats_dir + fname)\n        elif(fname.split('.')[0] == 'dog'):\n            copyfile(train_dir + fname,validation_dogs_dir + fname)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(os.listdir(train_dogs_dir))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.keras.preprocessing.image import ImageDataGenerator","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_datagen = ImageDataGenerator(rescale=1.0/255,rotation_range=40,width_shift_range=0.1,height_shift_range=0.1,zoom_range=0.1,shear_range=0.1,horizontal_flip=True,fill_mode='nearest')\nvalidation_datagen = ImageDataGenerator(rescale=1.0/255)\ntrain_generator = train_datagen.flow_from_directory(training_dir,target_size=(150,150),batch_size=256,class_mode='binary')\nvalidation_generator = validation_datagen.flow_from_directory(validation_dir,target_size=(150,150),batch_size=256,class_mode='binary')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = model.fit(train_generator,epochs=20,validation_data = validation_generator,verbose=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"acc = history.history['accuracy']\nval_acc = history.history['val_accuracy']\nloss = history.history['loss']\nval_loss = history.history['val_loss']\n\nepochs = range(len(acc))\n\nplt.plot(epochs, acc, 'r', label='Training accuracy')\nplt.plot(epochs, val_acc, 'b', label='Validation accuracy')\nplt.title('Training and validation accuracy')\n\nplt.figure()\n\nplt.plot(epochs, loss, 'r', label='Training Loss')\nplt.plot(epochs, val_loss, 'b', label='Validation Loss')\nplt.title('Training and validation loss')\nplt.legend()\n\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.keras.preprocessing.image import img_to_array\nfrom PIL import Image","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"os.listdir('../working/test/test')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"n_test = len(os.listdir('../working/test/test'))\ntst=[]\nfor fname in os.listdir('../working/test/test'):\n    img = np.array(Image.open('../working/test/test/'+fname).resize((150,150)))\n    tst.append(img)\ntest = np.asarray(tst).astype(float)#.reshape(n_test,150,150,3)\nprint(test.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predict = model.predict(test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample = pd.read_csv('/kaggle/input/dogs-vs-cats-redux-kernels-edition/sample_submission.csv')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample['label'] = predict","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(sample)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sample.to_csv(\"submission.csv\",index=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","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}