{"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)\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":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"!unzip -q /kaggle/input/dogs-vs-cats-redux-kernels-edition/test.zip\n!unzip -q /kaggle/input/dogs-vs-cats-redux-kernels-edition/train.zip","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"os.listdir('/kaggle/working/train')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom keras.preprocessing.image import load_img\n\nimg=load_img('/kaggle/working/train/cat.4910.jpg')\nplt.imshow(img)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train=pd.DataFrame({'path':os.listdir('/kaggle/working/train')})\ntrain['path']='/kaggle/working/train/'+train['path']\ntrain['label']=train['path'].apply(lambda x:x.split('/')[-1].split('.')[0])\ntrain.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test=pd.DataFrame({'path':os.listdir('/kaggle/working/test')})\ntest['path']='/kaggle/working/test/'+test['path']\ntest.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#훈련용, 검증용 나누기\n\nfrom sklearn.model_selection import train_test_split\n\nx_train,x_valid=train_test_split(train,test_size=0.1,stratify=train['label'],random_state=5)\nprint('훈련용',x_train['label'].value_counts())\nprint('검증용',x_valid['label'].value_counts())","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.preprocessing.image import ImageDataGenerator\n\ntrain_datagen=ImageDataGenerator(rescale=1./255,horizontal_flip=True,vertical_flip=True, fill_mode='nearset')\ntest_datagen=ImageDataGenerator(rescale=1./255)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_generator=train_datagen.flow_from_dataframe(x_train,x_col='path',y_col='label',batch_size=16,target_size=(300,300))\ntrain_generator=train_datagen.flow_from_dataframe(x_valid,x_col='path',y_col='label',batch_size=16,target_size=(300,300))\ntrain_generator=train_datagen.flow_from_dataframe(test,x_col='path',class_mode=None, shuffle=False,batch_size=16,target_size=(300,300))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#CNN 특징만 추출\n\nimport tensorflow as tf\nfrom tensorflow.keras.layers import Dense,Conv2D, MaxPool2D, Flatten, Dropout\nfrom tensorflow.keras.models import Sequential","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model= Sequential()\nmodel.add(Conv2D(128,(3,3),activation='relu',input_shape=(300,300,3)))\nmodel.add(MaxPool2D(pool_size=(2,2)))\n#은닉층의 노드를 꺼주는 방법? 지나치게 학습하지 않기 위해\nmodel.add(Dropout(0.5))\n\nmodel= Sequential()\nmodel.add(Conv2D(64,(3,3),activation='relu'))\nmodel.add(MaxPool2D(pool_size=(2,2)))\nmodel.add(Dropout(0.5))\n\nmodel= Sequential()\nmodel.add(Conv2D(32,(3,3),activation='relu'))\nmodel.add(MaxPool2D(pool_size=(2,2)))\nmodel.add(Dropout(0.5))\n\nmodel= Sequential()\nmodel.add(Conv2D(16,(3,3),activation='relu'))\nmodel.add(MaxPool2D(pool_size=(2,2)))\nmodel.add(Dropout(0.5))\n#----------------------특징을 추출해내는 층 (2차원)\n\nmodel.add(Flatten())\nmodel.add(Dense(512,activation='relu'))\nmodel.add(Dropout(0.5))\n\nmodel.add(Dense(128,activation='relu'))\nmodel.add(Dropout(0.5))\n\nmodel.add(Dense(2,activation='softmax'))\n\nmodel.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['acc'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.summary()","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}