{"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 in \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 \"../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# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"!unzip /kaggle/input/dogs-vs-cats/train.zip\n!unzip /kaggle/input/dogs-vs-cats/test1.zip","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import os\nall_files = os.listdir('/kaggle/working/train/')\nprint(all_files[0] , type(all_files[0]))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"all_files[0].split('.')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"file = []\nAnimal = []\nfor i in os.listdir('/kaggle/working/train/'):\n    file.append(i)\n    animal_name = i.split('.')[0]\n    Animal.append(animal_name)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport keras","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df = pd.DataFrame({'IMAGE_NAME':file , 'CATEGORY':Animal})\n\ntrain_df.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.preprocessing.image import ImageDataGenerator\n\ndata_gen = ImageDataGenerator(\n    rotation_range=20 ,\n    rescale=1/.255 ,\n    horizontal_flip=True,\n    validation_split=0.2\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_generator = data_gen.flow_from_dataframe(\n    train_df,\n    directory = '/kaggle/working/train/',\n    x_col='IMAGE_NAME',\n    y_col='CATEGORY',\n    target_size = (224,224),\n    class_mode='categorical',\n    batch_size=32,\n    shuffle=True,\n    subset='training'\n)\nvalidation_generator = data_gen.flow_from_dataframe(\n    train_df,\n    directory = '/kaggle/working/train/',\n    x_col='IMAGE_NAME',\n    y_col='CATEGORY',\n    target_size = (224,224),\n    class_mode='categorical',\n    batch_size=32,\n    shuffle=True,\n    subset='validation'\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import keras\nfrom keras.layers import Dense, Dropout , Flatten\nfrom keras.layers import Conv2D, MaxPooling2D, Activation , GlobalAveragePooling2D\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras import regularizers","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"vgg_model = keras.applications.vgg19.VGG19(include_top=False,input_shape=(224,224,3))\n\nmodel = vgg_model.layers[-2].output\nmodel = GlobalAveragePooling2D()(model)\nmodel = Dense(1024,activation='relu')(model)\nmodel =(Dropout(0.25)(model))\nmodel = (Dense(512,activation='relu')(model))\n\nmodel = Dropout(0.25)(model)\nmodel = (Dense(2,activation='softmax')(model))\n         \nnw_model = keras.Model(inputs = vgg_model.input , outputs=model)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for layer in nw_model.layers:\n    layer.trainable = False\nfor layer in nw_model.layers[-7:-1]:\n    layer.trainable=True","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"nw_model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"nw_model.fit_generator(train_generator, epochs=15 , \n                   validation_data=validation_generator,)","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}