{"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":"import zipfile","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"extract_files = ['train', 'test1']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for file in extract_files:\n    with zipfile.ZipFile(\"/kaggle/input/dogs-vs-cats/\"+file+\".zip\", \"r\") as z:\n        z.extractall(\".\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\n%matplotlib inline","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_directory = \"train/\"\ntest_directory = \"test1/\"\nvalidation_directory = \"validation/\"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import os\nimport shutil","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"os.mkdir(validation_directory)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for dir_name in ['cats','dogs']:\n    os.mkdir(\"train/\" + dir_name)\n    os.mkdir(validation_directory + dir_name)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"file_list = os.listdir(\"train/\")\nfor file_name in file_list:\n    if(file_name.startswith(\"cat\")):\n        shutil.move(\"train/\"+file_name, \"train/cats\")\n    elif(file_name.startswith(\"dog\")):\n        shutil.move(\"train/\"+file_name, \"train/dogs\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ls train/dogs | wc -l","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ls train/cats | wc -l","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"for i in range(0,6000):\n    shutil.move('train/cats/cat.' + str(i) + '.jpg', 'validation/cats')\n    shutil.move('train/dogs/dog.' + str(i) + '.jpg', 'validation/dogs')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"ls validation/cats | wc -l","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import tensorflow as tf","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"tf.__version__","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_generator = ImageDataGenerator(rescale=1./255)\nvalidation_generator = ImageDataGenerator(rescale=1./255)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_generator_data = train_generator.flow_from_directory(train_directory, target_size=(150,150), batch_size=15, class_mode='categorical')\nvalidation_generator_data = validation_generator.flow_from_directory(validation_directory, target_size=(150,150), batch_size=15, class_mode='categorical')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, Dropout, Flatten, Dense, BatchNormalization","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\n\nmodel.add(Conv2D(filters=32, kernel_size=(4,4), input_shape=(150,150,3), activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\n\nmodel.add(Dropout(0.25))\n\nmodel.add(Conv2D(filters=64, kernel_size=(4,4), activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\n\nmodel.add(Dropout(0.25))\n\nmodel.add(Conv2D(filters=128, kernel_size=(4,4), activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\n\nmodel.add(Dropout(0.5))\n\nmodel.add(Flatten())\nmodel.add(Dense(units=512, activation='relu'))\nmodel.add(BatchNormalization())\nmodel.add(Dropout(0.5))\n\nmodel.add(Dense(units=2, activation='softmax'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"**782** are the number of batches will run in every epoch."},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.keras.callbacks import EarlyStopping","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"earlystop = EarlyStopping(patience=5)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = model.fit_generator(train_generator_data, epochs=10, validation_data=validation_generator_data, callbacks=[earlystop])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# https://www.kaggle.com/uysimty/keras-cnn-dog-or-cat-classification\n# https://www.kaggle.com/bulentsiyah/dogs-vs-cats-classification-vgg16-fine-tuning","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}