{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"collapsed":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":"#packages required to build CNN\nfrom keras.models import Sequential\nfrom keras.layers import Convolution2D\nfrom keras.layers import MaxPooling2D\nfrom keras.layers import Flatten\nfrom keras.layers import Dense","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#initializing the CNN\nclassifier = Sequential()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#adding different layers\n#Step 1 :Convolution\nclassifier.add(Convolution2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#step 2 : pooling\nclassifier.add(MaxPooling2D(pool_size = (2,2)))\n#also check the following command\n#classifier.add(MaxPooling2D(pool_size = (2,2), strides = 2))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#adding another con layer to obtain better accuracy\nclassifier.add(Convolution2D(32, (3, 3), activation = 'relu'))\nclassifier.add(MaxPooling2D(pool_size = (2,2)))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#step 3: Flattening\nclassifier.add(Flatten())","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#step 4: Full Connection\n#hidden layer\nclassifier.add(Dense(units = 128, activation = 'relu'))\n\n#output layer\nclassifier.add(Dense(units = 1, activation = 'sigmoid'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#compile neural net\nclassifier.compile(optimizer= 'adam', loss = 'binary_crossentropy', metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#part 2: Fitting the CNN to images\nfrom keras.preprocessing.image import ImageDataGenerator\n\ntrain_datagen = ImageDataGenerator(\n        rescale=1./255,\n        shear_range=0.2,\n        zoom_range=0.2,\n        horizontal_flip=True)\n\ntest_datagen = ImageDataGenerator(rescale=1./255)\n\ntraining_set = train_datagen.flow_from_directory('/kaggle/input/cat-and-dog/training_set/training_set',target_size=(64, 64),batch_size=32,class_mode='binary')\n\ntest_set = test_datagen.flow_from_directory('/kaggle/input/cat-and-dog/test_set/test_set',target_size=(64, 64),batch_size=32,class_mode='binary')\n\nclassifier.fit_generator(training_set, steps_per_epoch=8005,epochs=10,validation_data=test_set,validation_steps=2023)","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}