{"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_minor":4,"nbformat":4,"cells":[{"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 20GB 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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# The below cells are related to arrangement of data so that we can readly consume it for model training","metadata":{}},{"cell_type":"code","source":"import zipfile\nzip_ref = zipfile.ZipFile('/kaggle/input/dogs-vs-cats/train.zip', 'r')\nzip_ref.extractall('/kaggle/temp/dogs-vs-cats/')\nzip_ref = zipfile.ZipFile('/kaggle/input/dogs-vs-cats/test1.zip', 'r')\nzip_ref.extractall('/kaggle/temp/dogs-vs-cats/')\nzip_ref.close()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.listdir('/kaggle/temp/dogs-vs-cats/')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.mkdir('/kaggle/temp/dogs-vs-cats/train/dog/')\nos.mkdir('/kaggle/temp/dogs-vs-cats/train/cat/')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import shutil\nfor file in os.listdir('/kaggle/temp/dogs-vs-cats/train/'):\n    if 'dog' in str(file) and 'jpg' in str(file):\n        shutil.move('/kaggle/temp/dogs-vs-cats/train/'+str(file),'/kaggle/temp/dogs-vs-cats/train/dog/'+str(file))\n    if 'cat' in str(file) and 'jpg' in str(file):\n        shutil.move('/kaggle/temp/dogs-vs-cats/train/'+str(file),'/kaggle/temp/dogs-vs-cats/train/cat/'+str(file))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(len(os.listdir('/kaggle/temp/dogs-vs-cats/train/dog/')))\nprint(len(os.listdir('/kaggle/temp/dogs-vs-cats/train/cat/')))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.mkdir('/kaggle/temp/dogs-vs-cats/validate/')\nos.mkdir('/kaggle/temp/dogs-vs-cats/validate/dog/')\nos.mkdir('/kaggle/temp/dogs-vs-cats/validate/cat/')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"os.listdir('/kaggle/temp/dogs-vs-cats/')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for file in os.listdir('/kaggle/temp/dogs-vs-cats/train/dog/')[:1250]:\n    shutil.move('/kaggle/temp/dogs-vs-cats/train/dog/'+str(file),'/kaggle/temp/dogs-vs-cats/validate/dog/'+str(file))\nfor file in os.listdir('/kaggle/temp/dogs-vs-cats/train/cat/')[:1250]:\n    shutil.move('/kaggle/temp/dogs-vs-cats/train/cat/'+str(file),'/kaggle/temp/dogs-vs-cats/validate/cat/'+str(file))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(len(os.listdir('/kaggle/temp/dogs-vs-cats/train/dog/')))\nprint(len(os.listdir('/kaggle/temp/dogs-vs-cats/train/cat/')))\nprint(len(os.listdir('/kaggle/temp/dogs-vs-cats/validate/dog/')))\nprint(len(os.listdir('/kaggle/temp/dogs-vs-cats/validate/cat/')))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# The below cells are for defining a TensorFlow model and preparing the Image Generator for feeding it to the model.fit","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\n\nTRAIN_DIR = '/kaggle/temp/dogs-vs-cats/train/'\nVALIDATION_DIR ='/kaggle/temp/dogs-vs-cats/validate/'\n\ntrain_datagen = ImageDataGenerator(rescale=1/255,rotation_range=40, width_shift_range=0.15,\n    height_shift_range=0.15,shear_range=0.2, zoom_range=0.2,\n    horizontal_flip=True, vertical_flip=True)\ntrain_generator = train_datagen.flow_from_directory(\n    TRAIN_DIR,\n    batch_size = 32,\n    shuffle=True,\n    target_size=(300,300),\n    class_mode='binary'\n)\n\n\nvalidation_datagen = ImageDataGenerator(rescale=1/255)\nvalidation_generator = validation_datagen.flow_from_directory(\n    VALIDATION_DIR,\n    batch_size = 32,\n    target_size=(300,300),\n    class_mode='binary'\n)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = tf.keras.models.Sequential(\n[\n    tf.keras.layers.Conv2D(16,(3,3),input_shape=(300,300,3)),\n    tf.keras.layers.MaxPool2D(2,2),\n    tf.keras.layers.Conv2D(32,(3,3),activation='relu'),\n    tf.keras.layers.MaxPool2D(2,2),\n    tf.keras.layers.Conv2D(64,(3,3),activation='relu'),\n    tf.keras.layers.MaxPool2D(2,2),\n    tf.keras.layers.Flatten(),\n    tf.keras.layers.Dense(512,activation='relu'),\n    tf.keras.layers.Dense(128,activation='relu'),\n    tf.keras.layers.Dense(1,activation='sigmoid')\n])\nmodel.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\nmodel.summary()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"history = model.fit(train_generator,\n                              epochs=500,\n                              validation_data=validation_generator,\n                              callbacks=[\n                                  tf.keras.callbacks.EarlyStopping(monitor='loss', patience=3),\n                                  tf.keras.callbacks.ModelCheckpoint(\n                                        filepath='/kaggle/working/',\n                                        save_weights_only=True,\n                                        monitor='val_accuracy',\n                                        mode='max',\n                                        save_best_only=True)\n                              ])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# The below cells visualize the model accuracy and loss to check the performance","metadata":{}},{"cell_type":"code","source":"try:\n    from matplotlib import pyplot as plt\n    plt.plot(history.history['accuracy'])\n    plt.plot(history.history['val_accuracy'])\n    plt.title('model accuracy')\n    plt.ylabel('accuracy')\n    plt.xlabel('epoch')\n    plt.legend(['train', 'val'], loc='upper left')\n    plt.show()\nexcept:\n    pass\n\ntry:\n    from matplotlib import pyplot as plt\n    plt.plot(history.history['loss'])\n    plt.plot(history.history['val_loss'])\n    plt.title('model loss')\n    plt.ylabel('loss')\n    plt.xlabel('epoch')\n    plt.legend(['train', 'val'], loc='upper left')\n    plt.show()\nexcept:\n    pass","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}