{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nimport keras\nfrom keras.preprocessing.image import ImageDataGenerator, load_img\nfrom keras.utils import to_categorical\nfrom keras.models import Sequential\nfrom sklearn.model_selection import train_test_split\nfrom keras_preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Conv2D, MaxPool2D , Flatten\nfrom keras import regularizers, optimizers\nimport matplotlib.pyplot as plt\nimport random\n\nimport os","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import zipfile\n\nDataset = \"train\"\n\n# Will unzip the files so that you can see them..\nwith zipfile.ZipFile(\"/kaggle/input/dogs-vs-cats/\"+Dataset+\".zip\",\"r\") as z:\n    z.extractall(\".\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"filenames = os.listdir(\"train\")\ncategories = []\nfor filename in filenames:\n    category = filename.split('.')[0]\n    if category == 'dog':\n        categories.append(\"dog\")\n    else:\n        categories.append(\"cat\")\n\ndf = pd.DataFrame({\n    'filename': filenames,\n    'category': categories\n})","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_df, validate_df = train_test_split(df, test_size=0.1)\ntrain_df = train_df.reset_index()\nvalidate_df = validate_df.reset_index()\n\ntotal_train = train_df.shape[0]\ntotal_validate = validate_df.shape[0]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_datagen = ImageDataGenerator(\n    rotation_range=15,\n    #rescale=1./255,\n    shear_range=0.2,\n    zoom_range=0.2,\n    horizontal_flip=True,\n    fill_mode='nearest',\n    width_shift_range=0.1,\n    height_shift_range=0.1\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_generator = train_datagen.flow_from_dataframe(\n    train_df, \n    \"train\",\n    x_col='filename',\n    y_col='category',\n    class_mode='binary',\n    target_size=(224, 224)\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"validation_datagen = ImageDataGenerator()\nvalidation_generator = validation_datagen.flow_from_dataframe(\n    validate_df, \n    \"train\",\n    x_col='filename',\n    y_col='category',\n    class_mode='binary',\n    target_size=(224, 224)\n)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\nmodel.add(Conv2D(input_shape=(224,224,3),filters=64,kernel_size=(3,3),padding=\"same\", activation=\"relu\"))\nmodel.add(Conv2D(filters=64,kernel_size=(3,3),padding=\"same\", activation=\"relu\"))\nmodel.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))\nmodel.add(Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\nmodel.add(Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\nmodel.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))\nmodel.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\nmodel.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\nmodel.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\nmodel.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))\nmodel.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\nmodel.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\nmodel.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\nmodel.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))\nmodel.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\nmodel.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\nmodel.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\nmodel.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))\nmodel.add(Flatten())\nmodel.add(Dense(units=4096,activation=\"relu\"))\nmodel.add(Dense(units=4096,activation=\"relu\"))\nmodel.add(Dense(units=2, activation=\"softmax\"))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.optimizers import Adam\nopt = Adam(lr=0.001)\nmodel.compile(optimizer=opt, loss=keras.losses.categorical_crossentropy, metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"hist = model.fit_generator(\n    steps_per_epoch=1000,\n    generator=train_generator,\n    validation_data=validation_generator, \n    validation_steps=10,\n    epochs=1\n)","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}