{"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\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":"from __future__ import print_function\nimport imageio\nfrom PIL import Image\nimport numpy as np\nimport keras\nimport tensorflow as tf\nfrom keras.preprocessing.image import ImageDataGenerator\nimport zipfile","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"  resnet = tf.keras.applications.ResNet50(\n      include_top=True,\n      weights=\"imagenet\",\n      classes=1000\n  )\n  resnet.trainable = False","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def model(input_shape = (224,224,3), classes = 2):\n  X_input = tf.keras.Input((224,224,3))\n  X = resnet(X_input,training = False)\n  X = tf.keras.layers.Flatten()(X)\n  X = tf.keras.layers.Dense(2,activation = tf.nn.softmax,name = 'end')(X)\n  model = tf.keras.models.Model(inputs = X_input, outputs = X ,name = 'model_full')\n  return model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = model(input_shape=(224,224,3),classes = 2)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"opt = tf.keras.optimizers.Adam(learning_rate = 0.01 , epsilon = 0.001)\nmodel.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"with zipfile.ZipFile(\"../input/dogs-vs-cats/train.zip\",\"r\") as z:\n    z.extractall(\".\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"IMAGE_DIR = \"../working/train\"\nfilenames = os.listdir(IMAGE_DIR)\nlabels = [x.split(\".\")[0] for x in filenames]\ndf = pd.DataFrame({\"filename\": filenames, \"label\": labels})\ndf.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_datagen = ImageDataGenerator(preprocessing_function = tf.keras.applications.resnet50.preprocess_input)\ntrain_generator = train_datagen.flow_from_dataframe(df, IMAGE_DIR,x_col=\"filename\", y_col=\"label\",\n                                                    target_size=(224,224), class_mode=\"categorical\",\n                                                   batch_size=100, shuffle=True, seed=42)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"hist = model.fit_generator(train_generator,epochs = 1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"hist = model.fit_generator(train_generator,epochs = 1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"with zipfile.ZipFile(\"../input/dogs-vs-cats/test1.zip\",\"r\") as z:\n    z.extractall(\".\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"IMAGE_DIR = \"../working/test1\"\nfilenames = os.listdir(IMAGE_DIR)\nlabels = [x.split(\".\")[0] for x in filenames]\ndf = pd.DataFrame({\"filename\": filenames, \"label\": labels})\ndf.head()","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}