{"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":"data = pd.read_csv('/kaggle/input/humpback-whale-identification/train.csv')\ndata.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import cv2\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def input_img(data,dataset):\n    m  = data.shape[0]\n    X_train = np.zeros((m,128,128,3))\n    c = 0;\n    for lab in data['Image']:\n        path = \"/kaggle/input/humpback-whale-identification/\" + dataset + \"/\" + lab \n        print(c)\n        img = cv2.imread(path)\n        img = cv2.resize(img,(128,128), interpolation=cv2.INTER_CUBIC)\n        img = np.array(img)\n        img = img/255\n        X_train[c] = img;\n        c = c+1\n    return X_train","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X = input_img(data,\"train\")\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\nfrom sklearn.preprocessing import OneHotEncoder","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def label():\n    values = np.array(data['Id'])\n    label_encoder = LabelEncoder()\n    integer_encoded = label_encoder.fit_transform(values)\n    onehot_encoder = OneHotEncoder(sparse=False)\n    integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)\n    onehot_encoded = onehot_encoder.fit_transform(integer_encoded)\n    return onehot_encoded","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y = label()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train, X_val, Y_train, Y_val = train_test_split(X,y, test_size=0.01, random_state=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.applications.resnet50 import ResNet50","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"res_50_model = ResNet50(weights='imagenet', include_top=False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.layers import *\nfrom keras.models import Sequential\nfrom keras.applications.resnet50 import ResNet50\n\nmodel = Sequential()\nmodel.add(ResNet50(include_top=False, pooling='max', weights='imagenet'))\nmodel.add(Dense(y.shape[1], activation='softmax'))\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_model = model.fit(X, y,\n                  batch_size= 128,\n                  epochs=64,\n                  verbose=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":1}