{"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\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","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport os\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Convolution2D, MaxPooling2D, Flatten, Dense, Dropout, BatchNormalization","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"path =('../input/dlai3/datasets/datasets')\nprint(os.listdir(path))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test=('../input/dlai3/datasets/datasets/test')\ntrain=('../input/dlai3/datasets/datasets/train')\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import cv2","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"img=cv2.imread('../input/dlai3/datasets/datasets/test/positive/1-s2.0-S0140673620303706-fx1_lrg.jpg')\nimg2=cv2.imread('../input/dlai3/datasets/datasets/test/negative/00001303_001.png')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.subplot(1, 2, 1)\nplt.imshow(img2)\nplt.title('negative')\n\nplt.subplot(1, 2, 2)\nplt.imshow(img)\nplt.title('positive')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.preprocessing.image import ImageDataGenerator","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\ntrain_datagen = ImageDataGenerator(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(train,\n                                                target_size = (200,200),\n                                                batch_size = 10,\n                                                class_mode = 'binary',\n                                                color_mode = 'grayscale')\n\ntest_set = test_datagen.flow_from_directory(test,\n                                            target_size = (200,200),\n                                            batch_size = 10,\n                                            class_mode = 'binary',\n                                            color_mode = 'grayscale')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\nmodel.add(Convolution2D(64, (3, 3), input_shape = (200, 200, 1), activation = 'relu'))\nmodel.add(MaxPooling2D(pool_size = (2, 2)))\nmodel.add(Dropout(0.20))\nmodel.add(Convolution2D(128, (3, 3), activation = 'relu'))\nmodel.add(MaxPooling2D(pool_size = (2, 2)))\nmodel.add(Dropout(0.20))\nmodel.add(Flatten())\nmodel.add(Dense(512, activation = 'relu'))\nmodel.add(Dense(1, activation = 'sigmoid'))\n\nmodel.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = model.fit(training_set, epochs = 10, validation_data= test_set)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nplt.figure(figsize = (10, 8))\nplt.plot(history.history['accuracy'], label = 'Train')\nplt.plot(history.history['val_accuracy'], label = 'Test')\nplt.title(\"Test Accuracy vs Validation Accuracy\")\nplt.xlabel(\"Epochs\")\nplt.legend()\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","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}