{"cells":[{"metadata":{"trusted":true},"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport os\nimport keras\nfrom keras.models import load_model\n\nfrom keras.preprocessing.image import ImageDataGenerator","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"img_x = 224\nimg_y = 224\nbat_siz = 32\nnum_epok = 32\n# In[2]:\n\n\ndata_generator = ImageDataGenerator(\n        zoom_range = 0.4,\n        vertical_flip  = True,\n        horizontal_flip = True,\n        rescale=1.0/255.0\n        )\n\nmodel = load_model('../input/aptos-densenet-train-submit/densenet_plus_five')\n\ntest_data_labels = pd.read_csv(\"../input/aptos2019-blindness-detection/test.csv\")\ntest_data_labels['id_code'] = test_data_labels['id_code'] + '.png'\n\n\ntest_generator = data_generator.flow_from_dataframe(dataframe = test_data_labels,\n                                                     directory = os.path.join('..', 'input','aptos2019-blindness-detection','test_images'),\n        target_size = (img_x, img_y), \n    \n        x_col = 'id_code',\n        class_mode = None,\n        batch_size = bat_siz\n        )\n\npredictions = model.predict_generator(test_generator,\n                                      steps = test_data_labels.shape[0]/bat_siz)\n\npred_holder = []\nfor x in predictions:\n    pred_holder.append(np.argmax(x))\n    \n\n\noutput_df = pd.DataFrame({'diagnosis':pred_holder, \n                          'id_code':test_data_labels.id_code.str.replace(pat = \"\\.png\", repl = \"\")})\n\n\noutput_df.to_csv(\"submission.csv\", mode = \"w\")\noutput_df.head()","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.6.6"}},"nbformat":4,"nbformat_minor":1}