{"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 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":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true,"collapsed":true},"cell_type":"code","source":"import tensorflow as tf\nfrom keras.preprocessing.image import ImageDataGenerator\n\ndata=pd.read_csv(\"../input/cassava-leaf-disease-classification/train.csv\")\nprint(data.head())\n\n\ntrain_datagen = ImageDataGenerator(rescale = 1./255,\n                                   shear_range = 0.2,\n                                   zoom_range = 0.2,\n                                   horizontal_flip = True)\n\ntraining_set = train_datagen.flow_from_directory('../input/cassava-leaf-disease-classification/',classes = ['train_images'],target_size = (64, 64), batch_size = 210,class_mode = 'categorical')\ncnn = tf.keras.models.Sequential()\ncnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu', input_shape=[64, 64, 3]))\ncnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))\ncnn.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation='relu'))\ncnn.add(tf.keras.layers.MaxPool2D(pool_size=2, strides=2))\ncnn.add(tf.keras.layers.Flatten())\ncnn.add(tf.keras.layers.Dense(units=32, activation='relu'))\ncnn.add(tf.keras.layers.Dense(units=5, activation='sigmoid'))\ncnn.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])\ncnn.fit(x = training_set, epochs = 3)\nimport numpy as np\nfrom keras.preprocessing import image\ntest_image = image.load_img('../input/cassava-leaf-disease-classification/test_images/2216849948.jpg', target_size = (64, 64))\ntest_image = image.img_to_array(test_image)\ntest_image = np.expand_dims(test_image, axis = 0)\nresult = cnn.predict(test_image)\ntraining_set.class_indices\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# The lines below shows how to save predictions in format used for competition scoring\n# Just uncomment them.\nprint(result)\nimport glob\nprint(cnn)\ntestimgdir ='../input/cassava-leaf-disease-classification/test_images'\ntestfile = glob.glob(testimgdir+'/*.jpg')\ntestfile = [os.path.basename(fn) for fn in testfile]\nprint(testfile)\n\n\n","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}