{"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_minor":4,"nbformat":4,"cells":[{"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","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-02-21T18:21:17.271699Z","iopub.execute_input":"2022-02-21T18:21:17.272566Z","iopub.status.idle":"2022-02-21T18:21:17.289482Z","shell.execute_reply.started":"2022-02-21T18:21:17.272516Z","shell.execute_reply":"2022-02-21T18:21:17.288438Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!unzip /kaggle/input/cassava-disease/train.zip\n!unzip /kaggle/input/cassava-disease/test.zip\n!pwd\n!ls","metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","execution":{"iopub.status.busy":"2022-02-21T18:21:17.29177Z","iopub.execute_input":"2022-02-21T18:21:17.292343Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import os\nimport pandas as pd\nimport tensorflow as tf\nfrom tensorflow.keras.optimizers import RMSprop\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\ntrain_path = '/kaggle/working/train/'   # cmd, healthy, cgm, cbsd, cbb 2658+316+773+1443+466 == 6122\ntest_path = '/kaggle/working/test/0'\nprint(len(os.listdir(train_path)))\nprint(len(os.listdir(test_path)))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"split_size = 4897 #80-20\nBATCH_SIZE = 128\ntrain_dir = '/kaggle/working/train/'\ntrain_datagen = ImageDataGenerator(\n    resize = 1.0/255.0,\n    validation_split = 0.25\n)\n\nvalidation_datagen = ImageDataGenerator(\n    resize = 1.0/255.0,\n    fill_mode = 'nearest'\n)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_generator = train_datagen.flow_from_directory(\n    train_dir,\n    batch_size = BATCH_SIZE,\n    class_mode = 'sparse',\n    target_size = (150,150),\n    shuffle = True,\n    subset = 'training'\n)\n\nvalidation_generator = train_datagen.flow_from_directory(\n    train_dir,\n    batch_size = BATCH_SIZE,\n    class_mode = 'sparse',\n    target_size = (150,150),\n    shuffle = True,\n    subset = 'validation'\n)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = tf.keras.Sequential([\n    tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape = (150, 150, 3)),\n    tf.keras.layers.MaxPooling2D(2,2),\n    tf.keras.layers.Conv2D(32, (3,3), activation='relu'),\n    tf.keras.layers.MaxPooling2D(2,2),\n    tf.keras.layers.Conv2D(16, (3,3), activation='relu'),\n    tf.keras.layers.MaxPooling2D(2,2),\n    tf.keras.layers.Flatten(),\n    tf.keras.layers.Dense(256, activation='relu'),\n    tf.keras.layers.Dense(128, activation='relu'),\n    tf.keras.layers.Dense(5, activation='softmax')\n])\n\nmodel.compile(optimizer=RMSprop(lr=0.001), loss='sparse_categorical_crossentropy', metrics=['accuracy'])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.summary()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"history = model.fit(train_generator,\n                             epochs=30,\n                             verbose=1,\n                             validation_data=validation_generator) #validation_generator","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nacc = history.history['accuracy']\nval_acc = history.history['val_accuracy']\nloss = history.history['loss']\nval_loss = history.history['val_loss']\n\nepochs = range(len(acc))\n\nplt.plot(epochs, acc, 'r', label='Training Accuracy')\nplt.plot(epochs, val_acc, 'b', label='Validation Accuracy')\nplt.title('Training and Validation Accuracy')\nplt.legend()\nplt.figure()\n\n\nplt.plot(epochs, loss, 'r', label = 'Training Loss')\nplt.plot(epochs, val_loss, 'b', label = 'Validation Loss')\nplt.title('Training and Validation Loss')\nplt.legend()\nplt.figure()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]}