{"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":"markdown","source":"## Cassava Leaf Disease Classification models using ResNet50\n\n\n### ResNet50\n\nResNet50 is a variant of ResNet model which has 48 Convolution layers along with 1 MaxPool and 1 Average Pool layer. It has 3.8 x 10^9 Floating points operations. It is a widely used ResNet model and we have explored ResNet50 architecture in depth.\n\n\n[![image.png](attachment:image.png)](http://)\n\n\n\n\n### Description\n\n[train/test]_images the image files. The full set of test images will only be available to your notebook when it is submitted for scoring. Expect to see roughly 15,000 images in the test set.\n\ntrain.csv\n\n* image_id the image file name.\n\n* label the ID code for the disease.\n\nsample_submission.csv A properly formatted sample submission, given the disclosed test set content.\n\n* image_id the image file name.\n\n* label the predicted ID code for the disease.\n\n#### Dataset:\n\n[Link](https://www.kaggle.com/c/cassava-leaf-disease-classification/data)","metadata":{"papermill":{"duration":0.01625,"end_time":"2021-04-05T12:45:45.735388","exception":false,"start_time":"2021-04-05T12:45:45.719138","status":"completed"},"tags":[]},"attachments":{"image.png":{"image/png":"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"}}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sn\nfrom PIL import Image\nimport cv2\nimport os\nimport keras\nimport skimage.io\nimport keras.backend as K\nimport tensorflow as tf\nfrom tensorflow.keras import Input, Model\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.keras.layers import Dense, Flatten, Dropout,BatchNormalization ,Activation,add\nfrom tensorflow.keras.models import Model, Sequential\nfrom tensorflow.keras.applications import DenseNet201\nfrom keras.applications.mobilenet import MobileNet\nfrom keras.applications.densenet import DenseNet169\nfrom keras.applications.vgg19 import VGG19\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.utils import to_categorical\nfrom keras.layers.experimental.preprocessing import RandomFlip, RandomRotation, RandomCrop, Rescaling, RandomTranslation\nfrom keras import Sequential\nfrom tqdm import tqdm\nfrom tensorflow.keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, EarlyStopping\nfrom tensorflow.keras.optimizers import Adam","metadata":{"papermill":{"duration":9.064543,"end_time":"2021-04-05T12:45:54.817136","exception":false,"start_time":"2021-04-05T12:45:45.752593","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"root_dir = '../input/cassava-leaf-disease-classification'\n\ntrain_df = pd.read_csv(os.path.join(root_dir, 'train.csv'))\nprint(\"there are \" + str(train_df.shape[0]) + \" train samples\" )\ntrain_df.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_img_dir = os.path.join(root_dir, 'train_images')  \n\nfigure = plt.figure(figsize = (20,20))\n\ncont = 0\n    \nfor i in range(5):\n    \n    speci = train_df[train_df['label'] == i]\n    \n    for j in range(5):\n        \n        img = Image.open(os.path.join(train_img_dir, speci.iloc[j,0]))\n        \n        plt.subplot(5,5, cont+1)\n        \n        plt.imshow(img)\n        \n        cont = cont + 1","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"image_preprocessor = Sequential([\n    RandomFlip(\"horizontal_and_vertical\"),\n    RandomCrop(150,150),\n    RandomTranslation(0.3, 0.3),\n    RandomRotation(0.5),\n    Rescaling(1./255)])\n\ndef custom_gen(batch_size, image_dir, h = 150, w = 150):\n    \n    start = 0\n    end = batch_size\n    images = train_df['image_id']\n    labels = train_df['label']\n    while 1:\n        \n        if end >= train_df.shape[0]:\n            start = 0\n            end = batch_size \n            continue\n        else:\n        \n            batch = []\n\n            if start == 0:\n                names = images[:end]\n                y = to_categorical(labels[:end], num_classes = 5)\n            else:\n                names = images[start:end]\n                y = to_categorical(labels[start:end], num_classes = 5)\n\n            for name in names:\n\n                img = cv2.imread(os.path.join(image_dir,name))\n                img = np.expand_dims(img, axis = 0)\n                img = image_preprocessor(img)\n                img = np.squeeze(img, axis = 0)\n                batch.append(img)\n\n\n\n            end = end + batch_size\n            start = start +  batch_size\n\n\n            yield np.array(batch), y","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"base_model = tf.keras.applications.ResNet50(input_shape=(150,150,3),include_top=False,weights=\"imagenet\")","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Freezing Layers\n\nfor layer in base_model.layers[:-4]:\n    layer.trainable=False","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Building Model\n\nmodel=Sequential()\nmodel.add(base_model)\nmodel.add(Dropout(0.2))\nmodel.add(Flatten())\nmodel.add(BatchNormalization())\nmodel.add(Dense(128,kernel_initializer='he_uniform'))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(64,kernel_initializer='he_uniform'))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(32,kernel_initializer='he_uniform'))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(Dense(5, activation = 'softmax'))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Model Summary\n\nmodel.summary()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from tensorflow.keras.utils import plot_model\nfrom IPython.display import Image\nplot_model(model, to_file='convnet.png', show_shapes=True,show_layer_names=True)\nImage(filename='convnet.png') ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"lrd = ReduceLROnPlateau(monitor = 'val_loss',patience = 2,verbose = 1,factor = 0.75, min_lr = 1e-4)\n\nmcp = ModelCheckpoint('model.h5')\n\nes = EarlyStopping(verbose=1, patience=2)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.compile(optimizer = 'adam', loss = 'categorical_crossentropy',metrics=['accuracy'])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"batch_size = 64\nepochs = 20\nsteps_per_epoch = train_df.shape[0] // batch_size\ntrain_gen = custom_gen(batch_size, train_img_dir)\n%time\nhistory = model.fit(train_gen, epochs = epochs, steps_per_epoch = steps_per_epoch,verbose = 1,callbacks=[lrd,mcp,es] )","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# summarize history for loss\nplt.plot(history.history['loss'])\nplt.title('model loss')\nplt.ylabel('loss')\nplt.xlabel('epoch')\nplt.legend(['train', 'test'], loc='upper left')\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_leaf = \"../input/cassava-leaf-disease-classification/test_images\"\n\ntest_names = pd.Series(os.listdir(test_leaf))\n\n\nfor j in range(3):\n\n    for i in tqdm(range(len(test_names))):\n\n        image = cv2.imread(os.path.join(test_leaf, test_names[i]))\n        image = np.expand_dims(image, axis = 0)\n        image = image_preprocessor(image)\n        if i ==0:\n\n            pred = model.predict(image)\n        else:\n            pred = np.concatenate([pred, model.predict(image)])\n            \n    if j ==0:\n        final = pred\n    else:\n        final = final +pred\n     \npred = pd.Series(np.argmax(final, axis = 1))\n\n\ntest_df = pd.concat([test_names, pred], axis = 1)\ntest_df = test_df.rename(columns = {0: 'image_id', 1: 'label'})\n\ntest_df.to_csv('submission.csv', index = False)\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}