{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"from shutil import copyfile\nimport cv2\nimport numpy as np\nimport pandas as pd\nimport os\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport keras\nfrom PIL import Image\nfrom tensorflow.keras import layers","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"## train path => /kaggle/input/cassava-leaf-disease-classification/train_images\n## train csv path => /kaggle/input/cassava-leaf-disease-classification/train.csv\n\n\n## test images => /kaggle/input/cassava-leaf-disease-classification/test_images\ntrainFrame = pd.read_csv('/kaggle/input/cassava-leaf-disease-classification/train.csv')\ntrainFrame.label = trainFrame.label.astype(str)\n\nmp1 = {'0': 'Cassava Bacterial Blight (CBB)',\n '1': 'Cassava Brown Streak Disease (CBSD)',\n '2': 'Cassava Green Mottle (CGM)',\n '3': 'Cassava Mosaic Disease (CMD)',\n '4': 'Healthy'}\n\ntrainFrame.label = trainFrame.label.map(mp1)\n\n\ndata_generator = tf.keras.preprocessing.image.ImageDataGenerator(\n    rescale=1/255.,\n    validation_split=0.25,\n)\n\ntrain_data_loader = data_generator.flow_from_dataframe(\n    trainFrame,\n    directory= '/kaggle/input/cassava-leaf-disease-classification/train_images',\n    x_col=\"image_id\",\n    y_col=\"label\",\n    target_size=(224, 224),\n    subset='training'\n)\n\nval_data_loader = data_generator.flow_from_dataframe(\n    trainFrame,\n    directory= '/kaggle/input/cassava-leaf-disease-classification/train_images',\n    x_col=\"image_id\",\n    y_col=\"label\",\n    target_size=(224, 224),\n    subset='validation'\n)\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"## Defining Cnn\n## lets define our CNN\n### this model will not be used but other will\nmodel =  tf.keras.models.Sequential([\n    layers.BatchNormalization(),\n    layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu'),\n    layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu'),\n    layers.Conv2D(filters=32, kernel_size=(3,3), activation='relu'),\n    layers.MaxPooling2D(),\n    \n    layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu'),\n    layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu'),\n    layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu'),\n    layers.MaxPooling2D(),\n    \n    layers.Conv2D(filters=128, kernel_size=(3,3), activation='relu'),\n    layers.Conv2D(filters=128, kernel_size=(3,3), activation='relu'),\n    layers.Conv2D(filters=128, kernel_size=(3,3), activation='relu'),\n    layers.MaxPooling2D(),\n    \n    layers.Conv2D(filters=256, kernel_size=(3,3), activation='relu'),\n    layers.Conv2D(filters=256, kernel_size=(3,3), activation='relu'),\n    layers.Conv2D(filters=256, kernel_size=(3,3), activation='relu'),\n    layers.MaxPooling2D(),\n    \n    layers.Conv2D(filters=512, kernel_size=(3,3), activation='relu'),\n    layers.Conv2D(filters=512, kernel_size=(3,3), activation='relu'),\n    layers.Conv2D(filters=512, kernel_size=(3,3), activation='relu'),\n    layers.MaxPooling2D(),\n    \n    layers.Flatten(),\n    \n    layers.Dense(5, activation='softmax')\n])\nmodel.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"## Transfer learning Model\nfrom keras.applications.inception_resnet_v2 import InceptionResNetV2\nfrom tensorflow.keras.applications.inception_resnet_v2 import preprocess_input\nfrom tensorflow.keras.layers import Input, Conv2D, MaxPool2D, Dense, Flatten\nfrom tensorflow.keras.models import Model\n\npretrained = InceptionResNetV2(include_top=True,weights='../input/keras-pretrained-models/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5')\n\nx=pretrained.layers[-2].output\nfc1 = Dense(5,activation='softmax')(x)\nmodel = Model(inputs=pretrained.input,outputs=fc1)\n\nfor l in model.layers[:-35]:\n    l.trainable = False\n\n    \n\nmodel.compile(optimizer='adam',loss='categorical_crossentropy', metrics=['accuracy'])\n\n\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def scheduler(epoch, lr):\n    if epoch < 18:\n        return lr\n    else:\n        return lr * 0.3\n\n    \ncallback = tf.keras.callbacks.LearningRateScheduler(scheduler)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"retVal = model.fit(train_data_loader, validation_data=val_data_loader, callbacks=[callback], batch_size= 200, epochs= 20)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.plot(retVal.history['loss'], label = 'training loss')\nplt.plot(retVal.history['accuracy'], label = 'training accuracy')\nplt.legend()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.plot(retVal.history['val_loss'], label = 'validation loss')\nplt.plot(retVal.history['val_accuracy'], label = 'validation accuracy')\nplt.legend()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"frame = {'image_id' : [],\n            'label' : []\n           }\n\ndef predictor():\n    path = '/kaggle/input/cassava-leaf-disease-classification/test_images'\n    files = os.listdir(path)\n    for mem in files:\n        img = Image.open(path + '/' + mem)\n        img = cv2.resize(np.asarray(img),(224, 224))\n        img = np.array([img/255])\n        snakeName = np.argmax (model.predict(img))\n        frame['image_id'].append(mem)\n        frame['label'].append (snakeName)\n        #print (snakeName)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictor()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"frame = pd.DataFrame(frame)\nframe.to_csv('submission.csv', index= False)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"frame","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}