{"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 in \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 \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array, array_to_img\nfrom keras.layers import Conv2D, Flatten, MaxPooling2D, Dense\nfrom keras.models import Sequential\n\nimport glob, os, random","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nimport os\nbase_path='../input/garbage classification/Garbage classification'\nimg_list = glob.glob(os.path.join(base_path, '*/*.jpg'))\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\npath=plt.imread(\"../input/garbage classification/Garbage classification/trash/trash6.jpg\")\nplt.imshow(path)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_datagen = ImageDataGenerator(\n    rescale=1./255,\n    shear_range=0.1,\n    zoom_range=0.1,\n    width_shift_range=0.1,\n    height_shift_range=0.1,\n    horizontal_flip=True,\n    vertical_flip=True,\n    validation_split=0.1\n)\n\ntest_datagen = ImageDataGenerator(\n    rescale=1./255,\n    validation_split=0.1\n)\n\ntrain_generator = train_datagen.flow_from_directory(\n    base_path,\n    target_size=(300, 300),\n    batch_size=16,\n    class_mode='categorical',\n    subset='training',\n    seed=0\n)\nvalidation_generator = test_datagen.flow_from_directory(\n    base_path,\n    target_size=(300, 300),\n    batch_size=16,\n    class_mode='categorical',\n    subset='validation',\n    seed=0\n)\n\nlabels = (train_generator.class_indices)\nlabels = dict((v,k) for k,v in labels.items())\n\nprint(labels)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential([\n    Conv2D(filters=32, kernel_size=3, padding='same', activation='relu', input_shape=(300, 300, 3)),\n    MaxPooling2D(pool_size=2),\n\n    Conv2D(filters=64, kernel_size=3, padding='same', activation='relu'),\n    MaxPooling2D(pool_size=2),\n    \n    Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'),\n    MaxPooling2D(pool_size=2),\n    \n    Conv2D(filters=32, kernel_size=3, padding='same', activation='relu'),\n    MaxPooling2D(pool_size=2),\n\n    Flatten(),\n\n    Dense(64, activation='relu'),\n\n    Dense(6, activation='softmax')\n])\n\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit_generator(train_generator, epochs=20, validation_data=validation_generator,steps_per_epoch=1000,validation_steps=100)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}