{"cells":[{"metadata":{"id":"czLIkc1qe642","colab_type":"code","colab":{},"trusted":false},"cell_type":"code","source":"\n#module for model\n\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers.core import Dense, Dropout, Activation, Flatten\nfrom keras.layers.convolutional import Convolution2D, MaxPooling2D\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Model\nfrom keras.models import clone_model\nfrom keras.layers import Dense,Conv2D,Flatten,MaxPool2D,Dropout,BatchNormalization, Input\nfrom keras.optimizers import Adam\nfrom keras.callbacks import ReduceLROnPlateau\n\n#for data handaling\nfrom tqdm.auto import tqdm\nimport numpy as np\nimport pandas as pd \n\n#for image processing\nimport cv2\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n\n#for calc accuaracy and spliting the data\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\n%matplotlib inline","execution_count":0,"outputs":[]},{"metadata":{"id":"PysGwY9tgqY1","colab_type":"code","outputId":"575cb6fa-299b-4286-e3e8-3f1339a2801e","colab":{"base_uri":"https://localhost:8080/","height":34},"trusted":false},"cell_type":"code","source":"from google.colab import drive\ndrive.mount('/content/drive')","execution_count":0,"outputs":[]},{"metadata":{"id":"YewtOGYWgmuO","colab_type":"code","colab":{},"trusted":false},"cell_type":"code","source":"PATH='/content/drive/My Drive/bengaliai-cv19/'\nHEIGHT = 137\nWIDTH = 236\nSIZE = 128\nbatch_size = 256\nepochs = 64","execution_count":0,"outputs":[]},{"metadata":{"id":"jSXbTzw_kG82","colab_type":"text"},"cell_type":"markdown","source":"Function for Cropping and resiing the given image data to own size"},{"metadata":{"id":"DVQo3cg2ZCP7","colab_type":"text"},"cell_type":"markdown","source":""},{"metadata":{"id":"yK2HlfiPg87a","colab_type":"code","colab":{},"trusted":false},"cell_type":"code","source":"def crop_resize_image(image_df):\n    cropped_resized_img={}\n    for i in tqdm(range(len(image_df))):\n        image=image_df.iloc[i].values.reshape(HEIGHT,WIDTH)\n        _, thresh = cv2.threshold(image, 30, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n        contours, _ = cv2.findContours(thresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)[-2:]\n        x_min=[]\n        x_max=[]\n        y_min=[]\n        y_max=[]\n        for cordinate in contours:\n            x,y,w,h=cv2.boundingRect(cordinate)\n            x_min.append(x)\n            x_max.append(x+w)\n            y_min.append(y)\n            y_max.append(y+h)\n        x1=min(x_min)\n        x2=max(x_max)\n        y1=min(y_min)\n        y2=max(y_max)\n        cropped_img=image[y1:y2,x1:x2]\n        resized_img=cv2.resize(cropped_img,(SIZE,SIZE),interpolation=cv2.INTER_AREA)\n        cropped_resized_img[i]=resized_img.reshape(-1)\n    return pd.DataFrame(cropped_resized_img).T","execution_count":0,"outputs":[]},{"metadata":{"id":"7v24cwf2kgUr","colab_type":"text"},"cell_type":"markdown","source":"making the model to learn and predict the bangali handwritten root, vowel, consonant"},{"metadata":{"id":"Cec-Nx8jkXv0","colab_type":"code","colab":{},"trusted":false},"cell_type":"code","source":"inputs= Input(shape=(SIZE,SIZE,1))\nmodel=Conv2D(filters=32,kernel_size=(3,3),padding='SAME',activation='relu',input_shape=(SIZE,SIZE,1))(inputs)\nmodel=MaxPool2D(pool_size=(2,2))(model)\n\nmodel=Conv2D(filters=64,kernel_size=(3,3),padding='SAME',activation='relu')(model)\nmodel=MaxPool2D(pool_size=(2,2))(model)\n\nmodel=Conv2D(filters=128,kernel_size=(3,3),padding='SAME',activation='relu')(model)\nmodel=MaxPool2D(pool_size=(2,2))(model)\n\nmodel=Conv2D(filters=128,kernel_size=(3,3),padding='SAME',activation='relu')(model)\nmodel=MaxPool2D(pool_size=(2,2))(model)\n\nmodel=Conv2D(filters=128,kernel_size=(3,3),padding='SAME',activation='relu')(model)\nmodel=MaxPool2D(pool_size=(2,2))(model)\n\nmodel=Dropout(0.3)(model)\nmodel=BatchNormalization(momentum=0.15)(model)\nmodel=Flatten()(model)\nmodel=Dense(1000,activation='relu')(model)\nmodel=Dropout(0.3)(model)\nmodel=Dense(500,activation='relu')(model)\n\nroot=Dense(168,activation='softmax',name='root')(model)\nvowel=Dense(11,activation='softmax',name='vowel')(model)\nconsonant=Dense(7,activation='softmax',name='consonant')(model)\n\nmodel=Model(inputs=inputs,outputs=[root,vowel,consonant])","execution_count":0,"outputs":[]},{"metadata":{"id":"_iBwo0N_k-fB","colab_type":"code","outputId":"165e3371-b0de-4f03-c7f9-b2a6a9159802","colab":{"base_uri":"https://localhost:8080/","height":823},"trusted":false},"cell_type":"code","source":"model.summary()","execution_count":0,"outputs":[]},{"metadata":{"id":"TgSkpDE7lMu9","colab_type":"code","outputId":"6e32f77e-245f-4150-b138-553b623ef3f6","colab":{"base_uri":"https://localhost:8080/","height":1000},"trusted":false},"cell_type":"code","source":"from keras.utils import plot_model\nplot_model(model, to_file=PATH+'model.png')","execution_count":0,"outputs":[]},{"metadata":{"id":"2RobLKPjl2ut","colab_type":"text"},"cell_type":"markdown","source":"Setting up the reduce learning rate to increase accuracy"},{"metadata":{"id":"UK-TliX2ltTe","colab_type":"code","colab":{},"trusted":false},"cell_type":"code","source":"lr_root=ReduceLROnPlateau(monitor='root_acc',factor=0.9,patience=3,min_lr=0.00001,verbose=1)\nlr_vowel=ReduceLROnPlateau(monitor='vowel_acc',factor=0.9,patience=3,min_lr=0.00001,verbose=1)\nlr_consonant=ReduceLROnPlateau(monitor='consonant_acc',factor=0.9,patience=3,min_lr=0.00001,verbose=1)","execution_count":0,"outputs":[]},{"metadata":{"id":"VpdvBMEflQ77","colab_type":"code","colab":{},"trusted":false},"cell_type":"code","source":"model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])","execution_count":0,"outputs":[]},{"metadata":{"id":"NToUOm1GlaM_","colab_type":"text"},"cell_type":"markdown","source":"https://github.com/keras-team/keras/issues/12639#issuecomment-506338552\n\nfor multiclassoutput class by a single model"},{"metadata":{"id":"7O1tVoN9lW_3","colab_type":"code","colab":{},"trusted":false},"cell_type":"code","source":"class MultiOutputDataGenerator(keras.preprocessing.image.ImageDataGenerator):\n\n    def flow(self,\n             x,\n             y=None,\n             batch_size=batch_size,\n             shuffle=True,\n             sample_weight=None,\n             seed=None,\n             save_to_dir=None,\n             save_prefix='',\n             save_format='png',\n             subset=None):\n\n        targets = None\n        target_lengths = {}\n        ordered_outputs = []\n        for output, target in y.items():\n            if targets is None:\n                targets = target\n            else:\n                targets = np.concatenate((targets, target), axis=1)\n            target_lengths[output] = target.shape[1]\n            ordered_outputs.append(output)\n\n\n        for flowx, flowy in super().flow(x, targets, batch_size=batch_size,shuffle=shuffle):\n            target_dict = {}\n            i = 0\n            for output in ordered_outputs:\n                target_length = target_lengths[output]\n                target_dict[output] = flowy[:, i: i + target_length]\n                i += target_length\n\n            yield flowx, target_dict","execution_count":0,"outputs":[]},{"metadata":{"id":"oCq1q-7BSvvZ","colab_type":"code","outputId":"d7d366a7-de13-40e5-cd33-15e3e04c7816","colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["4f4b222bc07946d8b0e48ee3e7d37286","39f39256c4bc40e5b14dbfe170abf16a","96db7b7b26614b71a13fc57e885dbbf1","0128a43493d347b2a76750735fef0338","c707834665ed45a9b2575784779cbe9e","a6533b20231c49c8a8de1289d7094903","f187a4fc5e26475c9f38e7db8a112f94","fa404c312832497880924d1c3ae15506"]},"trusted":false},"cell_type":"code","source":"train_image=pd.read_parquet(PATH+'train_image_data_3.parquet').drop(['image_id'],axis=1)\ntrain_image_info=pd.read_csv(PATH+'train.csv')\n\nX_train=crop_resize_image(train_image).values.reshape(-1,SIZE,SIZE,1)\nY_train=train_image_info[3*50210:(3+1)*50210]\n\n_, x_test, _, y_test = train_test_split(X_train, Y_train, test_size=0.99, random_state=420)","execution_count":0,"outputs":[]},{"metadata":{"id":"oEZHkncbTbCD","colab_type":"code","colab":{},"trusted":false},"cell_type":"code","source":"y_test_root = pd.get_dummies(y_test['grapheme_root']).values\ny_test_vowel = pd.get_dummies(y_test['vowel_diacritic']).values\ny_test_consonant = pd.get_dummies(y_test['consonant_diacritic']).values\n#print(y_test_root.shape)","execution_count":0,"outputs":[]},{"metadata":{"id":"2uXaCJwSWnR7","colab_type":"code","colab":{},"trusted":false},"cell_type":"code","source":"del train_image\ndel X_train\ndel Y_train","execution_count":0,"outputs":[]},{"metadata":{"id":"mVEzZSd1bHiG","colab_type":"code","colab":{},"trusted":false},"cell_type":"code","source":"def appendHist(h1, h2):\n    if h1 == {}:\n        return h2\n    else:\n        dest = {}\n        for key, value in h1.items():\n            dest[key] = value + h2[key]\n        return dest","execution_count":0,"outputs":[]},{"metadata":{"id":"rCcct2tOllFC","colab_type":"code","outputId":"7da64d05-8ad2-4734-d0a6-8e311088403d","colab":{"base_uri":"https://localhost:8080/"},"trusted":false},"cell_type":"code","source":"\nrecords={}\n\nfor i in range(4):\n    train_image=pd.read_parquet(PATH+'train_image_data_'+str(i)+'.parquet').drop(['image_id'],axis=1)\n    \n    y_train=train_image_info[i*50210:(i+1)*50210]\n    x_train=crop_resize_image(train_image).values.reshape(-1,SIZE,SIZE,1)\n    #plt.imshow(train_image.iloc[100].values.reshape(HEIGHT,WIDTH))\n    del train_image\n\n    #plt.imshow(X_train[100])\n    #print(Y_train.iloc[100])\n    #print('splitting about to start')\n\n\n    #print('splitting completed')\n\n    data_generator=MultiOutputDataGenerator()\n    data_generator.fit(x_train)\n    #print('fitting completed')\n\n    y_train_root = pd.get_dummies(y_train['grapheme_root']).values\n    y_train_vowel = pd.get_dummies(y_train['vowel_diacritic']).values\n    y_train_consonant = pd.get_dummies(y_train['consonant_diacritic']).values\n    \n    del y_train\n\n\n    #print('learning about to start')\n    result=model.fit_generator(\n        data_generator.flow(\n            x_train,\n            {\n                'root':y_train_root,'vowel':y_train_vowel,'consonant':y_train_consonant\n            },\n            batch_size=batch_size\n        ),\n        epochs=epochs,\n        validation_data=(x_test,[y_test_root,y_test_vowel,y_test_consonant]),\n        steps_per_epoch=y_train_root.shape[0]//batch_size,\n        callbacks=[lr_root,lr_vowel,lr_consonant]\n    )\n    del x_train\n    del y_train_root\n    del y_train_vowel\n    del y_train_consonant\n\n    records=appendHist(records,result.history) ","execution_count":0,"outputs":[]},{"metadata":{"id":"NaPHoq8eNbmJ","colab_type":"code","outputId":"5123cc08-726b-436b-a7f6-20f960666760","colab":{"base_uri":"https://localhost:8080/","height":54},"trusted":false},"cell_type":"code","source":"print(records)\n","execution_count":0,"outputs":[]},{"metadata":{"id":"aPVToYw3LDL0","colab_type":"code","colab":{},"trusted":false},"cell_type":"code","source":"def plot_graph(x,s):\n    plt.figure(figsize=(10,10))\n    plt.plot(x['val_root_'+s])\n    plt.plot(x['val_vowel_'+s])\n    plt.plot(x['val_consonant_'+s])\n    plt.plot(x['root_'+s])\n    plt.plot(x['vowel_'+s])\n    plt.plot(x['consonant_'+s])\n    plt.title('Learning Dataset '+s)\n    plt.ylabel(s)\n    plt.xlabel('epoch')\n    if s=='acc':\n        plt.legend(['val_root_'+s,'val_vowel_'+s,'val_consonant_'+s,'root_'+s,'vowel_'+s,'consonant_'+s], loc='best')\n    else:\n        plt.plot(x['val_'+s])\n        plt.plot(x[s])\n        plt.legend(['val_root_'+s,'val_vowel_'+s,'val_consonant_'+s,'root_'+s,'vowel_'+s,'consonant_'+s,'val_'+s,s], loc='best')\n    plt.show()","execution_count":0,"outputs":[]},{"metadata":{"id":"79vFvd3OLevQ","colab_type":"code","outputId":"a0049d5d-65f0-4574-8897-27970d7d806c","colab":{"base_uri":"https://localhost:8080/","height":1000},"trusted":false},"cell_type":"code","source":"plot_graph(records,'acc')\nplot_graph(records,'loss')","execution_count":0,"outputs":[]},{"metadata":{"id":"0LMakgq5XaW9","colab_type":"code","colab":{},"trusted":false},"cell_type":"code","source":"preds_dict = {\n    'grapheme_root': [],\n    'vowel_diacritic': [],\n    'consonant_diacritic': []\n}","execution_count":0,"outputs":[]},{"metadata":{"id":"13f7lq3ZNIuJ","colab_type":"code","outputId":"37b1ecf3-e58f-44f4-982e-861cf95481fa","colab":{"base_uri":"https://localhost:8080/","height":766,"referenced_widgets":["1c6b029374d746e48525b4c25644a393","9f56f6dc57534e04a671ca5e5657e19d","9de3b736c36242b29f3457c71935a82b","35f31767bb1e4077a0ff5ccda3f13fa8","d397d75a76ac4d9a8904ce403d55c107","72441945da12492bbcf7fef7d82f3dfe","8cf47e70822a4a3091795587951f3957","86339aea4d2f42f5b554c6b4adb8d460","5153b21a8f764a4b9602318fec171780","e373a21769b5487387bad79de9ff087c","9ddfa3ba46994df0acd1038f5ca4b80d","1ff50aa6f8c248b0a38176af1e33a6f3","a5ae519f46d74de49c6f575b1bac0ea2","3c82a88be2764a67bf77f41ebf61c481","12d22de1aa6c41c18d49a29937c0d7eb","eeb5ae0323254f93a9a06f84860b6c06","f4f2b6e1c1d645d8b0e1ecab97b88546","ded46fb227284f30b310eef0a0496610","1ce8e942f2d14942bbb188263f8f6913","b9a58cd7c49e4c0dbdd1c0721144d7c0","40c1dccbf9e24fc3badd1dcd75420ab1","4d14fb9dd8a5431dbf6633eab790ef32","e71339dadebf467bb476d49017bb6250","d7d1823a1b4d44f7a64bb6cffb7b8fc7","59ff8891ac0d41b88a513dc5b095dce8","adfde469e1d04afcadc60760cf60482f","8ae4d79748134ce5a0d24086b6bb9e11","21cc7db98ac44dadb8bafee227cf8db4","2a883f072aec413d8bd57696f8c25799","f5199cfb6579482ab0468717b6e22a82","29ed3a2d20504ee398c06339472aef8c","192bc69279d44dca96d0e85457d94478"]},"trusted":false},"cell_type":"code","source":"row_id=[]\ntarget=[]\nfor i in range(4):\n    test_image=pd.read_parquet(PATH+'test_image_data_'+str(i)+'.parquet')\n    test_image.set_index('image_id',inplace=True)\n    x_test=crop_resize_image(test_image).values.reshape(-1,SIZE,SIZE,1)\n    preds = model.predict(x_test)\n    \n    for j, p in enumerate(preds_dict):\n        preds_dict[p] = np.argmax(preds[j], axis=1)\n    \n    for k,id in enumerate(test_image.index.values):\n        row_id+=[id+'_grapheme_root',id+'_vowel_diacritic',id+'_consonant_diacritic']\n        target+=[preds_dict['grapheme_root'][k],preds_dict['vowel_diacritic'][k],preds_dict['consonant_diacritic'][k]]\n\nsubmission = pd.DataFrame({'row_id': row_id, 'target': target})\nsubmission.to_csv('submission.csv', index=False)\nprint(submission)","execution_count":0,"outputs":[]}],"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.4"},"colab":{"name":"CNN Bangali handwittern for 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