{"cells":[{"metadata":{},"cell_type":"markdown","source":"# What to expect from this kernel?\n\n- Train_labels data (y)\n- Train image data (X)\n- CNN Models for Vowel, Consonant, Grapheme (Accu: 86%) \n\nDownload the train data to directly train your own deep-learning or machine learning model. You can download the prepared training data from this kernel ( Link provided below).\n\n### _Please upvote if you like it._\n# Model flow\n\n![image.png](attachment:image.png)\n\n","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{},"cell_type":"markdown","source":"# Introduction\nBengali language origninated from the Prakrit or Middle Indo-Aryan, which is descended from Sanskrit. \nThe map below shows Begali or Bangla speaking regions.\n![image.png](attachment:image.png)\n\n\n","attachments":{"image.png":{"image/png":"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"}}},{"metadata":{},"cell_type":"markdown","source":"\n\n```Jana Gana Mana``` is the national anthem of India. \nIt was first written in Bengali, it is the first of five stanzas of a poem written and later set to notations by novel loriat Rbindranath Tagore (1861-1941).\n\n- Rabindranath Tagore won the Nobel Prize for Literature Language: Bengali; English) in 1913.\n\n\n### Objective:\nFor this competition, you’re given the image of a handwritten Bengali grapheme and are challenged to separately classify three constituent elements in the image: grapheme root, vowel diacritics, and consonant diacritics."},{"metadata":{},"cell_type":"markdown","source":"Steps:\n- Read train_label data and make it same as submission.csv file i.e. with only two columns. The image_id merge it with the three classes i.e. grapheme_root, vowel_diacritics, and consonant_diacritics e.g. Train_0_grapheme_root."},{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport gc\nfrom PIL import Image\n\nimport dask.dataframe as dd\nfrom tqdm.auto import tqdm\nimport cv2\n","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"print(os.listdir(\"../input/bengaliai-cv19/\"))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## File\n### train.csv\n\n- ```image_id:``` the foreign key for the parquet files\n- ```grapheme_root:``` the first of the three target classes\n- ```vowel_diacritic:``` the second target class\n- ```consonant_diacritic:``` the third target class\n- ```grapheme:``` the complete character. Provided for informational purposes only, you should not need to use this.\n\n### test.csv\n\nEvery image in the test set will require three rows of predictions, one for each component. This csv specifies the exact order for you to provide your labels.\n- ```row_id:``` foreign key to the sample submission \n- ```image_id:``` foreign key to the parquet file \n- ```component:``` the required target class for the row (grapheme_root, vowel_diacritic, or consonant_diacritic)"},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train_labels = pd.read_csv(\"../input/bengaliai-cv19/train.csv\")\ndf_train_labels.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(df_train_labels.grapheme_root.unique())","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(df_train_labels.consonant_diacritic.unique())","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(df_train_labels.vowel_diacritic.unique())","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train_labels.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train_labels = df_train_labels.drop(['grapheme'], axis=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_train_labels.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#df_train_labels.image_id.stack()\ndf_tmp = pd.melt(df_train_labels, id_vars=['image_id'], value_vars=['grapheme_root',\t'vowel_diacritic',\t'consonant_diacritic'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_tmp.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_tmp[df_tmp['image_id']=='Train_0']","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"- Add a column name 'row_id' by combining image_id and variable column values."},{"metadata":{"trusted":true},"cell_type":"code","source":"df_tmp['row_id'] = df_tmp['image_id']+'_'+df_tmp['variable']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_tmp.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"- Rename the column-names i.e. 'variable' to 'component'."},{"metadata":{"trusted":true},"cell_type":"code","source":"df_tmp= df_tmp.rename(columns={\"variable\": \"component\"}, errors=\"raise\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_test_labels = pd.read_csv(\"../input/bengaliai-cv19/test.csv\")\ndf_test_labels.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"- Make three separate dataframe each for 'compnent' ```consonant_discritic```, ```grapheme_root```, and ```vowel_diacritic```.\n\n- These are the train_target values. i.e. the values in column 'value'."},{"metadata":{"trusted":true},"cell_type":"code","source":"df_consonant = df_tmp[df_tmp['component'] =='consonant_diacritic']\ndf_grapheme = df_tmp[df_tmp['component'] =='grapheme_root']\ndf_vowel = df_tmp[df_tmp['component'] =='vowel_diacritic']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(df_consonant.shape)\nprint(df_grapheme.shape)\nprint(df_vowel.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_consonant.head()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Sneak peak at distribution of the three class"},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.catplot(x=\"vowel_diacritic\", data=df_train_labels, kind=\"count\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.catplot(x=\"consonant_diacritic\", data=df_train_labels, kind=\"count\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.catplot(x=\"grapheme_root\", data=df_train_labels, kind=\"count\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Let us look at some of the Bengali images"},{"metadata":{},"cell_type":"markdown","source":"### Helper function to read and see parquet"},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"HEIGHT = 137\nWIDTH = 236\n\ndef load_as_npa(file):\n    df = pd.read_parquet(file)\n    return df.iloc[:, 1:].values.reshape(-1, HEIGHT, WIDTH)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true},"cell_type":"code","source":"#images0 = load_as_npa('/kaggle/input/bengaliai-cv19/train_image_data_0.parquet')\n#images1 = load_as_npa('/kaggle/input/bengaliai-cv19/train_image_data_1.parquet')\n#images2 = load_as_npa('/kaggle/input/bengaliai-cv19/train_image_data_2.parquet')\n#images3 = load_as_npa('/kaggle/input/bengaliai-cv19/train_image_data_3.parquet')\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_kg_hide-input":true,"_kg_hide-output":true},"cell_type":"code","source":"#f, ax = plt.subplots(4, 4, figsize=(12, 8))\n#ax = ax.flatten()\n\n#for i in range(16):\n#    ax[i].imshow(images0[i], cmap='Greys')","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Merge the 3 parquet image_matrix into one matrix"},{"metadata":{"trusted":true},"cell_type":"code","source":"#final_train_images = np.concatenate((images0, images1, images2, images3), axis=0)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Free some memory. Here my memory usage was 13 Gb, after running the garbage collector, it reduced to 7 Gb."},{"metadata":{"trusted":true},"cell_type":"code","source":"#del [[images0, images1, images2, images3, final_train_images]]\n#del [[final_train_images]]\n#gc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#final_train_images.shape","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"Read each parquet file again\n\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"#import pyarrow.parquet as pq","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#able = pq.read_table(file_path, nthreads=4)\n#df_image_0 = pq.read_table('/kaggle/input/bengaliai-cv19/train_image_data_0.parquet')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def resize(df, size=46, need_progress_bar=True):\n    resized = {}\n    if need_progress_bar:\n        for i in tqdm(range(df.shape[0])):\n            image = cv2.resize(df.loc[df.index[i]].values.reshape(137,236),(size,size))\n            resized[df.index[i]] = image.reshape(-1)\n    else:\n        for i in range(df.shape[0]):\n            image = cv2.resize(df.loc[df.index[i]].values.reshape(137,236),(size,size))\n            resized[df.index[i]] = image.reshape(-1)\n    resized = pd.DataFrame(resized).T\n    return resized","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_image_0 = pd.read_parquet('/kaggle/input/bengaliai-cv19/train_image_data_0.parquet')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_image_0.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"type(df_image_0)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_image_0.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_image_0 = df_image_0.iloc[:,1:]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_image_0 = resize(df_image_0)/255\n#X_train = resize(X_train)/255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_image_0 = df_image_0.to_numpy() # Convert the dataframe to matrix ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_image_0.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"del [[df_image_0]]\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_image_1 = pd.read_parquet('/kaggle/input/bengaliai-cv19/train_image_data_1.parquet')\ndf_image_1= df_image_1.iloc[:,1:]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_image_1 = resize(df_image_1)/255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_image_1 = df_image_1.to_numpy() # Convert the dataframe to matrix ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_image_1.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#del [[df_image_1]]\ndel df_image_1\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_image_2 = pd.read_parquet('/kaggle/input/bengaliai-cv19/train_image_data_2.parquet')\ndf_image_2= df_image_2.iloc[:,1:]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_image_2 = resize(df_image_2)/255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_image_2 = df_image_2.to_numpy() # Convert the dataframe to matrix ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_image_2.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#del [[df_image_2]]\ndel df_image_2\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_image_3 = pd.read_parquet('/kaggle/input/bengaliai-cv19/train_image_data_3.parquet')\ndf_image_3= df_image_3.iloc[:,1:]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"df_image_3 = resize(df_image_3)/255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_image_3 = df_image_3.to_numpy() # Convert the dataframe to matrix ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_image_3.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"del [[df_image_3]]\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Final data for training"},{"metadata":{},"cell_type":"markdown","source":"Here, the ```final_train_images``` combines the image pixels with the three labels i.e. ```grapheme_root```, ```vowel_diacritic```, ```consonant_diacritic``` which can be used to train the model.\n\nWe will use this to train a model."},{"metadata":{"trusted":true},"cell_type":"code","source":"#final_train_images","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#image_size = 137 * 236\n#final_train_images.reshape(image_size)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#X = final_train_images/255","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":""},{"metadata":{"trusted":true},"cell_type":"code","source":"#X = pd.merge([X_image_0, X_image_1, X_image_2, X_image_3])\nX= np.concatenate((X_image_0, X_image_1, X_image_2, X_image_3), axis=0)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"len(X)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"del X_image_0\ndel X_image_1\ndel X_image_2\ndel X_image_3\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Saving the training data: Download\n\nHere, following files are being saved:\n- X ( It has all the training images, pixel in matrix format- each row in the matrix represents an image)\n\nAdditionally, the three target columns for three separate model one each for ```consonant_discritic```: 7, ```grapheme_root```: 168, and ``vowel_diacritic```: 11.\n\n- ```df_consonant.shape```\n- ```df_grapheme.shape``` and\n- ```df_vowel.shape``` \n\nEach one will work as target for our three separte models."},{"metadata":{"trusted":true},"cell_type":"code","source":"type(X)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#from tempfile import TemporaryFile\n#train_all_image_file = TemporaryFile()\n\n\n#from joblib import dump\n#dump(X, 'all_image_4_train.joblib', compress=3)\n#import pickle\n#f=open('all_image_4_train','w')\n#pickle.dump(X, f, protocol=4)\n#f.close()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Download form the output folder of thsi kernel\ndf_consonant.to_csv(\"target_4_consonant.csv\")\ndf_grapheme.to_csv(\"target_4_grapheme.csv\")\ndf_vowel.to_csv(\"target_4_vowel.csv\")","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"```consonant_discritic```: 7, \n\n```grapheme_root```: 168, and \n\n```vowel_diacritic```: 11\n\n#### Download the above files and load in your kernel before carryingout the below steps."},{"metadata":{"trusted":true},"cell_type":"code","source":"#X.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X= X.reshape(-1,46, 46,1)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Model_consonant"},{"metadata":{"trusted":true},"cell_type":"code","source":"n_classes = 7","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y= df_consonant.value","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from tensorflow.keras.utils import to_categorical","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y = to_categorical(y, n_classes)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from sklearn.model_selection import train_test_split","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state=42)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#X = np.divide(X, 255)\n#import dask.array as da\n\n#X = np.arange(1000)  #arange is used to create array on values from 0 to 1000\n#y = da.from_array(X, chunks=(100))  #converting numpy array to dask array\n\n#y.div(255).compute()  #computing mean of the array","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten, BatchNormalization\nfrom keras import Sequential","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model_consonant = Sequential()\n\nmodel_consonant .add(Conv2D(32, kernel_size=(3,3), activation='relu', input_shape=(46, 46,1)))\n\nmodel_consonant .add(Conv2D(32, kernel_size=(3,3), activation='relu'))\nmodel_consonant .add(MaxPooling2D(pool_size=(2,2)))\nmodel_consonant .add(Dropout(0.25))\n\nmodel_consonant .add(Conv2D(64, kernel_size=(3,3), activation='relu'))\nmodel_consonant .add(MaxPooling2D(pool_size=(2,2)))\nmodel_consonant .add(Dropout(0.25))\n\nmodel_consonant .add(Flatten())\n\nmodel_consonant .add(Dense(128, activation='relu'))\n#model.add(Dense(128, activation='relu'))\nmodel_consonant.add(Dropout(0.5))\n\nmodel_consonant .add(Dense(n_classes, activation='softmax'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model_consonant.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model_consonant.compile(loss='categorical_crossentropy',\n             optimizer='nadam',\n             metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model_consonant.fit(X_train, y_train, batch_size=32, epochs=20, validation_data=(X_test, y_test))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#history = model.fit(X_train, \n#                    y_train, \n#                    batch_size=128, \n#                    epochs=100,\n#                    verbose=1,\n#                    validation_data=(X_test, y_test)\n#                   )\n#history = model_consonant.fit(X, \n#                    y, \n#                    batch_size=128, \n#                    epochs=1,\n#                    verbose=1\n#                    #validation_data=(X_test, y_test)\n#                   )","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"### Save model_consonant\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"# Save the weights\nmodel_consonant.save_weights('model_consonant_weight.h5')\n\n# Save the model architecture\nwith open('model_consonant_architecture.json', 'w') as f:\n    f.write(model_consonant.to_json())\n    \n## READ weight and architecture\n#from keras.models import model_from_json\n\n## Model reconstruction from JSON file\n#with open('model_consonant_architecture.json', 'r') as f:\n#    model = model_from_json(f.read())\n\n## Load weights into the new model\n#model.load_weights('model_consonant_weight.h5')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions_consonant = model_consonant.predict(X_test)\npredictions_consonant = np.argmax(predictions_consonant, axis=1) ","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# calculate accuracy\n#from sklearn import metrics\n#print(metrics.accuracy_score(y_test, predictions_consonant))\n#print(metrics.confusion_matrix(y_test, predictions_consonant))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"del X_train\ndel X_test\ndel y_train\ndel y_test\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Model grapheme"},{"metadata":{"trusted":true},"cell_type":"code","source":"n_classes = 168\ny= df_grapheme.value\ny = to_categorical(y, n_classes)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state=42)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model_grapheme = Sequential()\n\nmodel_grapheme.add(Conv2D(32, kernel_size=(3,3), activation='relu', input_shape=(46, 46,1)))\n\nmodel_grapheme.add(Conv2D(32, kernel_size=(3,3), activation='relu'))\nmodel_grapheme.add(MaxPooling2D(pool_size=(2,2)))\nmodel_grapheme.add(Dropout(0.25))\n\nmodel_grapheme.add(Conv2D(64, kernel_size=(3,3), activation='relu'))\nmodel_grapheme.add(MaxPooling2D(pool_size=(2,2)))\nmodel_grapheme.add(Dropout(0.25))\n\nmodel_grapheme.add(Flatten())\n\nmodel_grapheme.add(Dense(128, activation='relu'))\n#model.add(Dense(128, activation='relu'))\nmodel_grapheme.add(Dropout(0.5))\n\nmodel_grapheme.add(Dense(n_classes, activation='softmax'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model_grapheme.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model_grapheme.compile(loss='categorical_crossentropy',\n             optimizer='nadam',\n             metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model_grapheme.fit(X_train, y_train, batch_size=32, epochs=20, validation_data=(X_test, y_test))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions_grapheme = model_grapheme.predict(X_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Save the weights\nmodel_grapheme.save_weights('model_grapheme_weight.h5')\n\n# Save the model architecture\nwith open('model_grapheme_architecture.json', 'w') as f:\n    f.write(model_grapheme.to_json())\n    \n## READ weight and architecture\n#from keras.models import model_from_json\n\n## Model reconstruction from JSON file\n#with open('model_consonant_architecture.json', 'r') as f:\n#    model = model_from_json(f.read())\n\n## Load weights into the new model\n#model.load_weights('model_consonant_weight.h5')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"del X_train\ndel X_test\ndel y_train\ndel y_test\ngc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"## Model vowel"},{"metadata":{"trusted":true},"cell_type":"code","source":"#n_classes = 11\n#y= df_vowel.value\n#y = to_categorical(y, n_classes)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state=42)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#model_vowel = Sequential()\n\n#model_vowel.add(Conv2D(32, kernel_size=(3,3), activation='relu', input_shape=(46, 46,1)))\n\n#model_vowel.add(Conv2D(32, kernel_size=(3,3), activation='relu'))\n#model_vowel.add(MaxPooling2D(pool_size=(2,2)))\n#model_vowel.add(Dropout(0.25))\n\n#model_vowel.add(Conv2D(64, kernel_size=(3,3), activation='relu'))\n#model_vowel.add(MaxPooling2D(pool_size=(2,2)))\n#model_vowel.add(Dropout(0.25))\n\n#model_vowel.add(Flatten())\n\n#model_vowel.add(Dense(128, activation='relu'))\n##model.add(Dense(128, activation='relu'))\n#model_vowel.add(Dropout(0.5))\n\n#model_vowel.add(Dense(n_classes, activation='softmax'))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#model_vowel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#model_vowel.compile(loss='categorical_crossentropy',\n#             optimizer='nadam',\n#             metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#model_vowel.fit(X_train, y_train, batch_size=32, epochs=20, validation_data=(X_test, y_test))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#predictions_vowel = model_vowel.predict(X_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"## Save the weights\n#model_vowel.save_weights('model_vowel_weight.h5')\n\n## Save the model architecture\n#with open('model_vowel_architecture.json', 'w') as f:\n#    f.write(model_vowel.to_json())\n    \n## READ weight and architecture\n#from keras.models import model_from_json\n\n## Model reconstruction from JSON file\n#with open('model_consonant_architecture.json', 'r') as f:\n#    model = model_from_json(f.read())\n\n## Load weights into the new model\n#model.load_weights('model_consonant_weight.h5')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#del X_train\n#del X_test\n#del y_train\n#del y_test\n#gc.collect()","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"# Prediction & Submission"},{"metadata":{"trusted":true},"cell_type":"code","source":"#components = ['consonant_diacritic', 'grapheme_root', 'vowel_diacritic']\n#target=[] # model predictions placeholder\n#row_id=[] # row_id place holder\n#n_cls = [7,168,11] # number of classes in each of the 3 targets\n#IMG_SIZE = 46\n#IMG_SIZE= 46\n#N_CHANNELS = 1\n#for i in range(4):\n#    df_test_img = pd.read_parquet('/kaggle/input/bengaliai-cv19/test_image_data_{}.parquet'.format(i)) \n#    df_test_img.set_index('image_id', inplace=True)\n\n#    X_test = resize(df_test_img)/255\n#    X_test = X_test.values.reshape(-1, IMG_SIZE, IMG_SIZE, N_CHANNELS)\n\n#    for pred in preds_dict:\n#        preds_dict[pred]=np.argmax(model_dict[pred].predict(X_test), axis=1)\n\n#    for k,id in enumerate(df_test_img.index.values):  \n#        for i,comp in enumerate(components):\n#            id_sample=id+'_'+comp\n#            row_id.append(id_sample)\n#            target.append(preds_dict[comp][k])\n\n#df_sample = pd.DataFrame(\n#    {'row_id': row_id,\n#    'target':target\n#    },\n#    columns =['row_id','target'] \n#)\n#df_sample.to_csv('submission.csv',index=False)","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":1}