{"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\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 read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import os\n\nos.listdir('/kaggle/input/ai-club-inductions')\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import tensorflow as tf \nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D\nimport pickle\nfrom keras.models import model_from_json\nfrom keras.models import load_model\nimport matplotlib.pyplot as plt","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submissions = pd.read_csv('/kaggle/input/ai-club-inductions/submission.csv')\nx = np.load(\"/kaggle/input/ai-club-inductions/X.npy\")\nx_test = np.load('/kaggle/input/ai-club-inductions/X_test.npy')\ny = np.load('/kaggle/input/ai-club-inductions/Y.npy')","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"np.unique(y)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"x_test.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import keras\nfrom sklearn.model_selection import train_test_split\ny = keras.utils.to_categorical(y,num_classes = 6)\nx = x.reshape((34000, 28, 28,1))\nx_test = x_test.reshape((6000, 28, 28,1))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\nx_train,x_test,y_train,y_test = train_test_split(x,y,test_size = 0.2,random_state = 4)"},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.add(Conv2D(32, 3,data_format = 'channels_last', input_shape = (28,28,1)))\nmodel.add(Activation(\"relu\"))\nmodel.add(MaxPooling2D(pool_size=(2,2)))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.add(Flatten())\nmodel.add(Dense(100))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(6))\nmodel.add(Activation('softmax'))\nmodel.compile(loss = 'categorical_crossentropy',optimizer = 'adadelta',metrics = ['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.fit(x,y,validation_data = (x,y))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.predict(x[1:2])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\nmodel.evaluate(x,y)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"out = model.predict(x_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"out.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"max(out[1])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"final = []\nnos = []\nfor i in range(6000):\n    nos.append(i)\n    temp = max(out[i])\n    for j in range(6):\n        if temp == out[i][j]:\n            final.append(j)\nfinal_df = pd.DataFrame({'index':nos,\n                         'class':final,})\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"final_df","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submissions","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"final_df.to_csv('final_1.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":4}