{"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 5GB 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":"TrainingData = pd.read_csv(\"/kaggle/input/digit-recognizer/train.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train = TrainingData.iloc[:,1:]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_train = TrainingData.iloc[:,:1]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"#Scale\nX_train = X_train.astype(float) / 255.","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"X_train.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_train_cat.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import tensorflow as tf\ny_train_cat = tf.keras.utils.to_categorical(y_train)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"def mlp_clf():\n    # define two sets of inputs\n    input_layer = tf.keras.layers.Input(shape=(784)) \n    \n    mod1 = tf.keras.layers.Dense(units=1000, activation=tf.keras.layers.PReLU() ,  kernel_initializer='normal')(input_layer)\n    mod1 = tf.keras.layers.BatchNormalization()(mod1)\n    mod1 = tf.keras.layers.Dense(units=1000, activation=tf.keras.layers.LeakyReLU() , kernel_initializer='normal')(mod1)\n    mod1 = tf.keras.layers.Dropout(0.1)(mod1)\n    mod1 = tf.keras.layers.Dense(units=1000, activation=\"relu\" , kernel_initializer='normal')(mod1)\n    mod1 = tf.keras.layers.BatchNormalization()(mod1)\n    mod1 = tf.keras.layers.Dense(units=1000, activation=tf.keras.layers.PReLU() , kernel_initializer='normal')(mod1)\n    mod1 = tf.keras.layers.Dropout(0.1)(mod1)\n    mod1 = tf.keras.layers.Dense(units=1000, activation=\"relu\" , kernel_initializer='normal')(mod1)\n    mod1 = tf.keras.layers.BatchNormalization()(mod1)\n    mod1 = tf.keras.layers.Dense(units=1000, activation=tf.keras.layers.PReLU() , kernel_initializer='normal')(mod1)\n    mod1 = tf.keras.layers.Dropout(0.1)(mod1)\n    mod1 = tf.keras.layers.Dense(units=1000, activation=\"relu\" , kernel_initializer='normal')(mod1)\n    mod1 = tf.keras.layers.BatchNormalization()(mod1)\n    mod1 = tf.keras.layers.Dense(units=1000, activation=tf.keras.layers.PReLU() , kernel_initializer='normal')(mod1)\n    mod1 = tf.keras.layers.Dropout(0.1)(mod1)\n    mod1 = tf.keras.layers.Dense(units=1000, activation=\"relu\" , kernel_initializer='normal')(mod1)\n    mod1 = tf.keras.layers.BatchNormalization()(mod1)\n    mod1 = tf.keras.layers.Dense(units=1000, activation=tf.keras.layers.PReLU() , kernel_initializer='normal')(mod1)\n    mod1 = tf.keras.layers.Dropout(0.1)(mod1)\n    mod1 = tf.keras.layers.Dense(units=1000, activation=tf.keras.layers.LeakyReLU() , kernel_initializer='normal')(mod1)\n    mod1 = tf.keras.layers.BatchNormalization()(mod1)\n    mod1 = tf.keras.layers.Dense(units=1000, activation=\"relu\" , kernel_initializer='normal')(mod1)\n    mod1 = tf.keras.layers.Dropout(0.1)(mod1)\n    output_layer = tf.keras.layers.Dense(10, activation=\"softmax\" , kernel_initializer='normal')(mod1)\n    model = tf.keras.models.Model(inputs=input_layer, outputs=output_layer)\n    model.compile(loss=tf.keras.losses.categorical_crossentropy,  optimizer=tf.keras.optimizers.Adam(), metrics=[tf.keras.metrics.CategoricalAccuracy()])\n    return model","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = mlp_clf()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"history = model.fit(x=X_train,y=y_train_cat, epochs=100,batch_size=256,validation_split=0.2,shuffle=True)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.figure(figsize=(30,8))\nplt.plot(history.history['loss'], label=\"Training loss\")\nplt.plot(history.history['val_loss'], label=\"Validation loss\")\nplt.plot(history.history['categorical_accuracy'], label=\"Training accuracy\")\nplt.plot(history.history['val_categorical_accuracy'], label=\"Validation accuracy\")\nplt.legend()\nplt.show()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"TestData = pd.read_csv(\"/kaggle/input/digit-recognizer/test.csv\")","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# Scale\nX_test = TestData.astype(float) / 255.","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"# predict results\nresults = model.predict(X_test)\n\n# select the indix with the maximum probability\nresults = np.argmax(results,axis = 1)\n\nresults = pd.Series(results,name=\"Label\")\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission = pd.concat([pd.Series(range(1,28001),name = \"ImageId\"),results],axis = 1)\n\nsubmission.to_csv(\"cnn_mnist_pred.csv\",index=False)","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}