{"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)\nimport tensorflow as tf\nfrom tensorflow.keras.optimizers import RMSprop\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\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":"train=pd.read_csv('/kaggle/input/digit-recognizer/train.csv')\ntest=pd.read_csv('/kaggle/input/digit-recognizer/test.csv')\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train_images=train.drop('label',axis=1)\ntrain_label=train['label']","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"test=np.array(test).reshape(28000,28,28,1)\ntrain_images=np.array(train_images).reshape(42000,28,28,1)\ntrain_images=train_images/255\ntest=test/255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"class myCallback(tf.keras.callbacks.Callback):\n    def on_epoch_end(self, epoch, logs={}):\n        if(logs.get('accuracy')>0.998):\n            print(\"\\nReached 99.9% accuracy so cancelling training!\")\n            self.model.stop_training = True\n\n  # Your Code\n\ncallbacks = myCallback()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import tensorflow as tf\nmodel = tf.keras.models.Sequential([\n    # YOUR CODE STARTS HERE\n    tf.keras.layers.Conv2D(32,(3,3),activation='relu',input_shape=(28,28,1)),\n    tf.keras.layers.MaxPool2D((2,2)),\n    tf.keras.layers.Conv2D(64,(3,3),activation='relu'),\n    tf.keras.layers.MaxPool2D((2,2)),\n    tf.keras.layers.Flatten(),\n    tf.keras.layers.Dense(256,activation='relu'),\n    tf.keras.layers.Dense(128,activation='relu'),\n    tf.keras.layers.Dense(10,activation='softmax')\n    # YOUR CODE ENDS HERE\n])\nmodel.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])\nmodel.summary()\nmodel.fit(train_images,train_label,epochs=25,callbacks=[callbacks])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predict=model.predict_classes(test)\nsubmission=pd.read_csv('/kaggle/input/digit-recognizer/sample_submission.csv')\nsubmission['Label']=predict\nsubmission.to_csv('submit.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}