{"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 in \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 \"../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# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"cell_type":"code","source":"import tensorflow as tf\nprint(tf.__version__)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"mnist = tf.keras.datasets.mnist","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"(x_train,y_train),(x_test,y_test) = mnist.load_data()\nx_val = x_train,x_test\ny_val = y_train,y_test\nx_train,x_test = x_train/255.0,x_test/255.0\nx_train.shape","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = tf.keras.models.Sequential([\n    tf.keras.layers.Flatten(input_shape=(28,28)),\n    tf.keras.layers.Dense(128,activation='relu'),\n    tf.keras.layers.Dropout(0.2),\n    tf.keras.layers.Dense(10,activation='softmax')\n])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(optimizer='adam',\n             loss='sparse_categorical_crossentropy',\n             metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"r = model.fit(x_train,y_train,validation_data=(x_test,y_test),epochs=50 )","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nplt.plot(r.history['loss'],label = 'loss')\nplt.plot(r.history['val_loss'],label = 'val_loss')\nplt.legend()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"plt.plot(r.history['accuracy'],label='acc')\nplt.plot(r.history['val_accuracy'],label = 'val_acc')\nplt.legend()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"print(model.evaluate(x_test,y_test))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"predictions  = model.predict_classes(x_test)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"y_true = np.argmax(y_test)\n\n\ncorrect = np.nonzero(predictions==y_true)[0]\nincorrect = np.nonzero(predictions!=y_true)[0]\n\nprint(incorrect.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"final_predictions = model.predict_classes(x_test)\n\nsubmit = pd.DataFrame(final_predictions,columns=[\"Label\"])\nsubmit[\"ImageId\"] = pd.Series(range(1,(len(final_predictions)+1)))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission = submit[[\"ImageId\",\"Label\"]]\nsubmission.shape\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"submission.to_csv(\"submission.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}