neural-net-movie-review-classifier.ipynb

{"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)\nfrom scipy.sparse import csr_matrix\nfrom tqdm import tqdm, trange\nfrom sklearn.datasets import load_svmlight_file\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input/movie-ratings\"))\n\n# Any results you write to the current directory are saved as output.","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"training_data, training_labels = load_svmlight_file('../input/movie-ratings/movie-ratings/data-splits/data.train')\ntesting_data, testing_labels = load_svmlight_file('../input/movie-ratings/movie-ratings/data-splits/data.test')\ntesting_data = csr_matrix((testing_data.data, testing_data.indices, testing_data.indptr), shape=(testing_data.shape[0], training_data.shape[1]))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"1251c31a55706de89be3889b920f40f75ec6f20e"},"cell_type":"code","source":"print(training_data.shape, training_labels.shape)\nprint(testing_data.shape, testing_labels.shape)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"3609f58bf349c7b87265e8427a29e8029836140b"},"cell_type":"code","source":"import keras","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"1175051653fb97909c327e8022ebfd31e9d9c671","_kg_hide-output":true,"scrolled":false},"cell_type":"code","source":"model = keras.Sequential()\nmodel.add(keras.layers.Dense(500, input_shape=(training_data.shape[1],), activation='relu'))\nmodel.add(keras.layers.Dense(30, activation='relu'))\nmodel.add(keras.layers.Dense(1, activation='sigmoid'))\n\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"2c3eeba50a1da3dd78e4ca1ef94fc87ca09f65b9"},"cell_type":"code","source":"model.compile(optimizer='adam',\n              loss='binary_crossentropy',\n              metrics=['accuracy'])","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"dbebcb08c80a08fb81f2e4df6a4bdc4b57df3460"},"cell_type":"code","source":"history = model.fit(training_data,\n                    training_labels,\n                    epochs=250,\n                    batch_size=512,\n                    validation_data=(testing_data, testing_labels),\n                    verbose=1)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"9f62f00f186c374e0735c409978b152ee06c4e80"},"cell_type":"code","source":"eval_data, _ = load_svmlight_file('../input/movie-ratings/movie-ratings/data-splits/data.eval.anon')\neval_data = csr_matrix((eval_data.data, eval_data.indices, eval_data.indptr), shape=(eval_data.shape[0], training_data.shape[1]))\nsubmission_pred = model.predict_classes(eval_data)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"2d2b06270ef29e6c3fa3669bb0c2e1dd26467b14"},"cell_type":"code","source":"submission_pred = submission_pred.flatten()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true,"_uuid":"8e72b1320d005ee1fe8a60e84b2de58b7abca859","scrolled":false},"cell_type":"code","source":"with open('submission.csv', 'w') as submission:\n    with open('../input/movie-ratings/movie-ratings/data-splits/data.eval.anon.id', 'r') as example_ids:\n        submission.write('example_id,label\\n')\n        for example_id, label in zip(example_ids, submission_pred):\n            submission.write('{},{}\\n'.format(example_id.strip(), int(label)))","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}