{"id":15159310,"url":"https://github.com/ashwinpn/binary-classification-using-tensorflow","last_synced_at":"2026-01-19T23:02:46.584Z","repository":{"id":201600051,"uuid":"243881839","full_name":"ashwinpn/Binary-Classification-using-Tensorflow","owner":"ashwinpn","description":null,"archived":false,"fork":false,"pushed_at":"2020-02-29T03:01:46.000Z","size":15,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-02-13T18:49:30.231Z","etag":null,"topics":["deep-learning","machine-learning","multilayer-perceptron-network","neural-network","research","statistical-analysis","tensorflow","tensorflow-examples","tensorflow-experiments","tensorflow-tutorials"],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ashwinpn.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2020-02-29T01:00:13.000Z","updated_at":"2020-03-02T15:47:29.000Z","dependencies_parsed_at":null,"dependency_job_id":"342d9df4-af00-4b61-b85c-5238a6bfb542","html_url":"https://github.com/ashwinpn/Binary-Classification-using-Tensorflow","commit_stats":null,"previous_names":["ashwinpn/binary-classification-using-tensorflow"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ashwinpn%2FBinary-Classification-using-Tensorflow","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ashwinpn%2FBinary-Classification-using-Tensorflow/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ashwinpn%2FBinary-Classification-using-Tensorflow/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ashwinpn%2FBinary-Classification-using-Tensorflow/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ashwinpn","download_url":"https://codeload.github.com/ashwinpn/Binary-Classification-using-Tensorflow/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247687206,"owners_count":20979419,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","machine-learning","multilayer-perceptron-network","neural-network","research","statistical-analysis","tensorflow","tensorflow-examples","tensorflow-experiments","tensorflow-tutorials"],"created_at":"2024-09-26T21:03:48.947Z","updated_at":"2026-01-19T23:02:46.577Z","avatar_url":"https://github.com/ashwinpn.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Binary-Classification-using-Tensorflow\n\n## Terminology\n\n* Batch Size\u003c/br\u003e\n  It is a hyperparameter which defines the number of samples the algorithm would work through before the internal parameters are updated.\n* Epoch\u003c/br\u003e\n  Number of epochs reflects the number of iterations the learning algorithm will undergo while working through the entire dataset.\n* Perceptrons\u003c/br\u003e\n  A machine learning construct usually used for classification tasks.\n* Stochastic Gradient Descent\u003c/br\u003e\n  When the batch_size = 1.\n* Batch Gradient Descent\u003c/br\u003e\n  When epoch = 1.\n\n## Important considerations\n\n* What should be the batch_size?\n* Gradient descent vs stochastic gradient descent vs mini-batch gradient descent\n\n## Common questions / issues\n* Can we use np.mean instead of tf.reduce_mean? Is there a difference?\u003c/br\u003e\n  The functionality is the same. However, tf.reduce_mean would work only within a session.\n  \n  Try this:\n   \n  ```\n  c = np.array([[3.,4], [5.,6], [6.,7]])\n  print(np.mean(c,1))\n\n  Mean = tf.reduce_mean(c,1)\n  with tf.Session() as sess:\n  result = sess.run(Mean)\n  print(result)\n    \n  ```\n  Output:\n  \n  ```\n  [ 3.5  5.5  6.5]\n  [ 3.5  5.5  6.5]\n  ```\n  \n## Results\n  | Batch size |    Cost       | Accuracy|\n  |------------|---------------|---------|\n  |    32      |  0.079790163  | 0.97696 |\n  |    100     |  0.073474707  | 0.98096 |\n  \n  \n## Checklist\n\n- [x] Classification (normally distributed clusters)\n- [ ] Plot learning curve of model vaildation error against training time\n- [ ] Tune batch size and learning rate \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fashwinpn%2Fbinary-classification-using-tensorflow","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fashwinpn%2Fbinary-classification-using-tensorflow","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fashwinpn%2Fbinary-classification-using-tensorflow/lists"}