{"id":19865207,"url":"https://github.com/angelhtml/tensorflow_tutorial","last_synced_at":"2026-03-06T00:30:49.276Z","repository":{"id":110677733,"uuid":"538646508","full_name":"angelhtml/Tensorflow_tutorial","owner":"angelhtml","description":null,"archived":false,"fork":false,"pushed_at":"2023-06-18T18:50:50.000Z","size":8,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-01T00:32:48.759Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"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/angelhtml.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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-09-19T18:37:50.000Z","updated_at":"2022-09-19T18:37:50.000Z","dependencies_parsed_at":"2025-01-11T15:46:05.882Z","dependency_job_id":null,"html_url":"https://github.com/angelhtml/Tensorflow_tutorial","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/angelhtml/Tensorflow_tutorial","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/angelhtml%2FTensorflow_tutorial","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/angelhtml%2FTensorflow_tutorial/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/angelhtml%2FTensorflow_tutorial/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/angelhtml%2FTensorflow_tutorial/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/angelhtml","download_url":"https://codeload.github.com/angelhtml/Tensorflow_tutorial/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/angelhtml%2FTensorflow_tutorial/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":30156285,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-03-05T22:39:40.138Z","status":"ssl_error","status_checked_at":"2026-03-05T22:39:24.771Z","response_time":93,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.6:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":[],"created_at":"2024-11-12T15:21:40.416Z","updated_at":"2026-03-06T00:30:49.257Z","avatar_url":"https://github.com/angelhtml.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\u003ch1\u003eTensorflow tutorial\u003c/h1\u003e              \n\n\u003ch2\u003eCreate a tensor of n-dimension 📜\u003c/h2\u003e\nYou begin with the creation of a tensor with one dimension, namely a scalar\u003cbr/\u003e\nTo create a tensor, you can use \u003cb\u003etf.constant()\u003c/b\u003e as shown in the below TensorFlow tensor shape example:   \n\n``` python\n\ntf.constant(value, dtype, name = \"\")\ntf.constant(value, shape=(col, row) )\n'''\n- `value`: Value of n dimension to define the tensor. Optional\n- `dtype`: Define the type of data:       \n    - `tf.float32`: Float variable    \n    - `tf.int16`: Integer variable\n    - `tf.string`: String variable \n    - `tf.bool`: Boolean variable\n- \"name\": Name of the tensor. Optional. By default, `Const_1:0`\n'''\n\n```\n\nBelow, a float tensor is converted to integer using you use the method cast (\u003cdtype: 'float32'\u003e)\n\n``` python\n\n# Change type of data\ntype_float = tf.constant(3.123456789, tf.float32)\ntype_int = tf.cast(type_float, dtype=tf.int32)\nprint(type_float.dtype)\nprint(type_int.dtype)\n\n```\n\n\u003ch2\u003eCreating operator ➗\u003c/h2\u003e\nSome Useful TensorFlow operators\nYou know how to create a tensor with TensorFlow. It is time to learn how to perform mathematical operations.\nTensorFlow contains all the basic operations. You can begin with a simple one. You will use TensorFlow method to compute the square of a number. This operation is straightforward because only one argument is required to construct the tensor.\nThe square of a number is constructed with \u003cb\u003etf.sqrt(x)\u003c/b\u003e with x as a floating number\n\n``` python\n\nx = tf.constant([4.0], dtype = tf.float32)\nprint(tf.sqrt(x))\n\n```\n\nThe output returned a tensor object and not the result of the square of 4. In the example, you print the definition of the tensor and not the actual evaluation of the operation. In the next section, you will learn how TensorFlow works to execute the operations.\nFollowing is a list of commonly used operations. The idea is the same. Each operation requires one or more arguments\n\u003cul\u003e\n\u003cli\u003etf.add(a, b)\u003c/li\u003e\n\u003cli\u003etf.substract(a, b)\u003c/li\u003e\n\u003cli\u003etf.multiply(a, b)\u003c/li\u003e\n\u003cli\u003etf.div(a, b)\u003c/li\u003e\n\u003cli\u003etf.pow(a, b)\u003c/li\u003e\n\u003cli\u003etf.exp(a)\u003c/li\u003e\n\u003cli\u003etf.sqrt(a)\u003c/li\u003e\n\u003c/ul\u003e\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fangelhtml%2Ftensorflow_tutorial","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fangelhtml%2Ftensorflow_tutorial","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fangelhtml%2Ftensorflow_tutorial/lists"}