{"action":"published","release":{"url":"https://api.github.com/repos/transcendent-ai-labs/DynaML/releases/10017668","assets_url":"https://api.github.com/repos/transcendent-ai-labs/DynaML/releases/10017668/assets","upload_url":"https://uploads.github.com/repos/transcendent-ai-labs/DynaML/releases/10017668/assets{?name,label}","html_url":"https://github.com/transcendent-ai-labs/DynaML/releases/tag/v1.5.3-beta.1","id":10017668,"tag_name":"v1.5.3-beta.1","target_commitish":"master","name":"1.5.3-beta.1","draft":false,"author":{"login":"mandar2812","id":1389553,"avatar_url":"https://avatars3.githubusercontent.com/u/1389553?v=4","gravatar_id":"","url":"https://api.github.com/users/mandar2812","html_url":"https://github.com/mandar2812","followers_url":"https://api.github.com/users/mandar2812/followers","following_url":"https://api.github.com/users/mandar2812/following{/other_user}","gists_url":"https://api.github.com/users/mandar2812/gists{/gist_id}","starred_url":"https://api.github.com/users/mandar2812/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mandar2812/subscriptions","organizations_url":"https://api.github.com/users/mandar2812/orgs","repos_url":"https://api.github.com/users/mandar2812/repos","events_url":"https://api.github.com/users/mandar2812/events{/privacy}","received_events_url":"https://api.github.com/users/mandar2812/received_events","type":"User","site_admin":false},"prerelease":true,"created_at":"2018-03-09T14:35:14Z","published_at":"2018-03-09T14:58:37Z","assets":[],"tarball_url":"https://api.github.com/repos/transcendent-ai-labs/DynaML/tarball/v1.5.3-beta.1","zipball_url":"https://api.github.com/repos/transcendent-ai-labs/DynaML/zipball/v1.5.3-beta.1","body":"## Additions\r\n\r\n### Tensorflow Utilities\r\n\r\n**Package** [`dynaml.tensorflow`](https://github.com/transcendent-ai-labs/DynaML/blob/master/dynaml-core/src/main/scala-2.11/io/github/mandar2812/dynaml/tensorflow/package.scala#L411)\r\n \r\n - The `dtfpipe` object is created to house data pipelines and workflows around tensorflow primitives.\r\n\r\n   * `dtfpipe.gaussian_standardization` performs Gaussian Scaling of the data and returns `GaussianScalerTF` objects, one each for the input and output data.\r\n   *  `dtfpipe.minmax_standardization` performs `[0, 1]` scaling of the features and ouputs, returning `MinMaxScalerTF` objects.\r\n\r\n**Usage**\r\n\r\n```scala\r\nimport io.github.mandar2812.dynaml.tensorflow._\r\nimport org.platanios.tensorflow.api._\r\n\r\nval (inputs, outputs): (Tensor, Tensor) = ...\r\nval (scaledData, (features_scaler, targets_scaler)) = \r\n  dtfpipe. dtfpipe.gaussian_standardization(inputs, outputs)\r\n```\r\n\r\n**Package** `dynaml.tensorflow.utils`\r\n - Added [`GaussianScalerTF`](https://github.com/transcendent-ai-labs/DynaML/blob/master/dynaml-core/src/main/scala-2.11/io/github/mandar2812/dynaml/tensorflow/utils/GaussianScalerTF.scala) and [`MinMaxScalerTF`](https://github.com/transcendent-ai-labs/DynaML/blob/master/dynaml-core/src/main/scala-2.11/io/github/mandar2812/dynaml/tensorflow/utils/MinMaxScalerTF.scala), to enable scaling-rescaling of tensorflow data sets.\r\n\r\n**Package** `dynaml.tensorflow.layers`\r\n\r\n#### Dynamical Systems: Continuous Time RNN\r\n\r\nThe [continuous time recurrent neural network](https://www.sciencedirect.com/science/article/pii/S089360800580125X); CTRNN, when discretised for a finite time horizon is represented as the computational layer [`FiniteTimeCTRNN`](https://github.com/transcendent-ai-labs/DynaML/blob/master/dynaml-core/src/main/scala-2.11/io/github/mandar2812/dynaml/tensorflow/layers/FiniteHorizonCTRNN.scala).\r\n\r\n\r\n\r\n**Package** `dynaml.tensorflow.learn`\r\n\r\n - Added [`MVTimeSeriesLoss`](https://github.com/transcendent-ai-labs/DynaML/blob/master/dynaml-core/src/main/scala-2.11/io/github/mandar2812/dynaml/tensorflow/learn/MVTimeSeriesLoss.scala) which helps quantify the average L2 loss over a finite time slice of a multivariate time series."}}
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