logistic_regression ( layers, y ) classifier = skflow. dnn ( X, , keep_prob = 0.5 ) return skflow. load_iris () def my_model ( X, y ): '''This is DNN with 10, 20, 10 hidden layers, and dropout of 0.5 probability.''' layers = skflow. Here’s an example of 3 layer deep neural network with 10, 20 and 10 hidden units in each layer respectively:įrom sklearn import datasets, metrics iris = datasets. Scikit Flow provides a set of high level model classes that you can use to easily integrate with your existing Scikit-learn pipeline code. It’s Scikit-learn compatible so you can also benefit from Scikit-learn features like GridSearch and Pipeline. You can start by using fit/predict and slide into TensorFlow APIs as you are getting comfortable. It helps smooth the transition from the Scikit-learn world of one-liner machine learning into the more open world of building different shapes of ML models. Scikit Flow is a simplified interface for TensorFlow, to get people started on predictive analytics and data mining. That’s where Scikit Flow comes in to help. However, there’s a large number of potential users, including some researchers, data scientists, and students who may be familiar with many data science concepts/algorithms already but who never get involved in deep learning research/applications, may found it really hard to start hacking. TensorFlow provides a good backbone for building different shapes of machine learning applications. TensorFlow is generally very straightforward to use in a sense that most of the researchers in the research area without experience of using this library could understand what’s happening behind the code blocks. Its flexible implementation and architecture enables you to focus on building the computation graph and deploy the model with little efforts on heterogeous platforms such as mobile devices, hundreds of machines, or thousands of computational devices. In November, 2015, Google open-sourced its numerical computation library called TensorFlow using data flow graphs.

Hidden markov model scikit learn