All the feature columns of the dataset can be viewed using Tensorflow and Estimator with the help of the ‘DenseFeatures’ method. This data is converted into Numpy array so that it can be viewed on the console.
We will use the Keras Sequential API, which is helpful in building a sequential model that is used to work with a plain stack of layers, where every layer has exactly one input tensor and one output tensor.
A neural network that contains at least one layer is known as a convolutional layer. We can use the Convolutional Neural Network to build learning model.
We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.
Estimators use feature columns to describe how the model would interpret the raw input features. An Estimator expects a vector of numeric inputs, and feature columns will help describe how the model should convert every feature in the dataset. Selecting and using the right set of feature columns is essential to learning an effective model. A feature column can be one of the raw inputs in the original features dict, or a new column created using transformations that are defined on one or multiple base columns.
print("Viewing all feature column transformation together") tf.keras.layers.DenseFeatures(feature_columns)(example).numpy()
Viewing all feature column transformation together array([[22. , 1. , 0. , 1. , 0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 1. , 0. , 0. , 0. , 7.25, 1. , 0. , 0. , 0. , 0. , 0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. , 1. , 0. ]], dtype=float32)