A specific column in the titanic dataset can be inspected by accessing the column-to-be-inspected and using the ‘DenseFeatures’ and converting it into a Numpy array.
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.
An Estimator is TensorFlow's high-level representation of a complete model. It is designed for easy scaling and asynchronous training. We will train a logistic regression model using the tf.estimator API. The model is used as a baseline for other algorithms. We use the titanic dataset with the goal of predicting passenger survival, given characteristics such as gender, age, class, etc.
print("Results of a specific column are being inspected") age_column = feature_columns tf.keras.layers.DenseFeatures([age_column])(feature_batch).numpy()
Results of a specific column are being inspected array([[61. ], [17. ], [19. ], [55.5], [26. ], [20. ], [24. ], [ 9. ], [31. ], [28. ]], dtype=float32)