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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.

**Read More:**
What is TensorFlow and how Keras work with TensorFlow to create Neural Networks?

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[7] tf.keras.layers.DenseFeatures([age_column])(feature_batch).numpy()

Code credit −https://www.tensorflow.org/tutorials/estimator/linear

Results of a specific column are being inspected array([[61. ], [17. ], [19. ], [55.5], [26. ], [20. ], [24. ], [ 9. ], [31. ], [28. ]], dtype=float32)

- The result of a specific feature column is inspected.
- This is done with the help of the tf.keras.layers.DenseFeatures layer.

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