How can Tensorflow be used with Estimators to inspect the titanic dataset using Python?

TensorflowPythonServer Side ProgrammingProgramming

The titanic dataset can be inspected using Tensorflow and estimators, by iterating through the features and converting the features into a list, and displaying it on the console. 

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. 

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.

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.

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. 

Example

print("The dataset is being inspected")
ds = make_input_fn(dftrain, y_train, batch_size=10)()
for feature_batch, label_batch in ds.take(1):
print('Some feature keys are:', list(feature_batch.keys()))
print()
print('A batch of class:', feature_batch['class'].numpy())
print()
print('A batch of Labels:', label_batch.numpy())

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

Output

The dataset is being inspected
Some feature keys are: ['sex', 'age', 'n_siblings_spouses', 'parch', 'fare', 'class', 'deck', 'embark_town', 'alone']
A batch of class: [b'First' b'First' b'First' b'Third' b'Third' b'Third' b'First' b'Third'
b'Second' b'Third']
A batch of Labels: [0 1 1 0 0 0 1 0 0 0]

Explanation

  • The dataset is inspected.
  • The feature keys, the labels, and the classes are displayed on the console.
  • It is done by iterating over a batch of dataset.
raja
Published on 12-Feb-2021 12:15:08
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