How can Tensorflow be used to predict a score for stackoverflow question dataset on every label using Python?

Tensorflow is a machine learning framework that is provided by Google. It is an open-source framework used in conjunction with Python to implement algorithms, deep learning applications, and much more. It is used in research and for production purposes. It has optimization techniques that help in performing complicated mathematical operations quickly. This is because it uses NumPy and multi-dimensional arrays. These multi-dimensional arrays are also known as ‘tensors’. The framework supports working with a deep neural network.

The ‘tensorflow’ package can be installed on Windows using the below line of code −

pip install tensorflow

Tensor is a data structure used in TensorFlow. It helps connect edges in a flow diagram. This flow diagram is known as the ‘Data flow graph’. Tensors are nothing but a multidimensional array or a list.

They can be identified using three main attributes −

  • Rank − It tells about the dimensionality of the tensor. It can be understood as the order of the tensor or the number of dimensions in the tensor that has been defined.

  • Type − It tells about the data type associated with the elements of the Tensor. It can be a one dimensional, two dimensional, or n-dimensional tensor.

  • Shape − It is the number of rows and columns together.

We are using 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.


Following is the code snippet −

print("Predicting a score for every label")
def get_string_labels(predicted_scores_batch):
   predicted_int_labels = tf.argmax(predicted_scores_batch, axis=1)
   predicted_labels = tf.gather(raw_train_ds.class_names, predicted_int_labels)
   return predicted_labels

Code credit −


Predicting a score for every label


  • The ‘predict’ method is called on the model that was built.

  • This will take a raw string as input and predict a score for every label.

  • The function finds the label that has the maximum score.

  • This data is displayed on the console.