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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 deep neural network. It is highly scalable, and comes with many popular datasets. It uses GPU computation and automates the management of resources. It comes with multitude of machine learning libraries, and is well-supported and documented. The framework has the ability to run deep neural network models, train them, and create applications that predict relevant characteristics of the respective datasets.

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 multidimensional array or a list. They can be identified using three main attributes −

The aim behind a regression problem is to predict the output of a continuous or discrete variable, such as a price, probability, whether it would rain or not and so on.

The dataset we use is called the ‘Auto MPG’ dataset. It contains fuel efficiency of 1970s and 1980s automobiles. It includes attributes like weight, horsepower, displacement, and so on. With this, we need to predict the fuel efficiency of specific vehicles.

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.

Following is the code snippet −

hrspwr = np.array(train_features['Horsepower']) print("The data is being normalized") hrspwr_normalizer = preprocessing.Normalization(input_shape=[1,]) hrspwr_normalizer.adapt(hrspwr) hrspwr_model = tf.keras.Sequential([ hrspwr_normalizer, layers.Dense(units=1) ]) print("The statistical data sample ") hrspwr_model.summary() print("The predicted output ") hrspwr_model.predict(hrspwr[:7]) print("The model is being compiled : ") hrspwr_model.compile( optimizer=tf.optimizers.Adam(learning_rate=0.1), loss='mean_absolute_error')

**Code credit** − https://www.tensorflow.org/tutorials/keras/regression

The ‘MPG’ value from ‘Horsepower’ needs to be predicted.

A Keras model is trained by defining the architecture of the model.

The model defined here is a ‘sequential’ model. It indicates a sequence of steps.

First, the ‘horsepower’ input is normalized.

The linear transformation (y= mx + b) is applied which will produce an output with the help of dense layer ‘layers.Dense’.

The ‘horsepower’ normalization layer is created.

- Related Questions & Answers
- How can predictions be made on Auto MPG dataset using TensorFlow?
- How can data be normalized to predict the fuel efficiency with Auto MPG dataset using TensorFlow?
- How can data be cleaned to predict the fuel efficiency with Auto MPG dataset using TensorFlow?
- How can data be imported to predict the fuel efficiency with Auto MPG dataset (basic regression) using TensorFlow?
- How can data be split and inspected to predict the fuel efficiency with Auto MPG dataset using TensorFlow?
- How can model be fit to data with Auto MPG dataset using TensorFlow?
- How can a sequential model be built on Auto MPG dataset using TensorFlow?
- How can a DNN (deep neural network) model be built on Auto MPG dataset using TensorFlow?
- How can model be evaluated based on Auto MPG using TensorFlow?
- How can a DNN (deep neural network) model be used to predict MPG values on Auto MPG dataset using TensorFlow?
- How can a sequential model be built on Auto MPG using TensorFlow?
- How can Tensorflow be used with Estimators to display metadata about the dataset?
- How can Tensorflow be used to decode the predictions using Python?
- How can Tensorflow be used to check the predictions using Python?
- How can TensorFlow be used to make predictions for Fashion MNIST dataset in Python?

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