python-tensorflowHow do I implement a regression example with Python and TensorFlow?

To implement a regression example with Python and TensorFlow, the following steps can be taken:

1. Install TensorFlow: pip install tensorflow
2. Import the TensorFlow library: import tensorflow as tf
3. Create the feature columns and assign them to a variable:

feature_columns = [tf.feature_column.numeric_column('x', shape=[1])]

4. Create the estimator model:

estimator = tf.estimator.LinearRegressor(feature_columns=feature_columns)

5. Create the input data:

x_train = np.array([1., 2., 3., 4.])
y_train = np.array([0., -1., -2., -3.])

6. Create the input function:

input_fn = tf.estimator.inputs.numpy_input_fn(
    {'x':x_train}, y_train, batch_size=4, num_epochs=None, shuffle=True)

7. Train the model:

estimator.train(input_fn=input_fn, steps=1000)

The output of the code will be the trained model.

Helpful links

• https://www.tensorflow.org/tutorials/estimators/linear
• https://www.tensorflow.org/api_docs/python/tf/estimator/LinearRegressor

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