Getting Started with Image Classification with PyTorch/

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Serving a TensorFlow Model

Serving a TensorFlow Model

Learn to use a TensorFlow image classification model for inference.

We'll cover the following...

We can use the TensorFlow model for inference on our datasets.

Utility functions

First, we need to define the softmax utility function.

Python
import tensorflow as tf
import numpy as np
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
def softmax(vec):
    exponential = np.exp(vec)
    probabilities = exponential / np.sum(exponential)
    return probabilities
# test softmax function
dummy_vec = np.array([1.5630065, -0.24305986, -0.08382231, -0.4424621])
print('The output probabilities after softmax is:', softmax(dummy_vec))

Note: The softmax utility function will convert the model prediction into probabilities that sum to 1. ...