Inception score python

Inception score is a metric used to evaluate the quality of generated images. It measures the diversity and quality of generated images by calculating the entropy of class predictions of the Inception model trained on ImageNet dataset.

Here's the Python code to calculate Inception score using TensorFlow:

import tensorflow as tf
import numpy as np

def get_inception_score(images, splits=10):
    # Load Inception-v3 model
    inception_model = tf.keras.applications.InceptionV3(include_top=False, pooling='avg')

    # Preprocess images
    images = tf.keras.applications.inception_v3.preprocess_input(images)

    # Get predictions from Inception model
    preds = inception_model.predict(images)

    # Calculate entropy of class predictions
    scores = []
    for i in range(splits):
        part = preds[i * (preds.shape[0] // splits): (i + 1) * (preds.shape[0] // splits), :]
        kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, axis=0), 0)))
        kl = np.mean(np.sum(kl, axis=1))
        scores.append(np.exp(kl))

    # Calculate mean and standard deviation of scores
    is_mean, is_std = np.mean(scores), np.std(scores)

    return is_mean, is_std

You can use this function to calculate the Inception score for generated images. The `images` parameter should be a numpy array of shape `(num_images, height, width, channels)`. The `splits` parameter determines the number of splits used to calculate the score. A higher number of splits leads to more accurate scores, but also increases computation time.