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- import facenet
- import numpy as np
- import tensorflow as tf
- FLAGS = tf.app.flags.FLAGS
- tf.app.flags.DEFINE_integer('people_per_batch', 45,
- """Number of people per batch.""")
- tf.app.flags.DEFINE_integer('alpha', 0.2,
- """Positive to negative triplet distance margin.""")
- embeddings = np.zeros((1800,128))
- np.random.seed(123)
- for ix in range(embeddings.shape[0]):
- for jx in range(embeddings.shape[1]):
- rnd = 1.0*np.random.randint(1,2**32)/2**32
- embeddings[ix][jx] = rnd
- emb_array = embeddings
- image_data = np.zeros((1800,96,96,3))
- num_per_class = [40 for i in range(45)]
- np.random.seed(123)
- apn, nrof_random_negs, nrof_triplets = facenet.select_triplets(emb_array, num_per_class, image_data)
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