Train-inception.py

from fr_utils import *
from inception_blocks_v2 import *


def triplet_loss(y_true, y_pred, alpha=0.3):
    """
    Implementation of the triplet loss as defined by formula (3)

    Arguments:
    y_pred -- python list containing three objects:
            anchor -- the encodings for the anchor images, of shape (None, 128)
            positive -- the encodings for the positive images, of shape (None, 128)
            negative -- the encodings for the negative images, of shape (None, 128)

    Returns:
    loss -- real number, value of the loss
    """

    anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]

    # Step 1: Compute the (encoding) distance between the anchor and the positive, you will need to sum over axis=-1
    pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), axis=-1)
    # Step 2: Compute the (encoding) distance between the anchor and the negative, you will need to sum over axis=-1
    neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), axis=-1)
    # Step 3: subtract the two previous distances and add alpha.
    basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), alpha)
    # Step 4: Take the maximum of basic_loss and 0.0. Sum over the training examples.
    loss = tf.reduce_sum(tf.maximum(basic_loss, 0.0))

    return loss

def main():
    FRmodel = faceRecoModel(input_shape=(3, 96, 96))
    FRmodel.compile(optimizer='adam', loss=triplet_loss, metrics=['accuracy'])
    FRmodel.save('face-rec_Google.h5')
    print_summary(model)

main()