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()
The error shown in this code is as follows
Got inputs shapes: %s' % (input_shape))
ValueError: A Concatenate layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 128, 12, 192), (None, 32, 12, 192), (None, 32, 12, 102), (None, 64, 12, 192)]
I try looking on the internet for he error but did not find a solution