# Face recognition triplet loss - loss = tf.Tensor(527.2598, shape=(), dtype=float32) not matching expected output of loss 527.2598

Need some help.

My code returns:

loss = tf.Tensor(527.2598, shape=(), dtype=float32)

but I think this is not correct, as I am not getting the “all tests passed” message.

My code:

``````  # Step 1: Compute the (encoding) distance between the anchor and the positive

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

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 = 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), axis=None)
``````

Here is the output of the next section

# BEGIN UNIT TEST

tf.random.set_seed(1)

y_true = (None, None, None) # It is not used

y_pred = (tf.keras.backend.random_normal([3, 128], mean=6, stddev=0.1, seed = 1),

``````      tf.keras.backend.random_normal([3, 128], mean=1, stddev=1, seed = 1),

tf.keras.backend.random_normal([3, 128], mean=3, stddev=4, seed = 1))
``````

loss = triplet_loss(y_true, y_pred)

assert type(loss) == tf.python.framework.ops.EagerTensor, “Use tensorflow functions”

print("loss = " + str(loss))

y_pred_perfect = ([1., 1.], [1., 1.], [1., 1.,])

loss = triplet_loss(y_true, y_pred_perfect, 5)

assert loss == 5, “Wrong value. Did you add the alpha to basic_loss?”

y_pred_perfect = ([1., 1.],[1., 1.], [0., 0.,])

loss = triplet_loss(y_true, y_pred_perfect, 3)

assert loss == 1., “Wrong value. Check that pos_dist = 0 and neg_dist = 2 in this example”

y_pred_perfect = ([1., 1.],[0., 0.], [1., 1.,])

loss = triplet_loss(y_true, y_pred_perfect, 0)

assert loss == 2., “Wrong value. Check that pos_dist = 2 and neg_dist = 0 in this example”

y_pred_perfect = ([0., 0.],[0., 0.], [0., 0.,])

loss = triplet_loss(y_true, y_pred_perfect, -2)

assert loss == 0, “Wrong value. Are you taking the maximum between basic_loss and 0?”

y_pred_perfect = ([[1., 0.], [1., 0.]],[[1., 0.], [1., 0.]], [[0., 1.], [0., 1.]])

loss = triplet_loss(y_true, y_pred_perfect, 3)

assert loss == 2., “Wrong value. Are you applying tf.reduce_sum to get the loss?”

y_pred_perfect = ([[1., 1.], [2., 0.]], [[0., 3.], [1., 1.]], [[1., 0.], [0., 1.,]])

loss = triplet_loss(y_true, y_pred_perfect, 1)

if (loss == 4.):

``````raise Exception('Perhaps you are not using axis=-1 in reduce_sum?')
``````

assert loss == 5, “Wrong value. Check your implementation”

# END UNIT TEST

loss = tf.Tensor(527.2598, shape=(), dtype=float32)

Expected Output:
loss 527.2598

You posted the actual test code. If you had bothered to actually read it, you’d see that there is no “All tests pass” message in that set of tests. If you get to the end without an exception being thrown, then you are good to go.

Maybe you think you are saving yourself time by not reading things carefully, but it’s clear you are in a hurry here.

Very much in a hurry. There are many things dependent on me finishing this course, and I do not have much time to do so. Thank you for understanding and thank you for the assistance along the way.