keras model with tf.contrib.losses.metric_learning.triplet_semihard_loss Assertion error

Question

I am using python 3 with anaconda, and trying to use a tf.contrib loss function with a Keras model.

The code is the following

from keras.layers import Dense, Flatten
from keras.optimizers import Adam
from keras.models import Sequential
from tensorflow.contrib.losses import metric_learning
model = Sequential()
model.add(Flatten(input_shape=input_shape))
model.add(Dense(50,  activation="relu"))
model.compile(loss=metric_learning.triplet_semihard_loss, optimizer=Adam())

I get the following error:

File "/home/user/.local/lib/python3.6/site-packages/keras/engine/training_utils.py", line 404, in weighted score_array = fn(y_true, y_pred) File "/home/user/anaconda3/envs/siamese/lib/python3.6/site-packages/tensorflow/contrib/losses/python/metric_learning/metric_loss_ops.py", line 179, in triplet_semihard_loss assert lshape.shape == 1 AssertionError

When I am using the same network with a keras loss function it works fine, I tried to wrap the tf loss function in a function like so

def func(y_true, y_pred): 
    import tensorflow as tf
    return tf.contrib.losses.metric_learning.triplet_semihard_loss(y_true, y_pred) 

And still getting the same error

What am I doing wrong here?

update: When changing the func to return the following

return K.categorical_crossentropy(y_true, y_pred)

everything works fine! But i cant get it to work with the specific tf loss function...

When i go into tf.contrib.losses.metric_learning.triplet_semihard_loss and remove this line of code: assert lshape.shape == 1 it runs fine

Thanks

Answer 1

The problem is that you pass wrong input to the loss function.

According to triplet_semihard_loss docstring you need to pass labels and embeddings.

So your code have to be:

def func(y, embeddings): 
    return tf.contrib.losses.metric_learning.triplet_semihard_loss(labels=y, embeddings=embeddings) 

And two more notes about network for embeddings:

1. Last dense layer has to be without activation

2. Don't forget to normalise output vector model.add(Lambda(lambda x: K.l2_normalize(x, axis=1)))