Unsupervised loss function in Keras - machine-learning

Question

Is there any way in Keras to specify a loss function which does not need to be passed target data?
I attempted to specify a loss function which omitted the y_true parameter like so:
def custom_loss(y_pred):
But I got the following error:
Traceback (most recent call last):
File "siamese.py", line 234, in <module>
model.compile(loss=custom_loss,optimizer=Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0))
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 911, in compile
sample_weight, mask)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 436, in weighted
score_array = fn(y_true, y_pred)
TypeError: custom_loss() takes exactly 1 argument (2 given)
I then tried to call fit() without specifying any target data:
model.fit(x=[x_train,x_train_warped, affines], batch_size = bs, epochs=1)
But it looks like not passing any target data causes an error:
Traceback (most recent call last):
File "siamese.py", line 264, in <module>
model.fit(x=[x_train,x_train_warped, affines], batch_size = bs, epochs=1)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1435, in fit
batch_size=batch_size)
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 1322, in _standardize_user_data
in zip(y, sample_weights, class_weights, self._feed_sample_weight_modes)]
File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 577, in _standardize_weights
return np.ones((y.shape[0],), dtype=K.floatx())
AttributeError: 'NoneType' object has no attribute 'shape'
I could manually create dummy data in the same shape as my neural net's output but this seems extremely messy. Is there a simple way to specify an unsupervised loss function in Keras that I am missing?

Answer 1

I think the best solution is customizing the training instead of using the model.fit method.
The complete walkthrough is published in the Tensorflow tutorials page.

Answer 2

Write your loss function as if it had two arguments:
y_true
y_pred
If you don't have y_true, that's fine, you don't need to use it inside to compute the loss, but leave a placeholder in your function prototype, so keras wouldn't complain.
def custom_loss(y_true, y_pred):
# do things with y_pred
return loss
Adding custom arguments
You may also need to use another parameter like margin inside your loss function, even then your custom function should only take in those two arguments. But there is a workaround, use lambda functions
def custom_loss(y_pred, margin):
# do things with y_pred
return loss
but use it like
model.compile(loss=lambda y_true, y_pred: custom_loss(y_pred, margin), ...)