Custom loss function: perform a model.predict on the data in y_pred - tensorflow

Question

I am training a network to denoise images, for this I am using the CIFAR10 dataset. I am trying to generate a custom loss function so that the loss is mse / classification_accuracy.
Given that my network receives as input 32x32 (noisy) images and predicts 32x32 (denoised) images, I am assuming that y_pred and Y_true would be arrays of 32x32 images. Thus my custom loss functions looks like this:
def custom_loss():
def joint_optimized_loss(y_true, y_pred):
mse = K.mean(K.square(y_pred - y_true), axis=-1)
preds = classif_model.predict(y_pred)
correctPreds = 0
totPreds = 0
for pred in preds:
predictedClass = pred.index(max(pred))
totPreds += 1
if predictedClass == currentClass:
correctPreds += 1
classifAccuracy = correctPreds / totPreds
loss = mse / classifAccuracy
return loss
return joint_optimized_loss
myModel.compile(optimizer='adadelta', loss=custom_loss())
classif_model is a pre-trained model that classifies CIFAR10 images into one of the 10 classes. It receives an array of 32x32 images.
However when I run my code I get the following error:
Traceback (most recent call last):
File "myCode.py", line 94, in
myModel.compile(optimizer='adadelta', loss=custom_loss())
File "/home/rvidalma/anaconda2/envs/tensorUpdated/lib/python2.7/site-packages/keras/engine/training.py",
line 850, in compile
sample_weight, mask)
File "/home/rvidalma/anaconda2/envs/tensorUpdated/lib/python2.7/site-packages/keras/engine/training.py",
line 450, in weighted
score_array = fn(y_true, y_pred)
File "myCode.py", line 57, in joint_optimized_loss
preds = classif_model.predict(y_pred)
File "/home/rvidalma/anaconda2/envs/tensorUpdated/lib/python2.7/site-packages/keras/models.py",
line 913, in predict
return self.model.predict(x, batch_size=batch_size, verbose=verbose)
File "/home/rvidalma/anaconda2/envs/tensorUpdated/lib/python2.7/site-packages/keras/engine/training.py",
line 1713, in predict
verbose=verbose, steps=steps)
File "/home/rvidalma/anaconda2/envs/tensorUpdated/lib/python2.7/site-packages/keras/engine/training.py",
line 1260, in _predict_loop
batches = _make_batches(num_samples, batch_size)
File "/home/rvidalma/anaconda2/envs/tensorUpdated/lib/python2.7/site-packages/keras/engine/training.py",
line 374, in _make_batches
num_batches = int(np.ceil(size / float(batch_size)))
AttributeError: 'Dimension' object has no attribute 'ceil'
I think this has something to do with the fact that y_true and y_pred are both tensors that, before training, are empty thus classif_model.predict fails as it is expecting an array. However I am not sure on how to fix this...
I tried getting instead the value of y_pred using K.get_value(y_pred), but that gives me the following error:
tensorflow.python.framework.errors_impl.InvalidArgumentError: Shape
[-1,32,32,3] has negative dimensions [[Node: input_1 =
Placeholderdtype=DT_FLOAT, shape=[?,32,32,3],
_device="/job:localhost/replica:0/task:0/cpu:0"]]

Answer 1

You cannot use accuracy as a loss function, as it is not differentiable. This is why upper bounds on accuracy like the cross-entropy are used instead.
Additionally, the way you implemented accuracy is also non-symbolic, you should have used only functions in keras.backend to implement a loss for it to work properly.

Answer 2

I had almost same problem, and I tried this and it worked for me.
Instead of:
preds = classif_model.predict(y_pred)
try:
preds = classif_model(y_pred)
I am not sure about the reason but it is because when we use model.predict(y) it need batch_size and while compiling we don't have any, so we can not use model.predict(y).
Please correct me if this is wrong.