Accuracy metric fails with trivial custom Keras loss function

Question

I am playing around with custom loss functions on Keras models. My "custom" loss seems to fail (in terms of accuracy score), even though I am only using a wrapper that returns an original keras loss.

As a toy example, I am using the "Basic classification" Tensorflow/Keras tutorial that uses a simple NN on the fashion-MNIST data set and I am following the related Keras documentation and this SO post.

---

This is the model:

model = keras.Sequential([
    keras.layers.Flatten(input_shape=(28, 28)),
    keras.layers.Dense(128, activation='relu'),
    keras.layers.Dense(10, activation='softmax')
])

Now, if I leave the sparse_categorical_crossentropy as a string argument in compile() function, the training results to a ~ 87% accuracy which is fine:

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(train_images, train_labels, epochs=10)
test_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)
print('\nTest accuracy:', test_acc)

But when I just create a trivial wrapper function to call keras' cross-entropy I get a ~ 10% accuracy both on training and test sets:

from tensorflow.keras import losses

def my_loss(y_true, y_pred):
  return losses.sparse_categorical_crossentropy(y_true, y_pred)

model.compile(optimizer='adam',
              loss=my_loss,
              metrics=['accuracy'])

Epoch 1/10 60000/60000 [==============================] - 3s 51us/sample - loss: 0.5030 - accuracy: 0.1032
Epoch 2/10 60000/60000 [==============================] - 3s 45us/sample - loss: 0.3766 - accuracy: 0.1035

...

Test accuracy: 0.1013

---

By plotting a few images and checking their classified labels, it doesn't look like the results differ in each case, but accuracies printed are very different. So, is it the case that the default metrics do not play nicely with custom losses? Can it be the case that what I see is the error rather than the accuracy? Am I missing something from the documentation?

Edit: The values of the loss functions in both cases end up roughly the same, so training indeed takes place. The accuracy is the point of failure.

Answer 1

Here's the reason:
When you use inbuilt loss and use loss='sparse_categorical_crossentropy' at that time accuracy metric used is sparse_categorical_accuracy But when you use custom loss function at that time accuracy metric used is categorical_accuracy .

Example:

model.compile(optimizer='adam',
              loss=losses.sparse_categorical_crossentropy,
              metrics=['categorical_accuracy', 'sparse_categorical_accuracy'])
model.fit(train_images, train_labels, epochs=1)
'''
Train on 60000 samples
60000/60000 [==============================] - 5s 86us/sample - loss: 0.4955 - categorical_accuracy: 0.1045 - sparse_categorical_accuracy: 0.8255
'''


model.compile(optimizer='adam',
              loss=my_loss,
              metrics=['accuracy', 'sparse_categorical_accuracy'])
model.fit(train_images, train_labels, epochs=1)

'''
Train on 60000 samples
60000/60000 [==============================] - 5s 87us/sample - loss: 0.4956 - acc: 0.1043 - sparse_categorical_accuracy: 0.8256
'''