CODEWITHSUNDEEP
pythontensorflowmachine-learning
haemu
haemu

Reputation: 73

Custom loss function's results does not match with the inbuilt loss function's result

I have implemented a custom binary cross entropy loss function in tensorflow. To test this I had compared it with the inbuilt binary cross entropy loss function in Tensorflow. But, I got very different results in both cases. I am unable to understand this behaviour.

def custom_loss(eps,w1,w2):
    def loss(y_true, y_pred):
        ans = -1*(w1*y_true*tf.log(y_pred+eps) + w2*(1-y_true)*tf.log(y_pred+eps))
        return ans
    return loss

I had set eps to 1e-6, w1=1 and w2=1. The loss dropped to very small values when I used my implementation of the loss function. Whereas, there was a steady drop while using the inbuilt loss function in tensorflow.

Edit: Here are the outputs:

1: Using the custom implementation:

1/650 [..............................] - ETA: 46:37 - loss: 0.8810 - acc: 0.50

2/650 [..............................] - ETA: 41:27 - loss: 0.4405 - acc: 0.40

3/650 [..............................] - ETA: 39:38 - loss: 0.2937 - acc: 0.41

4/650 [..............................] - ETA: 38:44 - loss: 0.2203 - acc: 0.45

5/650 [..............................] - ETA: 38:13 - loss: 0.1762 - acc: 0.46

6/650 [..............................] - ETA: 37:47 - loss: 0.1468 - acc: 0.42

7/650 [..............................] - ETA: 37:29 - loss: 0.1259 - acc: 0

  1. Using the built in loss function with eps=1e-7.

1/650 [..............................] - ETA: 48:15 - loss: 2.4260 - acc: 0.31

2/650 [..............................] - ETA: 42:09 - loss: 3.1842 - acc: 0.46

3/650 [..............................] - ETA: 40:10 - loss: 3.4615 - acc: 0.47

4/650 [..............................] - ETA: 39:06 - loss: 3.9737 - acc: 0.45

5/650 [..............................] - ETA: 38:28 - loss: 4.5173 - acc: 0.47

6/650 [..............................] - ETA: 37:58 - loss: 5.1865 - acc: 0.45

7/650 [..............................] - ETA: 37:41 - loss: 5.8239 - acc: 0.43

8/650 [..............................] - ETA: 37:24 - loss: 5.6979 - acc: 0.46

9/650 [..............................] - ETA: 37:12 - loss: 5.5973 - acc: 0.47

The input is an image from the MURA dataset. To keep the test uniform same images are passed in both the tests.

Upvotes: 3

Views: 184

Answers (1)

Stewart_R
Stewart_R

Reputation: 14525

You have a slight error in your implementation.

You have:

ans = -1*(w1*y_true*tf.log(y_pred+eps) + w2*(1-y_true)*tf.log(y_pred + eps))

Whereas, I think you were aiming for:

ans = -1*(w1*y_true*tf.log(y_pred+eps) + w2*(1-y_true)*tf.log(1 - y_pred + eps))

Generally we also take the average of this loss so that makes our implementation:

def custom_loss(eps,w1,w2):
    def loss(y_true, y_pred):
        ans = -1*(w1*y_true*tf.log(y_pred+eps) + w2*(1-y_true)*tf.log(1-y_pred+eps))
        return tf.reduce_mean(ans)
    return loss

which we can now test against the out of the box implementation:

y_true = tf.constant([0.1, 0.2])
y_pred = tf.constant([0.11, 0.19])

custom_loss(y_true, y_pred)                         # == 0.41316
tf.keras.losses.binary_crossentropy(y_true, y_pred) # == 0.41317

and find that the results match to many decimal places (I can't account for the small difference - maybe a different epsilon value? - but I guess such a small difference is negligible)

Upvotes: 3

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