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Loss function in Keras does not match analogical function


By : Jeff Chiu
Date : September 15 2020, 06:00 AM
I wish this help you It's because the model.predict output shape is not same with Y_valid. If you get the transpose of the predictions it will give you almost same loss.
code :
>>> Y_valid.shape                                                          
(2000,)
>>> preds.shape                                                            
(2000, 1)
>>> np.abs(Y_valid - np.transpose(preds)).mean()


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


By : user2888981
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further 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. , You have a slight error in your implementation.
You have:
code :
ans = -1*(w1*y_true*tf.log(y_pred+eps) + w2*(1-y_true)*tf.log(y_pred + eps))
ans = -1*(w1*y_true*tf.log(y_pred+eps) + w2*(1-y_true)*tf.log(1 - y_pred + eps))
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
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

keras sparse_categorical_crossentropy loss function output shape didn't match


By : Thato Sello
Date : March 29 2020, 07:55 AM
hope this fix your issue loss='sparse_categorical_crossentropy' is not meant for one-hot encodings but for integer targets. You probably need a "Dense(..." as the output layer and use y_train directly.

keras "unknown loss function" error after defining custom loss function


By : J. Ouellet
Date : March 29 2020, 07:55 AM
Hope that helps In Keras we have to pass the custom functions in the load_model function:

Keras Custom Binary Cross Entropy Loss Function. Get NaN as output for loss


By : Oscar Lastera Sanche
Date : March 29 2020, 07:55 AM
I hope this helps . A naive implementation of Binary Cross Entropy will suffer numerical problem on 0 output or larger than one output, eg log(0) -> NaN. The formula you posted is reformulated to ensure stability and avoid underflow. The following deduction is from tf.nn.sigmoid_cross_entropy_with_logits.
code :
z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))
= z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))
= z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))
= z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))
= (1 - z) * x + log(1 + exp(-x))
= x - x * z + log(1 + exp(-x))
x - x * z + log(1 + exp(-x))
= log(exp(x)) - x * z + log(1 + exp(-x))
= - x * z + log(1 + exp(x))
max(x, 0) - x * z + log(1 + exp(-abs(x)))

Loading model with custom loss function: ValueError: 'Unknown loss function' in keras


By : Ash
Date : March 29 2020, 07:55 AM
this will help Compiling the model then saving. Then while loading model that time getting error. , Use custom_objects when loading your model:
code :
def def triplet_loss(y_true, y_pred, alpha = 0.3):
    anchor, positive, negative = y_pred[0], y_pred[1], y_pred[2]
    pos_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, positive)), axis=-1)
    neg_dist = tf.reduce_sum(tf.square(tf.subtract(anchor, negative)), axis=-1)
    basic_loss = tf.add(tf.subtract(pos_dist, neg_dist), alpha)
    loss = tf.reduce_sum(tf.maximum(basic_loss, 0.0))

    return loss

FRmodel = load_model('model.h5',custom_objects={'triplet_loss':triplet_loss})
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