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Losses#

Collection of Ivy loss functions.

ivy.binary_cross_entropy(true, pred, /, *, from_logits=False, epsilon=0.0, reduction='mean', pos_weight=None, axis=None, out=None)[source]#

Compute the binary cross entropy loss.

Parameters:

  • true (Union[Array, NativeArray]) – input array containing true labels.

  • pred (Union[Array, NativeArray]) – input array containing Predicted labels.

  • from_logits (bool, default: False) – Whether pred is expected to be a logits tensor. By default, we assume that pred encodes a probability distribution.

  • epsilon (float, default: 0.0) – a float in [0.0, 1.0] specifying the amount of smoothing when calculating the loss. If epsilon is 0, no smoothing will be applied. Default: 0.

  • reduction (str, default: 'mean') – 'none': No reduction will be applied to the output. 'mean': The output will be averaged. 'sum': The output will be summed. Default: 'none'.

  • pos_weight (Optional[Union[Array, NativeArray]], default: None) – a weight for positive examples. Must be an array with length equal to the number of classes.

  • axis (Optional[int], default: None) – Axis along which to compute crossentropy.

  • out (Optional[Array], default: None) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Array

Returns:

ret – The binary cross entropy between the given distributions.

Examples

With ivy.Array input:

>>> x = ivy.array([0, 1, 0, 0])
>>> y = ivy.array([0.2, 0.8, 0.3, 0.8])
>>> z = ivy.binary_cross_entropy(x, y)
>>> print(z)
ivy.array(0.60309976)

>>> x = ivy.array([[0, 1, 1, 0]])
>>> y = ivy.array([[2.6, 6.2, 3.7, 5.3]])
>>> z = ivy.binary_cross_entropy(x, y, reduction='mean')
>>> print(z)
ivy.array(7.6666193)

>>> x = ivy.array([[0, 1, 1, 0]])
>>> y = ivy.array([[2.6, 6.2, 3.7, 5.3]])
>>> pos_weight = ivy.array([1, 2, 3, 4])
>>> z = ivy.binary_cross_entropy(x, y, pos_weight=pos_weight, from_logits=True)
ivy.array(2.01348412)

>>> x = ivy.array([[0, 1, 1, 0]])
>>> y = ivy.array([[2.6, 6.2, 3.7, 5.3]])
>>> pos_weight = ivy.array([1, 2, 3, 4])
>>> z = ivy.binary_cross_entropy(x, y, pos_weight=pos_weight, from_logits=True, reduction='sum', axis=1)
>>> print(z)
ivy.array([8.05393649])

>>> x = ivy.array([[0, 1, 1, 0]])
>>> y = ivy.array([[2.6, 6.2, 3.7, 5.3]])
>>> z = ivy.binary_cross_entropy(x, y, reduction='none', epsilon=0.5)
>>> print(z)
ivy.array([[11.49992943,  3.83330965,  3.83330965, 11.49992943]])

>>> x = ivy.array([[0, 1, 0, 0]])
>>> y = ivy.array([[0.6, 0.2, 0.7, 0.3]])
>>> z = ivy.binary_cross_entropy(x, y, epsilon=1e-3)
>>> print(z)
ivy.array(1.02136981)

With ivy.NativeArray input:

>>> x = ivy.native_array([0, 1, 0, 1])
>>> y = ivy.native_array([0.2, 0.7, 0.2, 0.6])
>>> z = ivy.binary_cross_entropy(x, y)
>>> print(z)
ivy.array(0.32844672)

With a mix of ivy.Array and ivy.NativeArray inputs:

>>> x = ivy.array([0, 0, 1, 1])
>>> y = ivy.native_array([0.1, 0.2, 0.8, 0.6])
>>> z = ivy.binary_cross_entropy(x, y)
>>> print(z)
ivy.array(0.26561815)

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([1, 0, 0]),b=ivy.array([0, 0, 1]))
>>> y = ivy.Container(a=ivy.array([0.6, 0.2, 0.3]),b=ivy.array([0.8, 0.2, 0.2]))
>>> z = ivy.binary_cross_entropy(x, y)
>>> print(z)
{
    a: ivy.array(0.36354783),
    b: ivy.array(1.14733934)
}

With a mix of ivy.Array and ivy.Container inputs:

>>> x = ivy.array([1 , 1, 0])
>>> y = ivy.Container(a=ivy.array([0.7, 0.8, 0.2]))
>>> z = ivy.binary_cross_entropy(x, y)
>>> print(z)
{
   a: ivy.array(0.26765382)
}

Instance Method Examples

Using ivy.Array instance method:

>>> x = ivy.array([1, 0, 0, 0])
>>> y = ivy.array([0.8, 0.2, 0.2, 0.2])
>>> z = ivy.binary_cross_entropy(x, y)
>>> print(z)
ivy.array(0.22314337)
ivy.cross_entropy(true, pred, /, *, axis=None, epsilon=1e-07, reduction='mean', out=None)[source]#

Compute cross-entropy between predicted and true discrete distributions.

Parameters:

  • true (Union[Array, NativeArray]) – input array containing true labels.

  • pred (Union[Array, NativeArray]) – input array containing the predicted labels.

  • axis (Optional[int], default: None) – the axis along which to compute the cross-entropy. If axis is -1, the cross-entropy will be computed along the last dimension. Default: -1.

  • epsilon (float, default: 1e-07) – a float in [0.0, 1.0] specifying the amount of smoothing when calculating the loss. If epsilon is 0, no smoothing will be applied. Default: 1e-7.

  • out (Optional[Array], default: None) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Array

Returns:

ret – The cross-entropy loss between the given distributions

Examples

>>> x = ivy.array([0, 0, 1, 0])
>>> y = ivy.array([0.25, 0.25, 0.25, 0.25])
>>> print(ivy.cross_entropy(x, y))
ivy.array(0.34657359)

>>> z = ivy.array([0.1, 0.1, 0.7, 0.1])
>>> print(ivy.cross_entropy(x, z))
ivy.array(0.08916873)
ivy.sparse_cross_entropy(true, pred, /, *, axis=-1, epsilon=1e-07, reduction='mean', out=None)[source]#

Compute sparse cross entropy between logits and labels.

Parameters:

  • true (Union[Array, NativeArray]) – input array containing the true labels as logits.

  • pred (Union[Array, NativeArray]) – input array containing the predicted labels as logits.

  • axis (int, default: -1) – the axis along which to compute the cross-entropy. If axis is -1, the cross-entropy will be computed along the last dimension. Default: -1.

  • epsilon (float, default: 1e-07) – a float in [0.0, 1.0] specifying the amount of smoothing when calculating the loss. If epsilon is 0, no smoothing will be applied. Default: 1e-7.

  • out (Optional[Array], default: None) – optional output array, for writing the result to. It must have a shape that the inputs broadcast to.

Return type:

Array

Returns:

ret – The sparse cross-entropy loss between the given distributions

Examples

With ivy.Array input:

>> x = ivy.array([2]) >> y = ivy.array([0.1, 0.1, 0.7, 0.1]) >> print(ivy.sparse_cross_entropy(x, y)) ivy.array([0.08916873])

>>> x = ivy.array([3])
>>> y = ivy.array([0.1, 0.1, 0.7, 0.1])
>>> print(ivy.cross_entropy(x, y))
ivy.array(5.44832274)

>>> x = ivy.array([2,3])
>>> y = ivy.array([0.1, 0.1])
>>> print(ivy.cross_entropy(x, y))
ivy.array(5.75646281)

With ivy.NativeArray input:

>>> x = ivy.native_array([4])
>>> y = ivy.native_array([0.1, 0.2, 0.1, 0.1, 0.5])
>>> print(ivy.sparse_cross_entropy(x, y))
ivy.array([0.13862944])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([4]))
>>> y = ivy.Container(a=ivy.array([0.1, 0.2, 0.1, 0.1, 0.5]))
>>> print(ivy.sparse_cross_entropy(x, y))
{
    a: ivy.array([0.13862944])
}

With a mix of ivy.Array and ivy.NativeArray inputs:

>>> x = ivy.array([0])
>>> y = ivy.native_array([0.1, 0.2, 0.6, 0.1])
>>> print(ivy.sparse_cross_entropy(x,y))
ivy.array([0.57564628])

With a mix of ivy.Array and ivy.Container inputs:

>>> x = ivy.array([0])
>>> y = ivy.Container(a=ivy.array([0.1, 0.2, 0.6, 0.1]))
>>> print(ivy.sparse_cross_entropy(x,y))
{
    a: ivy.array([0.57564628])
}

Instance Method Examples

With ivy.Array input:

>>> x = ivy.array([2])
>>> y = ivy.array([0.1, 0.1, 0.7, 0.1])
>>> print(x.sparse_cross_entropy(y))
ivy.array([0.08916873])

With ivy.Container input:

>>> x = ivy.Container(a=ivy.array([2]))
>>> y = ivy.Container(a=ivy.array([0.1, 0.1, 0.7, 0.1]))
>>> print(x.sparse_cross_entropy(y))
{
    a: ivy.array([0.08916873])
}
ivy.ssim_loss(true, pred, out=None)[source]#

Calculate the Structural Similarity Index (SSIM) loss between two images.

Parameters:

  • true (A 4D image array of shape (batch_size, channels, height, width).) –

  • pred (A 4D image array of shape (batch_size, channels, height, width).) –

Return type:

Array

Returns:

ivy.Array: The SSIM loss measure similarity between the two images.

Examples

With ivy.Array input: >>> import ivy >>> x = ivy.ones((5, 3, 28, 28)) >>> y = ivy.zeros((5, 3, 28, 28)) >>> ivy.ssim_loss(x, y) ivy.array(0.99989986)

ivy.wasserstein_loss_discriminator(p_real, p_fake, out=None)[source]#

Compute the Wasserstein loss for the discriminator (critic).

Parameters:

  • (ivy.Array) (p_fake) –

  • (ivy.Array) –

Return type:

Array

Returns:

ivy.Array: Wasserstein loss for the discriminator.

ivy.wasserstein_loss_generator(pred_fake, out=None)[source]#

Compute the Wasserstein loss for the generator.

Parameters:

(ivy.Array) (pred_fake) –

Return type:

Array

Returns:

ivy.Array: Wasserstein loss for the generator.

This should have hopefully given you an overview of the losses submodule, if you have any questions, please feel free to reach out on our discord!

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