What are the Loss functions in lasagne objectives?

This recipe explains what are the Loss functions in lasagne objectives.

Recipe Objective - What are the Loss functions in lasagne objectives?

Lasagne provides "lasagne.objectives" class for loss functions.

These functions are used to build the loss expression for training or validating a neural network.

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Loss Functions:-

1. binary_crossentropy - Computes the binary cross-entropy between predictions and targets.

2. categorical_crossentropy - Computes the categorical cross-entropy between predictions and targets.

3. squared_error - Computes the element-wise squared difference between two tensors.

4. binary_hinge_loss - Computes the binary hinge loss between predictions and targets.

5. multiclass_hinge_loss - Computes the multi-class hinge loss between predictions and targets.

6. huber_loss - Computes the huber loss between predictions and targets.

For more related projects:-

https://www.projectpro.io/projects/data-science-projects/deep-learning-projects

https://www.projectpro.io/projects/data-science-projects/tensorflow-projects

Examples:-

from lasagne.layers import InputLayer, DenseLayer, get_output
from lasagne.nonlinearities import softmax, rectify
ly_in = InputLayer((100, 20))
ly_hid = DenseLayer(ly_in, num_units=30, nonlinearity=rectify)
ly_out = DenseLayer(ly_hid, num_units=3, nonlinearity=softmax)

# And Theano variables representing your network input and targets:
import theano
data = theano.tensor.matrix('data')
targets = theano.tensor.matrix('targets')

# first construct an element-wise loss expression:
from lasagne.objectives import categorical_crossentropy, aggregate
predictions = get_output(ly_out, data)
loss = categorical_crossentropy(predictions, targets)

loss = aggregate(loss, mode='mean')
loss

test_predictions = get_output(ly_out, data, deterministic=True)
test_loss = categorical_crossentropy(test_predictions, targets)
test_loss = aggregate(test_loss)
test_loss

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