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Testing

For those who want to implement their own modules, we suggest using the nn.Jacobian class for testing the derivatives of their class, together with the torch.Tester class. The sources of nn package contains sufficiently many examples of such tests.

nn.Jacobian

testJacobian(module, input, minval, maxval, perturbation)

Test the jacobian of a module w.r.t. to its input.

module takes as its input a random tensor shaped the same as input.
minval and maxval specify the range of the random tensor ([-2, 2] by default).
perturbation is used as finite difference (1e-6 by default).

Returns the L-inf distance between the jacobian computed by backpropagation and by finite difference.

testJacobianParameters(module, input, param, dparam, minval, maxval, perturbation)

Test the jacobian of a module w.r.t. its parameters (instead of its input).

The input and parameters of module are random tensors shaped the same as input and param.
minval and maxval specify the range of the random tensors ([-2, 2] by default).
dparam points to the gradient w.r.t. parameters.
perturbation is used as finite difference (1e-6 by default).

Returns the L-inf distance between the jacobian computed by backpropagation and by finite difference.

testJacobianUpdateParameters(module, input, param, minval, maxval, perturbation)

Test the amount of update of a module to its parameters.

The input and parameters of module are random tensors shaped the same as input and param.
minval and maxval specify the range of the random tensors ([-2, 2] by default).
perturbation is used as finite difference (1e-6 by default).

Returns the L-inf distance between the update computed by backpropagation and by finite difference.

forward(module, input, param, perturbation)

Compute the jacobian by finite difference.

module has parameters param and input input.
If provided, param is regarded as independent variables, otherwise input is the independent variables.
perturbation is used as finite difference (1e-6 by default).

Returns the jacobian computed by finite difference.

backward(module, input, param, dparam)

Compute the jacobian by backpropagation.

module has parameters param and input input.
If provided, param is regarded as independent variables, otherwise input is the independent variables.
dparam is the gradient w.r.t. parameters, it must present as long as param is present.

Returns the jacobian computed by backpropagation.