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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](https://github.com/torch/torch7/blob/master/doc/tester.md) class. The sources
of `nn` package contains sufficiently many examples of such tests.
## nn.Jacobian ##
<a name="nn.Jacobian.testJacobian"></a>
### 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.
<a name="nn.Jacobian.testJacobianParameters"></a>
### 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.
<a name="nn.Jacobian.testJacobianUpdateParameters"></a>
### 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.
<a name="nn.Jacobian.forward"></a>
### 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.
<a name="nn.Jacobian.backward"></a>
### 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.
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