`tests` – Tests¶

class theano.tests.breakpoint.PdbBreakpoint(name)[source]¶

This is an identity-like op with the side effect of enforcing a conditional breakpoint, inside a theano function, based on a symbolic scalar condition. It automatically detects available debuggers and uses the first available in the following order: pudb, ipdb, or pdb.

Parameters

name (String) – name of the conditional breakpoint. To be printed when the breakpoint is activated.

Note

WARNING. At least one of the outputs of the op must be used otherwise the op will be removed from the Theano graph due to its outputs being unused

Note

WARNING. Employing the function inside a theano graph can prevent: Theano from applying certain optimizations to improve performance, reduce memory consumption and/or reduce numerical instability.

Detailed explanation: As of 2014-12-01 the PdbBreakpoint op is not known by any optimization. Setting a PdbBreakpoint op in the middle of a pattern that is usually optimized out will block the optimization.

Example:

import theano
import theano.tensor as T
from theano.tests.breakpoint import PdbBreakpoint

input = T.fvector()
target = T.fvector()

# Mean squared error between input and target
mse = (input - target) ** 2

# Conditional breakpoint to be activated if the total MSE is higher
# than 100. The breakpoint will monitor the inputs, targets as well
# as the individual error values
breakpointOp = PdbBreakpoint("MSE too high")
condition = T.gt(mse.sum(), 100)
mse, monitored_input, monitored_target = breakpointOp(condition, mse,
                                                      input, target)

# Compile the theano function
fct = theano.function([input, target], mse)

# Use the function
print fct([10, 0], [10, 5]) # Will NOT activate the breakpoint
print fct([0, 0], [10, 5]) # Will activate the breakpoint

make_node(condition, *monitored_vars)[source]¶: Create a “apply” nodes for the inputs in that order.

perform(node, inputs, output_storage)[source]¶

Required: Calculate the function on the inputs and put the variables in the output storage. Return None.

Parameters

node (Apply instance) – Contains the symbolic inputs and outputs.
inputs (list) – Sequence of inputs (immutable).
output_storage (list) – List of mutable 1-element lists (do not change the length of these lists)

Notes

The output_storage list might contain data. If an element of output_storage is not None, it has to be of the right type, for instance, for a TensorVariable, it has to be a Numpy ndarray, with the right number of dimensions, and the correct dtype. Its shape and stride pattern, can be arbitrary. It not is guaranteed that it was produced by a previous call to impl. It could be allocated by another Op impl is free to reuse it as it sees fit, or to discard it and allocate new memory.

Raises: MethodNotDefined – The subclass does not override this method.

tests – Tests¶

`tests` – Tests¶