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import inspect |
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from blocks.extensions import SimpleExtension |
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class SharedVariableModifier(SimpleExtension): |
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"""Adjusts shared variable parameter using some function. |
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Applies a function to compute the new value of a shared parameter each |
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iteration. |
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This class can be used to adapt over the training process parameters |
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like learning rate, momentum, etc. |
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Parameters |
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---------- |
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parameter : :class:`~tensor.TensorSharedVariable` |
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Shared variable to be adjusted |
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function : callable |
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A function which outputs a numeric value to which the |
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given shared variable will be set and may take one or two |
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arguments. |
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In the first case, function that takes the total number of |
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iterations done (``int``) as an input. |
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In the second case, it is a function which takes number of |
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iterations done (``int``) and old value of the shared variable |
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(with the same dtype as `parameter`). |
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Notes |
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----- |
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This class includes a method ``function`` that calls the function |
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passed in the constructor and a ``num_args`` property which computes |
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the number of arguments to use by inspecting the function object. |
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Subclasses may override a method called ``function`` and/or |
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the ``num_args`` property and instead pass ``None`` to the superclass |
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constructor. This can be used to bypass certain serialization issues |
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on Legacy Python regarding the unpicklability of instance |
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method objects. |
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""" |
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def __init__(self, parameter, function, **kwargs): |
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kwargs.setdefault("after_batch", True) |
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super(SharedVariableModifier, self).__init__(**kwargs) |
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self.parameter = parameter |
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self._function = function |
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self._num_args = None |
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@property |
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def num_args(self): |
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if self._num_args is None: |
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self._num_args = len(inspect.getargspec(self._function.args)) |
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return self._num_args |
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def function(self, *args): |
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return self._function(*args) |
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def do(self, which_callback, *args): |
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iterations_done = self.main_loop.log.status['iterations_done'] |
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if self.num_args == 1: |
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new_value = self.function(iterations_done) |
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else: |
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old_value = self.parameter.get_value() |
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new_value = self.function(iterations_done, old_value) |
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self.parameter.set_value(new_value) |
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class TrackTheBest(SimpleExtension): |
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"""Check if a log quantity has the minimum/maximum value so far. |
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Parameters |
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---------- |
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record_name : str |
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The name of the record to track. |
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notification_name : str, optional |
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The name for the record to be made in the log when the current |
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value of the tracked quantity is the best so far. It not given, |
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'record_name' plus "best_so_far" suffix is used. |
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choose_best : callable, optional |
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A function that takes the current value and the best so far |
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and return the best of two. By default :func:`min`, which |
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corresponds to tracking the minimum value. |
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Attributes |
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---------- |
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best_name : str |
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The name of the status record to keep the best value so far. |
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notification_name : str |
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The name of the record written to the log when the current |
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value of the tracked quantity is the best so far. |
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Notes |
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----- |
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In the likely case that you are relying on another extension to |
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add the tracked quantity to the log, make sure to place this |
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extension *after* the extension that writes the quantity to the log |
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in the `extensions` argument to :class:`blocks.main_loop.MainLoop`. |
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""" |
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def __init__(self, record_name, notification_name=None, |
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choose_best=min, **kwargs): |
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self.record_name = record_name |
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if not notification_name: |
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notification_name = record_name + "_best_so_far" |
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self.notification_name = notification_name |
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self.best_name = "best_" + record_name |
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self.choose_best = choose_best |
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kwargs.setdefault("after_epoch", True) |
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super(TrackTheBest, self).__init__(**kwargs) |
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def do(self, which_callback, *args): |
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current_value = self.main_loop.log.current_row.get(self.record_name) |
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if current_value is None: |
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return |
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best_value = self.main_loop.status.get(self.best_name, None) |
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if (best_value is None or |
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(current_value != best_value and |
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self.choose_best(current_value, best_value) == |
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current_value)): |
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self.main_loop.status[self.best_name] = current_value |
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self.main_loop.log.current_row[self.notification_name] = True |
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mila-udem /
blocks
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Cyclic imports may cause partly loaded modules to be returned. This might lead to unexpected runtime behavior which is hard to debug.