prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  log_name of LogReport with keys causes AttributeError
  In MNIST example you change the code
  
  https://github.com/pfnet/chainer/blob/master/examples/mnist/train_mnist.py#L84
  
  ```
      # Write a log of evaluation statistics for each epoch
      trainer.extend(extensions.LogReport())
  ```
  
  to 
  
  ```
      # Write a log of evaluation statistics for each epoch
      trainer.extend(extensions.LogReport(log_name='log_{.iteration}'))
  ```
  
  run train_mnist.py and you'll get `AttributeError: 'dict' object has no attribute 'iteration'`
  
  
  </issue>
  <code>
  [start of chainer/training/extensions/log_report.py]
  1 import json
  2 import os
  3 import tempfile
  4 
  5 import six
  6 
  7 from chainer import reporter
  8 import chainer.serializer as serializer_module
  9 from chainer.training import extension
  10 import chainer.training.trigger as trigger_module
  11 
  12 
  13 class LogReport(extension.Extension):
  14 
  15     """Trainer extension to output the accumulated results to a log file.
  16 
  17     This extension accumulates the observations of the trainer to
  18     :class:`~chainer.DictSummary` at a regular interval specified by a supplied
  19     trigger, and writes them into a log file in JSON format.
  20 
  21     There are two triggers to handle this extension. One is the trigger to
  22     invoke this extension, which is used to handle the timing of accumulating
  23     the results. It is set to ``1, 'iteration'`` by default. The other is the
  24     trigger to determine when to emit the result. When this trigger returns
  25     True, this extension appends the summary of accumulated values to the list
  26     of past summaries, and writes the list to the log file. Then, this
  27     extension makes a new fresh summary object which is used until the next
  28     time that the trigger fires.
  29 
  30     It also adds ``'epoch'`` and ``'iteration'`` entries to each result
  31     dictionary, which are the epoch and iteration counts at the output.
  32 
  33     Args:
  34         keys (iterable of strs): Keys of values to accumulate. If this is None,
  35             all the values are accumulated and output to the log file.
  36         trigger: Trigger that decides when to aggregate the result and output
  37             the values. This is distinct from the trigger of this extension
  38             itself. If it is a tuple in the form ``<int>, 'epoch'`` or
  39             ``<int>, 'iteration'``, it is passed to :class:`IntervalTrigger`.
  40         postprocess: Callback to postprocess the result dictionaries. Each
  41             result dictionary is passed to this callback on the output. This
  42             callback can modify the result dictionaries, which are used to
  43             output to the log file.
  44         log_name (str): Name of the log file under the output directory. It can
  45             be a format string: the last result dictionary is passed for the
  46             formatting. For example, users can use '{.iteration}' to separate
  47             the log files for different iterations. If the log name is None, it
  48             does not output the log to any file.
  49 
  50     """
  51     def __init__(self, keys=None, trigger=(1, 'epoch'), postprocess=None,
  52                  log_name='log'):
  53         self._keys = keys
  54         self._trigger = trigger_module.get_trigger(trigger)
  55         self._postprocess = postprocess
  56         self._log_name = log_name
  57         self._log = []
  58 
  59         self._init_summary()
  60 
  61     def __call__(self, trainer):
  62         # accumulate the observations
  63         keys = self._keys
  64         observation = trainer.observation
  65         summary = self._summary
  66 
  67         if keys is None:
  68             summary.add(observation)
  69         else:
  70             summary.add({k: observation[k] for k in keys if k in observation})
  71 
  72         if self._trigger(trainer):
  73             # output the result
  74             stats = self._summary.compute_mean()
  75             stats_cpu = {}
  76             for name, value in six.iteritems(stats):
  77                 stats_cpu[name] = float(value)  # copy to CPU
  78 
  79             updater = trainer.updater
  80             stats_cpu['epoch'] = updater.epoch
  81             stats_cpu['iteration'] = updater.iteration
  82 
  83             if self._postprocess is not None:
  84                 self._postprocess(stats_cpu)
  85 
  86             self._log.append(stats_cpu)
  87 
  88             # write to the log file
  89             if self._log_name is not None:
  90                 log_name = self._log_name.format(stats_cpu)
  91                 fd, path = tempfile.mkstemp(prefix=log_name, dir=trainer.out)
  92                 with os.fdopen(fd, 'w') as f:
  93                     json.dump(self._log, f, indent=4)
  94                 os.rename(path, os.path.join(trainer.out, log_name))
  95 
  96             # reset the summary for the next output
  97             self._init_summary()
  98 
  99     @property
  100     def log(self):
  101         """The current list of observation dictionaries."""
  102         return self._log
  103 
  104     def serialize(self, serializer):
  105         # Note that this serialization may lose some information of small
  106         # numerical differences.
  107         if isinstance(serializer, serializer_module.Serializer):
  108             log = json.dumps(self._log)
  109             serializer('_log', log)
  110         else:
  111             log = serializer('_log', '')
  112             self._log = json.loads(log)
  113 
  114     def _init_summary(self):
  115         self._summary = reporter.DictSummary()
  116
  [end of chainer/training/extensions/log_report.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/chainer/training/extensions/log_report.py b/chainer/training/extensions/log_report.py
  --- a/chainer/training/extensions/log_report.py
  +++ b/chainer/training/extensions/log_report.py
  @@ -43,7 +43,7 @@
               output to the log file.
           log_name (str): Name of the log file under the output directory. It can
               be a format string: the last result dictionary is passed for the
  -            formatting. For example, users can use '{.iteration}' to separate
  +            formatting. For example, users can use '{iteration}' to separate
               the log files for different iterations. If the log name is None, it
               does not output the log to any file.
   
  @@ -87,7 +87,7 @@
   
               # write to the log file
               if self._log_name is not None:
  -                log_name = self._log_name.format(stats_cpu)
  +                log_name = self._log_name.format(**stats_cpu)
                   fd, path = tempfile.mkstemp(prefix=log_name, dir=trainer.out)
                   with os.fdopen(fd, 'w') as f:
                       json.dump(self._log, f, indent=4)
  
verification_info: {"golden_diff": "diff --git a/chainer/training/extensions/log_report.py b/chainer/training/extensions/log_report.py\n--- a/chainer/training/extensions/log_report.py\n+++ b/chainer/training/extensions/log_report.py\n@@ -43,7 +43,7 @@\n             output to the log file.\n         log_name (str): Name of the log file under the output directory. It can\n             be a format string: the last result dictionary is passed for the\n-            formatting. For example, users can use '{.iteration}' to separate\n+            formatting. For example, users can use '{iteration}' to separate\n             the log files for different iterations. If the log name is None, it\n             does not output the log to any file.\n \n@@ -87,7 +87,7 @@\n \n             # write to the log file\n             if self._log_name is not None:\n-                log_name = self._log_name.format(stats_cpu)\n+                log_name = self._log_name.format(**stats_cpu)\n                 fd, path = tempfile.mkstemp(prefix=log_name, dir=trainer.out)\n                 with os.fdopen(fd, 'w') as f:\n                     json.dump(self._log, f, indent=4)\n", "issue": "log_name of LogReport with keys causes AttributeError\nIn MNIST example you change the code\n\nhttps://github.com/pfnet/chainer/blob/master/examples/mnist/train_mnist.py#L84\n\n```\n    # Write a log of evaluation statistics for each epoch\n    trainer.extend(extensions.LogReport())\n```\n\nto \n\n```\n    # Write a log of evaluation statistics for each epoch\n    trainer.extend(extensions.LogReport(log_name='log_{.iteration}'))\n```\n\nrun train_mnist.py and you'll get `AttributeError: 'dict' object has no attribute 'iteration'`\n\n", "before_files": [{"content": "import json\nimport os\nimport tempfile\n\nimport six\n\nfrom chainer import reporter\nimport chainer.serializer as serializer_module\nfrom chainer.training import extension\nimport chainer.training.trigger as trigger_module\n\n\nclass LogReport(extension.Extension):\n\n    \"\"\"Trainer extension to output the accumulated results to a log file.\n\n    This extension accumulates the observations of the trainer to\n    :class:`~chainer.DictSummary` at a regular interval specified by a supplied\n    trigger, and writes them into a log file in JSON format.\n\n    There are two triggers to handle this extension. One is the trigger to\n    invoke this extension, which is used to handle the timing of accumulating\n    the results. It is set to ``1, 'iteration'`` by default. The other is the\n    trigger to determine when to emit the result. When this trigger returns\n    True, this extension appends the summary of accumulated values to the list\n    of past summaries, and writes the list to the log file. Then, this\n    extension makes a new fresh summary object which is used until the next\n    time that the trigger fires.\n\n    It also adds ``'epoch'`` and ``'iteration'`` entries to each result\n    dictionary, which are the epoch and iteration counts at the output.\n\n    Args:\n        keys (iterable of strs): Keys of values to accumulate. If this is None,\n            all the values are accumulated and output to the log file.\n        trigger: Trigger that decides when to aggregate the result and output\n            the values. This is distinct from the trigger of this extension\n            itself. If it is a tuple in the form ``<int>, 'epoch'`` or\n            ``<int>, 'iteration'``, it is passed to :class:`IntervalTrigger`.\n        postprocess: Callback to postprocess the result dictionaries. Each\n            result dictionary is passed to this callback on the output. This\n            callback can modify the result dictionaries, which are used to\n            output to the log file.\n        log_name (str): Name of the log file under the output directory. It can\n            be a format string: the last result dictionary is passed for the\n            formatting. For example, users can use '{.iteration}' to separate\n            the log files for different iterations. If the log name is None, it\n            does not output the log to any file.\n\n    \"\"\"\n    def __init__(self, keys=None, trigger=(1, 'epoch'), postprocess=None,\n                 log_name='log'):\n        self._keys = keys\n        self._trigger = trigger_module.get_trigger(trigger)\n        self._postprocess = postprocess\n        self._log_name = log_name\n        self._log = []\n\n        self._init_summary()\n\n    def __call__(self, trainer):\n        # accumulate the observations\n        keys = self._keys\n        observation = trainer.observation\n        summary = self._summary\n\n        if keys is None:\n            summary.add(observation)\n        else:\n            summary.add({k: observation[k] for k in keys if k in observation})\n\n        if self._trigger(trainer):\n            # output the result\n            stats = self._summary.compute_mean()\n            stats_cpu = {}\n            for name, value in six.iteritems(stats):\n                stats_cpu[name] = float(value)  # copy to CPU\n\n            updater = trainer.updater\n            stats_cpu['epoch'] = updater.epoch\n            stats_cpu['iteration'] = updater.iteration\n\n            if self._postprocess is not None:\n                self._postprocess(stats_cpu)\n\n            self._log.append(stats_cpu)\n\n            # write to the log file\n            if self._log_name is not None:\n                log_name = self._log_name.format(stats_cpu)\n                fd, path = tempfile.mkstemp(prefix=log_name, dir=trainer.out)\n                with os.fdopen(fd, 'w') as f:\n                    json.dump(self._log, f, indent=4)\n                os.rename(path, os.path.join(trainer.out, log_name))\n\n            # reset the summary for the next output\n            self._init_summary()\n\n    @property\n    def log(self):\n        \"\"\"The current list of observation dictionaries.\"\"\"\n        return self._log\n\n    def serialize(self, serializer):\n        # Note that this serialization may lose some information of small\n        # numerical differences.\n        if isinstance(serializer, serializer_module.Serializer):\n            log = json.dumps(self._log)\n            serializer('_log', log)\n        else:\n            log = serializer('_log', '')\n            self._log = json.loads(log)\n\n    def _init_summary(self):\n        self._summary = reporter.DictSummary()\n", "path": "chainer/training/extensions/log_report.py"}]}