To report at https://github.com/chainer/chainer/issues/3027

reproduce_bug.py

import chainer
import chainer.functions as F
import chainer.links as L
from chainer import training
from chainer.training import extensions

# Network definition
class MLP(chainer.Chain):
    def __init__(self, n_out):
        super(MLP, self).__init__()
        with self.init_scope():
            self.l1 = L.Linear(None, n_out)

    def __call__(self, x):
        return self.l1(x)

# Set up a neural network to train
# Classifier reports softmax cross entropy loss and accuracy at every
# iteration, which will be used by the PrintReport extension below.
model = L.Classifier(MLP(10))
# Make a specified GPU current
chainer.cuda.get_device_from_id(0).use()
model.to_gpu()  # Copy the model to the GPU

# Setup an optimizer
optimizer = chainer.optimizers.Adam()
optimizer.setup(model)

# Load the MNIST dataset
train, _ = chainer.datasets.get_mnist()

train_iter = chainer.iterators.SerialIterator(train, 32)

# Set up a trainer
updater = training.StandardUpdater(train_iter, optimizer, device=0)
trainer = training.Trainer(updater, (5, 'epoch'), out='result')

trainer.extend(extensions.ParameterStatistics(model))

# Write a log of evaluation statistics for each epoch
trainer.extend(extensions.LogReport())

# Run the training
trainer.run()