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Fix errors in beginner/blitz #79

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May 17, 2017
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Fix errors in beginner/blitz #79

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1588236
Use torch.arange instead of torch.range (to be deprecated)
desktable May 7, 2017
333cab9
Fix attribute names after refactoring
desktable May 7, 2017
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8 changes: 4 additions & 4 deletions beginner_source/blitz/autograd_tutorial.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,9 +36,9 @@

``Variable`` and ``Function`` are interconnected and build up an acyclic
graph, that encodes a complete history of computation. Each variable has
a ``.creator`` attribute that references a ``Function`` that has created
a ``.grad_fn`` attribute that references a ``Function`` that has created
the ``Variable`` (except for Variables created by the user - their
``creator is None``).
``grad_fn is None``).

If you want to compute the derivatives, you can call ``.backward()`` on
a ``Variable``. If ``Variable`` is a scalar (i.e. it holds a one element
Expand All @@ -61,8 +61,8 @@
print(y)

###############################################################
# ``y`` was created as a result of an operation, so it has a creator.
print(y.creator)
# ``y`` was created as a result of an operation, so it has a ``grad_fn``.
print(y.grad_fn)

###############################################################
# Do more operations on y
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10 changes: 5 additions & 5 deletions beginner_source/blitz/neural_networks_tutorial.py
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Expand Up @@ -157,15 +157,15 @@ def num_flat_features(self, x):
# For example:

output = net(input)
target = Variable(torch.range(1, 10)) # a dummy target, for example
target = Variable(torch.arange(1, 11)) # a dummy target, for example
criterion = nn.MSELoss()

loss = criterion(output, target)
print(loss)

########################################################################
# Now, if you follow ``loss`` in the backward direction, using it’s
# ``.creator`` attribute, you will see a graph of computations that looks
# ``.grad_fn`` attribute, you will see a graph of computations that looks
# like this:
#
# ::
Expand All @@ -181,9 +181,9 @@ def num_flat_features(self, x):
#
# For illustration, let us follow a few steps backward:

print(loss.creator) # MSELoss
print(loss.creator.previous_functions[0][0]) # Linear
print(loss.creator.previous_functions[0][0].previous_functions[0][0]) # ReLU
print(loss.grad_fn) # MSELoss
print(loss.grad_fn.next_functions[0][0]) # Linear
print(loss.grad_fn.next_functions[0][0].next_functions[0][0]) # ReLU

########################################################################
# Backprop
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