def _FlatOutputProcessor(inputs):
"""Returns a flattened list of 'processor(inputs)'."""
output, bucketing_key = processor(inputs)
if isinstance(output, list):
assert output
assert all(isinstance(x, tf.Tensor)
for x in output), '{}'.format(output)
else:
assert isinstance(output, py_utils.NestedMap), '{}'.format(output)
assert output
assert all(isinstance(x, tf.Tensor)
for x in output.Flatten()), '{}'.format(
output.DebugString())
bucketing_key = tf.to_int32(bucketing_key)
tf.logging.debug('Processor outputs=%s bucketing_key=%s', output,
bucketing_key)
output_tmpl.values = output
flat_output_tmpl = output_tmpl.Flatten()
tf.logging.debug('Processor flat outputs=%s', flat_output_tmpl)
tf.logging.debug('extra_inputs=%s extra_args=%s extra_vars=%s',
function.get_extra_inputs(),
function.get_extra_args(), function.get_extra_vars())
assert not function.get_extra_args(), (
'fns {} is not pure: extra_args={}'.format(
processor, function.get_extra_args()))
return flat_output_tmpl + [bucketing_key]
def Grad(x, y0):
if use_forward_func:
y = Model(x)
else:
y = _Model(x)
loss = tf.reduce_mean(tf.reduce_sum(y0 * tf.log(y), 1), 0)
dw, db = tf.gradients(loss, function.get_extra_args())
cvars.extend(function.get_extra_vars())
return loss, dw, db
def Grad(x, y0):
if use_forward_func:
y = Model(x)
else:
y = _Model(x)
loss = tf.reduce_mean(tf.reduce_sum(y0 * tf.log(y), 1), 0)
arg_w, arg_b = function.get_extra_args()
self.assertEqual(arg_w.get_shape(), tf.TensorShape([64, 64]))
self.assertEqual(arg_b.get_shape(), tf.TensorShape([64]))
dw, db = tf.gradients(loss, [arg_w, arg_b])
cvars.extend(function.get_extra_vars())
return loss, dw, db
def Grad(x, y0):
if use_forward_func:
y = Model(x)
else:
y = _Model(x)
loss = tf.reduce_mean(tf.reduce_sum(y0 * tf.log(y), 1), 0)
arg_w, arg_b = function.get_extra_args()
self.assertEqual(arg_w.get_shape(), tf.TensorShape([64, 64]))
self.assertEqual(arg_b.get_shape(), tf.TensorShape([64]))
dw, db = tf.gradients(loss, [arg_w, arg_b])
cvars.extend(function.get_extra_vars())
return loss, dw, db
def _FlatOutputProcessor(inputs):
"""Returns a flattened list of 'processor(inputs)'."""
outputs = processor(inputs)
tf.logging.debug('Processor outputs=%s', outputs)
assert len(outputs) > 1, outputs
# Add 'outputs' as a list so that each element will be flattened.
output_tmpl.values = list(outputs)
flat_outputs = output_tmpl.Flatten()
tf.logging.debug('Processor flat outputs=%s', flat_outputs)
tf.logging.debug('extra_inputs=%s extra_args=%s extra_vars=%s',
function.get_extra_inputs(),
function.get_extra_args(), function.get_extra_vars())
assert not function.get_extra_args(), (
'fns {} is not pure: extra_args={}'.format(
processor, function.get_extra_args()))
return flat_outputs
def _FlatOutputProcessor(source_id, record):
"""Returns a flattened list of 'processor(inputs)'."""
processor_spec = tf_inspect.getargspec(processor)
tf.logging.debug('GenericInput.processor.argspec=%s', processor_spec)
processor_args = set(processor_spec.args) - set(['self'])
if len(processor_args) == 1:
output, bucketing_key = processor(record)
elif processor_args == set(['source_id', 'record']):
output, bucketing_key = processor(source_id=source_id,
record=record)
else:
raise ValueError(
'GenericInput: processor should take either a single arg '
'or two args named as "source_id" and "record". '
'Actual: %s' % processor_args)
if isinstance(output, list):
assert output
assert all(isinstance(x, tf.Tensor)
for x in output), '{}'.format(output)
else:
assert isinstance(output, py_utils.NestedMap), '{}'.format(output)
assert output
assert all(isinstance(x, tf.Tensor)
for x in output.Flatten()), '{}'.format(
output.DebugString())
bucketing_key = tf.cast(bucketing_key, tf.int32)
tf.logging.debug('Processor outputs=%s bucketing_key=%s', output,
bucketing_key)
output_tmpl.out_values = output
flat_output_tmpl = output_tmpl.Flatten()
tf.logging.debug('Processor flat outputs=%s', flat_output_tmpl)
tf.logging.debug('extra_inputs=%s extra_args=%s extra_vars=%s',
function.get_extra_inputs(),
function.get_extra_args(), function.get_extra_vars())
assert not function.get_extra_args(), (
'fns {} is not pure: extra_args={}'.format(
processor, function.get_extra_args()))
return flat_output_tmpl + [bucketing_key]