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 _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]