def _get_datasets_and_inputs(outputs):
import tensorflow as tf
all_required_inputs = find_placeholders(outputs)
dataset = tf.get_collection(all_required_inputs[0].name)[0]
inputs = dataset.tensors
_check_the_same(all_required_inputs, inputs)
return dataset, inputs
def _expand_inputs(inputs, tensors_with_value, loss):
additional_inputs = []
additional_values = []
all_required_inputs = find_placeholders([loss])
all_required_inputs_names = [v.name for v in all_required_inputs]
if tensors_with_value:
for t, v in tensors_with_value.items():
if t.name in all_required_inputs_names:
additional_inputs.append(t)
additional_values.append(v)
if not isinstance(inputs, list):
inputs = nest.flatten(inputs)
return inputs, additional_inputs, additional_values
def _get_dataset_from_loss(loss):
import tensorflow as tf
all_required_inputs = find_placeholders([loss])
dataset = tf.get_collection(all_required_inputs[0].name)[0]
return dataset
