问题解决：module 'tensorflow.compat.v1' has no attribute '

from six.moves import xrange # pylint: disable=redefined-builtin from six.moves import zip # pylint: disable=redefined-builtin from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import embedding_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops import rnn from tensorflow.python.ops import rnn_cell_impl from tensorflow.python.ops import variable_scope from tensorflow.python.util import nest def sequence_loss_by_example(logits, targets, weights, average_across_timesteps=True, softmax_loss_function=None, name=None): if len(targets) != len(logits) or len(weights) != len(logits): raise ValueError("Lengths of logits, weights, and targets must be the same " "%d, %d, %d." % (len(logits), len(weights), len(targets))) with ops.name_scope(name, "sequence_loss_by_example", logits + targets + weights): log_perp_list = [] for logit, target, weight in zip(logits, targets, weights): if softmax_loss_function is None: # TODO(irving,ebrevdo): This reshape is needed because # sequence_loss_by_example is called with scalars sometimes, which # violates our general scalar strictness policy. target = array_ops.reshape(target, [-1]) crossent = nn_ops.sparse_softmax_cross_entropy_with_logits( labels=target, logits=logit) else: # crossent = softmax_loss_function(labels=target,logits=logit) # crossent = softmax_loss_function(labels=targets,logits=logits) # crossent = softmax_loss_function(target, logits=logit) # crossent = softmax_loss_function(target, logit) # crossent = softmax_loss_function(targets, logits=logit) # crossent = softmax_loss_function(labels,logits) # crossent = softmax_loss_function(targets,logits) crossent=softmax_loss_function(targets,logits) log_perp_list.append(crossent * weight) log_perps = math_ops.add_n(log_perp_list) if average_across_timesteps: total_size = math_ops.add_n(weights) total_size += 1e-12 # Just to avoid division by 0 for all-0 weights. log_perps /= total_size return log_perps