tensorflow_custom.py 文件源码

def model_with_buckets(encoder_inputs, decoder_inputs, targets, weights,
                       buckets, seq2seq, softmax_loss_function=None,
                       per_example_loss=False, name=None):

    if len(encoder_inputs) < buckets[-1][0]:
        raise ValueError("Length of encoder_inputs (%d) must be at least that of la"
                            "st bucket (%d)." % (len(encoder_inputs), buckets[-1][0]))
    if len(targets) < buckets[-1][1]:
        raise ValueError("Length of targets (%d) must be at least that of last"
                        "bucket (%d)." % (len(targets), buckets[-1][1]))
    if len(weights) < buckets[-1][1]:
        raise ValueError("Length of weights (%d) must be at least that of last"
                            "bucket (%d)." % (len(weights), buckets[-1][1]))

    all_inputs = encoder_inputs + decoder_inputs + targets + weights
    losses = []
    outputs = []
    with ops.op_scope(all_inputs, name, "model_with_buckets"):
        for j, bucket in enumerate(buckets):
            with variable_scope.variable_scope(variable_scope.get_variable_scope(),
                                                reuse=True if j > 0 else None):
                bucket_outputs, _, _ = seq2seq(encoder_inputs[:bucket[0]], decoder_inputs[:bucket[1]])

                outputs.append(bucket_outputs)
                if per_example_loss:
                    losses.append(sequence_loss_by_example(
                        outputs[-1], targets[:bucket[1]], weights[:bucket[1]],
                        softmax_loss_function=softmax_loss_function))
                else:
                    losses.append(sequence_loss(
                        outputs[-1], targets[:bucket[1]], weights[:bucket[1]],
                        softmax_loss_function=softmax_loss_function))

    return outputs, losses