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目前，我尝试使用 Tensorflow 的新 Estimator API 在自定义图像数据集上训练自动编码器。

到目前为止，一切正常。我唯一的问题是在模型处于评估模式时将输入和输出图像保存为摘要。我在训练模式下创建的所有图像摘要都会正确存储并显示在 Tensorboard 中。

这是我的代码：

def model_fn_autoencoder(features, labels, mode, params):
    is_training = mode == ModeKeys.TRAIN

    # Define model's architecture
    logits = architecture_autoencoder(features, is_training=is_training)

    # Loss, training and eval operations are not needed during inference.
    loss = None
    train_op = None
    #eval_metric_ops = {}

    if mode != ModeKeys.INFER:
        loss = tf.reduce_mean(tf.square(logits - features))
        train_op = get_train_op_fn(loss, params)

        #eval_metric_ops = get_eval_metric_ops(labels, predictions)

    if mode == ModeKeys.TRAIN:
        for i in range(10):
            tf.summary.image("Input/Train/" + str(i), tf.reshape(features[i],[1, 150, 150, 3]))
            tf.summary.image("Output/Train/" + str(i), tf.reshape(logits[i],[1, 150, 150, 3]))

    if mode == ModeKeys.EVAL:
        for i in range(10):
            tf.summary.image("Input/Eval/" + str(i), tf.reshape(features[i], [1, 150, 150, 3]))
            tf.summary.image("Output/Eval/" + str(i), tf.reshape(logits[i], [1, 150, 150, 3]))

    return tf.estimator.EstimatorSpec(
        mode=mode,
        predictions=logits,
        loss=loss,
        train_op=train_op,
        #eval_metric_ops=eval_metric_ops

也许有人可以告诉我我做错了什么？

更新 以下是估计器和实验创建的功能：

估算器：

def get_estimator(run_config, params):
    return tf.estimator.Estimator(
        model_fn=model_fn_autoencoder,  # First-class function
        params=params,  # HParams
        config=run_config  # RunConfig
    )

实验：

def experiment_fn(run_config, params):
    run_config = run_config.replace(save_checkpoints_steps=params.min_eval_frequency)

    estimator = get_estimator(run_config, params)

    tf_path = 'path/to/tfrecord'
    train_file = 'Crops-Faces-Negtives-150-150.tfrecord'
    val_file = 'Crops-Faces-Negtives-150-150-TEST.tfrecord'
    tfrecords_train = [os.path.join(tf_path, train_file)]
    tfrecords_test = [os.path.join(tf_path, val_file)]

    # Setup data loaders
    train_input_fn = get_train_inputs(batch_size=128, tfrecord_files=tfrecords_train)
    eval_input_fn = get_train_inputs(batch_size=128, tfrecord_files=tfrecords_test)

    # Define the experiment
    experiment = tf.contrib.learn.Experiment(
        estimator=estimator,  # Estimator
        train_input_fn=train_input_fn,  # First-class function
        eval_input_fn=eval_input_fn,  # First-class function
        train_steps=params.train_steps,  # Minibatch steps
        min_eval_frequency=params.min_eval_frequency,  # Eval frequency
        eval_steps=10  # Number of eval batches
    )

    return experiment
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1 回答 1

10

使用 TF1.4，您可以通过 tf.estimator.EstimatorSpec 评估挂钩。evaluation_hooks 是一个钩子列表，您必须向其中添加以下钩子：

# Create a SummarySaverHook
eval_summary_hook = tf.train.SummarySaverHook(
                                save_steps=1,
                                output_dir= self.job_dir + "/eval_core",
                                summary_op=tf.summary.merge_all())
# Add it to the evaluation_hook list
evaluation_hooks.append(eval_summary_hook)

#Now, return the estimator:
return tf.estimator.EstimatorSpec(
                mode=mode,
                predictions=predictions,
                loss=loss,
                train_op=train_op,
                training_hooks=training_hooks,
                eval_metric_ops=eval_metric_ops,
                evaluation_hooks=evaluation_hooks)

现在您可以简单地添加 tf.summary.image 并将其放在 Tensorboard 中。使用您在 eval_summary 挂钩中使用的指定输出目录的父目录打开 Tensrobaord。在我的示例中，它被称为“eval_core”，因此我在其父目录中打开了 Tensorboard，如下图所示，它很好地显示在一个蓝色框中。

在此处输入图像描述

于 2018-01-09T15:17:11.677 回答