tensorflow - 在构建 tf_estimator 时面临 tensorflow2.0 中的错误

Question

import custom_model as CM
import input_pipeline as IP
import tensorflow as tf

def custom_estimator(features, labels, mode):

  logits = CM.model_net(features=features, n_classes=5)
  prediction = tf.keras.layers.Activation('softmax')(logits)
  preds_dict = {'class': tf.argmax(input=prediction, axis=1),
                'probabilities': prediction,
                'logits': logits}
  if mode == tf.estimator.ModeKeys.PREDICT:
    return tf.estimator.EstimatorSpec(mode=mode, 
                                      predictions=preds_dict)
  # Compute loss
  labels = tf.reshape(labels, (BATCH_SIZE, 5))
  loss = tf.keras.losses.categorical_crossentropy(y_true=labels,
                                                  y_pred=prediction)
  # Compute evaluation metrics
  accuracy = custom_accuracy(labels, prediction)
  metrics = {'accuracy': accuracy}
  tf.summary.scalar('accuracy', accuracy)
  if mode == tf.estimator.ModeKeys.EVAL:
    return tf.estimator.EstimatorSpec(mode, loss=loss, 
                                      eval_metric_ops=metrics)

  optimizer = tf.keras.optimizers.Adam()
  train_op = optimizer.minimize(loss)
  return tf.estimator.EstimatorSpec(mode, loss=loss, 
                                    train_op=train_op)

# Build tf_estimator
classifier = tf.estimator.Estimator(model_fn=custom_estimator,
                                    model_dir=model_dir)

# Train the estimator
TRAIN_FILES, TRAIN_LABELS = IP.map_file_to_label(data_dir=data_dir)
TRAIN = classifier.train(input_fn=lambda:
  IP.imgs_input_fn(TRAIN_FILES, labels=TRAIN_LABELS,
                   perform_shuffle=True, repeat_count=EPOCHS,
                   batch_size=BATCH_SIZE),
                   steps=int(len(TRAIN_LABELS)/BATCH_SIZE))

这是我在使用 TensorFlow-2.0 时遇到的错误。此处附有相同的错误图像和代码。请帮忙。如果我输入 var_list=None 那么错误是“ValueError: Passed in object of type , not tf.Tensor”

标签： tensorflowmachine-learningdeep-learningtensorflow2.0

Answer 1

首先，我认为这个 Estimator 代码示例不符合 TensorFlow 2.0。在任何情况下，如果您使用的是 1.x 版本，请替换：

train_op = optimizer.minimize(loss)

有了这个：

train_op = optimizer.minimize(
        loss=average_loss, global_step=tf.train.get_global_step())

如果确实，您使用的是 TensorFlow 2.0，则替换为：

train_op = optimizer.minimize(
        loss=average_loss, global_step=tf.compat.v1.train.get_global_step())