我可以使用自己的数据在TensorFlow中训练模型。模型的输入和输出都是图像。现在我尝试获取预测的输出并将其保存到PNG图像文件中，以查看发生了什么。不幸的是，我在运行下面创建的用于测试预测的函数时遇到了错误。我的目标是保存预测，这也是一个图像，这样我就可以打开它与正常的图像查看器。
一些更多的代码。在我的主要我正在创建一个估计器

def predict_element(my_model, features):
  eval_input_fn = tf.estimator.inputs.numpy_input_fn(
    x=features,
    num_epochs=1,
    shuffle=False)

  eval_results = my_model.predict(input_fn=eval_input_fn)

  predictions = eval_results.next() #this returns a dict with my tensors
  prediction_tensor = predictions["y"] #get the tensor from the dict
  
  image_tensor = tf.reshape(prediction_tensor, [IMG_WIDTH, -1]) #reshape to a matrix due my returned tensor is a 1D flat one
  decoded_image = tf.image.encode_png(image_tensor)
  write_image = tf.write_file("output/my_output_image.png", decoded_image)
  
  with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    print(sess.run(write_image))

def get_input():
  filename_dataset = tf.data.Dataset.list_files("features/*.png")
  label_dataset = tf.data.Dataset.list_files("labels/*.png")

  # Make a Dataset of image tensors by reading and decoding the files.
  image_dataset = filename_dataset.map(lambda x: tf.cast(tf.image.decode_png(tf.read_file(x), channels=1),tf.float32))
  l_dataset = label_dataset.map(lambda x: tf.cast(tf.image.decode_png(tf.read_file(x),channels=1),tf.float32))

  image_reshape = image_dataset.map(lambda x: tf.reshape(x, [IM_WIDTH * IM_HEIGHT]))
  label_reshape = l_dataset.map(lambda x: tf.reshape(x, [IM_WIDTH * IM_HEIGHT]))

  iterator = image_reshape.make_one_shot_iterator()
  iterator2 = label_reshape.make_one_shot_iterator()

  next_img = iterator.get_next()
  next_lbl = iterator2.get_next()

  features = []
  labels = []
  # read all 10 images and labels and put it in the array
  # so we can pass it to the estimator
  with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(10):
      t1, t2 = sess.run([next_img, next_lbl])
      features.append(t1)
      labels.append(t2)

  return {"x": np.array(features)}, np.array(labels)

def main(unused_argv):
    features, labels = get_input() # creating the features dict {"x": }
    my_estimator = tf.estimator.Estimator(model_fn=my_cnn_model, model_dir="/tmp/my_model")

    predict_element(my_estimator, features)

错误是
图形已最终确定，无法修改
通过一些简单的print（）语句，我可以看到使用
评估结果=我的模型.预测（输入fn =评估输入fn）
很可能就是最终完成图形的那个。我完全不知道该做什么或者在哪里寻找解决方案。我该如何保存输出呢？
我在我的model_fn中尝试过：

#the last layer of my network is dropout
predictions = {
   "y": dropout
    }

  if mode == tf.estimator.ModeKeys.PREDICT:
    reshape1 = tf.reshape(dropout, [-1,IM_WIDTH, IM_HEIGHT])
    sliced = tf.slice(reshape1, [0,0,0], [1, IM_WIDTH, IM_HEIGHT])
    encoded = tf.image.encode_png(tf.cast(sliced, dtype=tf.uint8))
    outputfile = tf.write_file(params["output_path"], encoded)
    return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)

这里的问题是，我无法传递回"outputfile"节点以便使用它。