Saving predicted tensor to image in TensorFlow – Graph finalized

I was able to train a model in TensorFlow with my own data. Input and Output of the model are images. I now tried to get the output of the predictions and save it to an png image file to see what’s going on. Unfortunately I am getting an error when running the following function I created to test with predictions. My goal is to save the prediction that is also an image so I can open it with a normal image viewer.

Some more to the code. In my main I am creating an estimator

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)

The error is

Graph is finalized and cannot be modified

With some easy print() statements I could see that retrieving the dict with

eval_results = my_model.predict(input_fn=eval_input_fn)

is probable the one which finalizes the graph.
I absolutely don’t know what to do or where to look for a solution here. How could I save the output?

I tried this in my model_fn:

#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)

My problem here is that I can’t pass back the "outputfile" node so I can work with it.