Question

我应该如何使用tf.saved_model.simple_save保存经过训练的模型，以便可以使用tensorflow-serving进行请求

x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])
values = tf.placeholder(tf.float32, [None, 1])

layer = tf.add(tf.matmul(x, w), b)
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y, logits=layer))
optimize = tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy)
correct_pred = tf.equal(tf.argmax(layer, 1), tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

with tf.Session() as sess:
  sess.run(init)
  for _ in range(10000):
    batch = mnist.train.next_batch(100)
    sess.run(accuracy, feed_dict={x:batch[0],y:batch[1]})

  !rm -rf "/model"
  export_dir = "/model/1"

  #Problem here
  tf.saved_model.simple_save(
      sess,
      export_dir=export_dir,
      inputs={"x":x},
      outputs={"accuracy":accuracy}
  )

当我跑步时：

!saved_model_cli show --dir {export_dir} --all

I get:
MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['x'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 784)
        name: Placeholder:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['accuracy'] tensor_info:
        dtype: DT_FLOAT
        shape: ()
        name: Mean_1:0
  Method name is: tensorflow/serving/predict

我的输出是shape（）而不是（-1，x）或那种格式。当我发送请求时，没有任何回应。由于准确性是一项操作，所以我没有任何回应。如何将其更改为变量，或者如何在keras中使用{t.name for model.outputs中的t}？

Answer 1

问题出在代码的最后一行outputs={"accuracy":accuracy}中。如果将accuracy替换为'layer'，则会解决此问题。因此，代码如下所示：

tf.saved_model.simple_save(sess, export_dir=export_dir, inputs={"x":x},
outputs={"Predicted_Output":layer})

Answer 2

simple_save中的输出似乎不正确。它应该是分层的，但不是准确性。

在张量流服务中保存和服务模型时出错

2 个答案: