Google Cloud ML: Outer dimension for outputs must be unknown

user_1132150 0
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we got a working exported model in local that is falling to create a new model version in Google Cloud ML as follows:

Create Version failed. Model validation failed: Outer dimension for outputs must be unknown, outer dimension of 'Const_2:0' is 1 For more information on how to export Tensorflow SavedModel, seehttps://www.tensorflow.org/api_docs/python/tf/saved_model.

Our current exported model response is working in tensorflow-serve and gcloud predict local with this responses:

outputs {
  key: "categories"
  value {
    dtype: DT_STRING
    tensor_shape {
      dim {
        size: 1
      }
      dim {
        size: 17
      }
    }
    string_val: "Business Essentials"
    string_val: "Business Skills"
    string_val: "Communication"
    string_val: "Customer Service"
    string_val: "Desktop Computing"
    string_val: "Finance"
    string_val: "Health & Wellness"
    string_val: "Human Resources"
    string_val: "Information Technology"
    string_val: "Leadership"
    string_val: "Management"
    string_val: "Marketing & Advertising"
    string_val: "Personal Development"
    string_val: "Project Management"
    string_val: "Sales"
    string_val: "Technical Skills"
    string_val: "Training & Development"
  }
}
outputs {
  key: "category"
  value {
    dtype: DT_STRING
    tensor_shape {
      dim {
        size: 1
      }
    }
    string_val: "Training & Development"
  }
}
outputs {
  key: "class"
  value {
    dtype: DT_INT64
    tensor_shape {
      dim {
        size: 1
      }
    }
    int64_val: 16
  }
}
outputs {
  key: "prob"
  value {
    dtype: DT_FLOAT
    tensor_shape {
      dim {
        size: 1
      }
      dim {
        size: 17
      }
    }
    float_val: 0.051308773458
    float_val: 2.39087748923e-05
    float_val: 4.77133402232e-11
    float_val: 0.00015225057723
    float_val: 0.201782479882
    float_val: 2.11781745287e-17
    float_val: 3.61836161034e-09
    float_val: 0.104659214616
    float_val: 6.55719213682e-06
    float_val: 1.16744895001e-12
    float_val: 1.68323947491e-06
    float_val: 0.00510392058641
    float_val: 3.46840134738e-12
    float_val: 1.02085353504e-08
    float_val: 0.000151587591972
    float_val: 3.04983092289e-25
    float_val: 0.636809647083
  }
}

The issue must be in categories as all the other outputs were there already in the first working version of the output.

Any ideas??

google-cloud-ml tensorflow machine-learning python
2 Answers

Responding to my own question:

I need to use one of the existing tensors of the shape I need to create a [?, len(CATEGORIES)] tensor based on them.

For that purpose we need a tensor [?] as tf.argmax(logits, 1) for using tf.till over the categories_tensor and a tensor [?, len(CATEGORIES)] for using tf.reshape over the result of that. So

CATEGORIES # => ['dog', 'elephant']
n_classes = len(CATEGORIES) # => 2
categories_tensor = tf.constant(CATEGORIES) # => Shape [2]
pob_tensor = tf.nn.softmax(logits) 
# => Shape [?, 2] being ? the number of inputs to predict
class_tensor = tf.argmax(logits, 1) 
# => Shape [?, 1]

tiled_categories_tensor = tf.tile(categories_tensor, tf.shape(class_tensor)) # => Shape [2*?] 
# => ['dog', 'elephant', 'dog', 'elephant', ... (? times) , 'dog', 'elephant' ]
categories = tf.reshape(tiled_categories_tensor, tf.shape(prob_tensor)) # => Shape [?, 2] 
# => [['dog', 'elephant'], ['dog', 'elephant'], ... (? times) , ['dog', 'elephant'] ]

predictions_dict = {
      'category': tf.gather(CATEGORIES, tf.argmax(logits, 1)),
      'class': class_tensor,
      'prob': prob_tensor,
      'categories': categories
    }

Hope it helps to anyone facing this issue

user_11321500

I think you need to structure your graph so that the first dimension of each input is unknown so that you can support batching. I think you can do this by setting the size in the shape to None; see this question.

user_43927840
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