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Google Cloud ML:输出的外部尺寸必须未知

google-cloud-ml tensorflow machine-learning python

andresbravog · 技术社区 · 7 年前

我们在本地得到了一个正在工作的导出模型,该模型将在中创建一个新的模型版本具体如下:

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.

我们当前导出的模型响应正在 tensorflow-serve 和 gcloud predict local

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
  }
}

该问题必须分类,因为所有其他产出在第一个报告中已经存在工作输出的版本。

有什么想法吗??

2 回复 | 直到 7 年前

andresbravog 7 年前

回答我自己的问题:

我需要使用一个现有的形状张量,我需要创建一个 [?, len(CATEGORIES)]

为此,我们需要一个张量 [?] 像 tf.argmax(logits, 1) tf.till 超过 categories_tensor 和张量 [?,len(类别)] tf.reshape 而不是结果。所以

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
    }

希望这对任何面临这个问题的人都有帮助

Jeremy Lewi 7 年前

question