mlmodel的输出形状为空.为什么形状为空? [英] mlmodel's output shape is empty. Why is shape empty?
本文介绍了mlmodel的输出形状为空.为什么形状为空?的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我研究了本教程 https://www.tensorflow.org/tutorials/generative/cyclegan，并且在Windows上可以很好地完成模型.然后，我将此tf模型转换为mlmodel，但是模型的输出(MultyArray)的形状为空.我该如何解决这个问题...?(此型号的EPOCH为1)
I study this tutorial https://www.tensorflow.org/tutorials/generative/cyclegan and the completed model is well worked on windows. Then I convert this tf model to mlmodel, but the model's output(MultyArray) has empty shape. How do i solve this problem...? (This model's EPOCH is 1)
计算机::: Windows10/Tensorflow和-gpu 2.2/tfcoreml 1.1
computer::: windows10 / tensorflow and -gpu 2.2 / tfcoreml 1.1
这是转换代码
import tfcoreml
import coremltools
from tensorflow import keras
saved_model = keras.models.load_model('saved_model')
# get input, output node names for the TF graph from the Keras model
input_name = (saved_model.inputs[0].name.split(':')[0])[0:7]
keras_output_node_name = saved_model.outputs[0].name.split(':')[0]
graph_output_node_name = keras_output_node_name.split('/')[-1]
# Saving the Core ML model to a file.
model = tfcoreml.convert('saved_model',
image_input_names=input_name,
input_name_shape_dict={input_name: [1, 540, 540, 3]},
output_feature_names=[graph_output_node_name],
minimum_ios_deployment_target='13',
red_bias=-123.68,
green_bias=-116.78,
blue_bias=-103.94)
model.save('./saved_mlmodel/saved_model.mlmodel')
这是save_model.summary
this is saved_model.summary
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, None, None, 0
__________________________________________________________________________________________________
sequential_15 (Sequential) (None, None, None, 6 3072 input_2[0][0]
__________________________________________________________________________________________________
sequential_16 (Sequential) (None, None, None, 1 131328 sequential_15[0][0]
__________________________________________________________________________________________________
sequential_17 (Sequential) (None, None, None, 2 524800 sequential_16[0][0]
__________________________________________________________________________________________________
sequential_18 (Sequential) (None, None, None, 5 2098176 sequential_17[0][0]
__________________________________________________________________________________________________
sequential_19 (Sequential) (None, None, None, 5 4195328 sequential_18[0][0]
__________________________________________________________________________________________________
sequential_20 (Sequential) (None, None, None, 5 4195328 sequential_19[0][0]
__________________________________________________________________________________________________
sequential_21 (Sequential) (None, None, None, 5 4195328 sequential_20[0][0]
__________________________________________________________________________________________________
sequential_22 (Sequential) (None, None, None, 5 4195328 sequential_21[0][0]
__________________________________________________________________________________________________
sequential_23 (Sequential) (None, None, None, 5 4195328 sequential_22[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) multiple 0 sequential_23[0][0]
sequential_21[0][0]
sequential_24[0][0]
sequential_20[0][0]
sequential_25[0][0]
sequential_19[0][0]
sequential_26[0][0]
sequential_18[0][0]
sequential_27[0][0]
sequential_17[0][0]
sequential_28[0][0]
sequential_16[0][0]
sequential_29[0][0]
sequential_15[0][0]
__________________________________________________________________________________________________
sequential_24 (Sequential) (None, None, None, 5 8389632 concatenate_1[0][0]
__________________________________________________________________________________________________
sequential_25 (Sequential) (None, None, None, 5 8389632 concatenate_1[1][0]
__________________________________________________________________________________________________
sequential_26 (Sequential) (None, None, None, 5 8389632 concatenate_1[2][0]
__________________________________________________________________________________________________
sequential_27 (Sequential) (None, None, None, 2 4194816 concatenate_1[3][0]
__________________________________________________________________________________________________
sequential_28 (Sequential) (None, None, None, 1 1048832 concatenate_1[4][0]
__________________________________________________________________________________________________
sequential_29 (Sequential) (None, None, None, 6 262272 concatenate_1[5][0]
__________________________________________________________________________________________________
conv2d_transpose_15 (Conv2DTran (None, None, None, 3 6147 concatenate_1[6][0]
==================================================================================================
Total params: 54,414,979
Trainable params: 54,414,979
Non-trainable params: 0
这是mlmodel.spec说明
this is mlmodel.spec description
input {
name: "input_2"
type {
imageType {
width: 540
height: 540
colorSpace: RGB
}
}
}
output {
name: "Identity"
type {
multiArrayType {
dataType: FLOAT32
}
}
}
metadata {
userDefined {
key: "coremltoolsVersion"
value: "3.4"
}
}
推荐答案
这通常不是问题.当你运行模型时，你仍然会得到一个正确形状的多数组.
This is usually not a problem. When you run the model, you still get a multi-array of the correct shape.
如果您已经知道形状，则可以对其进行填充，以使其显示在mlmodel文件中，但这更多是出于文档目的.
If you already know the shape, you can fill it in so that it shows up in the mlmodel file, but this is more for documentation purposes than anything else.
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