A layer (....) which is an input to the Conv operator producing the output array model/re_lu_1/Relu, is lacking min/max data, which is necessary for quantization. If accuracy matters, either target a non-quantized output format, or run quantized training with your model from a floating point checkpoint to change the input graph to contain min/max information. If you don't care about accuracy, you can pass --default_ranges_min= and --default_ranges_max= for easy experimentation.
Batch Normalization Quantize Tensorflow 1.x does not have MinMax information
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For tensorflow 1.x, if you want to quantize, you have to place it with fake quantization nodes to activate the quantization of the model. There are 3 phases of quantization:
However, the most important factor is the configuration of batch_normalization in the model. After trying multiple configuration, the best one is using batch_normalization without fused option from
tensorflow.keras.layers
. The reason is because Tensorflow want to avoid the folding result to be quantized. Therefore, activation behind batchnorm wont work. Details in [here][1]In short, this layer should be attached only under
tensorflow.keras.layers.Conv2D
with parsed activation param, which is Relu/Relu6/IdentityIf you conduct the above process: Conv2d=>Activation=>BatchNorm
the layer will not yield errors
does not have MinMax information