Quantize tensor based on min/max of tensor values across all batches.

Inherits From: Quantizer

num_bits Number of bits for quantization
per_axis Whether to apply per_axis quantization. The last dimension is used as the axis.
symmetric If true, use symmetric quantization limits instead of training the minimum and maximum of each quantization range separately.
narrow_range In case of 8 bits, narrow_range nudges the quantized range to be [-127, 127] instead of [-128, 127]. This ensures symmetric range has 0 as the centre.



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Construct the weights required by the quantizer.

A quantizer may need to construct variables to hold the state for its algorithm. This function is invoked during the build stage of the layer that the quantizer is used for. Any variables constructed are under the scope of the layer and serialized as part of the layer.

tensor_shape Shape of tensor which needs to be quantized.
name Name of tensor.
layer Keras layer which is quantizing the tensors. The layer is needed to construct the weights, and is also the owner of the weights.

Returns: Dictionary of constructed weights. This dictionary will be passed to the quantizer's call function as a weights dictionary.


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Instantiates a Quantizer from its config.

config Output of get_config().

A Quantizer instance.


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Returns the config used to serialize the Quantizer.


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Quantize tensor.

inputs Input tensor to be quantized.
training Whether the graph is currently training.
weights Dictionary of weights the quantizer can use to quantize the tensor. This contains the weights created in the build function.
**kwargs Additional variables which may be passed to the quantizer.

Quantized tensor.


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Return self==value.


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Return self!=value.