tf.fake_quant_with_min_max_vars_gradient(gradients, inputs, min, max, name=None)

tf.fake_quant_with_min_max_vars_gradient(gradients, inputs, min, max, name=None)

See the guide: Tensor Transformations > Fake quantization

Compute gradients for a FakeQuantWithMinMaxVars operation.

Args:

  • gradients: A Tensor of type float32. Backpropagated gradients above the FakeQuantWithMinMaxVars operation.
  • inputs: A Tensor of type float32. Values passed as inputs to the FakeQuantWithMinMaxVars operation. min, max: Quantization interval, scalar floats.
  • min: A Tensor of type float32.
  • max: A Tensor of type float32.
  • name: A name for the operation (optional).

Returns:

A tuple of Tensor objects (backprops_wrt_input, backprop_wrt_min, backprop_wrt_max). * backprops_wrt_input: A Tensor of type float32. Backpropagated gradients w.r.t. inputs: gradients * (inputs >= min && inputs <= max). * backprop_wrt_min: A Tensor of type float32. Backpropagated gradients w.r.t. min parameter: sum(gradients * (inputs < min)). * backprop_wrt_max: A Tensor of type float32. Backpropagated gradients w.r.t. max parameter: sum(gradients * (inputs > max)).

Defined in tensorflow/python/ops/gen_array_ops.py.