本文整理汇总了Python中tensorflow.python.ops.array_ops.quantize_v2方法的典型用法代码示例。如果您正苦于以下问题：Python array_ops.quantize_v2方法的具体用法？Python array_ops.quantize_v2怎么用？Python array_ops.quantize_v2使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.ops.array_ops的用法示例。
在下文中一共展示了array_ops.quantize_v2方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: quantize_weight_eightbit
# 需要导入模块: from tensorflow.python.ops import array_ops [as 别名]
# 或者: from tensorflow.python.ops.array_ops import quantize_v2 [as 别名]
def quantize_weight_eightbit(input_node, quantization_mode):
"""Returns replacement nodes for input_node using the Dequantize op."""
base_name = input_node.name + "_"
quint8_const_name = base_name + "quint8_const"
min_name = base_name + "min"
max_name = base_name + "max"
float_tensor = tensor_util.MakeNdarray(input_node.attr["value"].tensor)
min_value = np.min(float_tensor.flatten())
max_value = np.max(float_tensor.flatten())
# Make sure that the range includes zero.
if min_value > 0.0:
min_value = 0.0
# min_value == max_value is a tricky case. It can occur for general
# tensors, and of course for scalars. The quantized ops cannot deal
# with this case, so we set max_value to something else.
# It's a tricky question what is the numerically best solution to
# deal with this degeneracy.
# TODO(petewarden): Better use a tolerance than a hard comparison?
if min_value == max_value:
if abs(min_value) < 0.000001:
max_value = min_value + 1.0
elif min_value > 0:
max_value = 2 * min_value
else:
max_value = min_value / 2.0
sess = session.Session()
with sess.as_default():
quantize_op = array_ops.quantize_v2(
float_tensor,
min_value,
max_value,
dtypes.quint8,
mode=quantization_mode)
quint8_tensor = quantize_op[0].eval()
shape = tensor_util.TensorShapeProtoToList(input_node.attr["value"]
.tensor.tensor_shape)
quint8_const_node = create_constant_node(
quint8_const_name, quint8_tensor, dtypes.quint8, shape=shape)
min_node = create_constant_node(min_name, min_value, dtypes.float32)
max_node = create_constant_node(max_name, max_value, dtypes.float32)
dequantize_node = create_node("Dequantize", input_node.name,
[quint8_const_name, min_name, max_name])
set_attr_dtype(dequantize_node, "T", dtypes.quint8)
set_attr_string(dequantize_node, "mode", quantization_mode)
return [quint8_const_node, min_node, max_node, dequantize_node]