tf.raw_ops.QuantizedConv2DAndReluAndRequantize

tf.raw_ops.QuantizedConv2DAndReluAndRequantize(
    input, filter, min_input, max_input, min_filter, max_filter, min_freezed_output,
    max_freezed_output, strides, padding, out_type=tf.dtypes.quint8, dilations=[1,
    1, 1, 1], padding_list=[], name=None
)

Args:

  • input: A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.
  • filter: A Tensor. Must be one of the following types: qint8, quint8, qint32, qint16, quint16.
  • min_input: A Tensor of type float32.
  • max_input: A Tensor of type float32.
  • min_filter: A Tensor of type float32.
  • max_filter: A Tensor of type float32.
  • min_freezed_output: A Tensor of type float32.
  • max_freezed_output: A Tensor of type float32.
  • strides: A list of ints.
  • padding: A string from: "SAME", "VALID".
  • out_type: An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.quint8.
  • dilations: An optional list of ints. Defaults to [1, 1, 1, 1].
  • padding_list: An optional list of ints. Defaults to [].
  • name: A name for the operation (optional).

Returns:

A tuple of Tensor objects (output, min_output, max_output).

  • output: A Tensor of type out_type.
  • min_output: A Tensor of type float32.
  • max_output: A Tensor of type float32.