tf.nn.quantized_conv2d

tf.nn.quantized_conv2d(
    input,
    filter,
    min_input,
    max_input,
    min_filter,
    max_filter,
    strides,
    padding,
    out_type=tf.qint32,
    dilations=[1, 1, 1, 1],
    name=None
)

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

See the guide: Neural Network > Candidate Sampling

Computes a 2D convolution given quantized 4D input and filter tensors.

The inputs are quantized tensors where the lowest value represents the real number of the associated minimum, and the highest represents the maximum. This means that you can only interpret the quantized output in the same way, by taking the returned minimum and maximum values into account.

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. filter's input_depth dimension must match input's depth dimensions.
  • min_input: A Tensor of type float32. The float value that the lowest quantized input value represents.
  • max_input: A Tensor of type float32. The float value that the highest quantized input value represents.
  • min_filter: A Tensor of type float32. The float value that the lowest quantized filter value represents.
  • max_filter: A Tensor of type float32. The float value that the highest quantized filter value represents.
  • strides: A list of ints. The stride of the sliding window for each dimension of the input tensor.
  • padding: A string from: "SAME", "VALID". The type of padding algorithm to use.
  • out_type: An optional tf.DType from: tf.qint8, tf.quint8, tf.qint32, tf.qint16, tf.quint16. Defaults to tf.qint32.
  • dilations: An optional list of ints. Defaults to [1, 1, 1, 1]. 1-D tensor of length 4. The dilation factor for each dimension of input. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of data_format, see above for details. Dilations in the batch and depth dimensions must be 1.
  • 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.