tf.compat.v1.nn.quantized_avg_pool

Produces the average pool of the input tensor for quantized types.

tf.compat.v1.nn.quantized_avg_pool(
    input: _atypes.TensorFuzzingAnnotation[TV_QuantizedAvgPool_T],
    min_input: _atypes.TensorFuzzingAnnotation[_atypes.Float32],
    max_input: _atypes.TensorFuzzingAnnotation[_atypes.Float32],
    ksize,
    strides,
    padding: str,
    name=None
)

Args
`input`	A `Tensor`. Must be one of the following types: `qint8`, `quint8`, `qint32`, `qint16`, `quint16`. 4-D with shape `[batch, height, width, channels]`.
`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.
`ksize`	A list of `ints`. The size of the window for each dimension of the input tensor. The length must be 4 to match the number of dimensions of the input.
`strides`	A list of `ints`. The stride of the sliding window for each dimension of the input tensor. The length must be 4 to match the number of dimensions of the input.
`padding`	A `string` from: `"SAME", "VALID"`. The type of padding algorithm to use.
`name`	A name for the operation (optional).

Returns
A tuple of `Tensor` objects (output, min_output, max_output).
`output`	A `Tensor`. Has the same type as `input`.
`min_output`	A `Tensor` of type `float32`.
`max_output`	A `Tensor` of type `float32`.

tf.compat.v1.nn.quantized_avg_pool

Args

Returns