def test_shape_function(self):
# size must be scalar.
with self.assertRaisesRegexp(
ValueError, "Shape must be rank 0 but is rank 1 for 'Bincount'"):
gen_math_ops.bincount([1, 2, 3, -1, 6, 8], [1], [])
# size must be positive.
with self.assertRaisesRegexp(ValueError, "must be non-negative"):
gen_math_ops.bincount([1, 2, 3, -1, 6, 8], -5, [])
# if size is a constant then the shape is known.
v1 = gen_math_ops.bincount([1, 2, 3, -1, 6, 8], 5, [])
self.assertAllEqual(v1.get_shape().as_list(), [5])
# if size is a placeholder then the shape is unknown.
s = array_ops.placeholder(dtype=dtypes.int32)
v2 = gen_math_ops.bincount([1, 2, 3, -1, 6, 8], s, [])
self.assertAllEqual(v2.get_shape().as_list(), [None])
def test_shape_function(self):
# size must be scalar.
with self.assertRaisesRegexp(
ValueError, "Shape must be rank 0 but is rank 1 for 'Bincount'"):
gen_math_ops.bincount([1, 2, 3, -1, 6, 8], [1], [])
# size must be positive.
with self.assertRaisesRegexp(ValueError, "must be non-negative"):
gen_math_ops.bincount([1, 2, 3, -1, 6, 8], -5, [])
# if size is a constant then the shape is known.
v1 = gen_math_ops.bincount([1, 2, 3, -1, 6, 8], 5, [])
self.assertAllEqual(v1.get_shape().as_list(), [5])
# if size is a placeholder then the shape is unknown.
s = array_ops.placeholder(dtype=dtypes.int32)
v2 = gen_math_ops.bincount([1, 2, 3, -1, 6, 8], s, [])
self.assertAllEqual(v2.get_shape().as_list(), [None])
def test_shape_function(self):
# size must be scalar.
with self.assertRaisesRegex(
(ValueError, errors.InvalidArgumentError),
"Shape must be rank 0 but is rank 1(?s).*Bincount"):
gen_math_ops.bincount([1, 2, 3, 1, 6, 8], [1], [])
# size must be positive.
with self.assertRaisesRegex((ValueError, errors.InvalidArgumentError),
"must be non-negative"):
gen_math_ops.bincount([1, 2, 3, 1, 6, 8], -5, [])
# if size is a constant then the shape is known.
v1 = gen_math_ops.bincount([1, 2, 3, 1, 6, 8], 5, [])
self.assertAllEqual(v1.get_shape().as_list(), [5])
# if size is a placeholder then the shape is unknown.
with ops.Graph().as_default():
s = array_ops.placeholder(dtype=dtypes.int32)
v2 = gen_math_ops.bincount([1, 2, 3, 1, 6, 8], s, [])
self.assertAllEqual(v2.get_shape().as_list(), [None])
def bincount(arr,
weights=None,
minlength=None,
maxlength=None,
dtype=dtypes.int32,
name=None,
axis=None,
binary_output=False):
"""Counts the number of occurrences of each value in an integer array.
If `minlength` and `maxlength` are not given, returns a vector with length
`tf.reduce_max(arr) + 1` if `arr` is non-empty, and length 0 otherwise.
If `weights` are non-None, then index `i` of the output stores the sum of the
value in `weights` at each index where the corresponding value in `arr` is
`i`.
```python
values = tf.constant([1,1,2,3,2,4,4,5])
tf.math.bincount(values) #[0 2 2 1 2 1]
```
Vector length = Maximum element in vector `values` is 5. Adding 1, which is 6
will be the vector length.
Each bin value in the output indicates number of occurrences of the particular
index. Here, index 1 in output has a value 2. This indicates value 1 occurs
two times in `values`.
```python
values = tf.constant([1,1,2,3,2,4,4,5])
weights = tf.constant([1,5,0,1,0,5,4,5])
tf.math.bincount(values, weights=weights) #[0 6 0 1 9 5]
```
Bin will be incremented by the corresponding weight instead of 1.
Here, index 1 in output has a value 6. This is the summation of weights
corresponding to the value in `values`.
**Bin-counting on a certain axis**
This example takes a 2 dimensional input and returns a `Tensor` with
bincounting on each sample.
>>> data = np.array([[1, 2, 3, 0], [0, 0, 1, 2]], dtype=np.int32)
>>> tf.math.bincount(data, axis=-1)
<tf.Tensor: shape=(2, 4), dtype=int32, numpy=
array([[1, 1, 1, 1],
[2, 1, 1, 0]], dtype=int32)>
**Bin-counting with binary_output**
This example gives binary output instead of counting the occurrence.
>>> data = np.array([[1, 2, 3, 0], [0, 0, 1, 2]], dtype=np.int32)
>>> tf.math.bincount(data, axis=-1, binary_output=True)
<tf.Tensor: shape=(2, 4), dtype=int32, numpy=
array([[1, 1, 1, 1],
[1, 1, 1, 0]], dtype=int32)>
Args:
arr: A Tensor, RaggedTensor, or SparseTensor whose values should be counted.
These tensors must have a rank of 2 if `axis=-1`.
weights: If non-None, must be the same shape as arr. For each value in
`arr`, the bin will be incremented by the corresponding weight instead of
1.
minlength: If given, ensures the output has length at least `minlength`,
padding with zeros at the end if necessary.
maxlength: If given, skips values in `arr` that are equal or greater than
`maxlength`, ensuring that the output has length at most `maxlength`.
dtype: If `weights` is None, determines the type of the output bins.
name: A name scope for the associated operations (optional).
axis: The axis to slice over. Axes at and below `axis` will be flattened
before bin counting. Currently, only `0`, and `-1` are supported. If None,
all axes will be flattened (identical to passing `0`).
binary_output: If True, this op will output 1 instead of the number of times
a token appears (equivalent to one_hot + reduce_any instead of one_hot +
reduce_add). Defaults to False.
Returns:
A vector with the same dtype as `weights` or the given `dtype`. The bin
values.
Raises:
`InvalidArgumentError` if negative values are provided as an input.
"""
name = "bincount" if name is None else name
with ops.name_scope(name):
# Somehow forward compatible needs to be False.
if not binary_output and axis is None:
arr = ops.convert_to_tensor(arr, name="arr", dtype=dtypes.int32)
array_is_nonempty = math_ops.reduce_prod(array_ops.shape(arr)) > 0
output_size = math_ops.cast(array_is_nonempty, dtypes.int32) * (
math_ops.reduce_max(arr) + 1)
if minlength is not None:
minlength = ops.convert_to_tensor(minlength,
name="minlength",
dtype=dtypes.int32)
output_size = gen_math_ops.maximum(minlength, output_size)
if maxlength is not None:
maxlength = ops.convert_to_tensor(maxlength,
name="maxlength",
dtype=dtypes.int32)
output_size = gen_math_ops.minimum(maxlength, output_size)
if weights is not None:
weights = ops.convert_to_tensor(weights, name="weights")
return gen_math_ops.unsorted_segment_sum(
weights, arr, output_size)
weights = constant_op.constant([], dtype)
arr = array_ops.reshape(arr, [-1])
return gen_math_ops.bincount(arr, output_size, weights)
if not isinstance(arr, sparse_tensor.SparseTensor):
arr = ragged_tensor.convert_to_tensor_or_ragged_tensor(arr,
name="arr")
if weights is not None:
if not isinstance(weights, sparse_tensor.SparseTensor):
weights = ragged_tensor.convert_to_tensor_or_ragged_tensor(
weights, name="weights")
if weights is not None and binary_output:
raise ValueError(
"Arguments `binary_output` and `weights` are mutually "
"exclusive. Please specify only one.")
if not arr.dtype.is_integer:
arr = math_ops.cast(arr, dtypes.int32)
if axis is None:
axis = 0
if axis not in [0, -1]:
raise ValueError(
f"Unsupported value for argument axis={axis}. Only 0 and"
" -1 are currently supported.")
if isinstance(arr, ragged_tensor.RaggedTensor):
array_is_nonempty = math_ops.reduce_prod(
array_ops.shape(arr.values)) > 0
else:
array_is_nonempty = math_ops.reduce_prod(array_ops.shape(arr)) > 0
if isinstance(arr, sparse_tensor.SparseTensor):
output_size = math_ops.cast(array_is_nonempty, arr.dtype) * (
math_ops.reduce_max(arr.values) + 1)
else:
output_size = math_ops.cast(
array_is_nonempty, arr.dtype) * (math_ops.reduce_max(arr) + 1)
if minlength is not None:
minlength = ops.convert_to_tensor(minlength,
name="minlength",
dtype=arr.dtype)
output_size = gen_math_ops.maximum(minlength, output_size)
if maxlength is not None:
maxlength = ops.convert_to_tensor(maxlength,
name="maxlength",
dtype=arr.dtype)
output_size = gen_math_ops.minimum(maxlength, output_size)
if axis == 0:
if isinstance(arr, sparse_tensor.SparseTensor):
if weights is not None:
weights = validate_sparse_weights(arr, weights, dtype)
arr = arr.values
elif isinstance(arr, ragged_tensor.RaggedTensor):
if weights is not None:
weights = validate_ragged_weights(arr, weights, dtype)
arr = arr.values
else:
if weights is not None:
weights = array_ops.reshape(weights, [-1])
arr = array_ops.reshape(arr, [-1])
if isinstance(arr, sparse_tensor.SparseTensor):
weights = validate_sparse_weights(arr, weights, dtype)
return gen_math_ops.sparse_bincount(indices=arr.indices,
values=arr.values,
dense_shape=arr.dense_shape,
size=output_size,
weights=weights,
binary_output=binary_output)
elif isinstance(arr, ragged_tensor.RaggedTensor):
weights = validate_ragged_weights(arr, weights, dtype)
return gen_math_ops.ragged_bincount(splits=arr.row_splits,
values=arr.values,
size=output_size,
weights=weights,
binary_output=binary_output)
else:
weights = validate_dense_weights(arr, weights, dtype)
return gen_math_ops.dense_bincount(input=arr,
size=output_size,
weights=weights,
binary_output=binary_output)
