If the input is a vector (rank=1), finds the k largest entries in the vector and outputs their values and indices as vectors. Thus result = tf.math.top_k([1, 2, 98, 1, 1, 99, 3, 1, 3, 96, 4, 1],
k=3)
result.values.numpy()
array([99, 98, 96], dtype=int32)
result.indices.numpy()
array([5, 2, 9], dtype=int32)
1 is the result = tf.math.top_k([1, 2, 98, 1, 1, 99, 3, 1, 3, 96, 4, 1],
k=3)
result.values.numpy()
array([99, 98, 96], dtype=int32)
result.indices.numpy()
array([5, 2, 9], dtype=int32)
2-th largest entry in result = tf.math.top_k([1, 2, 98, 1, 1, 99, 3, 1, 3, 96, 4, 1],
k=3)
result.values.numpy()
array([99, 98, 96], dtype=int32)
result.indices.numpy()
array([5, 2, 9], dtype=int32)
3, and its index is result = tf.math.top_k([1, 2, 98, 1, 1, 99, 3, 1, 3, 96, 4, 1],
k=3)
result.values.numpy()
array([99, 98, 96], dtype=int32)
result.indices.numpy()
array([5, 2, 9], dtype=int32)
4. result = tf.math.top_k([1, 2, 98, 1, 1, 99, 3, 1, 3, 96, 4, 1],
k=3)
result.values.numpy()
array([99, 98, 96], dtype=int32)
result.indices.numpy()
array([5, 2, 9], dtype=int32)
For matrices (resp. higher rank input), computes the top k entries in each row (resp. vector along the last dimension). Thus, input = tf.random.normal(shape=(3,4,5,6))
k = 2
values, indices = tf.math.top_k(input, k=k)
values.shape.as_list()
[3, 4, 5, 2]
values.shape == indices.shape == input.shape[:-1] + [k]
True
The indices can be used to result = tf.math.top_k([1, 2, 98, 1, 1, 99, 3, 1, 3, 96, 4, 1],
k=3)
result.values.numpy()
array([99, 98, 96], dtype=int32)
result.indices.numpy()
array([5, 2, 9], dtype=int32)
6 from a tensor who's shape matches result = tf.math.top_k([1, 2, 98, 1, 1, 99, 3, 1, 3, 96, 4, 1],
k=3)
result.values.numpy()
array([99, 98, 96], dtype=int32)
result.indices.numpy()
array([5, 2, 9], dtype=int32)
3. gathered_values = tf.gather(input, indices, batch_dims=-1)
assert tf.reduce_all(gathered_values == values)
If two elements are equal, the lower-index element appears first. result = tf.math.top_k([1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0],
k=3)
result.indices.numpy()
array([0, 1, 3], dtype=int32)
By default, indices are returned as type result = tf.math.top_k([1, 2, 98, 1, 1, 99, 3, 1, 3, 96, 4, 1],
k=3)
result.values.numpy()
array([99, 98, 96], dtype=int32)
result.indices.numpy()
array([5, 2, 9], dtype=int32)
8, however, this can be changed by specifying the result = tf.math.top_k([1, 2, 98, 1, 1, 99, 3, 1, 3, 96, 4, 1],
k=3)
result.values.numpy()
array([99, 98, 96], dtype=int32)
result.indices.numpy()
array([5, 2, 9], dtype=int32)
9. result = tf.math.top_k([1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0],
k=3, index_type=tf.int16)
result.indices.numpy()
array([0, 1, 3], dtype=int16)
Argsresult = tf.math.top_k([1, 2, 98, 1, 1, 99, 3, 1, 3, 96, 4, 1],
k=3)
result.values.numpy()
array([99, 98, 96], dtype=int32)
result.indices.numpy()
array([5, 2, 9], dtype=int32)
3 1-D or higher input = tf.random.normal(shape=(3,4,5,6))
k = 2
values, indices = tf.math.top_k(input, k=k)
values.shape.as_list()
[3, 4, 5, 2]
values.shape == indices.shape == input.shape[:-1] + [k]
True
1 with last dimension at least k.`k`0-D input = tf.random.normal(shape=(3,4,5,6))
k = 2
values, indices = tf.math.top_k(input, k=k)
values.shape.as_list()
[3, 4, 5, 2]
values.shape == indices.shape == input.shape[:-1] + [k]
True
1 of type input = tf.random.normal(shape=(3,4,5,6))
k = 2
values, indices = tf.math.top_k(input, k=k)
values.shape.as_list()
[3, 4, 5, 2]
values.shape == indices.shape == input.shape[:-1] + [k]
True
5, result = tf.math.top_k([1, 2, 98, 1, 1, 99, 3, 1, 3, 96, 4, 1],
k=3)
result.values.numpy()
array([99, 98, 96], dtype=int32)
result.indices.numpy()
array([5, 2, 9], dtype=int32)
8 or input = tf.random.normal(shape=(3,4,5,6))
k = 2
values, indices = tf.math.top_k(input, k=k)
values.shape.as_list()
[3, 4, 5, 2]
values.shape == indices.shape == input.shape[:-1] + [k]
True
7. Number of top element to look for along the last dimension (along each row for matrices). input = tf.random.normal(shape=(3,4,5,6))
k = 2
values, indices = tf.math.top_k(input, k=k)
values.shape.as_list()
[3, 4, 5, 2]
values.shape == indices.shape == input.shape[:-1] + [k]
True
8 If true the resulting k elements will be sorted by the values in descending order. result = tf.math.top_k([1, 2, 98, 1, 1, 99, 3, 1, 3, 96, 4, 1],
k=3)
result.values.numpy()
array([99, 98, 96], dtype=int32)
result.indices.numpy()
array([5, 2, 9], dtype=int32)
9 Optional dtype for output indices. gathered_values = tf.gather(input, indices, batch_dims=-1)
assert tf.reduce_all(gathered_values == values)
1 Optional name for the operation. ReturnsA tuple with two named fields: gathered_values = tf.gather(input, indices, batch_dims=-1)
assert tf.reduce_all(gathered_values == values)
2 The k largest elements along each last dimensional slice. gathered_values = tf.gather(input, indices, batch_dims=-1)
assert tf.reduce_all(gathered_values == values)
4 The indices of gathered_values = tf.gather(input, indices, batch_dims=-1)
assert tf.reduce_all(gathered_values == values)
2 within the last dimension of result = tf.math.top_k([1, 2, 98, 1, 1, 99, 3, 1, 3, 96, 4, 1],
k=3)
result.values.numpy()
array([99, 98, 96], dtype=int32)
result.indices.numpy()
array([5, 2, 9], dtype=int32)
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