Core ops

• Original Link : https://keras.io/api/ops/core/
• Last Checked at : 2024-11-25

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associative_scan function

keras.ops.associative_scan(f, elems, reverse=False, axis=0)

Performs a scan with an associative binary operation, in parallel.

This operation his similar to scan, with the key difference that associative_scan is a parallel implementation with potentially significant performance benefits, especially when jit compiled. The catch is that it can only be used when f is a binary associative operation (i.e. it must verify f(a, f(b, c)) == f(f(a, b), c)).

For an introduction to associative scans, refer to this paper: Blelloch, Guy E. 1990. Prefix Sums and Their Applications.

Arguments

• f: A Python callable implementing an associative binary operation with signature r = f(a, b). Function f must be associative, i.e., it must satisfy the equation f(a, f(b, c)) == f(f(a, b), c). The inputs and result are (possibly nested Python tree structures of) array(s) matching elems. Each array has a dimension in place of the axis dimension. f should be applied elementwise over the axis dimension. The result r has the same shape (and structure) as the two inputs a and b.
• elems: A (possibly nested Python tree structure of) array(s), each with an axis dimension of size num_elems.
• reverse: A boolean stating if the scan should be reversed with respect to the axis dimension.
• axis: an integer identifying the axis over which the scan should occur.

Returns

A (possibly nested Python tree structure of) array(s) of the same shape and structure as elems, in which the k‘th element of axis is the result of recursively applying f to combine the first k elements of elems along axis. For example, given elems = [a, b, c, ...], the result would be [a, f(a, b), f(f(a, b), c), ...].

Examples

>>> sum_fn = lambda x, y: x + y
>>> xs = keras.ops.arange(5)
>>> ys = keras.ops.associative_scan(sum_fn, xs, axis=0)
>>> ys
[0, 1, 3, 6, 10]

>>> sum_fn = lambda x, y: [x[0] + y[0], x[1] + y[1], x[2] + y[2]]
>>> xs = [keras.ops.array([[1, 2]]) for _ in range(3)]
>>> ys = keras.ops.associative_scan(sum_fn, xs, axis=0)
>>> ys
[[1, 3], [1, 3], [1, 3]]

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cast function

keras.ops.cast(x, dtype)

Cast a tensor to the desired dtype.

Arguments

• x: A tensor or variable.
• dtype: The target type.

Returns

A tensor of the specified dtype.

Example

>>> x = keras.ops.arange(4)
>>> x = keras.ops.cast(x, dtype="float16")

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cond function

keras.ops.cond(pred, true_fn, false_fn)

Conditionally applies true_fn or false_fn.

Arguments

• pred: Boolean scalar type
• true_fn: Callable returning the output for the pred == True case.
• false_fn: Callable returning the output for the pred == False case.

Returns

The output of either true_fn or false_fn depending on pred.

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convert_to_numpy function

keras.ops.convert_to_numpy(x)

Convert a tensor to a NumPy array.

Arguments

• x: A tensor.

Returns

A NumPy array.

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convert_to_tensor function

keras.ops.convert_to_tensor(x, dtype=None, sparse=None)

Convert a NumPy array to a tensor.

Arguments

• x: A NumPy array.
• dtype: The target type.
• sparse: Whether to keep sparse tensors. False will cause sparse tensors to be densified. The default value of None means that sparse tensors are kept only if the backend supports them.

Returns

A tensor of the specified dtype.

Example

>>> x = np.array([1, 2, 3])
>>> y = keras.ops.convert_to_tensor(x)

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custom_gradient function

keras.ops.custom_gradient(f)

Decorator to define a function with a custom gradient.

This decorator allows fine grained control over the gradients of a sequence for operations. This may be useful for multiple reasons, including providing a more efficient or numerically stable gradient for a sequence of operations.

Arguments

• f: Function f(*args) that returns a tuple (output, grad_fn), where:
  • args is a sequence of (nested structures of) tensor inputs to the function.
  • output is a (nested structure of) tensor outputs of applying operations in forward_fn to args.
  • grad_fn is a function with the signature grad_fn(*args, upstream) which returns a tuple of tensors the same size as (flattened) args: the derivatives of tensors in output with respect to the tensors in args. upstream is a tensor or sequence of tensors holding the initial value gradients for each tensor in output.

Returns

A function h(*args) which returns the same value as f(*args)[0] and whose gradient is determined by f(*args)[1].

Examples

1. Backend-agnostic example.

@ops.custom_gradient
def log1pexp(x):
    e = ops.exp(x)
    def grad(*args, upstream=None):
        if upstream is None:
            (upstream,) = args
        return ops.multiply(upstream, 1.0 - 1.0 / ops.add(1, e))
    return ops.log(1 + e), grad

Note that the grad function that returns gradient computation requires args as well as an upstream keyword argument, depending on the backend being set. With the JAX and TensorFlow backends, it requires only one argument, whereas it might use the upstream argument in the case of the PyTorch backend.

When working with TensorFlow/JAX backend, grad(upstream) is sufficient. With PyTorch, the grad function requires *args as well as upstream, e.g. def grad(*args, upstream). Follow the previous example to use @ops.custom_gradient in a way that is compatible with all backends.

1. Here’s JAX & TensorFlow-specific example:

@ops.custom_gradient
def log1pexp(x):
    e = ops.exp(x)
    def grad(upstream):
        return ops.multiply(upstream, 1.0 - 1.0 / ops.add(1, e))
    return ops.log(1 + e), grad

1. Lastly, here’s a PyTorch-specific example, using *args & upstream:

@ops.custom_gradient
def log1pexp(x):
    e = ops.exp(x)
    def grad(*args, upstream):
        return ops.multiply(upstream, 1.0 - 1.0 / ops.add(1, e))
    return ops.log(1 + e), grad

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dtype function

keras.ops.dtype(x)

Return the dtype of the tensor input as a standardized string.

Note that due to the standardization, the dtype will not compare equal to the backend-specific version of the dtype.

Arguments

• x: A tensor. This function will try to access the dtype attribute of the input tensor.

Returns

A string indicating the dtype of the input tensor, e.g. "float32".

Example

>>> x = keras.ops.zeros((8, 12))
>>> keras.ops.dtype(x)
'float32'

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erf function

keras.ops.erf(x)

Computes the error function of x, element-wise.

Arguments

• x: Input tensor.

Returns

A tensor with the same dtype as x.

Example

>>> x = np.array([-3.0, -2.0, -1.0, 0.0, 1.0])
>>> keras.ops.erf(x)
array([-0.99998 , -0.99532, -0.842701,  0.,  0.842701], dtype=float32)

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erfinv function

keras.ops.erfinv(x)

Computes the inverse error function of x, element-wise.

Arguments

• x: Input tensor.

Returns

A tensor with the same dtype as x.

Example

>>> x = np.array([-0.5, -0.2, -0.1, 0.0, 0.3])
>>> keras.ops.erfinv(x)
array([-0.47694, -0.17914, -0.08886,  0. ,  0.27246], dtype=float32)

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extract_sequences function

keras.ops.extract_sequences(x, sequence_length, sequence_stride)

Expands the dimension of last axis into sequences of sequence_length.

Slides a window of size sequence_length over the last axis of the input with a stride of sequence_stride, replacing the last axis with [num_sequences, sequence_length] sequences.

If the dimension along the last axis is N, the number of sequences can be computed by:

num_sequences = 1 + (N - sequence_length) // sequence_stride

Arguments

• x: Input tensor.
• sequence_length: An integer representing the sequences length.
• sequence_stride: An integer representing the sequences hop size.

Returns

A tensor of sequences with shape […, num_sequences, sequence_length].

Example

>>> x = keras.ops.convert_to_tensor([1, 2, 3, 4, 5, 6])
>>> extract_sequences(x, 3, 2)
array([[1, 2, 3],
   [3, 4, 5]])

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fori_loop function

keras.ops.fori_loop(lower, upper, body_fun, init_val)

For loop implementation.

Arguments

• lower: The initial value of the loop variable.
• upper: The upper bound of the loop variable.
• body_fun: A callable that represents the loop body. Must take two arguments: the loop variable and the loop state. The loop state should be updated and returned by this function.
• init_val: The initial value of the loop state.

Returns

The final state after the loop.

Example

>>> lower = 0
>>> upper = 10
>>> body_fun = lambda i, s: (i + 1, s + i)
>>> init_val = 0
>>> keras.ops.fori_loop(lower, upper, body_fun, init_val)
45

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in_top_k function

keras.ops.in_top_k(targets, predictions, k)

Checks if the targets are in the top-k predictions.

Arguments

• targets: A tensor of true labels.
• predictions: A tensor of predicted labels.
• k: An integer representing the number of predictions to consider.

Returns

A boolean tensor of the same shape as targets, where each element indicates whether the corresponding target is in the top-k predictions.

Example

>>> targets = keras.ops.convert_to_tensor([2, 5, 3])
>>> predictions = keras.ops.convert_to_tensor(
... [[0.1, 0.4, 0.6, 0.9, 0.5],
...  [0.1, 0.7, 0.9, 0.8, 0.3],
...  [0.1, 0.6, 0.9, 0.9, 0.5]])
>>> in_top_k(targets, predictions, k=3)
array([ True False  True], shape=(3,), dtype=bool)

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is_tensor function

keras.ops.is_tensor(x)

Check whether the given object is a tensor.

Note: This checks for backend specific tensors so passing a TensorFlow tensor would return False if your backend is PyTorch or JAX.

Arguments

• x: A variable.

Returns

True if x is a tensor, otherwise False.

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logsumexp function

keras.ops.logsumexp(x, axis=None, keepdims=False)

Computes the logarithm of sum of exponentials of elements in a tensor.

Arguments

• x: Input tensor.
• axis: An integer or a tuple of integers specifying the axis/axes along which to compute the sum. If None, the sum is computed over all elements. Defaults to None.
• keepdims: A boolean indicating whether to keep the dimensions of the input tensor when computing the sum. Defaults to False.

Returns

A tensor containing the logarithm of the sum of exponentials of elements in x.

Example

>>> x = keras.ops.convert_to_tensor([1., 2., 3.])
>>> logsumexp(x)
3.407606

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map function

keras.ops.map(f, xs)

Map a function over leading array axes.

Like Python’s builtin map, except inputs and outputs are in the form of stacked arrays. Consider using the vectorized_map() transform instead, unless you need to apply a function element by element for reduced memory usage or heterogeneous computation with other control flow primitives.

When xs is an array type, the semantics of map() are given by this Python implementation:

def map(f, xs):
    return np.stack([f(x) for x in xs])

Arguments

• f: Callable defines the function to apply element-wise over the first axis or axes of xs.
• xs: Values over which to map along the leading axis.

Returns

Mapped values.

Examples

>>> f = lambda x: x**2
>>> xs = keras.ops.arange(10)
>>> ys = keras.ops.map(f, xs)
>>> ys
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

>>> f = lambda x: {"y1": x**2, "y2": x * 10}  # Can have nested outputs
>>> ys = keras.ops.map(f, xs)
>>> ys["y1"]
[0, 1, 4, 9, 16, 25, 36, 49, 64, 81]
>>> ys["y2"]
[0, 10, 20, 30, 40, 50, 60, 70, 80, 90]

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rsqrt function

keras.ops.rsqrt(x)

Computes reciprocal of square root of x element-wise.

Arguments

• x: input tensor

Returns

A tensor with the same dtype as x.

Example

>>> x = keras.ops.convert_to_tensor([1.0, 10.0, 100.0])
>>> keras.ops.rsqrt(x)
array([1.0, 0.31622776, 0.1], dtype=float32)

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saturate_cast function

keras.ops.saturate_cast(x, dtype)

Performs a safe saturating cast to the desired dtype.

Saturating cast prevents data type overflow when casting to dtype with smaller values range. E.g. ops.cast(ops.cast([-1, 256], "float32"), "uint8") returns [255, 0], but ops.saturate_cast(ops.cast([-1, 256], "float32"), "uint8") returns [0, 255].

Arguments

• x: A tensor or variable.
• dtype: The target type.

Returns

A safely casted tensor of the specified dtype.

Example

Image resizing with bicubic interpolation may produce values outside original range.

>>> image2x2 = np.array([0, 1, 254, 255], dtype="uint8").reshape(1, 2, 2, 1)
>>> image4x4 = tf.image.resize(image2x2, (4, 4), method="bicubic")
>>> print(image4x4.numpy().squeeze())
>>> # [[-22.500004 -22.204624 -21.618908 -21.32353 ]
>>> #  [ 52.526054  52.82143   53.407146  53.70253 ]
>>> #  [201.29752  201.59288  202.17859  202.47395 ]
>>> #  [276.32355  276.61893  277.20465  277.50006 ]]

Casting this resized image back to uint8 will cause overflow.

>>> image4x4_casted = ops.cast(image4x4, "uint8")
>>> print(image4x4_casted.numpy().squeeze())
>>> # [[234 234 235 235]
>>> #  [ 52  52  53  53]
>>> #  [201 201 202 202]
>>> #  [ 20  20  21  21]]

Saturate casting to uint8 will clip values to uint8 range before casting and will not cause overflow.

>>> image4x4_saturate_casted = ops.saturate_cast(image4x4, "uint8")
>>> print(image4x4_saturate_casted.numpy().squeeze())
>>> # [[  0   0   0   0]
>>> #  [ 52  52  53  53]
>>> #  [201 201 202 202]
>>> #  [255 255 255 255]]

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scan function

keras.ops.scan(f, init, xs=None, length=None, reverse=False, unroll=1)

Scan a function over leading array axes while carrying along state.

When the type of xs is an array type or None, and the type of ys is an array type, the semantics of scan() are given roughly by this Python implementation:

def scan(f, init, xs, length=None):
    if xs is None:
        xs = [None] * length
    carry = init
    ys = []
    for x in xs:
        carry, y = f(carry, x)
        ys.append(y)
    return carry, np.stack(ys)

The loop-carried value carry (init) must hold a fixed shape and dtype across all iterations.

In TensorFlow, y must match carry in shape and dtype. This is not required in other backends.

Arguments

• f: Callable defines the logic for each loop iteration. This accepts two arguments where the first is a value of the loop carry and the second is a slice of xs along its leading axis. This callable returns a pair where the first represents a new value for the loop carry and the second represents a slice of the output.
• init: The initial loop carry value. This can be a scalar, tensor, or any nested structure. It must match the structure of the first element returned by f.
• xs: Optional value to scan along its leading axis. This can be a tensor or any nested structure. If xs is not provided, you must specify length to define the number of loop iterations. Defaults to None.
• length: Optional integer specifying the number of loop iterations. If length is not provided, it defaults to the sizes of leading axis of the arrays in xs. Defaults to None.
• reverse: Optional boolean specifying whether to run the scan iteration forward or in reverse, equivalent to reversing the leading axes of the arrays in both xs and in ys.
• unroll: Optional positive integer or boolean specifying how many scan iterations to unroll within a single iteration of a loop. If an integer is provided, it determines how many unrolled loop iterations to run within a single rolled iteration of the loop. If a boolean is provided, it will determine if the loop is completely unrolled (unroll=True) or left completely unrolled (unroll=False). Note that unrolling is only supported by JAX and TensorFlow backends.

Returns

A pair where the first element represents the final loop carry value and the second element represents the stacked outputs of f when scanned over the leading axis of the inputs.

Examples

>>> sum_fn = lambda c, x: (c + x, c + x)
>>> init = keras.ops.array(0)
>>> xs = keras.ops.array([1, 2, 3, 4, 5])
>>> carry, result = keras.ops.scan(sum_fn, init, xs)
>>> carry
15
>>> result
[1, 3, 6, 10, 15]

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scatter function

keras.ops.scatter(indices, values, shape)

Returns a tensor of shape shape where indices are set to values.

At a high level, this operation does zeros[indices] = updates and returns the output. It is equivalent to:

zeros = keras.ops.zeros(shape)
output = keras.ops.scatter_update(zeros, indices, values)

Arguments

• indices: A tensor or list/tuple specifying indices for the values in values.
• values: A tensor, the values to be set at indices.
• shape: Shape of the output tensor.

Example

>>> indices = [[0, 1], [1, 1]]
>>> values = np.array([1., 1.])
>>> keras.ops.scatter(indices, values, shape=(2, 2))
array([[0., 1.],
       [0., 1.]])

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scatter_update function

keras.ops.scatter_update(inputs, indices, updates)

Update inputs via updates at scattered (sparse) indices.

At a high level, this operation does inputs[indices] = updates. Assume inputs is a tensor of shape (D0, D1, ..., Dn), there are 2 main usages of scatter_update.

1. indices is a 2D tensor of shape (num_updates, n), where num_updates is the number of updates to perform, and updates is a 1D tensor of shape (num_updates,). For example, if inputs is zeros((4, 4, 4)), and we want to update inputs[1, 2, 3] and inputs[0, 1, 3] as 1, then we can use:

inputs = np.zeros((4, 4, 4))
indices = [[1, 2, 3], [0, 1, 3]]
updates = np.array([1., 1.])
inputs = keras.ops.scatter_update(inputs, indices, updates)

2 indices is a 2D tensor of shape (num_updates, k), where num_updates is the number of updates to perform, and k (k < n) is the size of each index in indices. updates is a n - k-D tensor of shape (num_updates, inputs.shape[k:]). For example, if inputs = np.zeros((4, 4, 4)), and we want to update inputs[1, 2, :] and inputs[2, 3, :] as [1, 1, 1, 1], then indices would have shape (num_updates, 2) (k = 2), and updates would have shape (num_updates, 4) (inputs.shape[2:] = 4). See the code below:

inputs = np.zeros((4, 4, 4))
indices = [[1, 2], [2, 3]]
updates = np.array([[1., 1., 1, 1,], [1., 1., 1, 1,])
inputs = keras.ops.scatter_update(inputs, indices, updates)

Arguments

• inputs: A tensor, the tensor to be updated.
• indices: A tensor or list/tuple of shape (N, inputs.ndim), specifying indices to update. N is the number of indices to update, must be equal to the first dimension of updates.
• updates: A tensor, the new values to be put to inputs at indices.

Returns

A tensor, has the same shape and dtype as inputs.

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segment_max function

keras.ops.segment_max(data, segment_ids, num_segments=None, sorted=False)

Computes the max of segments in a tensor.

Arguments

• data: Input tensor.
• segment_ids: A N-D tensor containing segment indices for each element in data. data.shape[:len(segment_ids.shape)] should match.
• num_segments: An integer representing the total number of segments. If not specified, it is inferred from the maximum value in segment_ids.
• sorted: A boolean indicating whether segment_ids is sorted. Defaults to False.

Returns

A tensor containing the max of segments, where each element represents the max of the corresponding segment in data.

Example

>>> data = keras.ops.convert_to_tensor([1, 2, 10, 20, 100, 200])
>>> segment_ids = keras.ops.convert_to_tensor([0, 0, 1, 1, 2, 2])
>>> num_segments = 3
>>> keras.ops.segment_max(data, segment_ids, num_segments)
array([2, 20, 200], dtype=int32)

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segment_sum function

keras.ops.segment_sum(data, segment_ids, num_segments=None, sorted=False)

Computes the sum of segments in a tensor.

Arguments

• data: Input tensor.
• segment_ids: A N-D tensor containing segment indices for each element in data. Num dims for segment ids should be strictly smaller or equal to number of dims in data.
• num_segments: An integer representing the total number of segments. If not specified, it is inferred from the maximum value in segment_ids.
• sorted: A boolean indicating whether segment_ids is sorted. Defaults to False.

Returns

A tensor containing the sum of segments, where each element represents the sum of the corresponding segment in data.

Example

>>> data = keras.ops.convert_to_tensor([1, 2, 10, 20, 100, 200])
>>> segment_ids = keras.ops.convert_to_tensor([0, 0, 1, 1, 2, 2])
>>> num_segments = 3
>>> keras.ops.segment_sum(data, segment_ids,num_segments)
array([3, 30, 300], dtype=int32)

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shape function

keras.ops.shape(x)

Gets the shape of the tensor input.

Note: On the TensorFlow backend, when x is a tf.Tensor with dynamic shape, dimensions which are dynamic in the context of a compiled function will have a tf.Tensor value instead of a static integer value.

Arguments

• x: A tensor. This function will try to access the shape attribute of the input tensor.

Returns

A tuple of integers or None values, indicating the shape of the input tensor.

Example

>>> x = keras.ops.zeros((8, 12))
>>> keras.ops.shape(x)
(8, 12)

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slice function

keras.ops.slice(inputs, start_indices, shape)

Return a slice of an input tensor.

At a high level, this operation is an explicit replacement for array slicing e.g. inputs[start_indices: start_indices + shape]. Unlike slicing via brackets, this operation will accept tensor start indices on all backends, which is useful when indices dynamically computed via other tensor operations.

inputs = np.zeros((5, 5))
start_indices = np.array([3, 3])
shape = np.array([2, 2])
inputs = keras.ops.slice(inputs, start_indices, shape)

Arguments

• inputs: A tensor, the tensor to be updated.
• start_indices: A list/tuple of shape (inputs.ndim,), specifying the starting indices for updating.
• shape: The full shape of the returned slice.

Returns

A tensor, has the same shape and dtype as inputs.

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slice_update function

keras.ops.slice_update(inputs, start_indices, updates)

Update an input by slicing in a tensor of updated values.

At a high level, this operation does inputs[start_indices: start_indices + updates.shape] = updates. Assume inputs is a tensor of shape (D0, D1, ..., Dn), start_indices must be a list/tuple of n integers, specifying the starting indices. updates must have the same rank as inputs, and the size of each dim must not exceed Di - start_indices[i]. For example, if we have 2D inputs inputs = np.zeros((5, 5)), and we want to update the intersection of last 2 rows and last 2 columns as 1, i.e., inputs[3:, 3:] = np.ones((2, 2)), then we can use the code below:

inputs = np.zeros((5, 5))
start_indices = [3, 3]
updates = np.ones((2, 2))
inputs = keras.ops.slice_update(inputs, start_indices, updates)

Arguments

• inputs: A tensor, the tensor to be updated.
• start_indices: A list/tuple of shape (inputs.ndim,), specifying the starting indices for updating.
• updates: A tensor, the new values to be put to inputs at indices. updates must have the same rank as inputs.

Returns

A tensor, has the same shape and dtype as inputs.

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stop_gradient function

keras.ops.stop_gradient(variable)

Stops gradient computation.

Arguments

• variable: A tensor variable for which the gradient computation is to be disabled.

Returns

The variable with gradient computation disabled.

Examples

>>> var = keras.backend.convert_to_tensor(
...     [1., 2., 3.],
...     dtype="float32"
... )
>>> var = keras.ops.stop_gradient(var)

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switch function

keras.ops.switch(index, branches, *operands)

Apply exactly one of the branches given by index.

If index is out of bounds, it is clamped to within bounds.

The semantics of switch are given roughly by this Python implementation:

def switch(index, branches, *operands):
    index = clamp(0, index, len(branches) - 1)
    return branches[index](*operands)

Arguments

• index: An integer scalar indicating which branch function to apply.
• branches: A sequence of functions to be applied based on index.
• operands: Inputs to whichever branch is applied.

Returns

The outputs of branch(*operands) for the branch that was selected based on index.

Examples

>>> add_fn = lambda x, y: x + y
>>> subtract_fn = lambda x, y: x - y
>>> x = keras.ops.array(2.0)
>>> y = keras.ops.array(0.5)
>>> branches = [add_fn, subtract_fn]
>>> keras.ops.switch(0, branches, x, y)
2.5

>>> keras.ops.switch(1, branches, x, y)
1.5

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top_k function

keras.ops.top_k(x, k, sorted=True)

Finds the top-k values and their indices in a tensor.

Arguments

• x: Input tensor.
• k: An integer representing the number of top elements to retrieve.
• sorted: A boolean indicating whether to sort the output in descending order. Defaults to True.

Returns

A tuple containing two tensors. The first tensor contains the top-k values, and the second tensor contains the indices of the top-k values in the input tensor.

Example

>>> x = keras.ops.convert_to_tensor([5, 2, 7, 1, 9, 3])
>>> values, indices = top_k(x, k=3)
>>> print(values)
array([9 7 5], shape=(3,), dtype=int32)
>>> print(indices)
array([4 2 0], shape=(3,), dtype=int32)

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unstack function

keras.ops.unstack(x, num=None, axis=0)

Unpacks the given dimension of a rank-R tensor into rank-(R-1) tensors.

Arguments

• x: The input tensor.
• num: The length of the dimension axis. Automatically inferred if None.
• axis: The axis along which to unpack.

Returns

A list of tensors unpacked along the given axis.

Example

>>> x = keras.ops.array([[1, 2], [3, 4]])
>>> keras.ops.unstack(x, axis=0)
[array([1, 2]), array([3, 4])]

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vectorized_map function

keras.ops.vectorized_map(function, elements)

Parallel map of function on axis 0 of tensor(s) elements.

Schematically, vectorized_map implements the following, in the case of a single tensor input elements:

def vectorized_map(function, elements)
    outputs = []
    for e in elements:
        outputs.append(function(e))
    return stack(outputs)

In the case of an iterable of tensors elements, it implements the following:

def vectorized_map(function, elements)
    batch_size = elements[0].shape[0]
    outputs = []
    for index in range(batch_size):
        outputs.append(function([e[index] for e in elements]))
    return np.stack(outputs)

In this case, function is expected to take as input a single list of tensor arguments.

source

while_loop function

keras.ops.while_loop(cond, body, loop_vars, maximum_iterations=None)

While loop implementation.

Arguments

• cond: A callable that represents the termination condition of the loop. Must accept a loop_vars like structure as an argument. If loop_vars is a tuple or list, each element of loop_vars will be passed positionally to the callable.
• body: A callable that represents the loop body. Must accept a loop_vars like structure as an argument, and return update value with the same structure. If loop_vars is a tuple or list, each element of loop_vars will be passed positionally to the callable.
• loop_vars: An arbitrary nested structure of tensor state to persist across loop iterations.
• maximum_iterations: Optional maximum number of iterations of the while loop to run. If provided, the cond output is AND-ed with an additional condition ensuring the number of iterations executed is no greater than maximum_iterations.

Returns

A list/tuple of tensors, has the same shape and dtype as inputs.

Examples

>>> i = 0
>>> cond = lambda i: i < 10
>>> body = lambda i: i + 1
>>> keras.ops.while_loop(cond, body, i)
10

>>> x, y = 0, 1
>>> cond = lambda x, y: x < 10
>>> body = lambda x, y: (x + 1, y + 1)
>>> keras.ops.while_loop(cond, body, (x, y))
10, 11