tf.debugging.assert_rank_at

TensorFlow 1 version

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Validate and return float type based on tensors and dtype.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.assert_same_float_dtype, tf.compat.v1.debugging.assert_same_float_dtype

tf.debugging.assert_same_float_dtype(
    tensors=None, dtype=None
)

For ops such as matrix multiplication, inputs and weights must be of the same float type. This function validates that all tensors are the same type, validates that type is dtype (if supplied), and returns the type. Type must be a floating point type. If neither tensors nor dtype is supplied, the function will return dtypes.float32.

Args
`tensors`	Tensors of input values. Can include `None` elements, which will be ignored.
`dtype`	Expected type.

Returns
Validated type.

Raises
`ValueError`	if neither `tensors` nor `dtype` is supplied, or result is not float, or the common type of the inputs is not a floating point type.

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tf.debugging / assert_rank_at_least

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Args

Returns

Raises