tf.assert_non_negative(x, data=None, summarize=None, message=None, name=None)

tf.assert_non_negative(x, data=None, summarize=None, message=None, name=None)

See the guide: Asserts and boolean checks

Assert the condition x >= 0 holds element-wise.

Example of adding a dependency to an operation:

with tf.control_dependencies([tf.assert_non_negative(x)]):
  output = tf.reduce_sum(x)

Non-negative means, for every element x[i] of x, we have x[i] >= 0. If x is empty this is trivially satisfied.

Args:

• x: Numeric Tensor.
• data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x.
• summarize: Print this many entries of each tensor.
• message: A string to prefix to the default message.
• name: A name for this operation (optional). Defaults to "assert_non_negative".

Returns:

Op raising InvalidArgumentError unless x is all non-negative.

Defined in tensorflow/python/ops/check_ops.py.