Defined in tensorflow/python/ops/

Assert the condition x and y are close element-wise.

Example of adding a dependency to an operation:

with tf.control_dependencies([tf.assert_near(x, y)]):
  output = tf.reduce_sum(x)

This condition holds if for every pair of (possibly broadcast) elements x[i], y[i], we have

tf.abs(x[i] - y[i]) <= atol + rtol * tf.abs(y[i]).

If both x and y are empty, this is trivially satisfied.

The default atol and rtol is 10 * eps, where eps is the smallest representable positive number such that 1 + eps != eps. This is about 1.2e-6 in 32bit, 2.22e-15 in 64bit, and 0.00977 in 16bit. See numpy.finfo.


  • x: Float or complex Tensor.
  • y: Float or complex Tensor, same dtype as, and broadcastable to, x.
  • rtol: Tensor. Same dtype as, and broadcastable to, x. The relative tolerance. Default is 10 * eps.
  • atol: Tensor. Same dtype as, and broadcastable to, x. The absolute tolerance. Default is 10 * eps.
  • data: The tensors to print out if the condition is False. Defaults to error message and first few entries of x, y.
  • 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_near".


Op that raises InvalidArgumentError if x and y are not close enough.

Numpy Compatibility

Similar to numpy.assert_allclose, except tolerance depends on data type. This is due to the fact that TensorFlow is often used with 32bit, 64bit, and even 16bit data.