RSVP สำหรับกิจกรรม TensorFlow Everywhere ในพื้นที่ของคุณวันนี้!


TensorFlow 2 version View source on GitHub

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

Example of adding a dependency to an operation:

with tf.control_dependencies([tf.compat.v1.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

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 != 1. 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.