Missed TensorFlow World? Check out the recap. Learn more

tf.contrib.eager.add_execution_callback

View source on GitHub

Add an execution callback to the default eager context.

tf.contrib.eager.add_execution_callback(callback)

An execution callback is invoked immediately after an eager operation or function has finished execution, providing access to the op's type, name input and output tensors. Multiple execution callbacks can be added, in which case the callbacks will be invoked in the order in which they are added. To clear all execution callbacks that have been added, use clear_execution_callbacks().

Example:

def print_even_callback(op_type, inputs, attrs, outputs, op_name):
  # A callback that prints only the even output values.
  if outputs[0].numpy() % 2 == 0:
    print("Even output from %s: %s" % (op_name or op_type,  outputs))
tfe.add_execution_callback(print_even_callback)

x = tf.pow(2.0, 3.0) - 3.0
y = tf.multiply(x, tf.add(1.0, 5.0))
# When the line above is run, you will see all intermediate outputs that are
# even numbers printed to the console.

tfe.clear_execution_callbacks()

Args:

  • callback: a callable of the signature f(op_type, inputs, attrs, outputs, op_name). op_type is the type of the operation that was just executed (e.g., MatMul). inputs is the list of input Tensor(s) to the op. attrs contains the attributes of the operation as a tuple of alternating attribute name and attribute value. outputs is the list of output Tensor(s) from the op. op_name is the name of the operation that was just executed. This name is set by the client who created the operation and can be None if it is unset. Return value(s) from the callback are ignored.