tf.print

tf.print(
    *inputs,
    **kwargs
)

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

Print the specified inputs.

Returns an operator that prints the specified inputs to a desired output stream or logging level. The inputs may be dense or sparse Tensors, primitive python objects, data structures that contain Tensors, and printable python objects. Printed tensors will recursively show the first and last summarize elements of each dimension.

With eager execution enabled and/or inside a tf.contrib.eager.defun this operator will automatically execute, and users only need to call tf.print without using the return value. When constructing graphs outside of a tf.contrib.eager.defun, one must either include the returned op in the input to session.run, or use the operator as a control dependency for executed ops by specifying with tf.control_dependencies([print_op]).

Example: Single-input usage:

tf.enable_eager_execution()
tensor = tf.range(10)
tf.print(tensor, output_stream=sys.stderr)

(This prints "[0 1 2 ... 7 8 9]" to sys.stderr)

Multi-input usage:

tf.enable_eager_execution()
tensor = tf.range(10)
tf.print("tensors:", tensor, {2: tensor * 2}, output_stream=sys.stdout)

(This prints "tensors: [0 1 2 ... 7 8 9] {2: [0 2 4 ... 14 16 18]}" to sys.stdout)

Usage in a defun:

tf.enable_eager_execution()

@tf.contrib.eager.defun
def f():
    tensor = tf.range(10)
    tf.print(tensor, output_stream=sys.stderr)
    return tensor

range_tensor = f()

(This prints "[0 1 2 ... 7 8 9]" to sys.stderr)

Usage when constructing graphs:

sess = tf.Session()
with sess.as_default():
    tensor = tf.range(10)
    print_op = tf.print("tensors:", tensor, {2: tensor * 2},
                        output_stream=sys.stdout)
    with tf.control_dependencies([print_op]):
      tripled_tensor = tensor * 3
    sess.run(tripled_tensor)

(This prints "tensors: [0 1 2 ... 7 8 9] {2: [0 2 4 ... 14 16 18]}" to sys.stdout)

Args:

  • *inputs: Positional arguments that are the inputs to print. Inputs in the printed output will be separated by spaces. Inputs may be python primitives, tensors, data structures such as dicts and lists that may contain tensors (with the data structures possibly nested in arbitrary ways), and printable python objects.
  • output_stream: The output stream or logging level to print to. Defaults to sys.stderr, but sys.stdout, tf.logging.info, tf.logging.warning, and tf.logging.error are also supported.
  • summarize: The first and last summarize elements within each dimension are recursively printed per Tensor. If None, then the first 3 and last 3 elements of each dimension are printed for each tensor. If set to -1, it will print all elements of every tensor.
  • name: A name for the operation (optional).

Returns:

A print operator that prints the specified inputs in the specified output stream or logging level.

Raises:

  • ValueError: If an unsupported output stream is specified.

Python2 Compatibility

In python 2.7, make sure to import the following: from __future__ import print_function