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大牛时代配资Print the specified inputs.


  • tf.compat.v1.print
  • tf.compat.v2.print

A TensorFlow 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 elements of each dimension to summarize.


大牛时代配资Single-input usage:

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

大牛时代配资(This prints "[0 1 2 ... 7 8 9]" to sys.stderr)

Multi-input usage:

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

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

Changing the input separator:

tensor_a = tf.range(2)
tensor_b = tensor_a * 2
tf.print(tensor_a, tensor_b, output_stream=sys.stderr, sep=',')

大牛时代配资(This prints "[0 1],[0 2]" to sys.stderr)

Usage in a tf.function:

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)

@compatibility(TF 1.x Graphs and Sessions) In graphs manually created outside of tf.function, this method returns the created TF operator that prints the data. To make sure the operator runs, users need to pass the produced op to tf.compat.v1.Session's run method, or to use the op as a control dependency for executed ops by specifying with tf.compat.v1.control_dependencies([print_op])大牛时代配资. @end_compatibility

Compatibility usage in TF 1.x graphs:

  sess = tf.compat.v1.Session()
  with sess.as_default():
      tensor = tf.range(10)
      print_op = tf.print("tensors:", tensor, {2: tensor * 2},
      with tf.control_dependencies([print_op]):
        tripled_tensor = tensor * 3

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

Note: In Jupyter notebooks and colabs, tf.print大牛时代配资 prints to the notebook cell outputs. It will not write to the notebook kernel's console logs.


  • *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, logging level, or file to print to. Defaults to sys.stderr, but sys.stdout,, tf.compat.v1.logging.warning, tf.compat.v1.logging.error,, absl.logging.warning and absl.loogging,error are also supported. To print to a file, pass a string started with "file://" followed by the file path, e.g., "file:///tmp/foo.out".
  • 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.
  • sep: The string to use to separate the inputs. Defaults to " ".
  • end: End character that is appended at the end the printed string. Defaults to the newline character.
  • name: A name for the operation (optional).


None when executing eagerly. During graph tracing this returns a TF operator that prints the specified inputs in the specified output stream or logging level. This operator will be automatically executed except inside of tf.compat.v1 graphs and sessions.


  • 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

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