tf.compat.v1.Print

Prints a list of tensors. (deprecated)

tf.compat.v1.Print(
    input_, data, message=None, first_n=None, summarize=None, name=None
)

Migrate to TF2

Caution: This API was designed for TensorFlow v1. Continue reading for details on how to migrate from this API to a native TensorFlow v2 equivalent. See the TensorFlow v1 to TensorFlow v2 migration guide for instructions on how to migrate the rest of your code.

This API is deprecated. Use tf.print instead. tf.print does not need the input_ argument.

tf.print works in TF2 when executing eagerly and inside a tf.function.

In TF1-styled sessions, an explicit control dependency declaration is needed to execute the tf.print operation. Refer to the documentation of tf.print for more details.

Description

Deprecated: THIS FUNCTION IS DEPRECATED. It will be removed after 2018-08-20. Instructions for updating: Use tf.print instead of tf.Print. Note that tf.print returns a no-output operator that directly prints the output. Outside of defuns or eager mode, this operator will not be executed unless it is directly specified in session.run or used as a control dependency for other operators. This is only a concern in graph mode. Below is an example of how to ensure tf.print executes in graph mode:

This is an identity op (behaves like tf.identity) with the side effect of printing data when evaluating.

Note: This op prints to the standard error. It is not currently compatible with jupyter notebook (printing to the notebook server's output, not into the notebook).

Args
input_	A tensor passed through this op.
data	A list of tensors to print out when op is evaluated.
message	A string, prefix of the error message.
first_n	Only log first_n number of times. Negative numbers log always; this is the default.
summarize	Only print this many entries of each tensor. If None, then a maximum of 3 elements are printed per input tensor.
name	A name for the operation (optional).

Returns
A Tensor. Has the same type and contents as input_. sess = tf.compat.v1.Session() with sess.as_default(): tensor = tf.range(10) print_op = tf.print(tensor) with tf.control_dependencies([print_op]): out = tf.add(tensor, tensor) sess.run(out)