def Print(input_, data, message=None, first_n=None, summarize=None, name=None): """Prints a list of tensors. 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_`. """ return gen_logging_ops._print(input_, data, message, first_n, summarize, name)
def Print(input_, data, message=None, first_n=None, summarize=None, name=None): """Prints a list of tensors. 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). @compatibility(TF2) 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. @end_compatibility 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_`. ```python 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) ``` """ return gen_logging_ops._print(input_, data, message, first_n, summarize, name)
def Print(input_, data, message=None, first_n=None, summarize=None, name=None): """Prints a list of tensors. This is an identity op with the side effect of printing `data` when evaluating. 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: Same tensor as `input_`. """ return gen_logging_ops._print(input_, data, message, first_n, summarize, name)
def Print(input_, data, message=None, first_n=None, summarize=None, name=None): """Prints a list of tensors. This is an identity op with the side effect of printing `data` when evaluating. 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. name: A name for the operation (optional). Returns: Same tensor as `input_`. """ return gen_logging_ops._print(input_, data, message, first_n, summarize, name)
def Print(input_, data, message=None, first_n=None, summarize=None, name=None): """Prints a list of tensors. 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). Additionally, to use tf.print in python 2.7, users must make sure to import the following: `from __future__ import print_function` 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_`. ```python 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) ``` """ return gen_logging_ops._print(input_, data, message, first_n, summarize, name)