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).
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)
