def print_v2(*inputs, **kwargs):
"""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])`.
@compatibility(python2)
In python 2.7, make sure to import the following:
`from __future__ import print_function`
@end_compatibility
Example:
Single-input usage:
```python
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:
```python
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:
```python
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:
```python
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)
Note: This op is only partially compatible with Jupyter notebooks and colabs.
Because it prints to the C++ standard out / standard error, this will go
in the notebook kernel's console output, not in the notebook cell output.
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, logging level, or file to print to.
Defaults to sys.stderr, but sys.stdout, tf.logging.info,
tf.logging.warning, and tf.logging.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.
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.
"""
# Because we are using arbitrary-length positional arguments, python 2
# does not support explicitly specifying the keyword arguments in the
# function definition. So, we manually get the keyword arguments w/ default
# values here.
output_stream = kwargs.pop("output_stream", sys.stderr)
name = kwargs.pop("name", None)
summarize = kwargs.pop("summarize", 3)
if kwargs:
raise ValueError("Unrecognized keyword arguments for tf.print: %s" % kwargs)
format_name = None
if name:
format_name = name + "_format"
# Match the C++ string constants representing the different output streams.
# Keep this updated!
output_stream_to_constant = {
sys.stdout: "stdout",
sys.stderr: "stderr",
tf_logging.INFO: "log(info)",
tf_logging.info: "log(info)",
tf_logging.WARN: "log(warning)",
tf_logging.warning: "log(warning)",
tf_logging.warn: "log(warning)",
tf_logging.ERROR: "log(error)",
tf_logging.error: "log(error)",
}
if _is_filepath(output_stream):
output_stream_string = output_stream
else:
output_stream_string = output_stream_to_constant.get(output_stream)
if not output_stream_string:
raise ValueError(
"Unsupported output stream, logging level, or file." +
str(output_stream) + ". Supported streams are sys.stdout, "
"sys.stderr, tf.logging.info, "
"tf.logging.warning, tf.logging.error. " +
"File needs to be in the form of 'file://<filepath>'.")
# If we are only printing a single string scalar, there is no need to format
if (len(inputs) == 1 and tensor_util.is_tensor(inputs[0])
and (not isinstance(inputs[0], sparse_tensor.SparseTensor))
and inputs[0].shape and (inputs[0].dtype == dtypes.string)):
formatted_string = inputs[0]
# Otherwise, we construct an appropriate template for the tensors we are
# printing, and format the template using those tensors.
else:
# For each input to this print function, we extract any nested tensors,
# and construct an appropriate template to format representing the
# printed input.
templates = []
tensors = []
tensor_free_structure = nest.map_structure(
lambda x: "" if tensor_util.is_tensor(x) else x,
inputs)
tensor_free_template = " ".join(pprint.pformat(x)
for x in tensor_free_structure)
placeholder = _generate_placeholder_string(tensor_free_template)
for input_ in inputs:
placeholders = []
# Use the nest utilities to flatten & process any nested elements in this
# input. The placeholder for a tensor in the template should be the
# placeholder string, and the placeholder for a non-tensor can just be
# the printed value of the non-tensor itself.
for x in nest.flatten(input_):
# support sparse tensors
if isinstance(x, sparse_tensor.SparseTensor):
tensors.extend([x.indices, x.values, x.dense_shape])
placeholders.append(
"SparseTensor(indices={}, values={}, shape={})".format(
placeholder, placeholder, placeholder)
)
elif tensor_util.is_tensor(x):
tensors.append(x)
placeholders.append(placeholder)
else:
placeholders.append(x)
if isinstance(input_, six.string_types):
# If the current input to format/print is a normal string, that string
# can act as the template.
cur_template = input_
else:
# We pack the placeholders into a data structure that matches the
# input data structure format, then format that data structure
# into a string template.
#
# NOTE: We must use pprint.pformat here for building the template for
# unordered data structures such as `dict`, because `str` doesn't
# guarantee orderings, while pprint prints in sorted order. pprint
# will match the ordering of `nest.flatten`.
# This even works when nest.flatten reorders OrderedDicts, because
# pprint is printing *after* the OrderedDicts have been reordered.
cur_template = pprint.pformat(
nest.pack_sequence_as(input_, placeholders))
templates.append(cur_template)
# We join the templates for the various inputs into a single larger
# template. We also remove all quotes surrounding the placeholders, so that
# the formatted/printed output will not contain quotes around tensors.
# (example of where these quotes might appear: if we have added a
# placeholder string into a list, then pretty-formatted that list)
template = " ".join(templates)
template = template.replace("'" + placeholder + "'", placeholder)
formatted_string = string_ops.string_format(
inputs=tensors, template=template, placeholder=placeholder,
summarize=summarize,
name=format_name)
return gen_logging_ops.print_v2(formatted_string,
output_stream=output_stream_string,
name=name)
def print_v2(*inputs, **kwargs):
"""Print the specified inputs.
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.
@compatibility(python2)
In python 2.7, make sure to import the following:
`from __future__ import print_function`
@end_compatibility
Example:
Single-input usage:
```python
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:
```python
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)
Changing the input separator:
```python
tensor_a = tf.range(2)
tensor_b = tensor_a * 2
tf.print(tensor_a, tensor_b, output_stream=sys.stderr, sep=',')
```
(This prints "[0 1],[0 2]" to sys.stderr)
Usage in a `tf.function`:
```python
@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])`.
@end_compatibility
Compatibility usage in TF 1.x graphs:
```python
sess = tf.compat.v1.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)
Note: In Jupyter notebooks and colabs, `tf.print` prints to the notebook
cell outputs. It will not write to the notebook kernel's console logs.
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, logging level, or file to print to.
Defaults to sys.stderr, but sys.stdout, tf.compat.v1.logging.info,
tf.compat.v1.logging.warning, tf.compat.v1.logging.error,
absl.logging.info, 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).
Returns:
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.
Raises:
ValueError: If an unsupported output stream is specified.
"""
# Because we are using arbitrary-length positional arguments, python 2
# does not support explicitly specifying the keyword arguments in the
# function definition. So, we manually get the keyword arguments w/ default
# values here.
output_stream = kwargs.pop("output_stream", sys.stderr)
name = kwargs.pop("name", None)
summarize = kwargs.pop("summarize", 3)
sep = kwargs.pop("sep", " ")
end = kwargs.pop("end", os.linesep)
if kwargs:
raise ValueError("Unrecognized keyword arguments for tf.print: %s" %
kwargs)
format_name = None
if name:
format_name = name + "_format"
# Match the C++ string constants representing the different output streams.
# Keep this updated!
output_stream_to_constant = {
sys.stdout: "stdout",
sys.stderr: "stderr",
tf_logging.INFO: "log(info)",
tf_logging.info: "log(info)",
tf_logging.WARN: "log(warning)",
tf_logging.warning: "log(warning)",
tf_logging.warn: "log(warning)",
tf_logging.ERROR: "log(error)",
tf_logging.error: "log(error)",
logging.INFO: "log(info)",
logging.info: "log(info)",
logging.INFO: "log(info)",
logging.WARNING: "log(warning)",
logging.WARN: "log(warning)",
logging.warning: "log(warning)",
logging.warn: "log(warning)",
logging.ERROR: "log(error)",
logging.error: "log(error)",
}
if _is_filepath(output_stream):
output_stream_string = output_stream
else:
output_stream_string = output_stream_to_constant.get(output_stream)
if not output_stream_string:
raise ValueError(
"Unsupported output stream, logging level, or file." +
str(output_stream) + ". Supported streams are sys.stdout, "
"sys.stderr, tf.logging.info, "
"tf.logging.warning, tf.logging.error. " +
"File needs to be in the form of 'file://<filepath>'.")
# If we are only printing a single string scalar, there is no need to format
if (len(inputs) == 1 and tensor_util.is_tensor(inputs[0])
and (not isinstance(inputs[0], sparse_tensor.SparseTensor))
and (inputs[0].shape.ndims == 0)
and (inputs[0].dtype == dtypes.string)):
formatted_string = inputs[0]
# Otherwise, we construct an appropriate template for the tensors we are
# printing, and format the template using those tensors.
else:
# For each input to this print function, we extract any nested tensors,
# and construct an appropriate template to format representing the
# printed input.
templates = []
tensors = []
tensor_free_structure = nest.map_structure(
lambda x: "" if tensor_util.is_tensor(x) else x, inputs)
tensor_free_template = " ".join(
pprint.pformat(x) for x in tensor_free_structure)
placeholder = _generate_placeholder_string(tensor_free_template)
for input_ in inputs:
placeholders = []
# Use the nest utilities to flatten & process any nested elements in this
# input. The placeholder for a tensor in the template should be the
# placeholder string, and the placeholder for a non-tensor can just be
# the printed value of the non-tensor itself.
for x in nest.flatten(input_):
# support sparse tensors
if isinstance(x, sparse_tensor.SparseTensor):
tensors.extend([x.indices, x.values, x.dense_shape])
placeholders.append(
"SparseTensor(indices={}, values={}, shape={})".format(
placeholder, placeholder, placeholder))
elif tensor_util.is_tensor(x):
tensors.append(x)
placeholders.append(placeholder)
else:
placeholders.append(x)
if isinstance(input_, six.string_types):
# If the current input to format/print is a normal string, that string
# can act as the template.
cur_template = input_
else:
# We pack the placeholders into a data structure that matches the
# input data structure format, then format that data structure
# into a string template.
#
# NOTE: We must use pprint.pformat here for building the template for
# unordered data structures such as `dict`, because `str` doesn't
# guarantee orderings, while pprint prints in sorted order. pprint
# will match the ordering of `nest.flatten`.
# This even works when nest.flatten reorders OrderedDicts, because
# pprint is printing *after* the OrderedDicts have been reordered.
cur_template = pprint.pformat(
nest.pack_sequence_as(input_, placeholders))
templates.append(cur_template)
# We join the templates for the various inputs into a single larger
# template. We also remove all quotes surrounding the placeholders, so that
# the formatted/printed output will not contain quotes around tensors.
# (example of where these quotes might appear: if we have added a
# placeholder string into a list, then pretty-formatted that list)
template = sep.join(templates)
template = template.replace("'" + placeholder + "'", placeholder)
formatted_string = string_ops.string_format(inputs=tensors,
template=template,
placeholder=placeholder,
summarize=summarize,
name=format_name)
if compat.forward_compatible(2019, 5, 27):
return gen_logging_ops.print_v2(formatted_string,
output_stream=output_stream_string,
name=name,
end=end)
else:
if end == os.linesep:
end = ""
return gen_logging_ops.print_v2(formatted_string + end,
output_stream=output_stream_string,
name=name)
def print_v2(*inputs, **kwargs):
"""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])`.
@compatibility(python2)
In python 2.7, make sure to import the following:
`from __future__ import print_function`
@end_compatibility
Example:
Single-input usage:
```python
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:
```python
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:
```python
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:
```python
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)
Note: This op is only partially compatible with Jupyter notebooks and colabs.
Because it prints to the C++ standard out / standard error, this will go
in the notebook kernel's console output, not in the notebook cell output.
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.
"""
# Because we are using arbitrary-length positional arguments, python 2
# does not support explicitly specifying the keyword arguments in the
# function definition. So, we manually get the keyword arguments w/ default
# values here.
output_stream = kwargs.pop("output_stream", sys.stderr)
name = kwargs.pop("name", None)
summarize = kwargs.pop("summarize", 3)
if kwargs:
raise ValueError("Unrecognized keyword arguments for tf.print: %s" % kwargs)
format_name = None
if name:
format_name = name + "_format"
# Match the C++ string constants representing the different output streams.
# Keep this updated!
output_stream_to_constant = {
sys.stdout: "stdout",
sys.stderr: "stderr",
tf_logging.INFO: "log(info)",
tf_logging.info: "log(info)",
tf_logging.WARN: "log(warning)",
tf_logging.warning: "log(warning)",
tf_logging.warn: "log(warning)",
tf_logging.ERROR: "log(error)",
tf_logging.error: "log(error)",
}
output_stream_string = output_stream_to_constant.get(output_stream)
if not output_stream_string:
raise ValueError(
"Unsupported output stream or logging level " +
str(output_stream) + ". Supported streams are sys.stdout, "
"sys.stderr, tf.logging.info, "
"tf.logging.warning, tf.logging.error")
# If we are only printing a single string scalar, there is no need to format
if (len(inputs) == 1 and tensor_util.is_tensor(inputs[0])
and (not isinstance(inputs[0], sparse_tensor.SparseTensor))
and inputs[0].shape and (inputs[0].dtype == dtypes.string)):
formatted_string = inputs[0]
# Otherwise, we construct an appropriate template for the tensors we are
# printing, and format the template using those tensors.
else:
# For each input to this print function, we extract any nested tensors,
# and construct an appropriate template to format representing the
# printed input.
templates = []
tensors = []
tensor_free_structure = nest.map_structure(
lambda x: "" if tensor_util.is_tensor(x) else x,
inputs)
tensor_free_template = " ".join(pprint.pformat(x)
for x in tensor_free_structure)
placeholder = _generate_placeholder_string(tensor_free_template)
for input_ in inputs:
placeholders = []
# Use the nest utilities to flatten & process any nested elements in this
# input. The placeholder for a tensor in the template should be the
# placeholder string, and the placeholder for a non-tensor can just be
# the printed value of the non-tensor itself.
for x in nest.flatten(input_):
# support sparse tensors
if isinstance(x, sparse_tensor.SparseTensor):
tensors.extend([x.indices, x.values, x.dense_shape])
placeholders.append(
"SparseTensor(indices={}, values={}, shape={})".format(
placeholder, placeholder, placeholder)
)
elif tensor_util.is_tensor(x):
tensors.append(x)
placeholders.append(placeholder)
else:
placeholders.append(x)
if isinstance(input_, six.string_types):
# If the current input to format/print is a normal string, that string
# can act as the template.
cur_template = input_
else:
# We pack the placeholders into a data structure that matches the
# input data structure format, then format that data structure
# into a string template.
#
# NOTE: We must use pprint.pformat here for building the template for
# unordered data structures such as `dict`, because `str` doesn't
# guarantee orderings, while pprint prints in sorted order. pprint
# will match the ordering of `nest.flatten`.
# This even works when nest.flatten reorders OrderedDicts, because
# pprint is printing *after* the OrderedDicts have been reordered.
cur_template = pprint.pformat(
nest.pack_sequence_as(input_, placeholders))
templates.append(cur_template)
# We join the templates for the various inputs into a single larger
# template. We also remove all quotes surrounding the placeholders, so that
# the formatted/printed output will not contain quotes around tensors.
# (example of where these quotes might appear: if we have added a
# placeholder string into a list, then pretty-formatted that list)
template = " ".join(templates)
template = template.replace("'" + placeholder + "'", placeholder)
formatted_string = string_ops.string_format(
inputs=tensors, template=template, placeholder=placeholder,
summarize=summarize,
name=format_name)
return gen_logging_ops.print_v2(formatted_string,
output_stream=output_stream_string,
name=name)