本文整理汇总了Python中tensorflow.python.lib.io.file_io.write_string_to_file方法的典型用法代码示例。如果您正苦于以下问题:Python file_io.write_string_to_file方法的具体用法?Python file_io.write_string_to_file怎么用?Python file_io.write_string_to_file使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow.python.lib.io.file_io
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在下文中一共展示了file_io.write_string_to_file方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _DoSanityCheck
# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import write_string_to_file [as 别名]
def _DoSanityCheck(self, prefix):
"""Sanity-check the content of the checkpoint."""
if not self._sanity_checks:
return
reader = tf.train.NewCheckpointReader(prefix)
content = {}
for variables, rule in self._sanity_checks:
args = []
for v in variables:
key = _VarKey(v)
if key in content:
args.append(content[key])
else:
value = reader.get_tensor(key)
content[key] = value
args.append(value)
if not rule.Check(*args):
# TODO(zhifengc): Maybe should return an explicit signal
# so that the caller (the controller loop) can Restore()
# the latest checkpoint before raise the error.
msg = "Checkpoint sanity check failed: {} {} {}\n".format(
prefix, ",".join([_VarKey(v) for v in variables]), rule)
# Also saves the error messge into a file.
file_io.write_string_to_file("{}.failed".format(prefix), msg)
raise tf.errors.AbortedError(None, None, msg)
示例2: visualize_embeddings
# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import write_string_to_file [as 别名]
def visualize_embeddings(summary_writer, config):
"""Stores a config file used by the embedding projector.
Args:
summary_writer: The summary writer used for writting events.
config: `tf.contrib.tensorboard.plugins.projector.ProjectorConfig`
proto that holds the configuration for the projector such as paths to
checkpoint files and metadata files for the embeddings. If
`config.model_checkpoint_path` is none, it defaults to the
`logdir` used by the summary_writer.
Raises:
ValueError: If the summary writer does not have a `logdir`.
"""
logdir = summary_writer.get_logdir()
# Sanity checks.
if logdir is None:
raise ValueError('Summary writer must have a logdir')
# Saving the config file in the logdir.
config_pbtxt = text_format.MessageToString(config)
file_io.write_string_to_file(
os.path.join(logdir, projector_plugin.PROJECTOR_FILENAME), config_pbtxt)
示例3: visualize_embeddings
# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import write_string_to_file [as 别名]
def visualize_embeddings(summary_writer, config):
"""Stores a config file used by the embedding projector.
Args:
summary_writer: The summary writer used for writting events.
config: `tf.contrib.tensorboard.plugins.projector.ProjectorConfig`
proto that holds the configuration for the projector such as paths to
checkpoint files and metadata files for the embeddings. If
`config.model_checkpoint_path` is none, it defaults to the
`logdir` used by the summary_writer.
Raises:
ValueError: If the summary writer does not have a `logdir`.
"""
logdir = summary_writer.get_logdir()
# Sanity checks.
if logdir is None:
raise ValueError('Summary writer must have a logdir')
# Saving the config file in the logdir.
config_pbtxt = text_format.MessageToString(config)
file_io.write_string_to_file(
os.path.join(logdir, PROJECTOR_FILENAME), config_pbtxt)
示例4: start
# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import write_string_to_file [as 别名]
def start(self):
"""Performs startup logic, including building graphs.
"""
if self._config.master:
# Save out job information for later reference alongside all other outputs.
job_args = ' '.join(self._model_builder.args._args).replace(' --', '\n--').split('\n')
job_info = {
'config': self._config._env,
'args': job_args
}
job_spec = yaml.safe_dump(job_info, default_flow_style=False)
job_file = os.path.join(self._output, 'job.yaml')
tfio.recursive_create_dir(self._output)
tfio.write_string_to_file(job_file, job_spec)
# Create a checkpoints directory. This is needed to ensure checkpoint restoration logic
# can lookup an existing directory.
tfio.recursive_create_dir(self.checkpoints_path)
# Build the graphs that will be used during the course of the job.
self._training, self._evaluation, self._prediction = \
self._model_builder.build_graph_interfaces(self._inputs, self._config)
示例5: _write_assets
# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import write_string_to_file [as 别名]
def _write_assets(assets_directory, assets_filename):
"""Writes asset files to be used with SavedModel for half plus two.
Args:
assets_directory: The directory to which the assets should be written.
assets_filename: Name of the file to which the asset contents should be
written.
Returns:
The path to which the assets file was written.
"""
if not file_io.file_exists(assets_directory):
file_io.recursive_create_dir(assets_directory)
path = os.path.join(
compat.as_bytes(assets_directory), compat.as_bytes(assets_filename))
file_io.write_string_to_file(path, "asset-file-contents")
return path
示例6: _do_mlengine_inference
# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import write_string_to_file [as 别名]
def _do_mlengine_inference(model, version, serialized_examples):
"""Performs inference on the model:version in CMLE."""
working_dir = tempfile.mkdtemp()
instances_file = os.path.join(working_dir, 'test.json')
json_examples = []
for serialized_example in serialized_examples:
# The encoding follows the example in:
# https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/quests/tpu/invoke_model.py
json_examples.append(
'{ "inputs": { "b64": "%s" } }' % base64.b64encode(serialized_example))
# print('\n'.join(json_examples))
file_io.write_string_to_file(instances_file, '\n'.join(json_examples))
gcloud_command = [
'gcloud', 'ml-engine', 'predict', '--model', model, '--version', version,
'--json-instances', instances_file
]
print(subprocess.check_output(gcloud_command))
示例7: _write_assets
# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import write_string_to_file [as 别名]
def _write_assets(assets_directory, assets_filename):
"""Writes asset files to be used with SavedModel for half plus two.
Args:
assets_directory: The directory to which the assets should be written.
assets_filename: Name of the file to which the asset contents should be
written.
Returns:
The path to which the assets file was written.
"""
if not file_io.file_exists(assets_directory):
file_io.recursive_create_dir(assets_directory)
path = os.path.join(
tf.compat.as_bytes(assets_directory), tf.compat.as_bytes(assets_filename))
file_io.write_string_to_file(path, "asset-file-contents")
return path
示例8: save
# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import write_string_to_file [as 别名]
def save(self, as_text=False):
"""Writes a `SavedModel` protocol buffer to disk.
The function writes the SavedModel protocol buffer to the export directory
in serialized format.
Args:
as_text: Writes the SavedModel protocol buffer in text format to disk.
Returns:
The path to which the SavedModel protocol buffer was written.
"""
if not file_io.file_exists(self._export_dir):
file_io.recursive_create_dir(self._export_dir)
if as_text:
path = os.path.join(
compat.as_bytes(self._export_dir),
compat.as_bytes(constants.SAVED_MODEL_FILENAME_PBTXT))
file_io.write_string_to_file(path, str(self._saved_model))
else:
path = os.path.join(
compat.as_bytes(self._export_dir),
compat.as_bytes(constants.SAVED_MODEL_FILENAME_PB))
file_io.write_string_to_file(path, self._saved_model.SerializeToString())
tf_logging.info("SavedModel written to: %s", path)
return path
示例9: _build_asset_collection
# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import write_string_to_file [as 别名]
def _build_asset_collection(self, asset_file_name, asset_file_contents,
asset_file_tensor_name):
asset_filepath = os.path.join(
compat.as_bytes(test.get_temp_dir()), compat.as_bytes(asset_file_name))
file_io.write_string_to_file(asset_filepath, asset_file_contents)
asset_file_tensor = constant_op.constant(
asset_filepath, name=asset_file_tensor_name)
ops.add_to_collection(ops.GraphKeys.ASSET_FILEPATHS, asset_file_tensor)
asset_collection = ops.get_collection(ops.GraphKeys.ASSET_FILEPATHS)
return asset_collection
示例10: testAssets
# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import write_string_to_file [as 别名]
def testAssets(self):
export_dir = os.path.join(test.get_temp_dir(), "test_assets")
builder = saved_model_builder.SavedModelBuilder(export_dir)
with self.test_session(graph=ops.Graph()) as sess:
self._init_and_validate_variable(sess, "v", 42)
# Build an asset collection.
ignored_filepath = os.path.join(
compat.as_bytes(test.get_temp_dir()), compat.as_bytes("ignored.txt"))
file_io.write_string_to_file(ignored_filepath, "will be ignored")
asset_collection = self._build_asset_collection("hello42.txt",
"foo bar baz",
"asset_file_tensor")
builder.add_meta_graph_and_variables(
sess, ["foo"], assets_collection=asset_collection)
# Save the SavedModel to disk.
builder.save()
with self.test_session(graph=ops.Graph()) as sess:
foo_graph = loader.load(sess, ["foo"], export_dir)
self._validate_asset_collection(export_dir, foo_graph.collection_def,
"hello42.txt", "foo bar baz",
"asset_file_tensor:0")
ignored_asset_path = os.path.join(
compat.as_bytes(export_dir),
compat.as_bytes(constants.ASSETS_DIRECTORY),
compat.as_bytes("ignored.txt"))
self.assertFalse(file_io.file_exists(ignored_asset_path))
示例11: run_analysis
# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import write_string_to_file [as 别名]
def run_analysis(args):
"""Builds an analysis file for training.
Uses BiqQuery tables to do the analysis.
Args:
args: command line args
Raises:
ValueError if schema contains unknown types.
"""
import google.datalab.bigquery as bq
if args.bigquery_table:
table = bq.Table(args.bigquery_table)
schema_list = table.schema._bq_schema
else:
schema_list = json.loads(
file_io.read_file_to_string(args.schema_file).decode())
table = bq.ExternalDataSource(
source=args.input_file_pattern,
schema=bq.Schema(schema_list))
# Check the schema is supported.
for col_schema in schema_list:
col_type = col_schema['type'].lower()
if col_type != 'string' and col_type != 'integer' and col_type != 'float':
raise ValueError('Schema contains an unsupported type %s.' % col_type)
run_numerical_analysis(table, schema_list, args)
run_categorical_analysis(table, schema_list, args)
# Save a copy of the schema to the output location.
file_io.write_string_to_file(
os.path.join(args.output_dir, SCHEMA_FILE),
json.dumps(schema_list, indent=2, separators=(',', ': ')))
示例12: test_numerics
# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import write_string_to_file [as 别名]
def test_numerics(self):
output_folder = tempfile.mkdtemp()
input_file_path = tempfile.mkstemp(dir=output_folder)[1]
try:
file_io.write_string_to_file(
input_file_path,
'\n'.join(['%s,%s,%s' % (i, 10 * i + 0.5, i + 0.5) for i in range(100)]))
schema = [{'name': 'col1', 'type': 'INTEGER'},
{'name': 'col2', 'type': 'FLOAT'},
{'name': 'col3', 'type': 'FLOAT'}]
features = {'col1': {'transform': 'scale', 'source_column': 'col1'},
'col2': {'transform': 'identity', 'source_column': 'col2'},
'col3': {'transform': 'target'}}
feature_analysis.run_local_analysis(
output_folder, [input_file_path], schema, features)
stats = json.loads(
file_io.read_file_to_string(
os.path.join(output_folder, analyze.constant.STATS_FILE)).decode())
self.assertEqual(stats['num_examples'], 100)
col = stats['column_stats']['col1']
self.assertAlmostEqual(col['max'], 99.0)
self.assertAlmostEqual(col['min'], 0.0)
self.assertAlmostEqual(col['mean'], 49.5)
col = stats['column_stats']['col2']
self.assertAlmostEqual(col['max'], 990.5)
self.assertAlmostEqual(col['min'], 0.5)
self.assertAlmostEqual(col['mean'], 495.5)
finally:
shutil.rmtree(output_folder)
示例13: test_categorical
# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import write_string_to_file [as 别名]
def test_categorical(self):
output_folder = tempfile.mkdtemp()
input_file_path = tempfile.mkstemp(dir=output_folder)[1]
try:
csv_file = ['red,apple', 'red,pepper', 'red,apple', 'blue,grape',
'blue,apple', 'green,pepper']
file_io.write_string_to_file(
input_file_path,
'\n'.join(csv_file))
schema = [{'name': 'color', 'type': 'STRING'},
{'name': 'type', 'type': 'STRING'}]
features = {'color': {'transform': 'one_hot', 'source_column': 'color'},
'type': {'transform': 'target'}}
feature_analysis.run_local_analysis(
output_folder, [input_file_path], schema, features)
stats = json.loads(
file_io.read_file_to_string(
os.path.join(output_folder, analyze.constant.STATS_FILE)).decode())
self.assertEqual(stats['column_stats']['color']['vocab_size'], 3)
# Color column.
vocab_str = file_io.read_file_to_string(
os.path.join(output_folder, analyze.constant.VOCAB_ANALYSIS_FILE % 'color'))
vocab = pd.read_csv(six.StringIO(vocab_str),
header=None,
names=['color', 'count'])
expected_vocab = pd.DataFrame(
{'color': ['red', 'blue', 'green'], 'count': [3, 2, 1]},
columns=['color', 'count'])
pd.util.testing.assert_frame_equal(vocab, expected_vocab)
finally:
shutil.rmtree(output_folder)
示例14: save_schema_features
# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import write_string_to_file [as 别名]
def save_schema_features(schema, features, output):
# Save a copy of the schema and features in the output folder.
file_io.write_string_to_file(
os.path.join(output, constant.SCHEMA_FILE),
json.dumps(schema, indent=2))
file_io.write_string_to_file(
os.path.join(output, constant.FEATURES_FILE),
json.dumps(features, indent=2))
示例15: test_make_transform_graph_numerics
# 需要导入模块: from tensorflow.python.lib.io import file_io [as 别名]
# 或者: from tensorflow.python.lib.io.file_io import write_string_to_file [as 别名]
def test_make_transform_graph_numerics(self):
output_folder = tempfile.mkdtemp()
stats_file_path = os.path.join(output_folder, feature_transforms.STATS_FILE)
try:
stats = {'column_stats':
{'num1': {'max': 10.0, 'mean': 9.5, 'min': 0.0}, # noqa
'num2': {'max': 1.0, 'mean': 2.0, 'min': -1.0},
'num3': {'max': 10.0, 'mean': 2.0, 'min': 5.0}}}
schema = [{'name': 'num1', 'type': 'FLOAT'},
{'name': 'num2', 'type': 'FLOAT'},
{'name': 'num3', 'type': 'INTEGER'}]
features = {'num1': {'transform': 'identity', 'source_column': 'num1'},
'num2': {'transform': 'scale', 'value': 10, 'source_column': 'num2'},
'num3': {'transform': 'scale', 'source_column': 'num3'}}
input_data = ['5.0,-1.0,10',
'10.0,1.0,5',
'15.0,0.5,7']
file_io.write_string_to_file(
stats_file_path,
json.dumps(stats))
results = self._run_graph(output_folder, features, schema, stats, input_data)
for result, expected_result in zip(results['num1'].flatten().tolist(),
[5, 10, 15]):
self.assertAlmostEqual(result, expected_result)
for result, expected_result in zip(results['num2'].flatten().tolist(),
[-10, 10, 5]):
self.assertAlmostEqual(result, expected_result)
for result, expected_result in zip(results['num3'].flatten().tolist(),
[1, -1, (7.0 - 5) * 2.0 / 5.0 - 1]):
self.assertAlmostEqual(result, expected_result)
finally:
shutil.rmtree(output_folder)