# Utility op. Open Source. TODO(touts): move to nn?
from tensorflow.python.training.learning_rate_decay import *
# Distributed computing support
from tensorflow.core.protobuf.tensorflow_server_pb2 import ClusterDef
from tensorflow.core.protobuf.tensorflow_server_pb2 import JobDef
from tensorflow.core.protobuf.tensorflow_server_pb2 import ServerDef
from tensorflow.python.training.server_lib import ClusterSpec
from tensorflow.python.training.server_lib import Server
from tensorflow.python.util.all_util import make_all
# Include extra modules for docstrings because:
# * Input methods in tf.train are documented in io_ops.
# * Saver methods in tf.train are documented in state_ops.
__all__ = make_all(__name__, [sys.modules[__name__], io_ops, state_ops])
# Symbols whitelisted for export without documentation.
# TODO(cwhipkey): review these and move to contrib or expose through
# documentation.
__all__.extend([
"BytesList",
"Example",
"Feature",
"FeatureList",
"FeatureLists",
"Features",
"FloatList",
"Int64List",
"LooperThread",
"SaverDef",
"""
logits, labels = _compute_sampled_logits(
weights, biases, inputs, labels, num_sampled, num_classes,
num_true=num_true,
sampled_values=sampled_values,
subtract_log_q=True,
remove_accidental_hits=remove_accidental_hits,
partition_strategy=partition_strategy,
name=name)
sampled_losses = nn_ops.softmax_cross_entropy_with_logits(logits, labels)
# sampled_losses is a [batch_size] tensor.
return sampled_losses
# TODO(cwhipkey): sigmoid and tanh should not be exposed from tf.nn.
__all__ = make_all(__name__)
__all__.append("zero_fraction") # documented in training.py
# Modules whitelisted for reference through tf.nn.
# TODO(cwhipkey): migrate callers to use the submodule directly.
__all__.extend(["nn_ops", "rnn_cell", "seq2seq"])
# Symbols whitelisted for export without documentation.
# TODO(cwhipkey): review these and move to contrib or expose through
# documentation.
__all__.extend([
"all_candidate_sampler",
"batch_norm_with_global_normalization",
"batch_normalization",
"bidirectional_rnn",
"conv2d_backprop_filter",
size_value = tensor_util.constant_value(op.inputs[1])
if size_value is not None:
height = size_value[0]
width = size_value[1]
else:
height = None
width = None
return [tensor_shape.TensorShape(
[input_shape[0], height, width, input_shape[3]])]
@ops.RegisterShape('CropAndResize')
def _crop_and_resize_shape(op):
"""Shape function for the CropAndResize op."""
image_shape = op.inputs[0].get_shape().with_rank(4)
box_shape = op.inputs[1].get_shape().with_rank(2)
crop_size = tensor_util.constant_value(op.inputs[3])
if crop_size is not None:
crop_height = crop_size[0]
crop_width = crop_size[1]
else:
crop_height = None
crop_width = None
return [tensor_shape.TensorShape(
[box_shape[0], crop_height, crop_width, image_shape[3]])]
__all__ = make_all(__name__)
# ResizeMethod is not documented, but is documented in functions that use it.
__all__.append('ResizeMethod')
from tensorflow.python.ops import io_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import script_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.ops import state_ops
# Don't export modules except for the few we really want
_whitelist = set([app, compat, contrib, errors, flags, gfile, image,
logging, nn, python_io, resource_loader, sysconfig, test,
train, user_ops])
# Export all symbols directly accessible from 'tf.' by drawing on the doc
# strings of other modules.
__all__ = make_all(__name__,
[framework_lib, array_ops, client_lib, constant_op,
control_flow_ops, functional_ops, histogram_ops, io_ops,
math_ops, nn, script_ops, sparse_ops, state_ops, train])
# Symbols whitelisted for export without documentation.
# TODO(cwhipkey): review these and move to contrib, expose through
# documentation, or remove.
__all__.extend([
'AttrValue',
'ClusterDef',
'ClusterSpec',
'ConfigProto',
'Event',
'GPUOptions',
'GRAPH_DEF_VERSION',
'GRAPH_DEF_VERSION_MIN_CONSUMER',
'GRAPH_DEF_VERSION_MIN_PRODUCER',
# Distributed computing support
from tensorflow.core.protobuf.tensorflow_server_pb2 import ClusterDef
from tensorflow.core.protobuf.tensorflow_server_pb2 import JobDef
from tensorflow.core.protobuf.tensorflow_server_pb2 import ServerDef
from tensorflow.python.training.server_lib import ClusterSpec
from tensorflow.python.training.server_lib import Server
from tensorflow.python.util.all_util import make_all
# Include extra modules for docstrings because:
# * Input methods in tf.train are documented in io_ops.
# * Saver methods in tf.train are documented in state_ops.
__all__ = make_all(__name__, [sys.modules[__name__], io_ops, state_ops])
# Symbols whitelisted for export without documentation.
# TODO(cwhipkey): review these and move to contrib or expose through
# documentation.
__all__.extend(
[
"BytesList",
"Example",
"Feature",
"FeatureList",
"FeatureLists",
"Features",
"FloatList",
"Int64List",
"LooperThread",
# Training data protos.
from tensorflow.core.example.example_pb2 import *
from tensorflow.core.example.feature_pb2 import *
from tensorflow.core.protobuf.saver_pb2 import *
# Utility op. Open Source. TODO(touts): move to nn?
from tensorflow.python.training.learning_rate_decay import exponential_decay
from tensorflow.python.util.all_util import make_all
# Include extra make_all calls because:
# * Input methods in tf.train are documented in io_ops.
# * Saver methods in tf.train are documented in state_ops.
__all__ = list(set(
make_all(__name__) +
make_all(__name__, io_ops.__name__) +
make_all(__name__, state_ops.__name__)))
# Symbols whitelisted for export without documentation.
# TODO(cwhipkey): review these and move to contrib or expose through
# documentation.
__all__.extend([
"BytesList",
"Example",
"Feature",
"FeatureList",
"FeatureLists",
"Features",
"FloatList",
"InferenceExample",
@@summarize_activations ## Utilities @@assert_same_float_dtype @@assert_scalar_int @@is_numeric_tensor """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys # pylint: disable=unused-import,wildcard-import from tensorflow.contrib.layers.python.framework.tensor_util import * from tensorflow.contrib.layers.python.layers import * from tensorflow.contrib.layers.python.ops import * from tensorflow.contrib.layers.python.ops import loss_ops from tensorflow.python.util.all_util import make_all # Include loss_ops to get the symbols in - but they are not documented in main # docs yet. # TODO(cwhipkey): get the loss_ops documented in the main documentation and do # this in a better way. __all__ = make_all(__name__, [sys.modules[__name__], loss_ops])
of `summarize_collection` to `VARIABLES`, `WEIGHTS` and `BIASES`, respectively. @@summarize_activations ## Utilities @@assert_same_float_dtype @@assert_scalar_int @@is_numeric_tensor """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import sys # pylint: disable=unused-import,wildcard-import from tensorflow.contrib.layers.python.framework.tensor_util import * from tensorflow.contrib.layers.python.layers import * from tensorflow.contrib.layers.python.ops import * from tensorflow.contrib.layers.python.ops import loss_ops from tensorflow.python.util.all_util import make_all # Include loss_ops to get the symbols in - but they are not documented in main # docs yet. # TODO(cwhipkey): get the loss_ops documented in the main documentation and do # this in a better way. __all__ = make_all(__name__, [sys.modules[__name__], loss_ops])
from tensorflow.python.ops import session_ops
from tensorflow.python.ops import sparse_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import string_ops
from tensorflow.python.ops import tensor_array_ops
# Don't export modules except for the few we really want
_whitelist = set([app, compat, contrib, errors, flags, gfile, image, logging,
nn, python_io, resource_loader, sysconfig, test, train,
user_ops])
# Export all symbols directly accessible from 'tf.' by drawing on the doc
# strings of other modules.
__all__ = make_all(__name__, [framework_lib, array_ops, client_lib, check_ops,
constant_op, control_flow_ops, functional_ops,
histogram_ops, io_ops, math_ops, nn, script_ops,
session_ops, sparse_ops, state_ops, string_ops,
summary, tensor_array_ops, train])
# Symbols whitelisted for export without documentation.
# TODO(cwhipkey): review these and move to contrib, expose through
# documentation, or remove.
__all__.extend([
'AttrValue',
'ConfigProto',
'DeviceSpec',
'Event',
'GPUOptions',
'GRAPH_DEF_VERSION',
'GRAPH_DEF_VERSION_MIN_CONSUMER',
'GRAPH_DEF_VERSION_MIN_PRODUCER',
of `summarize_collection` to `VARIABLES`, `WEIGHTS` and `BIASES`, respectively.
@@summarize_activations
## Utilities
@@assert_same_float_dtype
@@assert_scalar_int
@@is_numeric_tensor
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
# pylint: disable=unused-import,wildcard-import
from tensorflow.contrib.layers.python.framework.tensor_util import *
from tensorflow.contrib.layers.python.layers import *
from tensorflow.contrib.layers.python.ops import *
from tensorflow.contrib.layers.python.ops import loss_ops
from tensorflow.python.util.all_util import make_all
# Include loss_ops to get the symbols in - but they are not documented in main
# docs yet.
# TODO(cwhipkey): get the loss_ops documented in the main documentation and do
# this in a better way.
__all__ = (make_all(__name__) +
make_all(__name__, loss_ops.__name__))
from tensorflow.python.ops import sparse_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import string_ops
from tensorflow.python.ops import tensor_array_ops
# Don't export modules except for the few we really want
_whitelist = set([
app, compat, contrib, errors, flags, gfile, image, logging, nn, python_io,
resource_loader, sysconfig, test, train, user_ops
])
# Export all symbols directly accessible from 'tf.' by drawing on the doc
# strings of other modules.
__all__ = make_all(__name__, [
framework_lib, array_ops, client_lib, check_ops, constant_op,
control_flow_ops, functional_ops, histogram_ops, io_ops, math_ops, nn,
script_ops, session_ops, sparse_ops, state_ops, string_ops, summary,
tensor_array_ops, train
])
# Symbols whitelisted for export without documentation.
# TODO(cwhipkey): review these and move to contrib, expose through
# documentation, or remove.
__all__.extend([
'AttrValue',
'ConfigProto',
'DeviceSpec',
'Event',
'GPUOptions',
'GRAPH_DEF_VERSION',
'GRAPH_DEF_VERSION_MIN_CONSUMER',
'GRAPH_DEF_VERSION_MIN_PRODUCER',
# Training data protos.
from tensorflow.core.example.example_pb2 import *
from tensorflow.core.example.feature_pb2 import *
from tensorflow.core.protobuf.saver_pb2 import *
# Utility op. Open Source. TODO(touts): move to nn?
from tensorflow.python.training.learning_rate_decay import exponential_decay
from tensorflow.python.util.all_util import make_all
# Include extra make_all calls because:
# * Input methods in tf.train are documented in io_ops.
# * Saver methods in tf.train are documented in state_ops.
__all__ = list(
set(
make_all(__name__) + make_all(__name__, io_ops.__name__) +
make_all(__name__, state_ops.__name__)))
# Symbols whitelisted for export without documentation.
# TODO(cwhipkey): review these and move to contrib or expose through
# documentation.
__all__.extend([
"BytesList",
"Example",
"Feature",
"FeatureList",
"FeatureLists",
"Features",
"FloatList",
"InferenceExample",
"Int64List",