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tf.compat.v1.layers.experimental.keras_style_scope Use Keras-style variable management.
@tf_contextlib.contextmanager
tf.compat.v1.layers.experimental.keras_style_scope()
All tf.layers and tf RNN cells created in this scope use Keras-style variable management. Creating such layers with a scope= argument is disallowed... | tensorflow.compat.v1.layers.experimental.keras_style_scope |
tf.compat.v1.layers.experimental.set_keras_style Use Keras-style variable management.
tf.compat.v1.layers.experimental.set_keras_style()
All tf.layers and tf RNN cells created after keras style ha been enabled use Keras-style variable management. Creating such layers with a scope= argument is disallowed, and reuse=Tr... | tensorflow.compat.v1.layers.experimental.set_keras_style |
tf.compat.v1.layers.Flatten Flattens an input tensor while preserving the batch axis (axis 0). Inherits From: Flatten, Layer, Layer, Module
tf.compat.v1.layers.Flatten(
data_format=None, **kwargs
)
Arguments
data_format A string, one of channels_last (default) or channels_first. The ordering of the dimen... | tensorflow.compat.v1.layers.flatten |
tf.compat.v1.layers.Layer Base layer class. Inherits From: Layer, Module
tf.compat.v1.layers.Layer(
trainable=True, name=None, dtype=None, **kwargs
)
It is considered legacy, and we recommend the use of tf.keras.layers.Layer instead.
Arguments
trainable Boolean, whether the layer's variables should be tr... | tensorflow.compat.v1.layers.layer |
tf.compat.v1.layers.MaxPooling1D Max Pooling layer for 1D inputs. Inherits From: MaxPool1D, Layer, Layer, Module
tf.compat.v1.layers.MaxPooling1D(
pool_size, strides, padding='valid',
data_format='channels_last', name=None, **kwargs
)
Arguments
pool_size An integer or tuple/list of a single integer, ... | tensorflow.compat.v1.layers.maxpooling1d |
tf.compat.v1.layers.MaxPooling2D Max pooling layer for 2D inputs (e.g. images). Inherits From: MaxPool2D, Layer, Layer, Module
tf.compat.v1.layers.MaxPooling2D(
pool_size, strides, padding='valid',
data_format='channels_last', name=None, **kwargs
)
Arguments
pool_size An integer or tuple/list of 2 in... | tensorflow.compat.v1.layers.maxpooling2d |
tf.compat.v1.layers.MaxPooling3D Max pooling layer for 3D inputs (e.g. volumes). Inherits From: MaxPool3D, Layer, Layer, Module
tf.compat.v1.layers.MaxPooling3D(
pool_size, strides, padding='valid',
data_format='channels_last', name=None, **kwargs
)
Arguments
pool_size An integer or tuple/list of 3 i... | tensorflow.compat.v1.layers.maxpooling3d |
tf.compat.v1.layers.max_pooling1d Max Pooling layer for 1D inputs.
tf.compat.v1.layers.max_pooling1d(
inputs, pool_size, strides, padding='valid',
data_format='channels_last', name=None
)
Arguments
inputs The tensor over which to pool. Must have rank 3.
pool_size An integer or tuple/list of a s... | tensorflow.compat.v1.layers.max_pooling1d |
tf.compat.v1.layers.max_pooling2d Max pooling layer for 2D inputs (e.g. images).
tf.compat.v1.layers.max_pooling2d(
inputs, pool_size, strides, padding='valid',
data_format='channels_last', name=None
)
Arguments
inputs The tensor over which to pool. Must have rank 4.
pool_size An integer or tup... | tensorflow.compat.v1.layers.max_pooling2d |
tf.compat.v1.layers.max_pooling3d Max pooling layer for 3D inputs (e.g.
tf.compat.v1.layers.max_pooling3d(
inputs, pool_size, strides, padding='valid',
data_format='channels_last', name=None
)
volumes).
Arguments
inputs The tensor over which to pool. Must have rank 5.
pool_size An integer or tu... | tensorflow.compat.v1.layers.max_pooling3d |
tf.compat.v1.layers.SeparableConv1D Depthwise separable 1D convolution. Inherits From: SeparableConv1D, Layer, Layer, Module
tf.compat.v1.layers.SeparableConv1D(
filters, kernel_size, strides=1, padding='valid',
data_format='channels_last', dilation_rate=1, depth_multiplier=1,
activation=None, use_bias=Tru... | tensorflow.compat.v1.layers.separableconv1d |
tf.compat.v1.layers.SeparableConv2D Depthwise separable 2D convolution. Inherits From: SeparableConv2D, Layer, Layer, Module
tf.compat.v1.layers.SeparableConv2D(
filters, kernel_size, strides=(1, 1), padding='valid',
data_format='channels_last', dilation_rate=(1, 1), depth_multiplier=1,
activation=None, us... | tensorflow.compat.v1.layers.separableconv2d |
tf.compat.v1.layers.separable_conv1d Functional interface for the depthwise separable 1D convolution layer.
tf.compat.v1.layers.separable_conv1d(
inputs, filters, kernel_size, strides=1, padding='valid',
data_format='channels_last', dilation_rate=1, depth_multiplier=1,
activation=None, use_bias=True, depth... | tensorflow.compat.v1.layers.separable_conv1d |
tf.compat.v1.layers.separable_conv2d Functional interface for the depthwise separable 2D convolution layer.
tf.compat.v1.layers.separable_conv2d(
inputs, filters, kernel_size, strides=(1, 1), padding='valid',
data_format='channels_last', dilation_rate=(1, 1), depth_multiplier=1,
activation=None, use_bias=T... | tensorflow.compat.v1.layers.separable_conv2d |
Module: tf.compat.v1.linalg Operations for linear algebra. Modules experimental module: Public API for tf.linalg.experimental namespace. Classes class LinearOperator: Base class defining a [batch of] linear operator[s]. class LinearOperatorAdjoint: LinearOperator representing the adjoint of another operator. class Line... | tensorflow.compat.v1.linalg |
Module: tf.compat.v1.linalg.experimental Public API for tf.linalg.experimental namespace. Functions conjugate_gradient(...): Conjugate gradient solver. | tensorflow.compat.v1.linalg.experimental |
tf.compat.v1.linalg.l2_normalize Normalizes along dimension axis using an L2 norm. (deprecated arguments) View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.math.l2_normalize, tf.compat.v1.nn.l2_normalize
tf.compat.v1.linalg.l2_normalize(
x, axis=None, epsilon=1e-12, n... | tensorflow.compat.v1.linalg.l2_normalize |
Module: tf.compat.v1.lite Public API for tf.lite namespace. Modules constants module: Public API for tf.lite.constants namespace. experimental module: Public API for tf.lite.experimental namespace. Classes class Interpreter: Interpreter interface for TensorFlow Lite Models. class OpHint: A class that helps build tflite... | tensorflow.compat.v1.lite |
Module: tf.compat.v1.lite.constants Public API for tf.lite.constants namespace.
Other Members
FLOAT tf.dtypes.DType
FLOAT16 tf.dtypes.DType
GRAPHVIZ_DOT 3
INT16 tf.dtypes.DType
INT32 tf.dtypes.DType
INT64 tf.dtypes.DType
INT8 tf.dtypes.DType
QUANTIZED_UINT8 tf.dtypes.D... | tensorflow.compat.v1.lite.constants |
Module: tf.compat.v1.lite.experimental Public API for tf.lite.experimental namespace. Modules nn module: Public API for tf.lite.experimental.nn namespace. Functions convert_op_hints_to_stubs(...): Converts a graphdef with LiteOp hints into stub operations. get_potentially_supported_ops(...): Returns operations potentia... | tensorflow.compat.v1.lite.experimental |
tf.compat.v1.lite.experimental.convert_op_hints_to_stubs Converts a graphdef with LiteOp hints into stub operations.
tf.compat.v1.lite.experimental.convert_op_hints_to_stubs(
session=None, graph_def=None, write_callback=(lambda graph_def, comments: None)
)
This is used to prepare for toco conversion of complex in... | tensorflow.compat.v1.lite.experimental.convert_op_hints_to_stubs |
tf.compat.v1.lite.experimental.get_potentially_supported_ops Returns operations potentially supported by TensorFlow Lite.
tf.compat.v1.lite.experimental.get_potentially_supported_ops()
The potentially support list contains a list of ops that are partially or fully supported, which is derived by simply scanning op nam... | tensorflow.compat.v1.lite.experimental.get_potentially_supported_ops |
Module: tf.compat.v1.lite.experimental.nn Public API for tf.lite.experimental.nn namespace. Classes class TFLiteLSTMCell: Long short-term memory unit (LSTM) recurrent network cell. class TfLiteRNNCell: The most basic RNN cell. Functions dynamic_rnn(...): Creates a recurrent neural network specified by RNNCell cell. | tensorflow.compat.v1.lite.experimental.nn |
tf.compat.v1.lite.experimental.nn.dynamic_rnn Creates a recurrent neural network specified by RNNCell cell.
tf.compat.v1.lite.experimental.nn.dynamic_rnn(
cell, inputs, sequence_length=None, initial_state=None, dtype=None,
parallel_iterations=None, swap_memory=False, time_major=True, scope=None
)
Performs ful... | tensorflow.compat.v1.lite.experimental.nn.dynamic_rnn |
tf.compat.v1.lite.experimental.nn.TFLiteLSTMCell Long short-term memory unit (LSTM) recurrent network cell. Inherits From: RNNCell, Layer, Layer, Module
tf.compat.v1.lite.experimental.nn.TFLiteLSTMCell(
num_units, use_peepholes=False, cell_clip=None, initializer=None, num_proj=None,
proj_clip=None, num_unit_sh... | tensorflow.compat.v1.lite.experimental.nn.tflitelstmcell |
tf.compat.v1.lite.experimental.nn.TfLiteRNNCell The most basic RNN cell. Inherits From: RNNCell, Layer, Layer, Module
tf.compat.v1.lite.experimental.nn.TfLiteRNNCell(
num_units, activation=None, reuse=None, name=None, dtype=None, **kwargs
)
This is used only for TfLite, it provides hints and it also makes the var... | tensorflow.compat.v1.lite.experimental.nn.tfliternncell |
tf.compat.v1.lite.OpHint A class that helps build tflite function invocations.
tf.compat.v1.lite.OpHint(
function_name, level=1, children_inputs_mappings=None, **kwargs
)
It allows you to take a bunch of TensorFlow ops and annotate the construction such that toco knows how to convert it to tflite. This embeds a p... | tensorflow.compat.v1.lite.ophint |
tf.compat.v1.lite.OpHint.OpHintArgumentTracker Conceptually tracks indices of arguments of "OpHint functions".
tf.compat.v1.lite.OpHint.OpHintArgumentTracker(
function_name, unique_function_id, node_name_prefix, attr_name, level=1,
children_inputs_mappings=None
)
The inputs and arguments of these functions bo... | tensorflow.compat.v1.lite.ophint.ophintargumenttracker |
tf.compat.v1.lite.TFLiteConverter Convert a TensorFlow model into output_format.
tf.compat.v1.lite.TFLiteConverter(
graph_def, input_tensors, output_tensors, input_arrays_with_shape=None,
output_arrays=None, experimental_debug_info_func=None
)
This is used to convert from a TensorFlow GraphDef, SavedModel or ... | tensorflow.compat.v1.lite.tfliteconverter |
tf.compat.v1.lite.TocoConverter Convert a TensorFlow model into output_format using TOCO. This class has been deprecated. Please use lite.TFLiteConverter instead. Methods from_frozen_graph View source
@classmethod
from_frozen_graph(
graph_def_file, input_arrays, output_arrays, input_shapes=None
)
Creates a TocoCo... | tensorflow.compat.v1.lite.tococonverter |
tf.compat.v1.lite.toco_convert Convert a model using TOCO. (deprecated)
tf.compat.v1.lite.toco_convert(
input_data, input_tensors, output_tensors, *args, **kwargs
)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use lite.TFLiteConverter instead. Typically ... | tensorflow.compat.v1.lite.toco_convert |
tf.compat.v1.LMDBReader A Reader that outputs the records from a LMDB file. Inherits From: ReaderBase
tf.compat.v1.LMDBReader(
name=None, options=None
)
See ReaderBase for supported methods.
Args
name A name for the operation (optional).
options A LMDBRecordOptions object (optional). Eager Compa... | tensorflow.compat.v1.lmdbreader |
tf.compat.v1.load_file_system_library Loads a TensorFlow plugin, containing file system implementation. (deprecated)
tf.compat.v1.load_file_system_library(
library_filename
)
Warning: THIS FUNCTION IS DEPRECATED. It will be removed in a future version. Instructions for updating: Use tf.load_library instead. Pass ... | tensorflow.compat.v1.load_file_system_library |
tf.compat.v1.local_variables Returns local variables.
tf.compat.v1.local_variables(
scope=None
)
Local variables - per process variables, usually not saved/restored to checkpoint and used for temporary or intermediate values. For example, they can be used as counters for metrics computation or number of epochs th... | tensorflow.compat.v1.local_variables |
tf.compat.v1.local_variables_initializer Returns an Op that initializes all local variables. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.initializers.local_variables
tf.compat.v1.local_variables_initializer()
This is just a shortcut for variables_initializer(local_... | tensorflow.compat.v1.local_variables_initializer |
Module: tf.compat.v1.logging Logging and Summary Operations. Functions TaskLevelStatusMessage(...) debug(...) error(...) fatal(...) flush(...) get_verbosity(...): Return how much logging output will be produced. info(...) log(...) log_every_n(...): Log 'msg % args' at level 'level' once per 'n' times. log_first_n(...):... | tensorflow.compat.v1.logging |
tf.compat.v1.logging.debug
tf.compat.v1.logging.debug(
msg, *args, **kwargs
) | tensorflow.compat.v1.logging.debug |
tf.compat.v1.logging.error
tf.compat.v1.logging.error(
msg, *args, **kwargs
) | tensorflow.compat.v1.logging.error |
tf.compat.v1.logging.fatal
tf.compat.v1.logging.fatal(
msg, *args, **kwargs
) | tensorflow.compat.v1.logging.fatal |
tf.compat.v1.logging.flush
tf.compat.v1.logging.flush() | tensorflow.compat.v1.logging.flush |
tf.compat.v1.logging.get_verbosity Return how much logging output will be produced.
tf.compat.v1.logging.get_verbosity() | tensorflow.compat.v1.logging.get_verbosity |
tf.compat.v1.logging.info
tf.compat.v1.logging.info(
msg, *args, **kwargs
) | tensorflow.compat.v1.logging.info |
tf.compat.v1.logging.log
tf.compat.v1.logging.log(
level, msg, *args, **kwargs
) | tensorflow.compat.v1.logging.log |
tf.compat.v1.logging.log_every_n Log 'msg % args' at level 'level' once per 'n' times.
tf.compat.v1.logging.log_every_n(
level, msg, n, *args
)
Logs the 1st call, (N+1)st call, (2N+1)st call, etc. Not threadsafe.
Args
level The level at which to log.
msg The message to be logged.
n The number... | tensorflow.compat.v1.logging.log_every_n |
tf.compat.v1.logging.log_first_n Log 'msg % args' at level 'level' only first 'n' times.
tf.compat.v1.logging.log_first_n(
level, msg, n, *args
)
Not threadsafe.
Args
level The level at which to log.
msg The message to be logged.
n The number of times this should be called before it is logged... | tensorflow.compat.v1.logging.log_first_n |
tf.compat.v1.logging.log_if Log 'msg % args' at level 'level' only if condition is fulfilled.
tf.compat.v1.logging.log_if(
level, msg, condition, *args
) | tensorflow.compat.v1.logging.log_if |
tf.compat.v1.logging.set_verbosity Sets the threshold for what messages will be logged.
tf.compat.v1.logging.set_verbosity(
v
) | tensorflow.compat.v1.logging.set_verbosity |
tf.compat.v1.logging.TaskLevelStatusMessage
tf.compat.v1.logging.TaskLevelStatusMessage(
msg
) | tensorflow.compat.v1.logging.tasklevelstatusmessage |
tf.compat.v1.logging.vlog
tf.compat.v1.logging.vlog(
level, msg, *args, **kwargs
) | tensorflow.compat.v1.logging.vlog |
tf.compat.v1.logging.warn
tf.compat.v1.logging.warn(
msg, *args, **kwargs
) | tensorflow.compat.v1.logging.warn |
tf.compat.v1.logging.warning
tf.compat.v1.logging.warning(
msg, *args, **kwargs
) | tensorflow.compat.v1.logging.warning |
tf.compat.v1.LogMessage A ProtocolMessage
Attributes
level Level level
message string message
Class Variables
DEBUGGING 10
ERROR 40
FATAL 50
INFO 20
Level
UNKNOWN 0
WARN 30 | tensorflow.compat.v1.logmessage |
Module: tf.compat.v1.lookup Public API for tf.lookup namespace. Modules experimental module: Public API for tf.lookup.experimental namespace. Classes class KeyValueTensorInitializer: Table initializers given keys and values tensors. class StaticHashTable: A generic hash table that is immutable once initialized. class S... | tensorflow.compat.v1.lookup |
Module: tf.compat.v1.lookup.experimental Public API for tf.lookup.experimental namespace. Classes class DatasetInitializer: Creates a table initializer from a tf.data.Dataset. class DenseHashTable: A generic mutable hash table implementation using tensors as backing store. | tensorflow.compat.v1.lookup.experimental |
tf.compat.v1.lookup.StaticHashTable A generic hash table that is immutable once initialized. Inherits From: StaticHashTable
tf.compat.v1.lookup.StaticHashTable(
initializer, default_value, name=None
)
When running in graph mode, you must evaluate the tensor returned by tf.tables_initializer() before evaluating th... | tensorflow.compat.v1.lookup.statichashtable |
tf.compat.v1.lookup.StaticVocabularyTable String to Id table that assigns out-of-vocabulary keys to hash buckets. Inherits From: StaticVocabularyTable
tf.compat.v1.lookup.StaticVocabularyTable(
initializer, num_oov_buckets, lookup_key_dtype=None, name=None
)
For example, if an instance of StaticVocabularyTable is... | tensorflow.compat.v1.lookup.staticvocabularytable |
Module: tf.compat.v1.losses Loss operations for use in neural networks.
Note: All the losses are added to the GraphKeys.LOSSES collection by default.
Classes class Reduction: Types of loss reduction. Functions absolute_difference(...): Adds an Absolute Difference loss to the training procedure. add_loss(...): Adds a ... | tensorflow.compat.v1.losses |
tf.compat.v1.losses.absolute_difference Adds an Absolute Difference loss to the training procedure.
tf.compat.v1.losses.absolute_difference(
labels, predictions, weights=1.0, scope=None,
loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)
weights acts as a coefficient for the los... | tensorflow.compat.v1.losses.absolute_difference |
tf.compat.v1.losses.add_loss Adds a externally defined loss to the collection of losses.
tf.compat.v1.losses.add_loss(
loss, loss_collection=tf.GraphKeys.LOSSES
)
Args
loss A loss Tensor.
loss_collection Optional collection to add the loss to. | tensorflow.compat.v1.losses.add_loss |
tf.compat.v1.losses.compute_weighted_loss Computes the weighted loss.
tf.compat.v1.losses.compute_weighted_loss(
losses, weights=1.0, scope=None, loss_collection=tf.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)
Args
losses Tensor of shape [batch_size, d1, ... dN].
weights Optio... | tensorflow.compat.v1.losses.compute_weighted_loss |
tf.compat.v1.losses.cosine_distance Adds a cosine-distance loss to the training procedure. (deprecated arguments)
tf.compat.v1.losses.cosine_distance(
labels, predictions, axis=None, weights=1.0, scope=None,
loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS,
dim=None
)
Warnin... | tensorflow.compat.v1.losses.cosine_distance |
tf.compat.v1.losses.get_losses Gets the list of losses from the loss_collection.
tf.compat.v1.losses.get_losses(
scope=None, loss_collection=tf.GraphKeys.LOSSES
)
Args
scope An optional scope name for filtering the losses to return.
loss_collection Optional losses collection.
Returns a li... | tensorflow.compat.v1.losses.get_losses |
tf.compat.v1.losses.get_regularization_loss Gets the total regularization loss.
tf.compat.v1.losses.get_regularization_loss(
scope=None, name='total_regularization_loss'
)
Args
scope An optional scope name for filtering the losses to return.
name The name of the returned tensor.
Returns A... | tensorflow.compat.v1.losses.get_regularization_loss |
tf.compat.v1.losses.get_regularization_losses Gets the list of regularization losses.
tf.compat.v1.losses.get_regularization_losses(
scope=None
)
Args
scope An optional scope name for filtering the losses to return.
Returns A list of regularization losses as Tensors. | tensorflow.compat.v1.losses.get_regularization_losses |
tf.compat.v1.losses.get_total_loss Returns a tensor whose value represents the total loss.
tf.compat.v1.losses.get_total_loss(
add_regularization_losses=True, name='total_loss', scope=None
)
In particular, this adds any losses you have added with tf.add_loss() to any regularization losses that have been added by ... | tensorflow.compat.v1.losses.get_total_loss |
tf.compat.v1.losses.hinge_loss Adds a hinge loss to the training procedure.
tf.compat.v1.losses.hinge_loss(
labels, logits, weights=1.0, scope=None, loss_collection=tf.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)
Args
labels The ground truth output tensor. Its shape should match the... | tensorflow.compat.v1.losses.hinge_loss |
tf.compat.v1.losses.huber_loss Adds a Huber Loss term to the training procedure.
tf.compat.v1.losses.huber_loss(
labels, predictions, weights=1.0, delta=1.0, scope=None,
loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)
For each value x in error=labels-predictions, the followin... | tensorflow.compat.v1.losses.huber_loss |
tf.compat.v1.losses.log_loss Adds a Log Loss term to the training procedure.
tf.compat.v1.losses.log_loss(
labels, predictions, weights=1.0, epsilon=1e-07, scope=None,
loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)
weights acts as a coefficient for the loss. If a scalar is p... | tensorflow.compat.v1.losses.log_loss |
tf.compat.v1.losses.mean_pairwise_squared_error Adds a pairwise-errors-squared loss to the training procedure.
tf.compat.v1.losses.mean_pairwise_squared_error(
labels, predictions, weights=1.0, scope=None,
loss_collection=tf.GraphKeys.LOSSES
)
Unlike mean_squared_error, which is a measure of the differences b... | tensorflow.compat.v1.losses.mean_pairwise_squared_error |
tf.compat.v1.losses.mean_squared_error Adds a Sum-of-Squares loss to the training procedure.
tf.compat.v1.losses.mean_squared_error(
labels, predictions, weights=1.0, scope=None,
loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)
weights acts as a coefficient for the loss. If a ... | tensorflow.compat.v1.losses.mean_squared_error |
tf.compat.v1.losses.Reduction Types of loss reduction. Contains the following values:
NONE: Un-reduced weighted losses with the same shape as input.
SUM: Scalar sum of weighted losses.
MEAN: Scalar SUM divided by sum of weights. DEPRECATED.
SUM_OVER_BATCH_SIZE: Scalar SUM divided by number of elements in losses.
... | tensorflow.compat.v1.losses.reduction |
tf.compat.v1.losses.sigmoid_cross_entropy Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits.
tf.compat.v1.losses.sigmoid_cross_entropy(
multi_class_labels, logits, weights=1.0, label_smoothing=0, scope=None,
loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHT... | tensorflow.compat.v1.losses.sigmoid_cross_entropy |
tf.compat.v1.losses.softmax_cross_entropy Creates a cross-entropy loss using tf.nn.softmax_cross_entropy_with_logits_v2.
tf.compat.v1.losses.softmax_cross_entropy(
onehot_labels, logits, weights=1.0, label_smoothing=0, scope=None,
loss_collection=tf.GraphKeys.LOSSES, reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
... | tensorflow.compat.v1.losses.softmax_cross_entropy |
tf.compat.v1.losses.sparse_softmax_cross_entropy Cross-entropy loss using tf.nn.sparse_softmax_cross_entropy_with_logits.
tf.compat.v1.losses.sparse_softmax_cross_entropy(
labels, logits, weights=1.0, scope=None, loss_collection=tf.GraphKeys.LOSSES,
reduction=Reduction.SUM_BY_NONZERO_WEIGHTS
)
weights acts as... | tensorflow.compat.v1.losses.sparse_softmax_cross_entropy |
tf.compat.v1.make_template Given an arbitrary function, wrap it so that it does variable sharing.
tf.compat.v1.make_template(
name_, func_, create_scope_now_=False, unique_name_=None, custom_getter_=None,
**kwargs
)
This wraps func_ in a Template and partially evaluates it. Templates are functions that create... | tensorflow.compat.v1.make_template |
Module: tf.compat.v1.manip Operators for manipulating tensors. Functions batch_to_space_nd(...): BatchToSpace for N-D tensors of type T. gather_nd(...): Gather slices from params into a Tensor with shape specified by indices. reshape(...): Reshapes a tensor. reverse(...): Reverses specific dimensions of a tensor. roll(... | tensorflow.compat.v1.manip |
tf.compat.v1.map_fn Transforms elems by applying fn to each element unstacked on axis 0. (deprecated arguments)
tf.compat.v1.map_fn(
fn, elems, dtype=None, parallel_iterations=None, back_prop=True,
swap_memory=False, infer_shape=True, name=None, fn_output_signature=None
)
Warning: SOME ARGUMENTS ARE DEPRECATE... | tensorflow.compat.v1.map_fn |
Module: tf.compat.v1.math Math Operations.
Note: Functions taking Tensor arguments can also take anything accepted by tf.convert_to_tensor.
Note: Elementwise binary operations in TensorFlow follow numpy-style broadcasting.
TensorFlow provides a variety of math functions including: Basic arithmetic operators and ... | tensorflow.compat.v1.math |
tf.compat.v1.math.in_top_k Says whether the targets are in the top K predictions. View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.nn.in_top_k
tf.compat.v1.math.in_top_k(
predictions, targets, k, name=None
)
This outputs a batch_size bool array, an entry out[i] is... | tensorflow.compat.v1.math.in_top_k |
tf.compat.v1.math.log_softmax Computes log softmax activations. (deprecated arguments) View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.nn.log_softmax
tf.compat.v1.math.log_softmax(
logits, axis=None, name=None, dim=None
)
Warning: SOME ARGUMENTS ARE DEPRECATED: (di... | tensorflow.compat.v1.math.log_softmax |
tf.compat.v1.math.softmax Computes softmax activations. (deprecated arguments) View aliases Compat aliases for migration
See Migration guide for more details. tf.compat.v1.nn.softmax
tf.compat.v1.math.softmax(
logits, axis=None, name=None, dim=None
)
Warning: SOME ARGUMENTS ARE DEPRECATED: (dim). They will be... | tensorflow.compat.v1.math.softmax |
Module: tf.compat.v1.math.special Public API for tf.math.special namespace. Functions bessel_i0(...): Computes the Bessel i0 function of x element-wise. bessel_i0e(...): Computes the Bessel i0e function of x element-wise. bessel_i1(...): Computes the Bessel i1 function of x element-wise. bessel_i1e(...): Computes the B... | tensorflow.compat.v1.math.special |
tf.compat.v1.MetaGraphDef A ProtocolMessage
Attributes
asset_file_def repeated AssetFileDef asset_file_def
collection_def repeated CollectionDefEntry collection_def
graph_def GraphDef graph_def
meta_info_def MetaInfoDef meta_info_def
object_graph_def SavedObjectGraph object_graph_def
... | tensorflow.compat.v1.metagraphdef |
tf.compat.v1.MetaGraphDef.CollectionDefEntry A ProtocolMessage
Attributes
key string key
value CollectionDef value | tensorflow.compat.v1.metagraphdef.collectiondefentry |
tf.compat.v1.MetaGraphDef.MetaInfoDef A ProtocolMessage
Attributes
any_info Any any_info
function_aliases repeated FunctionAliasesEntry function_aliases
meta_graph_version string meta_graph_version
stripped_default_attrs bool stripped_default_attrs
stripped_op_list OpList stripped_op_li... | tensorflow.compat.v1.metagraphdef.metainfodef |
tf.compat.v1.MetaGraphDef.MetaInfoDef.FunctionAliasesEntry A ProtocolMessage
Attributes
key string key
value string value | tensorflow.compat.v1.metagraphdef.metainfodef.functionaliasesentry |
tf.compat.v1.MetaGraphDef.SignatureDefEntry A ProtocolMessage
Attributes
key string key
value SignatureDef value | tensorflow.compat.v1.metagraphdef.signaturedefentry |
Module: tf.compat.v1.metrics Evaluation-related metrics. Functions accuracy(...): Calculates how often predictions matches labels. auc(...): Computes the approximate AUC via a Riemann sum. (deprecated) average_precision_at_k(...): Computes average precision@k of predictions with respect to sparse labels. false_negative... | tensorflow.compat.v1.metrics |
tf.compat.v1.metrics.accuracy Calculates how often predictions matches labels.
tf.compat.v1.metrics.accuracy(
labels, predictions, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
The accuracy function creates two local variables, total and count that are used to compute the frequ... | tensorflow.compat.v1.metrics.accuracy |
tf.compat.v1.metrics.auc Computes the approximate AUC via a Riemann sum. (deprecated)
tf.compat.v1.metrics.auc(
labels, predictions, weights=None, num_thresholds=200, metrics_collections=None,
updates_collections=None, curve='ROC', name=None,
summation_method='trapezoidal', thresholds=None
)
Warning: THIS... | tensorflow.compat.v1.metrics.auc |
tf.compat.v1.metrics.average_precision_at_k Computes average precision@k of predictions with respect to sparse labels.
tf.compat.v1.metrics.average_precision_at_k(
labels, predictions, k, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
average_precision_at_k creates two local var... | tensorflow.compat.v1.metrics.average_precision_at_k |
tf.compat.v1.metrics.false_negatives Computes the total number of false negatives.
tf.compat.v1.metrics.false_negatives(
labels, predictions, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
If weights is None, weights default to 1. Use weights of 0 to mask values.
Args
lab... | tensorflow.compat.v1.metrics.false_negatives |
tf.compat.v1.metrics.false_negatives_at_thresholds Computes false negatives at provided threshold values.
tf.compat.v1.metrics.false_negatives_at_thresholds(
labels, predictions, thresholds, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
If weights is None, weights default to 1.... | tensorflow.compat.v1.metrics.false_negatives_at_thresholds |
tf.compat.v1.metrics.false_positives Sum the weights of false positives.
tf.compat.v1.metrics.false_positives(
labels, predictions, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
If weights is None, weights default to 1. Use weights of 0 to mask values.
Args
labels The ... | tensorflow.compat.v1.metrics.false_positives |
tf.compat.v1.metrics.false_positives_at_thresholds Computes false positives at provided threshold values.
tf.compat.v1.metrics.false_positives_at_thresholds(
labels, predictions, thresholds, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
If weights is None, weights default to 1.... | tensorflow.compat.v1.metrics.false_positives_at_thresholds |
tf.compat.v1.metrics.mean Computes the (weighted) mean of the given values.
tf.compat.v1.metrics.mean(
values, weights=None, metrics_collections=None, updates_collections=None,
name=None
)
The mean function creates two local variables, total and count that are used to compute the average of values. This avera... | tensorflow.compat.v1.metrics.mean |
tf.compat.v1.metrics.mean_absolute_error Computes the mean absolute error between the labels and predictions.
tf.compat.v1.metrics.mean_absolute_error(
labels, predictions, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
The mean_absolute_error function creates two local variable... | tensorflow.compat.v1.metrics.mean_absolute_error |
tf.compat.v1.metrics.mean_cosine_distance Computes the cosine distance between the labels and predictions.
tf.compat.v1.metrics.mean_cosine_distance(
labels, predictions, dim, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
The mean_cosine_distance function creates two local vari... | tensorflow.compat.v1.metrics.mean_cosine_distance |
tf.compat.v1.metrics.mean_iou Calculate per-step mean Intersection-Over-Union (mIOU).
tf.compat.v1.metrics.mean_iou(
labels, predictions, num_classes, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
Mean Intersection-Over-Union is a common evaluation metric for semantic image seg... | tensorflow.compat.v1.metrics.mean_iou |
tf.compat.v1.metrics.mean_per_class_accuracy Calculates the mean of the per-class accuracies.
tf.compat.v1.metrics.mean_per_class_accuracy(
labels, predictions, num_classes, weights=None, metrics_collections=None,
updates_collections=None, name=None
)
Calculates the accuracy for each class, then takes the mea... | tensorflow.compat.v1.metrics.mean_per_class_accuracy |
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